Reconstructing hydro-climatological data using dynamical downscaling of reanalysis products in data-sparse regions – Application to the Limpopo catchment in southern Africa

• Main Authors: Ditiro B. Moalafhi, Ashish Sharma, Jason P. Evans
• Format: Article
• Language: English
• Published: Elsevier 2017-08-01
• Series: Journal of Hydrology: Regional Studies
• Subjects: Reanalyses; Dynamical downscaling; Hydrological applications; Limpopo basin; Southern Africa
• Online Access: http://www.sciencedirect.com/science/article/pii/S2214581817302537

This study is conducted over the data-poor Limpopo basin centered over southern Africa using reanalysis downscaled to useful resolution. Reanalysis products are of limited value in hydrological applications due to the coarse spatial scales they are available at. Dynamical downscaling of these products over a domain of interest offers a means to convert them to finer spatial scales in a dynamically consistent manner. Additionally, this downscaling also offers a way to resolve dominantatmospheric processes, leading to improved accuracy in the atmospheric variables derived. This study thus evaluates high-resolution downscaling of an objectively chosen reanalysis (ERA-I) over the Limpopo basin using Weather Research and Forecasting (WRF) as a regional climate model. The model generally under-estimates temperature and over-estimates precipitation over the basin, although reasonably consistent with observations. The model does well in simulating observed sustained hydrological extremes as assessed using the Standardized Precipitation Index (SPI) although it consistently under-estimates the severity ofmoisture deficit for the wettest part of the year during the dry years. The basin's aridity index (I) is above the severe drought threshold during summer and is more severe in autumn. This practically restricts rain-fed agriculture to around 3 months in a year over the basin. This study presents possible beneficial use of the downscaled simulations foroptimal hydrologic design and water resources planning in data scarce parts of the world.