.promotie Edwin Sutanudjaja, op 14 december j.l. gepromoveerd aan de Universiteit Utrecht. Spaceborne remote sensing for groundwater modeling In areas without ground-based measurements, applications of satellite-based remote sensing for hydrological studies have received increased attention. Yet groundwater studies, which are very important for agricultural areas and ecosystems, rarely utilize the benefits of spaceborne remote sensing. 12 Geo.brief december 2012
In liquid form, groundwater is the largest accessible source of freshwater. It serves as an essential source for drinking water and it supplies water for agricultural and industrial activities. During times of drought, groundwater has a crucial role to sustain water flow in rivers, lakes and wetlands (and thus supports ecosystem habitat and biodiversity), and its large natural storage provides a buffer against water shortage. Nevertheless, groundwater is known as a vulnerable resource. In many areas, ground- water is being consumed faster than it is naturally replenished. Due to climate change and increased human population, the pressure upon groundwater resources is expected to intensify. Hence, monitoring and predicting groundwater variability and change are imperative. Remote sensing However, assessing groundwater condition – especially over large areas – is difficult because it requires large amounts of groundbased measurement data. The most common way to measure groundwater states is by measuring groundwater head or level in a drilled observation well. Yet, in-situ head measurements are often sparsely distributed and the coverage of such head monitoring networks is mostly limited. Consequently, large-extent groundwater assessments (at the sub-continental scale and comprising multiple basins, aquifers and countries) are still challenging. During the last decades, spaceborne remote sensing applications for hydrological studies have received increased attention due to their ability to provide spatially and temporally exhaustive maps of hydrological properties. Examples include mapping of global land surface elevation (SRTM: Shuttle Radar Topography Mission), precipitation (TRMM: Tropical Rainfall Measuring Mission) and soil moisture (AMSR-E: Advanced Microwave Scanning Radiometer-EOS, SMOS: Soil Moisture and Ocean Salinity satellite), as well as global land surface temperature, vegetation and land cover (MODIS: Moderate-resolution Imaging Spectroradiometer). Yet, the use of satellitebased data in groundwater hydrology is still limited. Up to now, only the mission of GRACE (Gravity Recovery and Climate Experiment satellite) is recognized as a groundwater assessment tool, specifically to detect groundwater storage dynamics at a coarse resolution (400 km). Soil Water Index (SWI) In my Ph.D. research, we intend to explore the possibilities of using a satellite-based soil moisture product – having a better spatial resolution (50 km) than the GRACE product – for large-scale groundwater studies. In this research, we used the spatiotemporal maps of Soil Water Index (SWI) – derived by Vienna University of Technology (Austria), based on the scatterometer signals from European Remote Sensing (ERS) satellites. We used the monthly time series of SWI – which represents the moisture content of the upper profile soil (≤ 1 m thickness). Figure 1d is a snapshot of SWI indicating areas with low soil moisture (dry, indicated in red) and high soil moisture (wet, blue) soil conditions. As a test-bed of this research, we used the Rhine-Meuse basin (Fig. 1), covering about 200,000 km 2 and supported by more than four thousands of in-situ groundwater head time series (Fig. 1c). In this research, we explore the potential of using SWI for large-scale groundwater modeling, specifically in an empirical Transfer Function-Noise (TFN) model and a physically-based groundwater flow model PCR-GLOBWB-MOD. Groundwater head prediction In the first part of the thesis, it is shown that a correlation exists between observed groundwater head and SWI dynamics. This is apparent mostly for shallow groundwater areas (see the example as shown in Fig. 2a). For deep groundwater areas, the correlation may become apparent if a time-lag is considered (indicating the response time between the moisture in upper soil layers and groundwater compartments). Given this correlation, a groundwater head prediction based on SWI should be feasible. Hence, we performed two exercises in which SWI time series were used as TFN model input. In the first exercise – focusing on forecasting in time – the TFN model parameters were estimated based on available groundwater head measurements in the period 1995-2000. Subsequently, groundwater head forecasts were made and validated in the period 2004-2007. In the second exercise – focusing on spatio-temporal prediction – the TFN model parameters were interpolated by using the global digital elevation map. Using these estimated parameters, spatio-temporal groundwater head maps were then made. Both exercises show good results. Results of temporal predictions from the first exercise well reproduce observed head time series, especially at shallow groundwater locations (see example in Figs. 2b and 2c). In such areas, groundwater head fluctuations are dominated by meteorological forcing that is also reflected in SWI soil moisture dynamics (as shown in the correlation analysis in Fig. 2a).The spatiotemporal prediction method is able to predict the head variation in terms of timing and amplitude reasonably well (see Fig. 2d). However, the absolute value of the groundwater head is hardly estimated (as indicated by the bias between observation and prediction, Fig. 2d). This is mainly due the limited accuracy of the currentlyavailable global digital elevation map (≤ 16 m). Fig. 1. The Rhine-Meuse basin used as the study area: The average groundwater head (a) and depth (b) calculated from the PCR-GLOBWB-MOD model; the head measurement stations used in the study (c); and a snapshot of SWI in August 1995 (d). Figure 1a includes the point x indicating the measurement station for the graphs in Fig. 2 and the points a, b, c, d, e and f indicating the measurement stations for the graphs in Fig. 3. december 2012 Geo.brief 13
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