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Using tree ring data as a proxy for transpiration to reduce predictive uncertainty of a model simulating groundwater?surface water?vegetation interactions
Summary The interactions between surface water, the vadose zone, groundwater, and vegetation are governed by complex feedback mechanisms. Numerical models simulating these interactions are essential in quantifying these processes. However, the notorious lack of field observations results in highly uncertain parameterizations. We suggest a new type of observation data to be included in the calibration data set for hydrological models simulating interactions with vegetation: Tree rings as a proxy for transpiration. We use the lower Tarim River as an example site for our approach. In order to forestall the loss of riparian ecosystems from reduced flow over a 300 km reach of the lower Tarim River, the Chinese government initiated periodical, ecological water releases. The water exchange processes in this region were simulated for a cross-section on the lower reaches of the Tarim River using a numerical model (Hydro-GeoSphere) calibrated against observations of water tables, as well as transpiration estimated from tree ring growth. A predictive uncertainty analysis quantifying the worth of different components of the observation dataset in reducing the uncertainty of model predictions was carried out. The flow of information from elements of the calibration dataset to the different parameters employed by the model was also evaluated. The flow of information and the uncertainty analysis demonstrate that tree ring records can significantly improve confidence in modeling ecosystem dynamics, even if these transpiration estimates are uncertain. To use the full potential of the historical information encapsulated in the Tarim River tree rings, however, the relationship between tree ring growth and transpiration rates has to be studied further.
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.
Equally likely inverse solutions to a groundwater flow problem including pattern information from remote sensing images
Groundwater flow modeling for large areas in arid and semiarid regions, like the Chobe region in Botswana, suffers from a severe lack of data. This study addresses the usefulness of remote sensing (RS) images to constrain the recharge rate estimates for a region. The estimates derived from METEOSAT and NOAA advanced very high resolution radar (AVHRR) images are correlated with recharge rate values estimated from chloride measurements and used jointly in the generation of multiple, equally likely recharge rate realizations with the colocated cosimulation algorithm. The colocated cosimulation algorithm is very suited to generate stochastic realizations of a parameter that includes information from a correlated covariable given on a regular, dense grid as in RS information. These equally likely recharge rate realizations, together with multiple equally likely transmissivity realizations, are conditioned by inversion to hydraulic head data and a digital elevation model. For the inverse conditioning an additional penalty term was added to the objective function, penalizing too large deviations of the recharge rate pattern from the RS image. As such, the recharge rate pattern observed with the RS images is still honored by the calibrated recharge rate realizations. It was observed that conditioning to the RS information reduces significantly the estimated ensemble variance of the recharge rates.
Using remote sensing data to automatically calibrate a groundwater model
Sustainable Water Management in Arid and Semi-arid Regions
"Arid and semi-arid regions face major challenges in the management of scarce freshwater resources under pressures of population, economic development, climate change, pollution and over-abstraction. Groundwater is commonly the most important water resource in these areas. Groundwater models are widely used globally to understand groundwater systems and to guide decisions on management. However, the hydrology of arid and semi-arid areas is very different from that of humid regions, and there is little guidance on the special challenges of groundwater modelling for these areas. This book brings together the experience of internationally-leading experts to fill a gap in the scientific and technical literature. It introduces state-of-the-art methods for modelling groundwater resources, illustrated with a wide-ranging set of illustrative examples from around the world. The book is valuable for researchers, practitioners in developed and developing countries, and graduate students in hydrology, hydrogeology, water resources management, environmental engineering and geography"-- "This book brings together the worldwide experience of internationally leading experts to fill this gap in the scientific and technical literature. It introduces state-of-the-art methods for the modelling of groundwater resources and their protection from pollution"--
Uncertainty analysis of an integrated hydrological model using posterior covariance matrix from automatic calibration
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.
Modelling for sustainable water management in arid and semi-arid environments
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.