Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods.

The Urmia lake in north-west Iran has dried up to perilously low levels in the past two decades. In this study, we investigate the drivers behind the decline in lake water level with the help of in-situ and remote sensing data. We use total water storage (TWS) changes from the gravity recovery and climate experiment (GRACE) satellite mission. TWS from GRACE includes all the water storage compartments in a column and is the only remote sensing product that can help in estimating groundwater storage (GWS) changes. The coarse spatial (approx. 300 km) resolution of GRACE does not allow us to identify local changes that may have led to the Urmia lake disaster. In this study, we tackle the poor resolution of the GRACE data by employing three machine learning (ML) methods including random forest (RF), support vector regression (SVR) and multi-layer perceptron (MLP). The methods predict the groundwater storage anomaly (GWSA), derived from GRACE, as a function of hydro-climatic variables such as precipitation, evapotranspiration, land surface temperature (LST) and normalized difference vegetation index (NDVI) on a finer scale of 0.25° × 0.25°. We found that i) The RF model exhibited highest R (0.98), highest NSE (0.96) and lowest RMSE (18.36 mm) values. ii) The RF downscaled data indicated that the exploitation of groundwater resources in the aquifers is the main driver of groundwater storage and changes in the regional ecosystem, which has been corroborated by few other studies as well. The impact of precipitation and evapotranspiration on the GWSA was found to be rather weak, indicating that the anthropogenic derivers had the most significant impact on the GWSA changes. iii) We generally observed a significant negative trend in GWSA, having also significant positive correlations with the well data. However, over regions with dam construction significant negative correlations were found.

Assessment of the groundwater salinity monitoring network of the Tehran region: application of the discrete entropy theory.

In this paper, a new entropy-based approach is developed for assessing the location of salinity monitoring stations in the Tehran Aquifer, Tehran, Iran. To find the optimal distance among stations, the measure of Transinformation in the Entropy Theory is used. Then a Transinformation-Distance (T-D) curve is developed and used in a multi-objective GA-based optimization model, which provides the best locations for monitoring stations. Because of the large area of the Tehran aquifer and significant spatial variations of the Electrical Conductivity (EC) of the groundwater in the study area, the C-means clustering method is used to classify the study area to some homogenous zones. The optimization model is applied to each zone to find the optimal location of monitoring stations. The results show the applicability and the efficiency of the model in assessing the groundwater monitoring systems.