Direct and indirect simulating and projecting hydrological drought using a supervised machine learning method

Mohammad Reza Eini; Farzaneh Najminejad; Mikołaj Piniewski

doi:10.1016/j.scitotenv.2023.165523

Direct and indirect simulating and projecting hydrological drought using a supervised machine learning method

Sci Total Environ. 2023 Nov 10:898:165523. doi: 10.1016/j.scitotenv.2023.165523. Epub 2023 Jul 14.

Authors

Mohammad Reza Eini¹, Farzaneh Najminejad², Mikołaj Piniewski³

Affiliations

¹ Department of Hydrology, Meteorology and Water Management, Institute of Environmental Engineering, Warsaw University of Life Sciences, Warsaw, Poland; Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Telegraphenberg A 31, 14473 Potsdam, Germany. Electronic address: mohammad_eini@sggw.edu.pl.
² College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.
³ Department of Hydrology, Meteorology and Water Management, Institute of Environmental Engineering, Warsaw University of Life Sciences, Warsaw, Poland.

PMID: 37454850
DOI: 10.1016/j.scitotenv.2023.165523

Abstract

There is a trend in using Artificial Intelligence methods as simulation tools in different aspects of hydrology, including river discharge simulations, drought predictions, and crop yield simulations. The motivation of this work was to assess two various concepts in applying these methods in simulations and projections of hydrological drought. In this study, Standardized Runoff Index (SRI) was simulated and projected using Artificial Neural Networks (ANNs). Maximum and minimum temperature, precipitation, and meteorological drought indicators (the Standardized Precipitation Index (SPI)) were selected as predictors. A direct approach (directly simulating and projecting SRI) and an indirect approach (simulating and projecting river discharge, then calculating SRI) were assessed. Our results show that the indirect approach performs better than the direct approach in simulations of SRI in four discharge stations in the Odra River Basin (a transboundary river basin in Central Europe) from 2000 to 2019. Moreover, a considerable difference between these two approaches was detected in projections of hydrological drought under the RCP8.5 emission scenario for two horizons (near future: 2021-2040, and far future: 2041-2060). Based on the run theory, both approaches show somewhat similar drought conditions for future projections.

Keywords: Climate change; Data-driven models; Hydrological drought; Standardized drought index.