The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations

Abstract

Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.

Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan

“…e spatial accuracy of all the GDPs was poor in northern Punjab, where the underestimation was strongest. e underestimation of air temperature in the northern parts of the country were also reported by different studies [88,89], which simulated the air temperature by using WRF model at the upper Indus Basin and Himalaya ranges, respectively. However, the prior studies reported the different ranges of temperature underestimations, which could be the outcome of different study periods, gauges, and resolution.…”

Characteristics of Historical Precipitation in High Mountain Asia Based on a 15-Year High Resolution Dynamical Downscaling

“…We use dynamically downscaled precipitation from the WRF regional climate simulation described and evaluated by Dars et al [30] to examine the precipitation climate of HMA. This simulation uses the Advanced Research WRF (hereafter WRF) version 3.8.1 with lateral boundary and initial atmospheric and land-surface conditions provided by the Climate Forecast System Reanalysis [31,32] for the period from 2000-2015.…”

“…We discard the first year (i.e., 2000) for spin-up and focus our analysis on the 15-year period from 2001-2015. The WRF was configured with three nested domains with 36-, 12-, and 4-km grid spacing (Figure 1; see [30] for WRF-model terrain). Ikeda et al [33] showed that regional climate simulations with <6-km grid spacing exhibit significant improvements in precipitation fidelity in Colorado.…”

“…These studies suggest there is not an optimal set of parameters for simulating all climate variables, and that the optimal parameters differ based on the goals of the analysis [25,34]. Based on initial benchmarking (see [30] for details), the final model configuration used here includes the Noah-MP land-surface [35], YSU boundary layer [36], Thompson cloud microphysics [37], RRTM longwave radiation [38], Dudhia shortwave radiation [39], revised MM5 surface layer, and Kain-Fritsch cumulus [40] parameterizations, the latter applied only on the 36-and 12-km outer domains so that the 4-km domain is convection-permitting. All three domains had 38 vertical levels.…”

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

“…Model experiments [44] further showed that regional GCM limitations are systematic and irrespective of the model horizontal resolution. The evaluation of high resolution (0.44 • ) RCMs under the Coordinated Regional Climate Downscaling Experiments over the South Asian domain [47,48] and fine-scale (up to 4 km resolution) WRF simulations also yielded cold biases over the UIB, e.g., [49,50]. 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].…”