Drought monitoring utility of satellite-based precipitation products across mainland China

Zhong, Rong; Chen, Xiaohong; Lai, Chengguang; Lian, Yanqing; Yu, Haoping; Wu, Xiaoqing

doi:10.1016/j.jhydrol.2018.10.072

Cited by 171 publications

(117 citation statements)

References 71 publications

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“…Drought is a natural disaster that occurs with high frequency and which has long-lasting impacts on agriculture production, the ecological environment, and the economy [1]. Agricultural drought can occur in many parts of the world but usually develops slowly and causes widespread devastation and economic loss [2]. During the growing seasons from 1988 to 2001, Canada suffered more than 5 billion dollars in economic losses per year due to agricultural drought [3].…”

Section: Introductionmentioning

confidence: 99%

Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin, China

Zhu

Luo

et al. 2019

Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin, China

Zhu

Luo

et al. 2019

Remote Sensing

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“…Drought can be the most frequent, longest duration, widespread, and serious natural disasters in the world [1]. Because of the spatiotemporal variability and complexity of drought [2], the general understanding of its evolutionary characteristics is limited and thus it is difficult to avoid serious drought-related losses to agriculture, ecology and society.…”

Section: Introductionmentioning

confidence: 99%

“…The initial cause of drought is inadequate precipitation over a certain period [15]. Therefore, precipitation has become a key meteorological variable to study drought and as input data to calculate drought indices (including the Palmer Drought Index, the Standardized Precipitation Evapotranspiration Index and the SPI) [1,16]. Because of the great differences in natural factors such as geographical location, topography, climate, meteorology, and the ecological environment, precipitation has considerable differences over various temporal and spatial scales [17].…”

Section: Introductionmentioning

confidence: 99%

Performance of Two Long-Term Satellite-Based and GPCC 8.0 Precipitation Products for Drought Monitoring over the Yellow River Basin in China

et al. 2019

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This study investigated the accuracy and drought monitoring application of two newly-released long-term satellite precipitation products (i.e., the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record, PERSIANN-CDR and the Climate Hazards Group Infrared Precipitation with Station data version 2.0 CHIRPS) and the latest reanalysis precipitation product (i.e., the Global Precipitation Climatology Centre full data monthly version 2018, GPCC 8.0). Satellite- and reanalysis-based precipitation sequences and standardized precipitation indices (SPIs) were compared comprehensively with background estimates of the China Gauge-based Daily Precipitation Analysis (CGDPA) dataset at spatial and multiple temporal scales over the Yellow River Basin (YRB) in China during 1983–2016. Results indicated the PERSIANN-CDR, CHIRPS and GPCC 8.0 precipitation products generally had good consistency with CGDPA (correlation coefficient, CC > 0.78). At spatial, monthly and seasonal scales, the consistency between GPCC 8.0 and CGDPA precipitation was found to be better than that of the two satellite products. Due to their good performance at the spatiotemporal scale, the satellite with long-time record and GPCC 8.0 products were evaluated and compared with CGDPA to derive SPI-1 (1-month SPI), SPI-3 (3-month SPI), and SPI-12 (12-month SPI) for drought monitoring in the YRB. The results showed that they had good application in monitoring droughts (CC > 0.65 at spatial scale, CC > 0.84 at temporal scale). The historical drought years (i.e., 1997, 1999, and 2006) and the spatial distribution of drought area in August 1997 were captured successfully, but the performance of GPCC 8.0 was found to be the best. Overall, GPCC 8.0 is considered best suited to complement precipitation datasets for long-term hydrometeorological research in the YRB.

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“…In this sense, Quesada-Montano et al [59] highlighted that the selection of the precipitation database was more important than the selection of the drought index. e use of CHIRPS precipitation estimates for drought monitoring based on the SPI has gained attraction nowadays, as shown by studies performed in Indonesia [60], China [1,28], Nepal [11], Morocco [54], Southeast Asia [27], Central America [59], and Chile [12].…”

Section: Discussionmentioning

confidence: 99%

“…Zambrano et al [12] concluded that, in order to use the CHIRPS dataset to monitor drought intensity conditions, the product should be calibrated to adjust for the overestimation/underestimation of rainfall geographically. Recently, Gao et al [1] and Zhong et al [28] indicated that CHIRPS successfully captured the spatial patterns of drought over China.…”

Section: Introductionmentioning

confidence: 99%

Using CHIRPS Dataset to Assess Wet and Dry Conditions along the Semiarid Central-Western Argentina

Rivera

Hinrichs

Marianetti

2019

Advances in Meteorology

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e Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset was conceived as a tool for monitoring drought and environmental change over land. Recent validation efforts along South America have assessed its suitability for reproducing the main spatial and temporal features of precipitation. Nevertheless, little has been done regarding the ability of CHIRPS for the assessment of wet and dry conditions, particularly in areas where in situ precipitation records are scarce. In this paper, we investigated the performance of CHIRPS for monitoring wet and dry events along the semiarid Central-Western Argentina. Using the Standardized Precipitation Index (SPI), we compared the CHIRPS database with records from 49 meteorological stations along the study area for the period 1987-2016. Results indicate that the CHIRPS dataset adequately reproduced the temporal variability of SPI on multiple timescales (1 month, 3 months, and 6 months), particularly in the region dominated by warm season precipitation. e large overestimation of the seasonal precipitation in the region dominated by cold season precipitation can introduce errors that are reflected in the performance of CHIRPS over the western portion of the domain. e frequency of wet and dry classes was accurately reproduced by CHIRPS on timescales larger than 1 month (SPI1), given the existence of a wet bias that produces an underestimation of the frequency of zero values. is bias is further translated to the evaluation of the SPI1 during the spatial and temporal assessment of historical dry (1998) and wet (2016) events, especially for the classification of extreme dry/wet months. e results from the evaluation indicate that CHIRPS is a suitable tool for assessing dry and wet conditions for timescales longer than 1 month and can support decision-making process within the hydrometeorological agencies over the region.

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Drought monitoring utility of satellite-based precipitation products across mainland China

Cited by 171 publications

References 71 publications

Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin, China

Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin, China

Performance of Two Long-Term Satellite-Based and GPCC 8.0 Precipitation Products for Drought Monitoring over the Yellow River Basin in China

Using CHIRPS Dataset to Assess Wet and Dry Conditions along the Semiarid Central-Western Argentina

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