The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations
| Autor: | Courtenay Strong, Ghulam Hussain Dars, Kamran Ansari, Syed Hammad Ali, Adam K. Kochanski |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Mean squared error Indus 0207 environmental engineering Climate change WRF-ARW model Terrain 02 engineering and technology Structural basin lcsh:Technology 01 natural sciences lcsh:Chemistry Karakoram region General Materials Science Pakistan Precipitation 020701 environmental engineering lcsh:QH301-705.5 Instrumentation 0105 earth and related environmental sciences Fluid Flow and Transfer Processes lcsh:T Process Chemistry and Technology General Engineering lcsh:QC1-999 Computer Science Applications climate change lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Climatology Weather Research and Forecasting Model Climate Forecast System Environmental science Upper Indus Basin lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences Volume 10 Issue 5 Applied Sciences, Vol 10, Iss 5, p 1765 (2020) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10051765 |
| Popis: | 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&ndash 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. |
| Databáze: | OpenAIRE |
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