Abstract
In this paper, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is analyzed for the assessment of meteorological drought. The evaluation is conducted over China at 0.5° spatial resolution against a ground-based gridded China monthly Precipitation Analysis Product (CPAP) from 1983 to 2014 (32 years). The Standardized Precipitation Index (SPI) at various time scales (1 month to 12 months) is calculated for detecting drought events. The results show that PERSIANN-CDR depicts similar drought behavior as the ground-based CPAP in terms of capturing the spatial and temporal patterns of drought events over eastern China, where the intensity of gauge networks and the frequency of droughts are high. 6-month SPI shows the best agreement with CPAP in identifying drought months. However, large differences between PERSIANN-CDR and CPAP in depicting drought patterns and identifying specific drought events are found over northwestern China, particularly in Xinjiang and Qinghai-Tibet Plateau region. Factors behind this may be due to the relatively sparse gauge networks, the complicated terrain and the performance of PERSIANN algorithm.
Highlights
Drought is one of the costliest and most widespread and one of the most complex and least understood natural hazards [1,2,3]
It is expected that the discrepancies in Standardized Precipitation Index (SPI) estimates can be reflected by the estimating errors in precipitation spatial pattern and statistics between PERSIANN-Climate Data Record (CDR) and China monthly Precipitation Analysis Product (CPAP)
Availability of the latest daily, 0.25 ̋ satellite-based PERSIANN-CDR provides an opportunity for long-term assessments of droughts at a finer resolution than previously possible
Summary
Drought is one of the costliest and most widespread and one of the most complex and least understood natural hazards [1,2,3]. Droughts may typically last for several months, or even years and can occur over most parts of the world including wet and humid regions [4]. Based on definitions and types, droughts can be grouped into four categories: (a) meteorological;. This study focuses on the meteorological drought, which is related to the negative precipitation departure under normal conditions over an extended period of time. Due to increasing vulnerability of environment, exposure of assets and the change of climate, drought impacts on agriculture, water supply and economy have increased over decades [1,6]. The development of drought monitoring tools is very necessary for drought risk management [7,8]
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