Introduction

Soil moisture represents a switch that controls the proportion of rainfall that percolates, runs off, or evaporates from land. Since the 1970s, a variety of coarse resolution soil moisture datasets have become available from active and passive microwave systems (i.e. ERS-1/2, METOP ASCAT, AMSR-E and SMOS) at coarse (>25km) spatial resolution. These have been applied to improve flood forecasting, numerical weather predictions and rainfall estimates as well as to study soil moisture trends and anomalies in relation to climate change [1–4].

While of excellent radiometric accuracy, the coarse spatial resolution datasets remained a constraint for data users operating at medium scale (<1km). It became obvious that applications such as coupled crop-climate modeling or soil moisture monitoring over heterogeneous landscapes or river runoff prediction at sub-basins scale may benefit the establishment of medium resolution (<1km) soil moisture dataset [5–7].

SHARE (Soil moisture for hydrometeorologic applications), the ESA’s DUE Tiger Initiative project, answered the need of hydrological and agricultural community for improved Earth Observation products by providing medium resolution (1 km) soil moisture service derived from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT [9]. Since its start in 2005 the SHARE service extended over Australian and portions of African and South American continent.

The algorithm for the ASAR Global Mode (GM) soil moisture product has been adopted from the already existing change detection algorithm for the ERS-1/2 scatterometer [8]. The basic idea behind the change detection is that the backscatter cross section of natural surfaces changes over short timescales mainly due to variations in soil moisture, while vegetation or surface roughness are assumed to be constant or only slowly varying [9]. It should be noted that the ASAR GM soil moisture product is an index scaled between 0 (dry conditions) and 1 (saturated conditions) and its conversion to absolute values may be required.

The SHARE project demonstrated in two important ways how data from medium resolution microwave instruments can be used to support flood monitoring efforts. Firstly, the data can continuously monitor how much water is stored in the soil (Figure 1) and thus determine the amount of runoff resulting from rain. Secondly, the data can support monitoring of inundated areas during a flood due to its capabilities to penetrate clouds and even rain.

Figure 1. The ASAR GM relative soil moisture product (monthly mean) over Victoria in Feb. 2007-2011.

Importantly, given the similar characteristics of the ASAR GM and the future Sentinel-1 sensor it is anticipated that the ASAR GM algorithm can be transformed to a potential soil moisture product retrieved from Sentinel-1.

Toward operational products

The development of operational water monitoring services is progressing rapidly. The requirement on the operationality is thus becoming a standard also for the Earth observation products. The ASAR GM soil moisture product is available semi-operationally, in other words it is automatically processed on a monthly basis. The development of the product includes algorithm development, data processing, data validation, algorithm improvement, automatization of the data processing and delivery, capacity building and support to data users. A fully automatic processing chain has been generated at the TU WIEN that reprocesses the ASAR Level 1 data into soil moisture Level 3 datasets and make previews available via the ASAR GM data viewer with a 1 month delay. The potential minimum delay is however only several hours and compares to the latency of the near-real-time coarse resolution soil moisture datasets from the SMOS, ASCAT and AMSR-E sensors. The ASAR GM georeferenced soil moisture product is available on request at no coast at the institute website.

The ASAR GM soil moisture product development and validation was in detail summarized elsewhere [9–11]. The latter works demonstrated a good potential of ASAR C-band observations to monitor variations in soil moisture on a quasi-operational basis. Additional works demonstrated a good agreement of ASAR GM soil moisture and the soil moisture output from an independent AWRA-L landscape hydrological model developed within the Australian Water Resources Assessment system (AWRA) [11], [12] over the Australian continent (Figure 2, left). Further, the observational error of the ASAR GM dataset was evaluated [11] (Figure 2, right) using the independent estimates from the AWRA-L model. The error estimates were less (25%) for forested areas and areas covered with rock outcrops in western, northern, and eastern coastal Australia. The percentage represents the maximum relative soil moisture that can be accounted by the ASAR GM error. The good understanding of the error together with the knowledge of the relationship between remotely-sensed and model variables are critical for a successful application of the product [13].

Figure 2. The simple Pearson correlation coefficient between ASAR GM and AWRA-L soil moisture (left). AWRA-L is a landscape hydrology model that explicitly models soil surface moisture dynamics. The ASAR GM error (right) is estimated by propagating sensor error through the ASAR GM retrieval algorithm.

Demonstrated and planned applications in hydrology

The major applications of the ASAR GM product are expected in hydrology and water management. While the added value of the coarse resolution soil moisture datasets in hydrological models have been demonstrated [3], [6] similar investigation with medium resolution ASAR GM data has began only recently. The preliminary studies demonstrated the potential of the ASAR GM data to identify saturated surfaces (Figure 1) [10], [14] which to a large extent contribute to surface runoff [15]. The ASAR GM data were also implemented to identify bias in precipitation datasets [16] and could resolve spatial patterns not observable in the ERS scatterometer measurements [14].

Further investigations are performed within the scope of the SHARE project; supported by combined efforts of TU WIEN (Vienna University of Technology) and CSIRO (Commonwealth Scientific and Industrial Research Organisation). CSIRO identified remote sensing datasets as crucial for the hydrological observation system (AWRA); this will soon become operational through the Bureau of Meteorology. Preliminary assessments have suggested potential of ASAR GM soil moisture to:

a) Characterise the relative errors of AWRA-L (AWRA landscape hydrological model) soil moisture (Figure 2);
b) Serve as an independent dataset for a multi-objective calibration of the AWRA-L model parameters;
c) Serve as an independent member for a generation of a blended soil moisture product at 5 km spatial resolution;
d) Support monitoring of large scale inundation events.

Figure 3. An example of the ASAR Wide Swath (WS) and Image Mode (IM) normalized backscatter images over Eastern Queensland, Australia, during dry (April 2010) and wet season (January, 2011). The inundated areas are shown as orange and red, characterized by very low backscatter values owing to the specular reflection of the radar signal on the flooded surface. The dark blue colors, which are a result of the high backscatter values, show inundated vegetation that reflects the signal back to the sensor (double bounce effect).

As this work is ongoing and will continue beyond the duration of the SHARE project only first findings are here summarized.

Several different ways of merging observations within the model-data system require different computational overheads. Blended dataset can be used as a stand-alone product for wide range of implications (i.e. agricultural decision making, drought detection). Also, it can be directly assimilated into a model rather than assimilating several datasets with independent error structures and often different spatial resolutions.

While the ability of the ASAR data from higher resolution modes (Wide Swath (WS) and Image Mode (IM) with 150m spatial resolution) to monitor large scale inundation events is evident (Figure 3), a generic classification approach applicable also on the ASAR GM data is under investigation [17–19]. Within the SHARE project a method [20] for inundation extent mapping using the ASAR GM data was developed that uses a thresholding approach to distinguish flooded and non-flooded areas and combines this with the MODIS Open Water Likelihood (OWL) index to retrieve water proportion within each ASAR GM pixel. The method demonstrated the ability of the ASAR GM data to detect open water bodies as well as water under vegetated areas (Figure 4).

Nevertheless, an overclassification of flooded regions was evident that occurred over areas where wet soil got mistaken with flooded vegetation (southeastern corner of Figure 4). Also, the total proportion of water within each pixel differed substantially between the ASAR GM and MODIS algorithms. The latter may be caused by the low spatial resolution of the ASAR GM data that provides mixed signature of flooded and non-flooded regions resulting in a mid-range backscatter; these may be consequently classified as only partly flooded. On the contrary, several irrigated areas were correctly detected by the ASAR GM data that could not be detected using the MODIS OWL index. These results suggest the synergistic combination of several remote sensing methods as the best approach for the characterization of inundation events.

Figure 4. The MODIS band composite (7, 2, 1) (left), the ASAR GM water map (percentage of water within each pixel) (center) and the corresponding MODIS water map (right) in January 2011.

Data users

It was an explicit aim of SHARE to get the widest possible user community actively involved. Two prime users were identified – the University of Kwazulu Natal (UKZN) and the Australian Commonwealth Scientific and Research Organization (CSIRO). These also acted as a bridgehead to the user community in Australia and Africa.

A data request form has been setup on the SHARE website. Since beginning of the project (December 2005) there have been more than 80 data requests that originated mostly in the African and European continent. The recently published journal papers and the representation of the SHARE project on international meetings raised the awareness on the product also by users from the USA, Australia, and variety of international organizations (Figure 5, right).

Figure 5. The number of users and their proposed application of the ASAR GM soil moisture product (left). The origin of the ESA DUE SHARE project data users (right). The numbers represent the number of users.

The application of the ASAR GM soil moisture parameter in variety of applied studies has been investigated (Figure 5, left). These range from crop yield estimates, runoff prediction [15] to climate variability studies. A number of comparison and validation studies with in-situ [9], modelled [12] and remote sensing datasets [21] has also been performed.

Final remarks

A continuation of research satellite missions and data service availability on operational bases is needed for successful and meaningful integration of the Earth Observation data into existing models. While the ENVISAT is slowly approaching its end a successive satellite mission – Sentinel – is foreseen to be operated over the period 2013 to 2030 that will provide data at improved spatial, temporal and radiometric resolution.

The results of the SHARE project have well prepared the ground for the future Sentinel SAR sensors by demonstrating the viability of the soil moisture and inundation extent retrieval. The future operationally available medium resolution soil moisture and inundation extent estimates from Sentinel-1 have the potential to be of a great benefit for crop growth and water balance monitoring and modeling in next decades.

[1] Y. Y. Liu, A. I. J. M. Van Dijk, R. A. M. De Jeu, and T. R. H. Holmes, “An analysis of spatiotemporal variations of soil and vegetation moisture from a 29-year satellite-derived data set over mainland Australia,” Water Resources Research, vol. 45, no. 7, p. art. no. W07405, 2009.

[2] W. T. Crow, G. J. Huffman, R. Bindlish, and T. J. Jackson, “Improving satellite-based rainfall accumulation estimates using spaceborne surface soil moisture retrievals,” Journal of Hydrometeorology, vol. 10, no. 1, pp. 199-212, 2009.

[3] L. Brocca et al., “Improving runoff prediction through the assimilation of the ASCAT soil moisture product,” Hydrology and Earth System Sciences Discussions, vol. 7 (4), no. 4, pp. 4113-4144, 2010.

[4] M. Drusch, “Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF’s Integrated Forecast System and the TMI soil moisture data set,” Journal of Geophysical Research, vol. 112, no. 3, pp. 1-14, 2007.

[5] T. Osborne, J. Slingo, D. Lawrence, and T. Wheeler, “Examining the interaction of growing crops with local climate using a coupled crop-climate model,” Journal of Climate, vol. 22, no. 6, pp. 1393-1411, 2009.

[6] J. Parajka, V. Naeimi, G. Blöschl, and J. Komma, “Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria,” Hydrology and Earth System Sciences, vol. 13, no. 2, pp. 259-271, 2009.

[7] P. Meier, A. Frömelt, and W. Kinzelbach, “Hydrological real-time modeling using remote sensing data,” Hydrology and Earth System Sciences Discussions, vol. 7, no. 6, pp. 8809-8835, 2010.

[8] W. Wagner, G. Lemoine, and H. Rott, “A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data,” Remote Sensing of Environment, vol. 70, no. 2, pp. 191-207, 1999.

[9] C. Pathe, W. Wagner, D. Sabel, M. Doubkova, and J. Basara, “Using ENVISAT ASAR Global Mode Data for Surface Soil Moisture Retrieval Over Oklahoma, USA,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 2, pp. 468-480, 2009.

