Modeling regional precipitation over the Indus River basin of Pakistan using statistical downscaling

Abstract

Complex processes govern spatiotemporal distribution of precipitation within the high-mountainous headwater regions (commonly known as the upper Indus basin (UIB)), of the Indus River basin of Pakistan. Reliable precipitation simulations particularly over the UIB present a major scientific challenge due to regional complexity and inadequate observational coverage. Here, we present a statistical downscaling approach to model observed precipitation of the entire Indus basin, with a focus on UIB within available data constraints. Taking advantage of recent high altitude (HA) observatories, we perform precipitation regionalization using K-means cluster analysis to demonstrate effectiveness of low-altitude stations to provide useful precipitation inferences over more uncertain and hydrologically important HA of the UIB. We further employ generalized linear models (GLM) with gamma and Tweedie distributions to identify major dynamic and thermodynamic drivers from a reanalysis dataset within a robust cross-validation framework that explain observed spatiotemporal precipitation patterns across the Indus basin. Final statistical models demonstrate higher predictability to resolve precipitation variability over wetter southern Himalayans and different lower Indus regions, by mainly using different dynamic predictors. The modeling framework also shows an adequate performance over more complex and uncertain trans-Himalayans and the northwestern regions of the UIB, particularly during the seasons dominated by the westerly circulations. However, the cryosphere-dominated trans-Himalayan regions, which largely govern the basin hydrology, require relatively complex models that contain dynamic and thermodynamic circulations. We also analyzed relevant atmospheric circulations during precipitation anomalies over the UIB, to evaluate physical consistency of the statistical models, as an additional measure of reliability. Overall, our results suggest that such circulation-based statistical downscaling has the potential to improve our understanding towards distinct features of the regional-scale precipitation across the upper and lower Indus basin. Such understanding should help to assess the response of this complex, data-scarce, and climate-sensitive river basin amid future climatic changes, to serve communal and scientific interests.

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

“…In the following sections, we provide a brief description of the data, the adapted regionalization scheme, and the model development process to give the necessary background. More detailed information can be found in Pomee et al (2020).…”

“…Most uncertainty in downscaling studies stems from the choice of models (e.g., Heo et al, 2014), how precipitation is derived from these models (Pomee et al, 2020), and This article is protected by copyright. All rights reserved.…”

Temperature Projections over the Indus River Basin of Pakistan Using Statistical Downscaling

“…New observational sites, particularly within the UIB, should help in this regard, but it is still an ongoing process. Meanwhile, exploiting the available observational profile complemented significantly by the recent high-altitude observatories within the UIB can offer new simulation advantages [43]. Following a sub-regional analysis, adopting a suitable statistical downscaling approach, uncertainty quantification, and implementing a model ensemble are some options that may improve temperature simulations in this region.…”

“…Ref. [43] argue that the basin characterization using climate patterns rather than arbitrarily defined sub-regions, e.g., refs. [36,40] can support a more realistic climate analysis.…”

“…As biases in different GCMs/RCMs are systematic, statistical treatments like bias corrections are necessary for realistic climate change analysis, e.g., [39,[51][52][53]. However, inadequate observational profile, e.g., [43,54] and unreliable observational proxies, e.g., ref. [55] may not accurately correct such systematic biases, particularly over high-altitude regions.…”