System Thinking Approach toward Reclamation of Regional Water Management under Changing Climate Conditions

This paper represents a streamflow prediction model with the approach of ensemble multi-GCM downscaling and system dynamics (SD) for the Aji-Chay watershed located in northwest Iran. In this study, firstly, the precipitation and temperature projection for the future was assessed according to the climate change impact using a statistical downscaling technique, i.e., Long Ashton Research Station Weather Generator (LARS-WG); secondly, a rainfall-runoff model for future horizons was developed according to artificial neural networks (ANN); finally, an SD model was developed according to plausible reclamation scenarios, i.e., cloud seeding, increasing the irrigation efficiency and reducing agricultural production, controlling policies on groundwater withdrawal as well as environmental awareness, and cultivation to reduce domestic consumption to achieve sustainable development. For downscaling purposes, the outputs of four general circulation models (GCMs) including EC-EARTH, HadGEM2, MIROC5, MPI-ESM from Coupled Model Intercomparison Project 5 (CMIP5) were applied. The results of multi-GCM downscaling indicated an ascending trend of 0.1 degrees C to +1.3 degrees C for temperature and a descending trend of 17 to 23% for precipitation by 2040 under representative concentration pathways (RCPs) of 4.5 and 8.5, respectively. Moreover, the results of the SD model revealed that none of the individual reclamation scenarios were impressive on water balance sustainable conditions; instead, the simultaneous implementation of all plausible scenarios managed to meet the requirements of socio-environment aspects as well as sustainability approaches.

Automated summary

This paper presents a streamflow prediction model for the Aji-Chay watershed in Iran, using ensemble multi-GCM downscaling and system dynamics. It assesses future precipitation and temperature using statistical downscaling, develops a rainfall-runoff model using artificial neural networks, and creates an SD model for sustainable development. The results indicate changes in temperature and precipitation and reveal the importance of implementing multiple strategies for sustainable water management.