[10] I. Mladenova, V. Lakshmi, J. P. Walker, R. Panciera, W. Wagner, and M. Doubkova, “Validation of the ASAR global monitoring mode soil moisture product using the NAFE’05 data set,” IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 6, pp. 2498-2508, 2010.

[11] M. Doubková, A. I. J. M. Van Dijk, G. Blöschl, D. Sabel, and W. Wagner, “Evaluation of predicted soil moisture retrieval error from C-Band SAR by comparison against soil moisture estimates over Australia,” Remote Sensing of Environment, 2011.

[12] A. I. J. M. Van Dijk and G. A. Warren, “AWRA Technical Report 4. Evaluation Against Observations.,” WIRADA/CSIRO Water for a Healthy Country Flagship, Canberra, 2010.

[13] A. I. J. M. van Dijk and L. J. Renzullo, “Water resource monitoring systems and the role of satellite observations,” Hydrology and Earth System Sciences, vol. 15, no. 1, pp. 39-55, Jan. 2011.

[14] C. Pathe, W. Wagner, D. Sabel, Z. Bartalis, M. Doubkova, and V. Naeimi, “Scatterometer and ScanSAR soil moisture observations of the contiguous United States,” in Proceedings of the IEEE National Radar Conference, IEEE National Radar Conference, 2009.

[15] A. Bartsch, M. Doubkova, C. Pathe, D. Sabel, P. Wolski, and W. Wagner, “River flow & wetland monitoring with ENVISAT ASAR Global mode in the Okavango Basin and Delta,” Proceedings of the Second IASTED Africa Conference, Water Resource Management (AfricaWRM 2008). Gaborone, Botswana, 8-10 September, 2008, pp. 152-156, 2008.

[16] C. Milzow, P. E. Krogh, and P. Bauer-Gottwein, “Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for model calibration and validation in a large ungauged catchment,” Hydrology and Earth System Sciences Discussions, vol. 7, no. 6, pp. 9123-9154, 2010.

[17] S. Martinis, a. Twele, and S. Voigt, “Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data,” Natural Hazards and Earth System Science, vol. 9, no. 2, pp. 303-314, Mar. 2009.

[18] P. Matgen et al., “Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the particle Filter: Proof of concept,” Hydrology and Earth System Sciences Discussions, vol. 7, no. 2, pp. 1785-1819, 2010.

[19] D. O’Grady, M. Leblanc, and D. Gillieson, “Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan,” Hydrology and Earth System Sciences Discussions, vol. 8, no. 3, pp. 5769-5809, Jun. 2011.

[20] C. J. Ticehurst, A. Bartsch, M. Doubkova, and A. I. J. M. van Dijk, “Comparison of ENVISAT ASAR GM, AMSR-E passive microwave, and MODIS optical remote sensing for flood monitoring in Australia,” in Earth Observation and Water Cycle Science Symposium, 2009, vol. ESA Specia, p. 8.

[21] D. Sabel et al., “Synergistic use of Scatterometer and ScanSAR Data for Extraction of Surface Soil Moisture Information in Australia,” in EUMETSAT Meteorological Satellite Conference, 8-12 September, 2008, Darmstadt, Germany, 2008.

Image of EarthCube logo.

To understand and predict the Earth system from the center of the sun to the center of the Earth is a bold call to action by the Advisory Committee for Geosciences Directorate at the National Science Foundation (NSF). Similar calls can be found through a search of the global scientific literature, e.g. see the National Academy’s Division on Earth and Life Studies. The implications of and for humanity is a dominate theme in all of these recent reports. It is important to note that almost all of these reports recognize the importance of the use of cyberinfrastructure (CI) to derive knowledge from a cornucopia of information and data about our planet, the sun, and near-space environment (the portion of space between the sun and the Earth).

Propelled by an industry-driven technology revolution over the last decade, geoscientists working with informaticists have created an ever-increasing array of cyberinfrastructure solutions that serve research and educational endeavors. There are a number of communities within the geosciences that have created, or are in the process of creating, highly functional and robust CI systems to increase the productivity and capability of research communities, e.g. UNIDATA (meteorology), IRIS (seismology), and OOI (oceanography).

Although outputs from these systems are of great value to the communities they serve, the outcome with respect to understanding and predicting the Earth as a single complex system remains to be fully realized. Insufficient community dialog and sharing of ideas, practices, data, etc., across disciplines within geosciences has created many cyber-technology enabled solutions to solve similar problems.

Without an overall guiding framework to promote convergence, the diversity of approaches becomes a barrier to the holistic study of the Earth system. Although there is evidence of a community movement toward increased compatibility through the use of common standards and software, this process, if left un-stimulated, would be too slow to allow the geosciences community to address the most pressing challenges outlined in various reports, e.g. the crossroad challenges articulated in the GEO Vision (download pdf report).

EarthCube is NSF’s effort to:

1) Accelerate the convergence process;
2) Frame a system that is scaleable as ever more complexity is investigated; and,
3) Transform to take advantage of emerging technologies.

EarthCube

The NSF is facilitating a community dialog with a goal of transforming the conduct of research in geosciences by supporting the development of a community-guided CI to integrate data and information for knowledge management across the geosciences.

The purpose of the project is to significantly increase the productivity and capability of researchers and educators by integrating all geosciences data, information, knowledge and practices in an open, transparent and inclusive manner. No integrated framework currently exists that is sufficiently functional and robust to allow a holistic view of the Earth system.

This is not for lack of investment in CI by NSF, other agencies, or international partners. Rather it is an outcome resulting from a long history of making needed tactical investments in sub-disciplines of geosciences. Most of these investments effectively serve the communities that have come to depend upon them. Through these investments and concurrent investments in people, other tools, and ideas, the community helped establish a strong CI foundation and user-savvy CI culture. However, recent surveys and community dialog reveal a frustration with CI-created incompatibilities across the geosciences and a readiness to strategically address the incompatibilities.

The challenge faced by funding agencies lies in transforming substantial previous CI investments in collecting, curating, and disseminating geosciences data so that these investments can become more “interworkable” and shared more uniformly with a myriad of end users. The good news is that technologies emerging from industry will create an opportunity to greatly facilitate the convergence process within the geosciences, because all the technologies used today by the sub-disciplines of geosciences will be completely refreshed over the next decade. The framework developed under the auspices of EarthCube will guide the refreshment choices toward establishing an interworkable structure to study the Earth system.

Early efforts

The Geosciences Directorate (GEO) and the Office of Cyberinfrastructure (OCI) established a partnership to address the multifaceted challenges of modern, data-intensive science and education. The EarthCube program is one manifestations of the NSF-wide program titled “Cyberinfrastructure for the 21st Century.”

A “Dear Colleague Letter” initiated EarthCube in June 2011, and was followed by several WebEx-enabled dialogs with the community. These and other events set the stage for a charrette held Nov. 1-4, 2011. The charrette provided the opportunity for the community to come together (face-to-face and virtually) to clarify the breadth and scope of EarthCube, identify potential new science that could be accomplished within a future framework, and develop a rough order to the set of capabilities that would be needed to realize the EarthCube vision. Information on the charrette and its outcomes is available at the EarthCube website.

A second “Dear Colleague Letter” was released on Dec. 16, 2011, and provided the guidance for proposals to NSF that would explore transformational ideas to enable EarthCube. On the planning horizon is another community event planned for the late spring or early summer of 2012. NSF will continue to facilitate a broad-based community dialogue through a variety of modern and traditional methods to further develop a strategic framework for EarthCube and encourage convergence of collaborations within the geosciences and beyond.

Clifford Jacobs is a senior advisor for Geosciences Directorate at the National Science Foundation. His career spans the private sector and government service and has engaged him in basic and applied research, teaching, and scientific program management. For more than 25 years, he served as the program officer for the National Center for Atmospheric Research, where he oversaw research activities and the provision of facilities to the university community, including a broad range of cyberinfrastructure activities.

Photo Credit: Till Westermayer

Abstract

This article discusses the importance of using hydrological models to study the increasing concerns over water resources due to future changes in climate variability. For this purpose, a hydrological model approach has been used to calibrate the runoff using rainfall and other climate variables.

1. Introduction

There have been many studies worldwide on changes in climate variables, for example, increasing trends in temperature and decreasing trends in precipitation and riverflow have been observed in China, Korea, Japan and New Zealand (Afzal et al., 2011). The effect of climate change on the hydrological regimes of Europe generally show an increasing trend in precipitation and runoff in northern Europe and a decreasing trend in southern Europe (Arnell, 1999). Fealy and Sweeney (2005) observed a salient turning point in the hydrological and climatic processes after the 1980s. Afzal et al. (2011) found a similar turning point in the precipitation data for Scotland around the 1980s, which was followed by an increasing trend.

The abrupt increase in precipitation has resulted in an increase of river runoff volume, increased surface area and the elevation of water levels within reservoirs. The hydro climate of catchment zones is considered to include the long-term average climate such as rainfall, evapotranspiration and streamflows across a catchment. The variation in the long-term average is known as hydro-climatic variability (McMahon et al., 2011).

Environmentally sustainable management of land water resources requires designing operating systems to cope with, and preferably prosper, during present and future hydro-climatic variability. Existing water resource engineering projects have been designed generally on the assumption of hydro-climatic stability (McMahon et al., 2011). To model these changes, climate change scenarios have been used to estimate the potential changes in river flow under climate change in a number of catchments in England and Wales. The model projected a decrease in the mean monthly riverflow in the summer and autumn and as a result led to the prediction that the annual riverflow in England and Wales could drop 10 to 15 percent by 2050 (Griffiths et al., 2008). Similarly, the UKCIP02 ‘medium-high’ greenhouse gas emission scenarios projected the reservoir yield could, on average, decrease by as much as 18 percent by the end of this century in north-western England (Fowler et al., 2007) and decrease by 30 percent during the summer across Britain (Arnell, 2004).

There are a number of adaptation strategies to counter the impact of climate change on the urban water supply. Adaptation strategies to climate change and climate variability were proposed for seven contrasting basins across the globe in the context of the ADAPT project (Droogers and Aerts, 2005). These studies proposed several strategies to increase the water storage capacity for agricultural purposes. Such adaptation strategies have the potential to be adapted for urban water storage systems (Fisher and Rubio, 1997).

Similar optimal water storage methodologies were adapted in Spain to measure the increase in uncertainty and variance in water storage systems. Strategies for climate variability and its effect on future water supplies also have been implemented in England and Wales by considering the historical drought conditions experienced in 1933 and 1934 and 1995 and 1996 (Subak, 2000). Various methods for mitigating the effects of climate change have been suggested, for example, by building reservoirs underground to prevent evaporation (IEMA, 2009). In addition, there has been an increased interest in harvesting stormwater runoff (Mitchell et al., 2008).

Figure 1. Typical river catchment and reservoir layout

2. Data Sources

Daily precipitation, maximum and minimum temperatures and wind speed data was downloaded from the (British Atmospheric Data Centre) BADC website. Supplementary data was provided by the Scottish Environmental Protection Agency

3. Methodology:

In order to estimate the reliability of a water reservoir supplied by a river, the hydrological model based on Moore (1985) has been used to calculate the outflow of the catchment, which is used as the input to a storage reservoir. Key variables used in the hydrological model are given in table 1, and figure 1 shows a schematic layout of a typical river catchment and reservoir.

Daily rainfall from selected river catchments is calculated by subtracting evaporation losses from the rainfall data to give the effective rainfall figure, which may be negative. The model uses the Penman equation to calculate the evapotranspiration using maximum and minimum temperatures and wind speed data. An outline of the hydrological model is given in Fig. 2.

