Model Development in Irrigated Data-Scarce Environments Using Publicly Available Datasets

Abstract:

Groundwater flow models typically require extensive local data, including hydraulic head measurements from wells, climate data, lithological data, and pumping rates. The expense and challenges of obtaining requisite data often limit the development of groundwater models in poor regions. In particular, obtaining accurate data on groundwater pumping for irrigation is difficult because it requires extensive monitoring infrastructure. This research develops and demonstrates a methodology to use global datasets in the construction of groundwater models at the sub-basin scale in irrigated areas where data is unavailable or unattainable. A 2-dimensional discretized groundwater flow model is developed to incorporate irrigation estimates using publicly accessible global datasets. Irrigation is calculated using the soil moisture balance equation and remotely sensed soil moisture, precipitation, and temperature data from the Global Land Data Assimilation System (GLDAS). Satellite data from the Gravity Recovery and Climate Experiment (GRACE) are processed using GLDAS data and then used to determine the change in hydraulic heads at the center of 1°by 1°grid cells over time. Boundary conditions are taken from a HydroSHEDs derived database of river widths and depths. Outflow is determined using the Hydrological Modeling and Analysis Platform (HyMAP). A simulation-optimization inverse modeling approach is applied to estimate hydraulic conductivity values in the groundwater model using spatio-temporal water table values based on GRACE data. The regional scale groundwater model is applied to the Khabour River sub-basin in Syria and Turkey, where data availability has been impacted by government secrecy and civil war.