Normalized difference vegetation index as the dominant predicting factor of groundwater recharge in phreatic aquifers: case studies across Iran

Esmaeel Parizi, Seiyed Mossa Hosseini, Behzad Ataie-Ashtiani, Craig T. Simmons

Research output: Contribution to journalArticlepeer-review

Abstract

The estimation of long-term groundwater recharge rate (GWr) is a pre-requisite for efficient management of groundwater resources, especially for arid and semi-arid regions. Precise estimation of GWr is probably the most difficult factor of all measurements in the evaluation of GW resources, particularly in semi-arid regions in which the recharge rate is typically small and/or regions with scarce hydrogeological data. The main objective of this study is to find and assess the predicting factors of GWr at an aquifer scale. For this purpose, 325 Iran’s phreatic aquifers (61% of Iran’s aquifers) were selected based on the data availability and the effect of eight predicting factors were assessed on GWr estimation. The predicting factors considered include Normalized Difference Vegetation Index (NDVI), mean annual temperature (T), the ratio of precipitation to potential evapotranspiration (P/ ETP), drainage density (Dd), mean annual specific discharge (Qs), Mean Slope (S), Soil Moisture (SM90), and population density (Popd). The local and global Moran’s I index, geographically weighted regression (GWR), and two-step cluster analysis served to support the spatial analysis of the results. The eight predicting factors considered are positively correlated to GWr and the NDVI has the greatest influence followed by the P/ ETP and SM90. In the regression model, NDVI solely explained 71% of the variation in GWr, while other drivers have only a minor modification (3.6%). The results of this study provide new insight into the complex interrelationship between GWr and vegetation density indicated by the NDVI. The findings of this study can help in better estimation of GWr especially for the phreatic aquifers that the hydrogeological ground-data requisite for establishing models are scarce.

Original languageEnglish
Article number17473
Number of pages19
JournalScientific Reports
Volume10
Issue number1
DOIs
Publication statusPublished - 20 Oct 2020

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