Figure 2: General structure of hydrological model

Q_b x storage

Where Q_b is the base flow coefficient and storage refers to the water storage into the reservoirs. Allowance is then made for the routing or translation of the rainfall from the point of contact with the ground to the outlet, i.e.:

Where Q_d(t) refers to the basin direct runoff and f (t) is the probability density function of translation time (t).

In above equation, λ refers to translation distribution coefficient and μ refers to translation distribution coefficient evaporation constants. A similar transformation is applied to the base flow and the low flows are then added, i.e.:

QT(t) = Q_d + Q_b(t)

Figure 3. Runoff production by simple storage reservoirs

The operation of the reservoir is modelled using a simple tank model where the storage volume is calculated from

S_n+1 = S_n + Q – D

where Q and D are the inflow and demand (outflow), respectively. The programme records the number of days when the volume is zero (Nf) and the reliability is calculated as:

R = (1 – Nf/365) * 100

It is assumed that the reservoir can be described by two parameters, which describe (a) the storage and (b) the demand in relation to the mean annual inflow. Both of these two parameters are key in finding the reliability of water resources.

4. Results:

Figure 4: Observed and actual flows at Paisley from 1964-2008, Scotland, UK

The runoff was calibrated using daily rainfall, maximum and minimum temperatures and wind speed data in the hydrological model. The calibrated runoff data was compared against the observed data for the validation of the hydrological model. As an example, Paisley’s actual daily riverflow data has been compared against the Paisley weather station’s calibrated data. Figure 3 shows the observed riverflow within the White Cart Catchment and the calculated riverflow at the Paisley weather station. To achieve the best closest observed and calculated values, the storage distribution coefficient and translation distribution coefficients were adjusted.

4.2 Reservoir reliability and future work:

Once the hydrological model is calibrated, it can be used to find the reliability of a reservoir. The reliability of a reservoir system depends on the level of demand, or outflow, the nature of inflow, and the amount of storage. To measure the reliability of a reservoir, the storage and demand factors have to be adjusted and the analysis is then repeated over all datasets for 20 to 30 years. In addition, the same storage demand factor values are also adjusted. The number of failures for each of the reservoirs, indicated as an empty reservoir, will then be counted for each dataset for 20 to 30 years, to compare the reliability of the reservoir against the measures of variability.

The next step of this research will be to correlate the reservoir reliability against measures of variability such as the intra-annual variance of precipitation and the intra-annual cumulative sum control chart range of precipitation for the number of reservoirs across the UK. To find the maximum number of reliability values and number of failures, the sequencing of the rainfall data will be manipulated in order to change the periodicity, variance and trends of a particular time series. Future work will investigate the potential changes in rainfall variability in climate change scenarios and their impacts on water supply systems. An estimation of the reliability of the water supply systems will be made under a number of different climate change scenarios. For this, the UKCP09 weather generator will be used to project rainfall and maximum and minimum temperatures under different climate change scenarios. The projected data will be used in the hydrological model to assess the water resource reliability. As a case study, at least two catchments and reservoirs will be selected from two contrasting study regions including a low- and a high-rainfall variability region.

5. Concluding remarks

The main inspiration of this study is the perception that climate change is likely to affect the hydrological cycle in many regions by increasing its variability, which in turn will result in increased uncertainty about the future availability of water resources. Hydrological models can be widely used to assess the future reliability of water resources as rainfall variability increases, as its subsequent impact on river flow will inevitably have an impact on water supply systems. Therefore, this work has implications in future hydrological system management and can be a platform for water resource planning and engineering.

6. Acknowledgment

The authors are thankful to the British Atmospheric Data Centre (BADC) for providing the daily precipitation data, wind speed, maximum and minimum temperatures, which were downloaded from badc.nerc.ac.uk/mybadc. The authors would also like to thank the Scottish Environmental Protection Agency (SEPA) for the provision of river-flow data.

7. References

AFZAL, M., MANSELL, M. G. & GAGNON, A. S. 2011. Trends and variability in daily precipitation in Scotland. Procedia Environmental Sciences, 6 15–26.

ARNELL, N. W. 1999. The effect of climate change on hydrological regimes in Europe: a continental perspective. Global Environmental Change-Human and Policy Dimensions, 9, 5-23.

ARNELL, N. W. 2004. Climate-change impacts on river flows in Britain: The UKCIP02 scenarios. . Water and Environment Journal, 18, 112-117.

DROOGERS, P. & AERTS, J. 2005. Adaptation strategies to climate change and climate variability: A comparative study between seven contrasting river basins. Physics and Chemistry of the Earth, 30, 339-346.

FEALY, R. & SWEENEY, J. 2005. Detection of a possible change point in atmospheric variability in the North Atlantic and its effect on Scandinavian glacier mass balance. International Journal of Climatology, 25, 1819-1833.

FISHER, A. C. & RUBIO, S. J. 1997. Adjusting to climate change: Implications of increased variability and asymmetric adjustment costs for investment in water reserves. Environmental Economics and Management, 34, 207-227.

FOWLER, H. J., KILSBY, C. G. & STUNELL, J. 2007. Modelling the impacts of projected future climate change on water resources in north-west England. Hydrology and Earth System Sciences, 11, 1115-1126.

GRIFFITHS, J., KELLER, V., MORRIS, D. & YOUNG, A. R. 2008. Continuous Estimation of River Flows (CERF). Using Science to create a better place. Bristol: Environment Agency.

IEMA 2009. Britain should prepare for the loss of landmass The Environmentalist. Institute of Environmental Management and Assessment.

MANSELL, M. G. 2000. Modelling the impacts of urban discharges on receiving waters. Proceedings on Institute of Civil Engineers, 142, 167-176.

MCMAHON, T. A., MURRAY, C. P., PEGRAM, G. S. & SMITH, I. N. 2011. A simple Methodology for Estimating Mean and Variability of Annual Runoff and Reservoir Yield under Present and Future Climates. American Meteorological Society 12, 135-146.

MITCHELL, V. G., MCCARTHY, D. T., DELETIC, A. & FLETCHER, T. D. 2008. Urban stormwater harvesting – sensitivity of a storage behaviour model. Environmental Modelling & Software 23, 782-793.

MOORE, R. J. 1985. The probability-distributed principle and runoff production at point and basin scales. Hydrological Sciences-Journal, 30 273-297.

SUBAK, S. 2000. Climate change adaptation in the UK water industry: Manager’s perceptions of past variability and future scenarios. Water Resources Management, 14, 137-156.

S. Sangita Mishra, Indian Institute of Technology, Bombay, India

R. Nagarjan, Indian Institute of Technology, Bombay, India

V. Balaji, Commonwealth of Learning, Vancouver, Canada

Figure 1. Location of Peddavagu basin, a tributary of Krishna River basin

Figure 2. Spatial Variation of Ten Years Average Annual Rainfall of the Peddavagu Basin, a tributary of Krishna River basin

Drought is a serious problem that significantly affects millions of people in the Semi Arid Tropics (SAT), which receives an average annual rainfall of less than 1,000 millimeters. Drought varies with regard to the time of occurrence, duration, intensity and extent of the area affected ([12], [16]). Drought starts with an extended period of reduced precipitation, although it may propagate throughout the hydrologic cycle at different temporal and spatial scales [14]. Drought is defined from the hydrological point of view as a sustained and regional extensive occurrence of below-average natural water availability ([1], [12]).

Drought is further classified into meteorological drought, agricultural drought, hydrological drought and socio-economic drought, based on water deficiency in a specific part of the hydrologic cycle. There is an element of connection between different droughts as drought in one stage can lead to a drought in another stage. Meteorological drought occurs when the precipitation is less than the normal amount of precipitation over a region. Agricultural drought occurs when the soil water content is low and not sufficient to support plant growth [1]. Hydrological drought occurs when there is a depletion of water in surface water bodies including irrigation tanks, streams, reservoirs, lakes and also a depletion of the groundwater level, and is further classified into stream-flow droughts and groundwater droughts [12]. The plight of the farmer has always been a matter of concern in India, and is only further reinforced by the recurrence of the droughts that cause untold hardships. Identification, monitoring and characterization of droughts in villages have been the topic of much research at the national and international levels.

Figure 3. Digital Elevation Model of Peddavagu basin, a tributary of Krishna River basin

Table 1. Ranking assigned to different Choropleth Maps accruing to their vulnerability to Drought

II. STUDY AREA

The Peddavagu basin, a tributary of Krishna River basin is located in the southern Telangana agri-climatic zone of the Mahabubnagar district of Andhra Pradesh (Fig. 1), which has been prone to recurrent droughts in the last two decades. The basin is 1,611 square kilometers, and lies between 77o 48’ 44.7” E to 78o 13’ 31.55” E longitudes and 16o 19’ 31.55” N to 16o 50’ 22.1” N latitudes. The basin’s topography is mostly flat with granitic hills in the upstream, and its climate transitions from a tropical to a subtropical climate. The climate of the study area is semi-arid with an average annual rainfall of 622 millimeters, received mainly during the monsoon period from June to October. Summers, which last from March to May, are hot, with temperatures ranging from 27 to 41.5 Celsius. The winter, which spans from November to January, has temperatures ranging from 16.9 to 19.1 Celsius. The main livelihood opportunities for rural communities in the Mahabubnagar district are agriculture and livestock rearing. This region has two major cropping seasons, viz, June-October (kharif) and November to March (rabi). The most important crop in the basin is rice during kharif and groundnut in rabi seasons. Other regularly cultivated crops include sorghum, pearl millet, finger millet, maize, groundnut, castor, sunflower, pigeon pea and vegetables. Cultivation in kharif is mostly dependent on rainfall, while groundwater is used in rabi. Levels of the groundwater aquifer level have been falling over the years because of exploitation and a lack of groundwater recharge. Most bore wells run dry after a bad monsoon year and only those boreholes near drainage tanks and river streams yield water.

Figure 4. Drainage density map of Peddavagu Basin, a tributary of Krishna River basin

A GIS-based framework developed by the Indian Institute of Technology, Bombay was tested on a pilot basis at Adakkal Mandal, Mahabubnagar to assess the micro-level drought vulnerability [6]. Based on this method, an integrated approach was developed to analyze various parameters that influence the drought. Rainfall, elevation, soils, drainage density and surface water availability were considered to be the most important parameters influencing the water availability in a village in this study.

Thematic maps showing the spatial variations of each of these parameters were prepared using remote sensing data and GIS. Spatial variation of annual rainfall over the basin was prepared using the Inverse Distance Weighting (IDW) interpolation technique. IDW makes interpolated estimates based on values at nearby locations weighted only by distance from the interpolation location [18]. The average rainfall data of the 10 meteorological stations were spatially interpolated to understand its variation over the entire study area. The rainfall varied between 626 to 749 millimeters, which was further divided into six classes and the class with the highest rainfall was given the highest rank, the subsequent class ranges and associated ranks can be seen in Table 1. The spatially interpolated rainfall map was classified into six color bands as shown in spatial variation of the ten-year average annual rainfall Map (Fig. 2).

Figure 5. Soil Map of Peddavagu basin, a tributary of Krishna River basin

The drainage map was prepared by digitizing the drainage network from the geo-referenced topographic map of the Survey of India on a 1-to-50000 scale. Further, the drainage density map was prepared using the drainage map in Arc GIS 9.2 software. From the drainage density map, it was found that the drainage lines were sparsely distributed in the upstream portion of the watershed, leading to water scarcity conditions. The drainage density values were again divided into six classes and ranked accordingly. The drainage density of the study area varied between 0 and 43.75 meters per square meter (Fig. 4).

