Precipitation projections over the Indus River Basin of Pakistan for the 21st century using a statistical downscaling framework

Abstract

We estimate future changes in precipitation over the entire Indus basin of Pakistan with a particular focus on the high-elevation Upper Indus Basin (UIB). A statistical downscaling approach is used. We consider the spatial variability of observed precipitation on seasonal scales. Large-scale atmospheric patterns are employed for general circulation model (GCM) selection and subsequent projections. First, we identify the precipitation governing predictors from ERA-Interim reanalysis. We further quantify the robustness of governing predictors against other reanalysis datasets (ERA5 and NCEP-NCAR-II) to assess future projections' fidelity. We perform S-mode Principal Component Analysis on predictor fields and compare loading patterns using Taylor diagrams to assess predictor correspondence between different reanalysis. Similarly, we compare ERA-Interim variables with model-simulated fields during the historical period to select better performing GCMs and quantify model uncertainty. The regional suitability of available GCMs in our study is also demonstrated. Ensemble (median) changes in regional precipitation derived through atmospheric fields show an elevation-dependent response of the UIB at representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, where increased precipitation will mostly fall at high elevations. However, the positive signals are more distinct during the winter and monsoon seasons, particularly over the central Karakoram. Meanwhile, a decrease in precipitation is robust during the pre-monsoon period, particularly over the northwestern regions. These signals intensify and become more robust during 2071-2100 under RCP8.5, and the better-performing models and signal-to-noise ratios further support this finding. The spatial patterns of projected changes suggest stronger (weaker) and further northward penetrating westerly systems during the winter (pre-monsoon) season.Increased warming will also strengthen monsoon circulations, and these will penetrate further into the northwestern and trans-Himalayan regions. The lower Indus shows a mixed seasonal response that is more uncertain. The present analysis provides an alternative perspective to the ongoing research of assessing climate responses in complex regions.

Spatio-Temporal Analysis of Future Precipitation Changes in the Huaihe River Basin Based on NEX-GDDP-CMIP6 Dataset and Monitoring Data

“…The supplementary literature shows a spatial correlation coefficient 0.69, indicating reasonable accuracy [29]. As a result, the findings strengthen their credibility and align with other studies that have reported high performance of CMIP6 models across various geographical settings [30]. Further bolstering the credibility of the findings is consistency across models.…”

Spatiotemporal Analysis of Future Precipitation Changes in the Huaihe River Basin Based on the NEX-GDDP-CMIP6 Dataset and Monitoring Data

“…The supplementary literature shows a spatial correlation coefficient of 0.69, indicating reasonable accuracy values [32]. As a result, the findings strengthen their credibility and align with other studies that report the good performance of CMIP6 models across various geographical settings [33]. Further bolstering the credibility of the findings is the consistency across models.…”

Integrated SWAT-MODFLOW Modeling-Based Groundwater Adaptation Policy Guidelines for Lahore, Pakistan under Projected Climate Change, and Human Development Scenarios

“…Both Tmax and Tmin will increase while Tmin will rise more than the Tmax. Previous studies report similar results for the study area and other parts of the world [16,[58][59][60][61]. Rising future temperatures will increase agricultural water demand due to an increase in evapotranspiration [62][63][64].…”