Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

Spatial and temporal estimation of pumping and recharge in groundwater system analysis

In many parts of the U.S., groundwater is used as the primary or secondary source of water for the public supply, agricultural, and industrial sectors. In southwestern Louisiana, a majority of the water demand is supplied by the Chicot aquifer. Numerical flow models are useful for managing and optimizing groundwater systems. However, many important model parameters are difficult to quantify, exhibit a certain degree of uncertainty, and may vary both spatially and temporally. Two of the most important and sensitive parameters for a regional groundwater model of the Chicot aquifer are pumping rates and recharge. The use of GIS-based methods for calculating these properties provide spatially-specific results, allow flexibility for incorporating various processes and input parameters, and are independent of groundwater model resolution. In the Chicot aquifer system, agricultural pumping accounts for a majority of the groundwater demand. The irrigation water requirement is a function of the crop type, crop development, soil properties, and rainfall; all of which vary in space and time. A portion of this dissertation work is directed at the development of a GIS-based method for estimating irrigation demand that incorporates these hydrological processes and agricultural properties. Results show that the technique is able to capture the spatial and temporal variability in agricultural water demand in southwestern Louisiana over an 11 year time period. The second part of this dissertation research involves the use of a GIS-based net water balance technique which incorporates rainfall, soil properties, runoff, soil moisture, storage, and evapotranspiration to estimate recharge rates over the aquifer. Results show how seasonal- and long-term variations in agricultural demand and rainfall can significantly impact the recharge. The pumping and recharge rates are incorporated into a regional groundwater model of the Chicot aquifer to simulate the groundwater flow over an 11 year period. Comparison of simulated and observed water levels at multiple locations shows how the use of the GIS-based estimates improves our ability to capture the spatial and seasonal- and long-term variability in the groundwater dynamics. Finally, the model is used to project the impact of several alternative scenarios on groundwater levels over the next ten years

Advances and visions in large-scale hydrological modelling: findings from the 11th Workshop on Large-Scale Hydrological Modelling

Large-scale hydrological modelling has become increasingly wide-spread during the last decade. An annual workshop series on large-scale hydrological modelling has provided, since 1997, a forum to the German-speaking community for discussing recent developments and achievements in this research area. In this paper we present the findings from the 2007 workshop which focused on advances and visions in large-scale hydrological modelling. We identify the state of the art, difficulties and research perspectives with respect to the themes "sensitivity of model results", "integrated modelling" and "coupling of processes in hydrosphere, atmosphere and biosphere". Some achievements in large-scale hydrological modelling during the last ten years are presented together with a selection of remaining challenges for the future

Temporal stability of soil moisture and radar backscatter observed by the advanced Synthetic Aperture Radar (ASAR)

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R-2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments

Evaluation of a global soil moisture product from finer spatial resolution sar data and ground measurements at Irish sites

In the framework of the European Space Agency Climate Change Initiative, a global, almost daily, soil moisture (SM) product is being developed from passive and active satellite microwave sensors, at a coarse spatial resolution. This study contributes to its validation by using finer spatial resolution ASAR Wide Swath and in situ soil moisture data taken over three sites in Ireland, from 2007 to 2009. This is the first time a comparison has been carried out between three sets of independent observations from different sensors at very different spatial resolutions for such a long time series. Furthermore, the SM spatial distribution has been investigated at the ASAR scale within each Essential Climate Variable (ECV) pixel, without adopting any particular model or using a densely distributed network of in situ stations. This approach facilitated an understanding of the extent to which geophysical factors, such as soil texture, terrain composition and altitude, affect the retrieved ECV SM product values in temperate grasslands. Temporal and spatial variability analysis provided high levels of correlation (p < 0.025) and low errors between the three datasets, leading to confidence in the new ECV SM global product, despite limitations in its ability to track the driest and wettest conditions