A soil map was prepared by digitizing the geo-referenced soil map obtained from the National Bureau of Soil Survey and Land Use Planning (NBSS andLUP) in GIS environment. The predominant soils in the basin include clay, cracking clay, gravelly clay , gravelly loam and loamy soils. The information about the soil type, structure, texture and water holding capacity was gathered using the NBSS soil maps at the village level. The water holding capacity of a soil has a direct relationship to the amount of water required for crop growth, and these soils were ranked according to their water holding capacity. The soil map overlaid with village boundaries is shown in Fig 5.

Figure 6. Landuse/land cover map of Peddavagu Watershed, a tributary of Krishna River basin

The land use-land cover map was prepared using the Landsat Enhanced Thematic Mapper (ETM) Imagery, 30 meter spatial resolution, was obtained from GLCF. The Landsat ETM and TM data, were classified using unsupervised and supervised classification techniques in the Earth Resource Data Analysis System (ERDAS) Imagine 8.6 Software package. A hierarchical classification system based on an Anderson classification scheme [9] was adopted for the classification.

In this study, the unsupervised classification technique along with visual interpretation was employed using limited ground truth data, topographic maps and Google Earth imagery. The unsupervised classification followed by progressive generalization [10] was used to derive the land use-land cover classes. Landsat imagery was classified using the ISODATA k-means cluster algorithm — the pixels cluster together based on the similarity of digital numbers into natural groups within a multispectral imagery. The image was initially classified into 30 classes, with a threshold value of 0.98 and a maximum of 30 iterations. The threshold value was set to 0.98 in order to force the ISODATA algorithm to run as much iteration as possible until 98 percent of the image remains unchanged. The initial 30 classes were merged by progressive generalization using ground truth data and Google Earth imagery. Ground truthing was done during April and August of 2010, so the investigation would coincide with the crop growth and development period of both rabi and kharif. Field surveys were conducted to collect the ground truth data using the Global Positioning System (GPS) Unit in Universal Transverse Mercator (UTM) and latitude and longitude coordinate system with WGS 84 datum. The ground truth data was used to identify and classify the image and for the accuracy assessment. Five to 20 samples were collected for each class, and class identification and labels were assigned based on ground truth data and Google Earth imagery. The gross cultivable land from the classified image was separated and used to mask out the agricultural area from kharif and rabi season satellite imagery. These masked out images were reclassified using the ISODATA algorithm to classify the kharif and rabi cropped areas. The study area was classified into 12 classes (Fig. 6) and the percentage of each class in the basin was estimated.

Table 2. Land use/land cover classes of Peddavagu basin.

All the classes of the above generated five choropleth maps, which were ranked according to the vulnerability of each class to drought as mentioned in Table 1. Using these thematic layers, a weighed overlay analysis was carried out to prepare a drought vulnerability map. The weighted overlay technique was applied to integrate the diverse and dissimilar thematic maps to create an integrated analysis to derive the drought vulnerability map. The drought vulnerability of each thematic map was evaluated by considering the influence of each parameter on water availability and scarcity. The drought vulnerability of the study area was classified into five vulnerable classes. Village boundaries were digitized from geo-referenced cadastral maps obtained from the land survey office, Hyderabad. The village boundary map was then overlapped over the drought vulnerability map to show the degree of vulnerability of each village to drought. The drought vulnerability of each village was color-coded in red, orange, yellow, light green and green for easy understanding.

IV. RESULTS AND DISCUSSION

The 10 years average annual rainfall variation map was presented in Figure 2. It is evident from the figure that the regions around Wanaparty and Mahabubnagar have better rainfall, while other regions around Bhoothpur, Addakal, Ghanpur, Peddamandadi and Gopalpet received very low rainfall. The Devarkadra and Kothakota regions received moderate rainfall.

Figure 7. Final drought vulnerability map of Peddavagu basin, a tributary of Krishna River basin

The integrated unsupervised and supervised classification carried out on the Landsat imagery had 12 generalized classes (Figure 6). The different land use/ land cover classes and their area in percentage of the total area of watershed in square kilometers were shown in Table 2. Agriculture is primarily rain-fed with a kharif area cultivation of 28.55% of the basin. More than one third of the basin is left fallow (36.75 %) during kharif due to lack of rainfall.

B. Drought Vulnerability

Thematic maps of rainfall variability, soil, drainage density, topography and land use or land cover were analyzed using weighted overlay analysis to demarcate the drought vulnerability of the study area. The study area was classified into five vulnerable classes from very highly vulnerable, highly vulnerable, medium vulnerable, low vulnerable to very low vulnerable regions (Fig. 7).

The figure revealed that Addakal, Ghanpur, Peddamandadi, Gopalpet and Bhootpur regions are very highly or extremely vulnerable to drought, primarily due to very low rainfall occurrence, lack of surface water storage, poor soils having low water holding capacity, high elevated topography and very sparse drainage. The Kothakota and Devarkadra regions experienced high-to-moderate drought vulnerability, with moderate rainfall, and comparatively better surface water storage, having gravelly loam soils. Open scrub, stone waste, and boulders are highly vulnerable whereas fallow lands with loamy soils are regions that are moderately vulnerable to drought. The Mahabubnagar and Wanaparthy regions receive relatively good rainfall, having very low or low vulnerability to drought with good surface water storage and water holding soils like cracking clay, and clayey soils. The regions with extremely high risk to drought, concentrated in the north, north-west, north-east and center part of the basin, corresponded very well, with kharif fallow lands that were not cultivated primarily due to lack of rainfall. Satellite imagery and ground truth information has also shown the surface water bodies in the red colored villages were either dry or infested with vegetation and accumulated with silt. The basin is dominated by fallow lands and kharif crop cultivation with 36.75% and 28.55%, respectively.

Figure 8. Drought vulnerability map of villages in the Peddavagu basin, a tributary of Krishna River basin

V. CONCLUSION

Drought preparedness is a priority of the disaster management authority of India, and implementation of drought preparedness programs at the micro-level require the assessment of a village’s vulnerability to drought. It is hoped this study will guide disaster management authorities to better water management and augmentation of water supplies to reduce risk. This study can be improved by incorporating a water balance model on village-level water supply and demand, and by considering groundwater recharge as an additional source of supply during scarce water conditions.

ACKNOWLEDGMENT

Support from National Agricultural Innovation Project (NAIP) of the Indian Council for Agricultural Research (ICAR) to ICRISAT is gratefully acknowledged. Thanks to the two anonymous reviewers who have provided thorough comments and suggestions.

REFERENCES

[1] A. K. Mishra and V. P. Singh (2010). A review on drought concepts. Journal of Hydrology. (391). 202-216.

[2] A.R. Huete, K. Diden, T. Miura, E.P. Rodriguez, X.Gao and L.G. Ferreria, Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 2002, Vol 83, pp. 195-213.

[3] B. Clausen and C.P. Pearson, Regional frequency analysis of annual maximum streamflow drought. J. Hydrol. 173 (1-4), 1995, pp. 111-130.

[4] B. McKee, N.J. Doesken and N. Kleist, “The relationship of drought frequency and duration to time scales.” In proc. of 8th Applied Meteorology, 1993, p. 179-184.

[5] F.N. Kogan. Application of Vegetation Index and Brightness Temperature for Drought Detection. Advances in Space Research, 1995, 15, 91-100.

[6] G. Dileepkumar, R. Nagarajan, K. RAO, and V. BALAJI. Village Knowledge Centers and the Use of GIS-derived Products. In Proceedings of Second International Conference on ICT in Development, jointly organized by IEEE and ACM, Bangalore, India 14-15 December 2007. Available: http://research.microsoft.com/workshops/ictd2007/ICTD2007_Proceedings_CD.pdf

[7] G. Sreedhar, R.Nagarajan, V.R. Kumar, N. Lavanya, A. G. Sylvester and V. Balaji. Disaster Preparedness using IT Tools: Case Studies on the use of ICT and GIS Derived tools for Micro-level Drought Preparedness. In Proceedings of IEEE Conference on Humanitarian Challenges, 28 August, 2009, Bangalore, India. http://ewh.ieee.org/r10/gujarat/htccon/.

[8] H.S. Shin and J. D. Salas. Regional drought analysis based on neural networks. J. Hydrol. Sci. J. 48 (5), 2000, pp. 809-820.

[9] J.R. Anderson, E.E.Hardy, J. T. Roach, and R. E. Witmer, ” A land use and land cover classification system for use with remote sensor data”, U.S. Geological Survey Professional Paper No. 964. USGS, Washington, D.C. 1976.

[10] J. Cihlar, Q. Xiao, J. Beaubien, K. Fung and R. Latifovic. (1998). Classification by progressive generalization: a new automated methodology for remote sensing multichannel data. International Journal of Remote Sensing, 19, pp. 2685-2704.

[11] K.C. Sinha Ray. ‘Role of Drought Early Warning Systems for Sustainable Agricultural Research in India’. Proceedings of an Expert Group Meeting Held September 5-7, 2000, in Lisbon, Portugal. Available at http://www.drought.unl.edu/monitor/EWS/EWS_WMO.html.

[12] L.M. Tallaksen & H.A.J. Lanen van (eds). Hydrological Drought. Processes and Estimation Methods for Streamflow and Groundwater. Developments in Water Science, 48, Elsevier, Amsterdam, 2004.

[13] M.D. Zaidman, H.G. Rees and A.R. Young. (2001). Spatial-temporal development of streamflow droughts in north-west Europe. Hydrology and Earth System Sciences, 5 (4), 733-751.

[14] O. Bonacci. Hydrological identification of drought. Hydrol. Processes, 7, 1993, pp. 249-262.

[15] R.P. Pandey and K.S. Ramasastri. Relationship between the common climatic parameters and average drought frequency. Hydrol. Processes. 15 (6), 2001, pp.1019 – 1032.

[16] R.P. Pandey and K.S. Ramasastri., Incidence of droughts in different climatic regions. Hydrol. Sci. J. 47, 2002, pp 31- 40.

[17] S. Naoum, & I. K. Tsanis. Ranking spatial interpolation techniques using a GIS based DSS. Global Nest: the Int. J. 6 (1), 2004, pp 1-20.

[18] T. R. Kjeldsen, A. Lundorf, and D. Rosbjerg. Use of a two-component exponential distribution in partial duration modelling of hydrological droughts in Zimbabwean rivers. Hydrol. Sci. J. 45 (2), 2000, pp.285-298.

[19] W.J. Gibbs and J.V. Maher. Rainfall deciles as drought indicators. Bureau of Meteorology Bulletin, 48, Commonwealth of Australia, 1967.

AUTHORS

G. Sreedhar is a research associate with the Knowledge Sharing and Innovation Department of ICRISAT and a PhD scholar in CSRE, Indian Institute of Technology, Bombay. He works on drought assessment and helps rural communities with drought preparedness using ICT tools in drought affected regions of India.

S. Sangita Mishra, is a research scholar in CSRE, Indian Institute of Technology, Bombay, and works on drought and natural hazards assessment and mitigation in the chronically drought affected regions of India.

R. Nagarajan, associate professor, Indian Institute of Technology, Bombay, works on the natural hazards assessment and mitigation in the chronically drought affected regions of India.

V. Balaji, director, Technology & Knowledge Management, Commonwealth of Learning, Vancouver, Canada, is a specialist in the area of Information and Communication Technology applied to rural development. Before joining the Commonwealth of Learning, he worked as the Global Leader for Knowledge Management and Sharing at ICRISAT.

S. Avery
Department of Geography, University of Leicester
(bundufundi@gmail.com)

D.M. Harper,
Department of Biology, University of Leicester
(dmh@le.ac.uk)

Figure 1. Lesser flamingos at Lake Bogoria, Kenya. Credit: David Harper.

