Abstract
The current study evaluates the potential of merged satellite precipitation datasets (MSPDs) against rain gauges (RGs) and satellite precipitation datasets (SPDs) in monitoring meteorological drought over Pakistan during 2000–2015. MSPDs evaluated in the current study include Regional Weighted Average Least Square (RWALS), Weighted Average Least Square (WALS), Dynamic Clustered Bayesian model Averaging (DCBA), and Dynamic Bayesian Model Averaging (DBMA) algorithms, while the set of SPDs is Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG-V06), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA 3B42 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and ERA-Interim (re-analyses dataset). Several standardized precipitation indices (SPIs), including SPI-1, SPI-3, and SPI-12, are used to evaluate the performances of RGs, SPDs, and MSPDs across Pakistan as well as on a regional scale. The Mann–Kendall (MK) test is used to assess the trend of meteorological drought across different climate regions of Pakistan using these SPI indices. Results revealed higher performance of MSPDs than SPDs when compared against RGs for SPI estimates. The seasonal evaluation of SPIs from RGs, MSPDs, and SPDs in a representative drought year (2008) revealed mildly to moderate wetness in monsoon season while mild to moderate drought in winter season across Pakistan. However, the drought severity ranges from mild to severe drought in different years across different climate regions. MAPD (mean absolute percentage difference) shows high accuracy (MAPD <10%) for RWALS-MSPD, good accuracy (10% < MAPD <20%) for WALS-MSPD and DCBA-MSPD, while good to reasonable accuracy (20% < MAPD < 50%) for DCBA in different climate regions. Furthermore, MSPDs show a consistent drought trend as compared with RGs, while SPDs show poor performance. Overall, this study demonstrated significantly improved performance of MSPDs in monitoring the meteorological drought.
Highlights
Population growth and their improving standard of living, increase in agricultural and industrial production, water quality deterioration, and several other factors influence and intensify our freshwater needs across the global and regional scales [1]
Despite these dependencies and uncertainties associated with satellite precipitation datasets (SPDs), merged satellite precipitation datasets (MSPDs) have a high potential for hydrological applications [57]
The obtained results are comprehensively explained in the following sub-sections: Section 3.1 presents the performance assessment of MSPDs and SPDs to monitor drought for a representative drought year using standardized precipitation indices (SPIs)-3, Section 3.2 presents the regional scale evaluation of SPDs and MSPDs for drought monitoring, and Section 3.3 shows trend analyses of drought in different climate regions of Pakistan
Summary
Population growth and their improving standard of living, increase in agricultural and industrial production, water quality deterioration, and several other factors influence and intensify our freshwater needs across the global and regional scales [1]. The infelicitous spatial and temporal distributions of water resources result in water scarcity in different regions at different times. Appropriate water management and planning policies become an arduous task to implement in several regions and results in potential conflicts between various stakeholders [2]. This problem becomes a devastating issue during the drought season when there are serious climate change implications on water security, environmental sustainability, and socio-economic developments. Climate change is anticipated to augment the duration, frequency, and intensity (severity) of droughts and Remote Sens. The arid (hyper-arid and semi-arid) regions are more prone to climate change mitigation, where the precipitation and soil moisture availability is already very low
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