Groundwater Management Using Remotely Sensed Data in High Plains Aquifer

Abstract

Groundwater monitoring in regional scales using conventional methods is challenging since it requires a dense network monitoring well system and regular measurements. Satellite measurement of time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) mission since 2002 provided an exceptional opportunity to observe the variations in Terrestrial Water Storage (TWS) from space. This study has been divided into 3 parts: First different satellite and hydrological model data have been used to validate the TSW measurements derived from GRACE in High Plains Aquifer (HPA). Terrestrial Water Storage derived from GRACE was compared to TWS derived from a water budget whose inputs determined from independent datasets. The results were similar to each other both in magnitude and timing with a correlation coefficient of 0.55. The seasonal groundwater storage changes are also estimated using GRACE and auxiliary data for the period of 2004 to 2009, and results are compared to the local in situ measurements to test the capability of GRACE in detecting groundwater changes in this region. The results from comparing seasonal groundwater changes from GRACE and in situ measurements indicated a good agreement both in magnitude and seasonality with a correlation coefficient of 0.71. This finding reveals the worthiness of GRACE satellite data in detecting the groundwater level anomalies and the benefits of using its data in regional hydrological modelling. In the second part of the study the feasibility of the GRACE TWS for predicting groundwater level changes is investigated in different locations of the High Plains Aquifer. The Artificial Neural Networks (ANNs) are used to predict the monthly groundwater level changes. The input data employed in the ANN include monthly gridded GRACE TWS based on Release-05 of GRACE Level-3, precipitation, minimum and maximum temperature which are estimated from Parameter elevation Regression on Independent Slopes Model (PRISM), and the soil moisture estimations derived from Noah Land Surface Model for the period of January 2004 to December 2009. All the values for mentioned datasets are extracted at the location of 21 selected wells for the study period. The input data is divided into 3 parts which 60% is dedicated to training, 20% to validation, and 20% to testing. The output to the developed ANNs is the groundwater level change which is compared to the US Geological Survey's National Water Information well data. Results from statistical downscaling of GRACE data leaded to a significant improvement in predicting groundwater level changes, and the trained ensemble multi-layer perceptron shows a "good" to a "very good" performance based on the obtained Nash-Sutcliff Efficiency which demonstrates the capability of these data for downscaling. In the third part of this study the soil moisture from 4 different Land Surface models (NOAH, VIC, MOSAIC, and CLM land surface models) which are accessible through NASA Global Land Data Assimilation System (GLDAS) is included in developing the ANNs and the results are compared to each other to quantify the effect of soil moisture in the downscaling process of GRACE. The relative importance of each predictor was estimated using connection weight technique and it was found that the GRACE TWS is a significant parameter in the performance of Artificial Neural Network ensembles, and based on the Root Mean Squared (RMSE) and the correlation coefficients associated to the models in which the soil moisture from Noah and CLM Land Surface Models are used, it is found that using these datasets in process of downscaling GRACE delivers a higher correlated simulation values to the observed values.