University of Leicester researchers from the Department of Biology and the Earth Observation Science Group have teamed up to work on an interdisciplinary project. The aim is to apply Earth observation data to investigating the connections between ecological and hydrological processes in alkaline-saline lakes in the Eastern Rift Valley (Kenya-Tanzania). This interdisciplinary approach has been adopted to give greater insight into the functioning of these water dependent ecosystems and the results will be used to inform future management strategies. The work will act as a demonstration of how satellite data can contribute to remote monitoring of ecosystems where in situ data are limited. The project forms the basis of a cross-departmental PhD study addressing the sustainability of soda lakes in Kenya and Tanzania critical to the life cycle of lesser flamingos.

Figure 2. Image of Arthrospira fusiformis, the dominant phytoplankton species in Lake Bogoria, enlarged x 100. Credit: Suzanne Mills (2).

Alkaline-saline lakes have a distinct ecology characterized by dense blooms of cyanobacteria (blue-green bacteria). Such blooms are often considered a hazard in freshwater lakes due to the toxins they may produce, but in alkaline-saline lakes they play a vital role in sustaining a population of lesser flamingos, which feed by filtering colonial cyanobacteria from the water of a dozen or so soda lakes in the Rift Valley [1]. Lesser flamingos are classified as a near-threatened species by the International Union for Conservation of Nature due to their decreasing numbers and limited breeding sites. These flamingos are of great economic importance as they attract tourists to the soda lakes of Kenya and Tanzania where they form a globally renowned spectacle; their numbers can reach one million individuals (Figure 1). This study aims to use remote sensing to determine the quantity and distribution of food available to lesser flamingos, thus providing valuable information for their future conservation.

Lesser flamingos feed primarily on cyanobacterium Arthrospira fusiformis (Figure 2) in deep lakes and also on benthic algae, which grow on the bottom of shallower soda lakes. The flamingos move from lake to lake in response to food availability, but little is known about the spatial and temporal distribution of their food supply. Occasionally a drastic reduction in cyanobacterial biomass – known as a “crash” or “die-off event” – is observed in the lakes. The causes of these events are poorly understood. Satellite observations will give insight into how often these crashes occur and how long they last. These lakes are in remote areas and there is no routine in situ monitoring so the ability to monitor these environments with satellite data is urgently needed. The main lake considered in this study is Lake Bogoria, Kenya (Figure 3). It is a key feeding site of lesser flamingos and it is dominated by one species of cyanobacteria, Arthrospira fusiformis (always over 80 percent).

Figure 3. Landsat ETM+ RGB image (3, 2, 1 band combination) of Lake Bogoria, Kenya. The lake appears green due to high cyanobacterial biomass. Credit: NASA Landsat Program.

Another lake of critical importance is Lake Natron as it is the only breeding site for flamingos in East Africa (Figure 4). The unique hydrology of the lake is believed to control the success of flamingo breeding events but the details are unknown. The lake is threatened by two proposed developments: a dam to be built on the Ewaso Ngiro (South) River, which provides 30 percent of the lake’s water, and a soda ash extraction factory that would pump saline water from the lake and extract the salts before returning the water to the lake. Both developments will have a significant effect on the hydrology and ecology of the lake and could potentially be disruptive to the flamingo breeding. Hence another aim of this project is to use archival satellite imagery to establish baseline data about the hydrology of the lake by looking at fluctuations in lake surface area.

Figure 4. Landsat ETM+ RGB image (3, 2, 1 band combination) of Lake Natron, Kenya-Tanzania, the sole breeding site for lesser flamingos in East Africa. The red color is due to extremophile bacteria that inhabit the lake’s hypersaline waters. Credit: NASA Landsat Program.

The spectral signatures of the lakes contain information about the optically active substances in the water column, including dissolved material, cyanobacteria and suspended sediment. A. fusiformis contains photosynthetic pigments, such as chlorophylls and carotenoids, which absorb light in the visible region and give it a characteristic reflectance spectrum. At high biomass concentrations, scattering by cells causes high reflectance in the near infrared region. Due to high biomass concentrations it is likely that the optical properties of Lake Bogoria will be dominated by cyanobacteria.

Chlorophyll retrieval algorithms (as an indicator for cyanobacterial biomass) have been developed for oceanic waters and freshwater lakes [3], but this is the first study to look specifically at remote sensing of chlorophyll in alkaline-saline lakes. Outputs of the project so far include an algorithm for estimating chlorophyll in Lake Bogoria from Landsat ETM+ satellite imagery [4, 5]. The ultimate aim is to develop a set of algorithms for assessing chlorophyll at the landscape scale for a chain of soda lakes in the Rift Valley. The algorithms will be used to recover a long term chlorophyll time series for each lake to assess the stability of the lesser flamingo food supply. Landsat ETM+ has been the focus of work so far because its high resolution imagery (30 m) is necessary due to the small size of the lakes (1-3 km across) and the need to observe small scale variations.

The algorithm was developed using a time series of Landsat ETM+ imagery and in situ chlorophyll measurements. The near infrared band of Landsat was found to be the best predictor of chlorophyll due to the high biomass concentrations (hundreds of µg/l Chl-a). The standard error in the algorithm is relatively large due to the separation between ground and satellite data of up to eight days (to allow matching of a sufficient number of images). The algorithm was applied to Landsat ETM+ images for the period 1999–2010 to produce a long-term chlorophyll time series for Lake Bogoria (Figure 5). The time series show key features such as the die-off event in 2003, which coincided with the in situ study, and other apparent die-offs in early 1999 and in 2005, 2006 and 2007. Large error bars mean, however, that fine variations in chlorophyll cannot be observed. Hence a field spectroscopy study was carried out to aid the development of an improved algorithm and to give a better understanding of other water parameters influencing the optical properties of the lake. Spectral measurements of the lake were made from a boat using a spectroradiometer and, at each site water samples were collected and analyzed for chlorophyll concentration, dissolved organic material and suspended sediment.

Figure 5. Chlorophyll-a time series for Lake Bogoria showing in situ data and satellite estimates.

A field spectroscopy study allowed the unique spectral properties of Lake Bogoria to be measured for the first time. Three distinct spectral shapes were observed (Figure 6). At one site, within the sediment plume of the Waseges River, a spectral shape typical of suspended sediment was observed; at another site, where cyanobacterial scum (a layer of cyanobacterial cells trapped in the surface tension on the lake surface) was present, a spectral shape similar to that of terrestrial vegetation was observed. At all other sites the shape of the reflectance was characteristic of cyanobacteria mixed in the water column. For these sites the height of the reflectance peak in the near infrared increased with increasing chlorophyll concentration, in agreement with the existing chlorophyll retrieval algorithm.

Figure 6. Different spectral shapes observed at Lake Bogoria (left). Reflectance spectra for sites where cyanobacteria mixed in the water column dominated the optical properties.

IV. FUTURE WORK

A. Improving the Chlorophyll Algorithm

Figure 7. Landsat ETM+ band 3 reflectance and NDVI images for Lake Bogoria showing potential for masking sediment and scum.

B. Hydrology of Lake Natron

The hydrology of Lake Natron could be significantly disrupted in the future by two proposed developments. In order to assess the past hydrological variability of Lake Natron a Normalized Difference Water Index will be applied to archival Landsat images of the lake in order to produce a time series of lake surface area. These results will be used in combination with radar altimetry data to determine changes in lake level over time. It is thought that the flamingos only breed at a certain lake level, and in this study we aim to confirm whether this is the case. Our results will inform the future management of the lake.

V. CONCLUSIONS

This work has shown that ecologically useful information about alkaline-soda lakes can be gained from satellite data. Remote sensing can be used to monitor long-term trends in biomass of primary producers in alkaline-saline lakes and assess the sustainability of the lesser flamingo food supply. Work is ongoing to refine the algorithm for Lake Bogoria and to develop algorithms for other lakes. Ultimately the chlorophyll results will be combined with information on lake surface area to determine connections between the hydrological and ecological processes. Our results will contribute to the conservation of lesser flamingos in alkaline-saline lakes within the East African Rift Valley thus preserving the biodiversity and economic value of these unique ecosystems.

VI. REFERENCES

[1] C.H. Tuite, “Standing crop densities and distribution of Spirulina and benthic diatoms in East African alkaline saline lakes,” Freshwater Biology, 11: 345–360, 1981.

[2] S. Mills, R. Boar, B. Childress, D.J. White, and D.M. Harper, Foraging by Lesser Flamingos, Phoeniconaias minor, in response to vertically migrating cyanobacterium, Arthrospira fusiformis. Manuscript submitted.

[3] T. Kutser, “Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters,” International Journal of Remote Sensing, 30(17): 4401–4425, 2009.

[4] E.J. Tebbs, J.J. Remedios, and D.M. Harper, “Remote sensing of chlorophyll a in saline-alkaline lakes using Landsat ETM+, with applications to lesser flamingo (Phoeniconaias minor) ecology,” in preparation.

[5] E.J.Tebbs, Remote Sensing of Chlorophyll in Saline Lakes, with applications to flamingo ecology: A feasibility study, Master’s Thesis, 2009.

[6] M.W. Matthews, S. Bernard, and K. Winter, “Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS,” Remote Sensing of Environment, 114 (9): 2070–2087, 2010.

Acknowledgements

Thanks to the Natural Environment Research Council Field Spectroscopy Facility for providing equipment and training. Emma Tebbs would like to acknowledge the support of the Centre for Interdisciplinary Science at the University of Leicester in funding this PhD study.

Community Links

As well as carrying out scientific research in the area, the University also works with local communities though several projects. Each year the Centre for Interdisciplinary Science runs an undergraduate field course during which students work with members of the local community on sustainability themed projects. Water themed projects are common since clean, safe, fresh water is such a valuable and limited resource in the area. In 2011 a student project on “Reduction of fluoride in drinking water” was particularly successful in raising awareness and transferring knowledge to the community. Although fluoride is added to drinking water in some areas of the world, very high levels of fluoride can have a negative effect on human health. Students found that drinking water from bore holes in the area had fluoride concentrations well above WHO guidelines. They tested and demonstrated a simple method of reducing fluoride concentration in drinking water, using carbonized animal bones. Another project in 2010 investigated the potential of rainwater harvesting. Students found that local people were knowledgeable about the benefits of harvesting rainwater but the main barrier was the cost of purchasing a water storage tank. They devised a method for building a cheap water tank using sticks, concrete and sand. The work done by undergraduate students is complemented by short films about sustainability made by Kenyan and Tanzanian film-makers, trained by a team of British filmmakers under a project funded by the Darwin Initiative. The project is called CBCF (Community-based Biodiversity Conservation Films), and is directed by Dr. David Harper. The films are used to raise awareness of sustainability issues in the area.

Emma Tebbs is a PhD student working on remote sensing for the study of eco-hydrology in East African river basins. She has a Masters in Physics with Space Science and Technology from the University of Leicester. A final year project on remote sensing of chlorophyll in saline lakes led to this PhD project.

Dr. David M. Harper is a Senior Lecturer in Ecology and Conservation Biology in the Biology Department and contributes to the Interdisciplinary Science degree in ecology and sustainability issues. He has conducted scientific research in Kenya and Tanzania for over 25 years, focused upon the sustainability of water – a highly limiting resource in an arid country like Kenya, and which will shortly become limiting in a country like Britain where so much is wasted.

