Drought Assessment of a Data-scarced Watershed – Quetta Valley, Pakistan

Abstract

Pakistan continuously remains under the threat of drought, as approximately 88% of its area falls under arid and semi-arid regions. The most affected province due to drought is Balochistan, facing migration, scarcity, famine and economic strain. The insufficiency of meteorolgical and hydrological data in the area intensifies the problem because of the late or non-diagnosis of drought. Therefore, this study proposes a methodology to quantify the drought in a watershed with inadequate meteorological and hydrological data. This study aims to design a method to find the extent and duration of drought conditions for watersheds where only basic meteorological data is available. For this purpose, Quetta Valley, Balochistan, Pakistan, is chosen as the study area. First, the hydrological components of water balance for the watershed are calibrated for 10 years using a distributed hydrological model (MIKE-SHE). The modeling results and other observed meteorological data are then used to evaluate eight drought indices to assess the existence and extent of drought, including those which use the hydrological parameters as input from MIKE-SHE. Reconnaissance drought and Palmer drought severity indices, which use detailed hydrological parameters, are found to provide more accurate results coupled with early drought detection of historical events. The results showed that the proposed method could be effectively used to determine the secondary parameters from the hydrological model, which in turn gives more realistic drought conditions for such regions. KEYWORDS: Meteorological drought indices, Hydrological modeling, MIKE SHE, Drought, Palmer drought severity index.

Predictive Assessment of Climate Change Impact on Water Yield in the Meta River Basin, Colombia: An InVEST Model Application

“…Valencia et al [9] used the annual water yield model from the INVEST package to quantify current water yields in specific subbasins in the Meta River basin, Colombia. The study [10] also demonstrated the application of a distributed hydrological model like MIKE-SHE in data-scarce watersheds to assess climate change impacts on water resources, using limited meteorological and hydrological data for model input.…”