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Calibration of a groundwater model using pattern information from remote sensing data
Due to the chronic lack of verification data, hydrologic models are notoriously over-parameterized. If a large number of parameters are estimated, while few verification data are available, the calibrated model may have little predictive value. However, recent development in remote sensing (RS) techniques allows generation of spatially distributed data that can be used to construct and verify hydrological models. These additional data reduce the ambiguity of the calibration process and thus increase the predictive value of the model. An example for such remotely sensed data is the spatial distribution of phreatic evaporation. In this modeling approach, we use the spatial distribution of phreatic evaporation obtained by remote sensing images as verification data Compared to the usual limited amount of head data, the spatial distribution of evaporation data provides a complete areal coverage. However, the absolute values of the evaporation data are uncertain and therefore three ways of using the spatial distribution pattern of evaporation were tested and compared. The first way is to directly use the evaporation pattern defined in a relative manner by dividing the evaporation rate in a pixel by the total evaporation of a selected rectangular area of interest. Alternatively, the discrete fourier transform (DFT) or the discrete wavelet transform (DWT) are applied to the relative evaporation pattern in the space domain defined before. Seven different combinations of using hydraulic head data and/or evaporation pattern data as conditioning information have been tested. The code PEST, based on the least-squares method, was used as an automatic calibration tool. From the calibration results, we can conclude that the evaporation pattern can replace the head data in the model calibration process, independently of the way the evaporation pattern is introduced into the calibration procedure. (C) 2009 Elsevier B.V All rights reserved.
Generating soil electrical conductivity maps at regional level by integrating measurements on the ground and remote sensing data
In arid and semi-arid areas, salinization of soil and water resources is one of the major threats to irrigated agriculture. For management purposes, quantifying both the extent and distribution of salinization is important, but accurate data with sufficient spatial resolution are often not available. Commonly used techniques such as soil sampling and geophysical methods are time-consuming and yield only point data. A method is described in which multispectral remote sensing images can be used to regionalize point data measured on the field. Field data consist of measurements of electrical conductivity and are obtained by the combination of geophysical methods and the analysis of field soil samples. Uncalibrated salinity maps were calculated with spectral correlation mapping using image-based reference spectra of saline areas. As an alternative indicator for soil salinity, the NDVI was used. The method was verified in the Yanqi Basin, northwestern China. Correlations between field data and the uncalibrated salinity maps were found over non-irrigated sites for all images. Good correlations (R-2 up to 0.85) resulted for images collected during the winter months. The high correlation coefficients allow the uncalibrated salinity maps to be scaled to electrical conductivity maps.
Extracting phreatic evaporation from remotely sensed maps of evapotranspiration
One of the most important parameters related to soil salinization is the direct evaporation from the groundwater (phreatic evaporation). If the groundwater table is sufficiently close to the surface, groundwater will evaporate through capillary rise. In recent years, several methods have been suggested to map evapotranspiration (ET) on the basis of remote sensing images. These maps represent the sum of both transpiration of vegetation and evaporation from the bare soil. However, identifying the amount of phreatic evaporation is important as it is the dominant flux in the salt balance of the soil. The interpretation of stable isotope profiles at nonirrigated areas in the unsaturated zone allows one to quantify phreatic evaporation independently of the transpiration of the vegetation. Such measurements were carried out at different locations with a different depth to groundwater. The benefit is twofold. (1) A relation between phreatic evaporation rates and the depth to groundwater can be established. (2) By subtracting the measured values of phreatic evaporation from remotely sensed values of ET, vadose ET consisting of transpiration and excess irrigation water in the unsaturated zone can be estimated at the sampling locations. A correlation between the normalized differential vegetation index and the calculated vadose ET rates could be established (R(2) = 0.89). With this correlation the contribution of phreatic evaporation can be estimated. This approach has been tested for the Yanqi basin located in western China. Finally, the distribution of phreatic evaporation was compared to a soil salinity map of the project area on a qualitative basis.
Topography representation methods for improving evaporation simulation in groundwater modeling
In a groundwater model, surface elevations which are used in simulating the phreatic evaporation process are usually incorporated as spatially constant over discretized cells. Traditionally, a modeler obtains the data for surface elevations from point data or a digital elevation model (DEM) by means of extrapolation or interpolation. In this way, a smoothing error of surface elevations is introduced, which via the depth to groundwater propagates into evaporation simulation. As a consequence, the evaporation simulation results can be biased. In order to explore the influence of surface elevations on evaporation simulation, three alternative methods of representation of topography in calculating evaporation were studied. The first one is a traditional method which uses cell-wise constant elevations obtained by averaging surface elevations from the DEM with higher resolution for the corresponding model cells. The second one retains some information on the sub-pixet statistics of surface elevations from the DEM by a perturbation approach, calculating the second order first moment of evaporation with a Taylor expansion. In the third method, a finer discretization is used to represent the topography in calculating evaporation than is used to compute global groundwater flow. This allows to take into account the smaller scale variations of the surface elevation as given in the high resolution DEM data. For all the three methods, two different evaporation functions, a linear segment function and an exponential function have been used individually. In this paper, a groundwater model with a discretization of 500 x 500 m has been established white DEM data with a resolution of 90 x 90 m are available and resampled to 100 x 100 m cells for convenience of model input. The evaporation rates from a groundwater model with a discretization of 100 x 100 m, which has the same spatial distribution pattern of hydraulic parameters as the 500 x 500 m model, is taken as validation data. The comparisons of evaporation rates were carried out on different averaging scales ranging from 500 m to 2 km. The compared evaporation rates for each scale are obtained by summing up the corresponding evaporation rates from the 500 x 500 m model and the 100 x 100 m model. It is shown that the third method, which uses a finer resolution of topography in the evaporation calculation, yields the best results no matter which evaporation function is used. It is also seen that the correlation between the evaporation rates from the 500 x 500 m model and the 100 x 100 m model increases and values converge when comparing the evaporation results on an increasingly coarser scale, independently of the selected method and evaporation function. (C) 2008 Elsevier B.V. All rights reserved.