Prof. John J. Remedios is head of the EOS team in the Physics and Astronomy Department at the University of Leicester. He has much experience in infrared radiative transfer, satellite surface temperature measurements, as well as satellite data for atmospheric composition studies of the troposphere and stratosphere, and trace gas profile retrieval using the optimal estimation technique.

Dr. Sean Avery has spent over 30 years working in the water and environmental sectors, initially in South-East Asia and then extensively throughout Africa. He has been resident in East Africa since 1979 and has worked throughout the continent focusing on water resources development. In 2008 he was appointed an Honorary Visiting Fellow of the University of Leicester. His professional field of special expertise is in engineering hydrology. He has a passion for arid lands in particular, and a special interest in Kenya’s conservation areas.

Aristotle University of Thessaloniki, School of Rural and Surveying Engineering

Figure 1: OBSERVE's Post GEO Workshop.

A Post-GEO Plenary Workshop was held by the OBSERVE, BALKANGEONET and EGIDA 7th Framework Programme projects on Nov. 18 and 19 in Istanbul. The workshop attracted 88 experts, stakeholders, local policy makers and academics who had the opportunity to learn about the latest developments regarding international and European Earth Observation, , and debate the future of the practice in the Balkans.

Motivation

Balkan countries do not have a coherent and continuous approach toward the challenge of implementing integrated Earth Observation (EO) applications in environmental monitoring and management. The defect in the implementation of EO applications and their use in the environmental decision making are manifested through the limited synergies among national and regional institutions, ineffective technological means and discontinuous record of participation to international organizations and committees. The increasing importance of a common approach toward effective environmental monitoring practices, for the benefit of the societal web of the broader Balkan region, calls for immediate action, setting as a starting point the build-up of regional institutional capacity and spillage of technology transfer.

Brief description

The Post-GEO workshop aimed to:

• Inform the audience on EO activities with a focus on European institutions’ aims, actions, and near-term future plans;
• Exchange views on how EO activities relate to activities in Southeastern Europe;
• Inform speakers and leading EO institutions on the needs regarding EO in Southeastern Europe;
• Build-up acquaintances, networks and co-operations, with focus on capacity building and more extensive use of EO in Southeastern Europe, including possible contributions of related EU projects in European activities.

Venue

The Post-GEO workshop was held in at the Maçka Campus of Istanbul Technical University, in the center of the Istanbul-Maçka district, just near the Taksım area. Participants had the opportunity to join the meetings of the workshop and enjoy the city center in the nights and during session breaks.

Speakers

Keynote and invited speakers at the workshop play vital roles in Earth Observation in Europe, especially in the development of environmental activities in the Balkan area:

• Mario Cornelis Vis – Adviser in charge of science, research, technology and industry, Bureau of European Policy Advisers, EC – TOWARDS AN EU SPACE POLICY;
• Hans Peter Plag – Nevada Bureau of Mines and Geology – Toward a GEOSS stakeholder network;
• Peter Zeil – Co-chair of GEO Capacity Building Committee – GEO Capacity building activities – a platform for cooperation;
• Vojko Bratina – research program officer, EC – EC initiatives in capacity building for Earth Observation in the Balkans;
• Ian Dowman – ISPRS First Vice President – Prospects for mapping from Earth Observation data and the role of ISPRS;
• Paolo Mazzetti – National Research Council of Italy – The EGIDA METHODOLOGY;
• Stuart Marsh – Geoscience Technologies, British Geological Survey – GMES and GEO for geohazard mitigation in the Balkans;
• Nando Foppa – Deputy Head of Staff Office Climate Division, Swiss GCOS Office – The role of satellite data within the national climate observing system(GCOS SWITZERLAND);
• Jay Pearlman – IEEE GRSS – Socio-economic benefits from the use of Earth Observation;
• Francesco Sarti – Scientific coordinator of Education and Training Activities, Director-ate of Earth Observation Programs, ESA – ESA Education and training EO;
• Vasilis Tritakis – Greek GEO Office Mariolopoulos Foundation for Environmental Re-search – Risks and cost of a possible climatic change in Greece and Eastern Mediterranean Cornelis Vis;
• Athina Trakas – Director of European Services – Open Standards and the EO Community – From Processes, Applications and the Value;
• Antti Jakobsson – EuroGeographics, Programmes Manager – European Location Framework and Inspire;
• Stefano Nativi – EGIDA project coordinator – EuroGEOSS: BROKERING APPROACH FOR THE GCI;
• Joan Maso – CREAF: Centre de Recerca Ecològica i Aplicacions Forestals of the Uni-versitat Autonoma de Barcelona – GeoViQua: The Quality Challenges for GEOSS.

Presentations from the plenary workshop are all available for download.

The final session ended with a roundtable discussion summarizing results and scheduling further activities to fulfill the aims of the workshop and the three 7th Framework projects that organized the event.

Program – Presentations and Workshop results
For the realization and organizing purposes of the workshop, a special website was created. Important information can be found at postgeo-ws.itu.edu.tr.

Additionally, workshop results and conclusions are presented in detail in the most recent issue of OBSERVE project D3.3, and all the workshop presentations can be downloaded from OBSERVE’s Knowledge Base Repository.

Petros Patias, OBSERVE coordinator, is a professor and ex-chairman at the School of Rural and Surveying Engineering, The Aristotle University of Thessaloniki, board member of the Department of Urban Planning, and Vice Rector at the University of Western Macedonia, Greece. His published work includes six books, four chapters in international books and 161 papers in journals and proceedings.

See also
An Introduction to OBSERVE, Strengthening and development of Earth Observation activities for the environment in the Balkan area.

Abstract

OBSERVE is the acronym of a research project with the full title “Strengthening and development of Earth Observation activities for the environment in the Balkan area.” It is funded under the scheme of Coordination and Support Action by the 7th Framework Programme. Its duration is 24 months and started in November 2010.

Since it began, the project has delivered five consortium meetings, one workshop, presence in three international conferences by way of academic papers and posters, and a long list of other public and internal deliverables including an Earth Observation (EO) stakeholders’ database, national thematic reports on EO capacities, questionnaires and other auxiliary documents. All of the results and objectives are targeted toward EO activities, and are especially focused in the Balkan area.

The OBSERVE project consortium consists of (Figure 1): Fifteen institutions from 13 different countries, eight of which belong to the Balkan region. Ten of the partners are universities or research organizations, while the other five are from the private sector.

Figure 1: Participants of OBSERVE.

Balkan countries do not have a coherent and continuous approach toward the challenge of implementing integrated Earth Observation (EO) applications in environmental monitoring and management. It should be mentioned the Balkan countries, except Greece and Romania, are not ESA members. Besides, Albania, Bulgaria, FYROM, Montenegro and Bosnia Herzegovina also are not members of the Group on Earth Observation (GEO).

The defect in the implementation of EO applications and their use in the environmental decision making are manifested through the limited synergies among national and regional institutions, ineffective technological means and discontinuous record of participation to international organizations and committees.

On the other hand, the increasing importance of a common approach toward effective environmental monitoring practices, for the benefit of the societal web of the broader Balkan region, calls for immediate action, setting as a starting point the build-up of regional institutional capacity and spillage of technology transfer.

The aim of the OBSERVE project is to collect and compile all the necessary information for delivering an integrated analysis on the current status of EO activities and networks in the Balkans regarding environmental monitoring, the potential benefit from the full exploitation of an integrated capacity development strategy, and the prospect of creating a relevant and permanent EO community in the broader region. OBSERVE has the ultimate goal of raising awareness and establishing firm links with the regional decision-making bodies on the importance of a mutual and enhanced EO application network on environmental monitoring according to the principles of the GEO.

Another key objective is to ensure a focused and strong dissemination strategy in the Balkan region, including EO and environmental decision makers, as well as national and regional government decision makers, the international research community, local stakeholders, the media and other valuable multipliers.

The Vision

OBSERVE has the vision of establishing a new Balkan EO community of multilevel stakeholders that will make use of state of the art technological developments, products and knowhow from the existing European EO community and industry.

Strategic Objectives

Figure 2: The distribution of official organizations using EO data in the OBSERVE Balkan countries as recorded through special survey and delivery of National Thematic Reports on EO capacities.

• Build a spatial database and web inventory with all existing dynamic elements related to the scope of the relevant analysis in order to reinforce new synergies in EO solutions for the benefit of environment;
• Raise awareness on the need to harmonize policies and practices in field EO applications to address the challenges described by the GEO societal benefit areas;
• Serve as an efficient mechanism for recording, monitoring and influencing policy in EO;
• Favor exploitation and development of EO activities and ensure coordination of these activities for the benefit of natural resources management;
• Promote the idea of permanent institutional links and mutual cooperation between Balkan states in the field of EO for environmental management;
• Ensure free access of Balkan countries to all advantages of Earth Observation techniques;
• Promote cooperation between Balkan States in the fields of training and sharing of staff and experiences in all aspects of EO.

Conclusion

During the short lifetime of OBSERVE, institutes from most of the Balkan countries have had the opportunity to cooperate and exchange experiences and knowhow to cope with environmental management issues, taking advantage of Earth Observation data and services. The recording of status quo for Earth Observation in the Balkan region through the OBSERVE surveys and deliverables (Figure 2) will be a powerful tool to:

• Explore strengths and weaknesses in the production, use and delivery of EO products and services;
• Find the gaps and emphasize certain problems each Balkan state is facing;
• Design a road map to overcome problems connected to the use of Earth Observation for environmental applications.

Petros Patias, OBSERVE coordinator, is a professor and ex-chairman at the School of Rural and Surveying Engineering, The Aristotle University of Thessaloniki, board member of the Department of Urban Planning, and Vice Rector at the University of Western Macedonia, Greece. His published work includes six books, four chapters in international books and 161 papers in journals and proceedings.

See also
A Post-GEO Plenary Workshop on Earth Observations for the Social Benefit of the Balkans.

Abstract

Water on Earth is an irreplaceable commodity. Improper selection of indicators of water resources can lead to underestimation or overestimation of water. Incorrect assessment of water resources contributes to an improper fulfillment of water management tasks, including addressing the needs of different types of water users, including, inter alia, population and the economy (industry, agriculture and forestry, hydropower, inland navigation, tourism and recreation).

The paper discusses the benefits of using Earth observation technology in the Global Monitoring for Environment and Security (GMES) program for estimating water resources in Poland. The authors conclude that a summary of the total amount of water resources in different components, including the availability of water for people, will allow for proper assessment of water resources.

Figure 1. Average annual precipitation in Europe for the period of 1940–1995 (Mathematical expressions absolute humidity) (Lazaridis, 2011).

Despite the vast amount of water on the planet, decades of unsustainable water management have caused that water shortages have reached a crisis point in many regions. Globally more than 50 percent of all renewable and accessible freshwater resources are used but billions still lack the most basic water services. As a result of prolonged periods of low rainfall or drought, the balance between water demand and availability has reached a critical level in many areas of Europe. Both water and population are unevenly distributed in Europe, and therefore countries and subregions experience varying degrees of water stress. The average annual precipitation in Europe for the period of 1940–1995 is presented in Figure 1.

Water availability problems occur when the demand for water exceeds the amount available during a certain period. Difficulties occur frequently in areas with low rainfall and high population density, and in areas with intensive agricultural or industrial activity. Apart from water supply issues, overexploitation of water has led to the drying out of natural areas in western and southern Europe and saltwater intrusion in aquifers (EEA 2008).

Figure 2. Drinking water resources in Europe (m³ per capita) (Wprost), 2011).

II. WATER RESOURCES AND WATER NEEDS IN POLAND

According to the principle of sustainable development (in terms of protecting water resources and water management), water use depends on the state of water resources of the country. In addition, water resources are understood as the general surface and groundwater volume occurring permanently or temporarily in a particular area. The volume and quality of these resources determine the economic development of societies and have an impact on the quality of life. Water resources are used from different sources including surface water, groundwater, precipitation or post-production water. The same water can be repeatedly used (power industry). Surface water, groundwater and rainwater resources are usually characterized by an uneven distribution. The reasons for this variation are topographical, geological and meteorological characteristics and various types of land use (Figure 3). In addition to their spatial variability, surface water and rainfall are characterized by a random variability in time, conditioned by meteorological phenomena. The annual cycle of these phenomena determines the average annual variation, both in the long term and within individual years. Renewability of water resources depends on, inter alia, the amount and intensity of precipitation, as well as on terrain, soil and other factors, all of which affect the volume of surface and soil runoff.

Figure 3. The main natural determinants of water resources in A-local, B- regional, and C - global scale. (Pociask-Karteczka, 2009).

P= (H’+H”) + E

According to the above equation, the total water resources are defined as the subtraction of precipitation and total evaporation, terrain and river runoff. Figure 4 shows the total volume of water resources in selected European countries in comparison with the surface of the country. It may be noted that the size of the country’s area is directly proportional to the water volume.

Considering the amount of total resources, in accordance with the presented data, Poland is on 19th place among 29 countries analyzed, and by precipitation in 11th place, following Austria, Switzerland, Ireland, Bulgaria and Romania. In terms of external flow, Poland is estimated to be at 17th place among 29 countries. Figure 5 presents a summary of total water resources and total consumption volume area per km².

Figure 4. Total resources of diverse income and disbursements in selected EU countries against area of the country (data: FAO, 2011).

Figure 5. Summary of total water resources in hm³ and total volume of consumption in Poland per km² compared to other countries (data: FAO, 2011.)

III. ESTIMATION OF WATER RESOURCES IN POLAND

Analysis of the size of groundwater resources within the catchment requires knowledge of the conditions and possibilities of their discharge and renewal within each hydrogeological unit. Groundwater resources are divided into static and dynamic. Static resources are below the long-term and seasonal fluctuations of groundwater and this is gravitational water, filling the utility levels below the lower limit of seasonal and perennial groundwater fluctuations. It can be either renewable (which is in hydraulic contact with the surface area) or non-renewable (isolated from the surface of the terrain and other aquifers). A correct assessment of deep groundwater resources involves careful evaluation. This information can change the opinion of the size of water resources in Poland (Mikulski, 1998). Consumable surface water resources are difficult to estimate because of the possibility of their reuse. International data bases of these resources are limited or incomplete and most importantly they vary in many aspects. There are no homogeneous and comparable data for long time periods that could be the meaningful bases for more thorough analysis. Water resources of the country are usually measured by volume of water produced in a specific area in a defined period of time as a result of atmospheric processes (Kaczmarek, 1978). The analysis of Polish water resources must take into account the amount of precipitation, river water supply, lakes and aquifers and the above-mentioned factors affecting the size of the disposable and consumable resources. Despite the relatively small resources, a water deficit in Poland does not result from the lack of water in general, but a lack of water in the right place and of adequate quality. Nowadays, more frequent droughts will become a major concern for the country’s economy. Moreover, increased demand for water is forecasted.

Figure 6. The size of water intakes in the main sectors of the national economy against the background of selected EU countries (data: FAO, 2011)

IV. THE CONSEQUENCES OF POOR WATER RESOURCE ASSESSMENT

Poland has a high rate of water consumption (FAO, 2011). Water per capita per year is probably the best synthetic indicator because it illustrates the richness of water for each country, but despite its advantages, it is not convincing. For an average citizen, this hypothetical amount remaining at their disposal usually exceeds their need by several times but for planners and strategists this indicator does not show the direct action that should be taken. Water management strategies need a more detailed analysis related to the extreme periods, types of demand or averaging capacity of reservoir discharges. Improper selection of indicators of water resources can lead to underestimation or overestimation of water. This can result in the mismanagement of water in the country, having negative impacts on water allocation for different sectors of the economy. Incorrect estimation of water resources contributes to the improper realization of water management tasks, including addressing the needs of different types of water users, including, inter alia, population and the economy (industry, agriculture and forestry, hydropower, inland navigation, tourism and recreation). For nonconsumers, such as hydropower and tourism, incorrect estimates are not as significant as for consumers, like public utilities, agriculture and industry, for which the consequences are sometimes serious limitations in meeting their needs. Relative needs, which are replaceable, can be met by substitution solutions, while the absolute water needs cannot be replaced in any way. Incorrect estimation of resources may also have direct or indirect impacts on water prices, which can lead to a significant increase of economic costs. All the above-mentioned factors tend to suggest changing the approach to estimation of water resources in Poland, thus changing the presentation and interpretation of the analytical results. Such a change should also affect the interpretation of the collected data and improve the opportunities for their utilization.

V. THE USE OF EARTH OBSERVATION SYSTEMS FOR THE PROPER ASSESSMENT OF WATER RESOURCES

Space images of the Earth provide a wide range of information that may be impossible or difficult to obtain using ground-based systems. Applications of these images are rapidly increasing – from geodesy, cartography, oceanography, forestry or marine science to the study of climate change – as are attempts to respond in advance to the threat of natural disasters and the assessment and effective management of their effects. The information obtained from satellite imagery can also be used to assess water resources. Remote sensing is a system of collecting information about objects and phenomena without direct contact with the sensor. Different methods of collecting information provide data that have complementary properties to each other. For example, airborne images are characterized by higher resolution and flexibility of implementation of data capture campaigns, while satellite systems have the potential to capture data at a global scale. Information derived from satellite systems is important in the meteorological and water management measurements due to the large swath extent of the imagery, which allows wide-scale observation and can be combined with simultaneous measurements. Another advantage for meteorology is the opportunity to obtain current information with the possibility of frequent and fast updates. Data analysis provides, inter alia, the possibility of obtaining detailed data for monitoring drought nationwide. Remote sensing has a very wide range of applications. For resources and water management these applications include meteorological observations and weather forecasting; analyzing changes in the environment to assess changes in climate processes, global warming and the impact of human activity; estimation of losses in reservoirs due to drought, floods and biological contamination; information for water cadastre; determination of the biological conditions of the aquatic environment; and the designation of areas exposed to flood hazards (Ryzenko, 2007).

The Global Earth Observation System of Systems (GEOSS), coordinated by the Group on Earth Observations (GEO) actively coordinates activities across societal benefits areas (SBAs) aiming to achieve a harmonized system on land, sea and air-space and to provide comprehensive data, information and analysis on the environment. Data and information in the nine SBAs provided by GEOSS are groups of complex issues that require accurate data on the spatial and temporal resolution. The main aims for these groups are (European Commission, 2011):

• Minimizing loss of life and property due to natural disasters and disasters caused by man
• Understanding the environmental factors affecting human health and well-being
• Improving management of energy resources
• Understanding impact assessment, mitigation and adaptation to climate change
• Improving management of water resources through a more detailed analysis of the water cycle
• Improving the quality of weather information, forecasting and warning systems
• Improving management and protection of terrestrial, marine and coastal areas
• Promoting sustainable agriculture and combating desertification
• Understanding, monitoring and conservation of biological diversity.

The Global Monitoring for Environment and Security program is being supported within the framework of GEOSS. The program aims to provide information about the Earth’s surface to maintain environmental sustainability. Subjects include the GMES environmental monitoring of the oceans and atmosphere, the Earth’s surface including crop forecasting, development of vegetation, providing an early warning system and the use of water resources for the sustainable management of these elements. Another advantage is integrating Earth observation data for GMES by products intended to support implementation of policy and European directives. The products allow the supply of information, dissemination and implementation of the Convention and integration models to prepare them for operational use (European Commission, 2011).

Pre-validation of GMES Global Service for Water Scarcity Information is conducted by the GLOWASIS project. It will be a portal providing water scarcity information by securing access to the monitoring data from satellites and in situ sensors, with improved forecasting models with improved monitoring data and linked statistical water data in forecasting. It also aims to promote GMES Services and European satellite utilization.

Earth observation technology allows examination of various factors in considering the observations of water resources and acquisition of information on their size. Satellite meteorological observations have great importance for estimating water resources. In discussing meteorological processes in the atmosphere with their varying spatial and temporal dynamics, attention should be paid to various possibilities of Earth observation technologies to provide information. The requirements of a particular analysis depend on the scale of meteorological phenomena, their dynamics, the possibility of classification and physical parameters. Imaging allows acquisition of information about fast-changing as well as slow-changing phenomena, their mutual correlation and relationship (Struzik, 2008). In addition, the study of water resources in the soil layers is important when assessing a drought. In Poland, due to scarce data and the local character, this assessment is currently difficult. Satellite observation gives wide opportunities for estimating water resources in a global, holistic and detailed way at a reliable scale (Usowicz, 2009).

VI. SUMMARY

Potential water deficit in Poland results not from the lack of water in general, but from the lack of water in the right place and of adequate quality. Reservoirs in Poland with a total capacity of about 4 billion m³ equal approximately to 6.5 percent of average annual runoff volume do not provide the full protection against floods and drought, and do not guarantee adequate water supply. Estimation of water resources in Poland turns out to be a complex process. Correct determination of water resources is very important in reference to water needs in Poland. Incorrect estimation of the amount of water in Poland led to the improper planning of water use. The consequence of this is the inadequate management of water resources. New research, analysis and technologies enable precise measurements of the amount of water circulating in the atmosphere, pedosphere, biosphere and hydrosphere. A summary of the cumulative volume of water resources in the various components, including the availability of water for the human use, will allow proper assessment of water resources in Poland. Such measurements are possible thanks to the utilization of Earth observation technologies. These systems provide a global, accurate, comparable and reliable measurement of water resources, and will enable the determination of the amount of water in Poland. Moreover it will show the real water quantity in Poland compared to other countries of the world.

VII. REFERENCES

BBC, Population seven billion: UN sets out challenges, Available: http://www.bbc.co.uk/news/world-15459643 2011-10-26, Retrieved 2011-10-27

European Commission, Research and Innovation, Available: http://europa.eu/pol/rd/index_en.htm, Retrieved 2011-10-25

European Environmental Agency (EEA), Average annual precipitation, Available: http://www.eea.europa.eu/data-and-maps/figures/average-annual-precipitation, Retrieved 2011-10-20

Food and Agriculture Organization (FAO), AQUASTAT online database. Water resources, http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en, Retrieved 2011-09-10

Kaczmarek Z., Zasoby wodne Polski i zasady ich racjonalnego wykorzystania, Nauka Polska nr 8 s. 43-58, 1978

Mikulski Z, Gospodarka wodna, Warszawa, PWN 1998

Pociask-Karteczka J., Water protection ad water resources. Natural conditions and other aspects of water resources in river catchments – ad memoriam veterum veritatum, FRUG , Gdańsk 2009.

Ryzenko J., Badurska A., Kobierzycka A., Kierunki rozwoju systemów satelitarnych, Polskie Biuro ds. Przestrzeni Kosmicznej, Warszawa, 2007

Struzik P., Satelity meteorologiczne od 40 lat w służbie Instytutu Meteorologii i Gospodarki Wodnej, Nauka Polska nr 4, s. 35-42, 2008

Usowicz B., Marczewski W., Lipiec J., Woda w glebie, pomiary naziemne i satelitarne w badaniach zmian klimatu, Polska Akademia Nauk, 2009

Wprost, Pistolet na wodę, Available: http://www.wprost.pl/ar/79264/Pistolet-na-wode/, Retrieved 2011-10-20

Acknowledgements

This work has been supported by the GLOWASIS project, a collaborative project aimed at pre-validation of a GMES Global Water Scarcity Information Service, and funded within the EU Seventh Framework Programme by the grant agreement 26225 under the call FP7-SPACE-2010.1.1-04.

Mieczyslaw Ostojski is director general of the Institute of Meteorology and Water Management in Poland. He has been closely involved in the activities of the World Meteorological Organization. His current activity is mainly focused on coupling meteorological and hydrological models to advance knowledge in flood forecasting and help build capacities in Flood Risk Assessment.

In order for this concept to work, there are a few key lessons learned from a recent session at the Fall American Geophysical Union (AGU) Meeting in San Francisco:

• Pick a topic you are passionate about and put a creative spin on it. Ignite talks push the boundaries. They are more visionary, provocative, inspiring and entertaining than a traditional oral presentation;

• 1 slide = 1 idea. Use images and graphics. Slides should be compelling and grab the audience. Do not try to put multiple concepts on a single slide. Fifteen seconds goes faster than you think;

• Practice! Fifteen seconds also can go slower than you think. Because the slides auto-advance, you have to get into the rhythm of the talk. This will not happen on your first try.

Many Ignite events allow the presenters to choose their topic, but for Ignite@AGU, we wanted the presentations to align with NASA’s Applied Sciences Program and connect science and research to real-world applications.

This was a broad enough window to bring together 18 presenters sharing a diverse range of topics from technical to socio-economic — from oceans to the atmosphere and disasters. The variety of experts had the desired effect of bringing together people from various focus areas. Many where quickly exposed to ideas and presenters that they would otherwise have never met.

The last distinction between Ignite@AGU and other oral presentations at AGU was that the talks were recorded and will be posted online at igniteshow.com/events/igniteagu.

Many left the event feeling like all presentations should follow the guidelines noted above. Try mixing it up at your next event with an Ignite session!

For more information, see “How to Produce an Ignite Event” at IgniteShow.com.

Erin Robinson is the Information and Virtual Community Director for the Federation of Earth Science Information Partners (ESIP). Her current research interests include improving scientific collaboration by enhancing connections. This ranges from coordinating events, like Ignite, to developing collaborative web sites that integrate social media and sharing Earth observations through standard data access.

Fig. 1. The Central Valley aquifer is outlined in black and is the study area for which C2VSIM calculates groundwater storage changes.

Abstract — The role of the NASA Applied Sciences DEVELOP student internship program is to use NASA satellite missions to explore Earth-based research questions in collaboration with state and federal agencies. One recent project focused on the use of the Gravity Recovery and Climate Experiment (GRACE), to estimate changes in groundwater storage in the Central Valley aquifer in collaboration with the California Department of Water Resources (DWR). GRACE can be used to measure gravitational anomalies on Earth which are further processed to remove other gravitational effects, and are obtained as measurements of equivalent water thickness, which relate to changes in total water storage (TWS) throughout the world. For this study, GRACE TWS anomalies were obtained from October 2002 to September 2009, for two hydrologic regions, the Sacramento River Basin and the San Joaquin River Basin, including the Tulare Lake Basin, encompassing the Central Valley aquifer. To calculate monthly groundwater storage estimates, additional variables such as soil moisture, snowpack, and surface water storage were combined with GRACE TWS values to estimate groundwater storage anomalies. The GRACE-derived changes in groundwater storage at the basin and regional level or the two basins combined, were then compared to modeled values calculated using the California Department of Water Resources’ Central Valley Groundwater-Surface Water Simulation Model (C2VSIM) and a Geographical Information Systems Change in Storage Tool (GIS CST). Groundwater storage estimates from GRACE and C2VSIM were comparable for the entire Central Valley, showing similar seasonal, annual, and long-term trends. This work has the potential to improve California’s groundwater resource management and the use of existing hydrologic models for the Central Valley.

I. INTRODUCTION TO NASA DEVELOP

The NASA Applied Sciences’ DEVELOP Program sponsors paid internships located at Ames Research Center for students to extend science research to local communities. DEVELOP is a NASA Science Mission Directorate Applied Sciences Program training and development internship. Students work on Earth science research projects, mentored by science advisors from NASA and partner agencies to extend research results to local communities. The NASA Ames DEVELOP program hosts graduate, undergraduate and high school students in a ten week summer internship. Students are involved in a wide range of projects such as vegetation mapping, air quality assessments, wetland restoration, and water availability. The DEVELOP program is student run and student lead, and is supported by science advisors and mentors.

Fig. 2. The GRACE satellite orbiting Earth {5}.

One pressing issue within California is the amount of available groundwater to support a growing population, agriculture and industry [1]. Groundwater, one of the most important resources on Earth, is difficult to understand and manage. The majority of groundwater in California is found in the Central Valley aquifer system (Figure 1). This aquifer supports agriculture that supplies nearly 7 percent of the United States’ food supply, with an estimated annual value of $21 billion [2]. California does not regulate groundwater pumping at the state level. Groundwater management is implemented at the local level and includes groundwater monitoring, basin management and water-use restrictions. Understanding groundwater availability is complicated and may benefit from using different methods. The California Department of Water Resources (DWR) has developed tools to assess groundwater storage changes within the Central Valley with the use of the Central Valley Groundwater-Surface Water Simulation Model (C2VSIM) and a Water Data Library (WDL) Geographic Information System (GIS) change in groundwater storage tool (CGST) [3].

The Central Valley aquifer is a basin, which is about 80 kilometers wide and 650 kilometers long, and is bounded by the coast ranges on the east and the Sierra Nevada on the west (Figure 1). The Central Valley aquifer is contained within three hydrologic regions –The Sacramento River Basin, the San Joaquin River Basin, and the Tulare Lake Basin (Figure 1). For this study, the San Joaquin River Basin and the Tulare Lake Basin were combined and will be referred to as the San Joaquin River Basin. The C2VSIM model also incorporates inflow from watersheds within the Sierra Nevada, and although the study areas from C2VSIM and GRACE data are significantly different, the Sierra Nevada is not conducive to groundwater storage, and it is assumed that the majority of changes within the GRACE defined study area are occurring within the Central Valley aquifer [3].

III. METHODS

Fig. 3. Area of the modeled GRACE dataset compared to the outline of the Sacramento and San Joaquin hydrologic regions.

Other sensors and field measurements can be used to detect surface water storage changes in California, and can aid in calculating changes in groundwater storage that may be due to pumping, drought or natural climate variability. The most important variables to use when converting from changes in TWS to changes in groundwater storage are changes in lakes or reservoirs, soil moisture and snowpack (Figure 4). For this study, changes in reservoir storage were obtained from the California Data Exchange Center (CDEC) [15], soil moisture can be estimated from AMSR-E) [15], and snowpack was obtained from data collected from various sources including satellite data by the National Oceanic and Atmospheric Administration (NOAA) [17]. The monthly anomalies for soil moisture (SMα), surface water (SWα) and snow pack (SPα) were then subtracted from monthly TWSα,GRACE values to calculate monthly GWα for the Central Valley, the Sacramento River Basin and the San Joaquin River Basin.

Equation (1)

GW_α = TWS_α,GRACE – ( SW_α + SM_α + SP_α )

Where:
GW_α = groundwater storage anomaly
TWS_α = total water storage anomaly
SW_α = surface water storage anomaly
SM_α = soil moisture storage anomaly
SP_α = snowpack storage anomaly

Fig. 4. Visual example of each of the variables of the hydrologic cycle that were considered in the groundwater calculation.

Changes in groundwater storage in the Central Valley were also calculated using two DWR tools, the C2VSIM model and groundwater level measurements that are incorporated into a GIS CGST. The C2VSIM also calculates changes in groundwater storage using a variety of inputs including precipitation, river discharge and aquifer characteristics. The C2VSIM then calculates the change in groundwater storage for each groundwater basin. Also, the GIS CGST uses groundwater elevation measurements taken each spring to estimate changes in groundwater storage. This tool was used in the Sacramento River Basin only. The groundwater storage estimates from each method were compared.

Fig. 5. Groundwater storage anomalies from GRACE and C2VSIM with linear trend lines {19}.

VI. RESULTS

GRACE and C2VSIM groundwater storage anomalies exhibited similar trends for the Central Valley region from October 2002 through September 2009 (Figure 5) [20]. This finding is important as it validates the usefulness of GRACE at scales that are less than or equal to 150,000 kilometers². It should be noted that GRACE and C2VSIM groundwater anomalies display marked differences during the seasonal peaks and troughs. The GRACE data appears to be more variable than C2VSIM, although the trends for the entire time period are similar. Also, climate variability is observed, and the drought period beginning in 2007 has a distinct negative trend in available groundwater.

Water storage anomalies produced from GRACE were also examined to understand climatic trends and long term differences in each basin. The San Joaquin River Basin exhibited the largest loss in TWS (-21.92 ± 1.92 kilometers³), snowpack (-3.90 ± 0.59 kilometers³) and groundwater storage anomalies (-16.43 ± 2.04 kilometers³) using the GRACE method. In contrast, the Sacramento River Basin displayed the largest loss in surface water storage (-4.22 ± 0.63 kilometers³) compared to the San Joaquin (-2.40 ± 0.36 kilometers³) [20]. Soil moisture storage values, however, remained relatively unchanged throughout the study period in both regions. The results presented here are consistent with the results presented by [1] and [13].

Fig. 6. Changes in groundwater for the Sacramento River Basin, the San Joaquin River Basin, and the Central Valley aquifer for GRACE and C2VSIM {19}.

Groundwater storage loss was calculated for the entire Central Valley, the Sacramento River Basin and the San Joaquin River Basin which includes the Tulare Lake Basin using GRACE and C2VSIM. The calculations of changes in groundwater storage for each river basin using both the GRACE-derived and the C2VSIM estimates did not produce comparable results. Although there are large differences on the hydrologic region level, both tools were similar for the entire Central Valley. For the study period, GRACE calculated a total loss of groundwater storage of -14.47 ± 1.49 kilometers³ or -11.7 ± 1.2 million acre-ft, and C2VSIM calculated a total loss of -15.01 ± 2.25 kilometers³ or -12.2 ± 1.8 million acre-ft, for the Central Valley (Figure 6) [20]. For a detailed analysis of results refer to Kuss et al., 2011 [20]. The large losses in groundwater storage in the Central Valley during the study period most likely resulted from a period of extended drought from 2007 to 2009.

V. CONCLUSIONS AND IMPLICATIONS FOR GROUNDWATER MANAGEMENT

The usefulness of the GRACE satellite to monitor changes in groundwater storage for the Central Valley aquifer was examined through a comparison approach. Satellites have been shown to be useful to detect changes in groundwater storage over large areas, and could be an important tool for groundwater management on large scales. The GRACE satellite and other sensors can supplement current groundwater management techniques for the Central Valley aquifer. This study also supports DWR’s modeling efforts for the Central Valley, and illustrates the usefulness of multiple techniques. Current spatial downscaling limitations of GRACE may result in decreased usefulness for smaller scale basin management. Estimated changes in groundwater storage by the C2VSIM and GRACE are comparable, while the estimated changes in groundwater storage for the Sacramento River Basin by the GIS CGST are significantly less than the estimates by the other two methods. Understanding how groundwater storage is changing in the Central Valley is an important aspect of sustainable groundwater resource management in California. This understanding will enhance the ability to make informed decisions on proper management techniques such as curbing water use, implementing artificial recharge and maximizing conservation and water-use efficiency efforts that will aid in sustainable groundwater practices.

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