Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS)

Shen, Zhehui; Yong, Bin; Gourley, Jonathan J.; Qi, Weiqing; Lü, Daren; Liu, Jiufu; Ren, Liliang; Hong, Yang; Zhang, Jianyun

doi:10.1016/j.jhydrol.2020.125284

Cited by 74 publications

(33 citation statements)

References 34 publications

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“…CHIRPS have negative biases (−2.01%) before 2000, while this underestimation was corrected after 2000. Temporal analysis and intensity distribution of the gauge-adjusted CHIRPS estimates are in line with the estimates of the Global Precipitation Climatology Centre (GPCC) in several regions of the world, such as the United States, Europe, and Africa [39].…”

Section: Precipitation Parametersupporting

confidence: 74%

Drainage Network Patterns Determinism: A Comparison in Arid, Semi-Arid and Semi-Humid Area of Morocco Using Multifactorial Approach

et al. 2020

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Drainage network patterns influence the hydrological response of the watersheds and must be taken into account in the management of the water resource. In this context, it is important to identify the factors that control the configuration of drainage networks in and beyond specific climatic conditions. Here, we study 318 basins spread over three sectors (arid, semi-arid, and semi-humid) of Morocco where seven drainage network patterns have been identified. From each basin, 14 parameters were extracted, describing the relief, geology, morphometry, drainage network, land cover, precipitation, and time of concentration (Tc). Principal component analysis (PCA) and discriminant analysis (DA) processing were performed on the entire database and on each sector separately. The results show that the drainage network pattern is a feature of the landscape that contributes significantly to the variance of the basins. They suggest that the distribution of network patterns is controlled by the relationship between the different parameters, mainly those related to the relief, more than by the variations of each parameter taken individually. The network discrimination rate is 63.8%, which improves when each sector is treated separately. Confusion in discrimination are similar across all sectors and can be explained by similar conditions (active tectonic, deformation, and uplift) or transitions from one network pattern to another, due to the landscape evolution of certain sectors. A contribution of climatic variables appears locally but was attributed to a statistical coincidence, these parameters presenting a distribution close to that of the relief and geology variables.

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Section: Precipitation Parametersupporting

confidence: 74%

Drainage Network Patterns Determinism: A Comparison in Arid, Semi-Arid and Semi-Humid Area of Morocco Using Multifactorial Approach

et al. 2020

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“…And these features may reflect improvements in terms of updates to the algorithms, or changes in available data sources. In future studies, we can further partition the study period and compare the characteristics of the errors over different study periods (Shen et al, 2020;Tang et al, 2020). Spatial averaging poses similar problems, but our approach is generalized to different spatial and temporal scales.…”

Section: Discussionmentioning

confidence: 99%

New Insights Into Error Decomposition for Precipitation Products

Zhang

Nguyen

et al. 2021

Geophysical Research Letters

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Precipitation is not only a key variable in climate studies, but also a crucial forcing factor of the hydrological cycle (Do et al., 2020;Jiang & Bauer-Gottwein, 2019;Yao et al., 2020). Currently, various precipitation products are developed through different technologies, such as gauge interpolation, radar observation, satellite retrieval, and assimilation reanalysis (Foufoula-Georgiou et al., 2020;Sun et al., 2018). However, despite the significant advances of these products, there still exists errors associated with them due to many factors (e.g., algorithms, data sources, bias correction). Therefore, it is important to identify and quantify these error sources before proceeding to applications, which is also beneficial to the iterative updates of products (Alijanian et al., 2019;Sorooshian et al., 2000).Many efforts have been done to address this task, including the comparison of gauge observations, intercomparison, and hydrological validation (

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“…A recent global-scale evaluation of CHIRPS monthly precipitation from 2000 to 2016 with the Global Precipitation Climatology Center gauge-based precipitation data reported error (including random and bias components) within ±2.5% across Europe, Africa, Australia, United States, and South America (Shen et al, 2020). The Southeast China region had a relatively larger error at 5.6% (Shen et al, 2020); however, this region does not cover the large cropland areas included in this study. Additionally, these errors are at a monthly scale and the annual scale error is smaller at −0.06% (Shen et al, 2020).…”

Section: Model Parameter Estimation Uncertaintiesmentioning

confidence: 64%

“…The Southeast China region had a relatively larger error at 5.6% (Shen et al, 2020); however, this region does not cover the large cropland areas included in this study. Additionally, these errors are at a monthly scale and the annual scale error is smaller at −0.06% (Shen et al, 2020).…”

Section: Model Parameter Estimation Uncertaintiesmentioning

confidence: 90%

“…The CHIRPS precipitation data have been widely validated across the globe (Paredes-Trejo et al, 2017;Prakash, 2019;Tarek et al, 2020). A recent global-scale evaluation of CHIRPS monthly precipitation from 2000 to 2016 with the Global Precipitation Climatology Center gauge-based precipitation data reported error (including random and bias components) within ±2.5% across Europe, Africa, Australia, United States, and South America (Shen et al, 2020). The Southeast China region had a relatively larger error at 5.6% (Shen et al, 2020); however, this region does not cover the large cropland areas included in this study.…”

Section: Model Parameter Estimation Uncertaintiesmentioning

confidence: 99%

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Dynamics of Green and Blue Water Supply Stress Index Across Major Global Cropland Basins

et al. 2021

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Global food and water insecurity could be serious problems in the upcoming decades with growing demands from the increasing global population and more frequent effect of climatic extremes. As the available water resources are diminishing and facing continuous stress, it is crucial to monitor water demand and water availability to understand the associated water stresses. This study assessed the water stress by applying the water supply stress index (WaSSI) in relation to green (WaSSIG) and blue (WaSSIB) water resources across six major cropland basins including the Mississippi (North America), San Francisco (South America), Nile (Africa), Danube (Europe), Ganges-Brahmaputra (Asia), and Murray-Darling (Australia) for the past 17-years (2003–2019). The WaSSIG and WaSSIB results indicated that the Murray-Darling Basin experienced the most severe (maximum WaSSIG and WaSSIB anomalies) green and blue water stresses and the Mississippi Basin had the least. All basins had both green and blue water stresses for at least 35% (6 out of 17 years) of the study period. The interannual variations in green water stress were driven by both crop water demand and green water supply, whereas the blue water stress variations were primarily driven by blue water supply. The WaSSIG and WaSSIB provided a better understanding of water stress (blue or green) and their drivers (demand or supply driven) across cropland basins. This information can be useful for basin-specific resource mobilization and interventions to ensure food and water security.

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Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS)

Cited by 74 publications

References 34 publications

Drainage Network Patterns Determinism: A Comparison in Arid, Semi-Arid and Semi-Humid Area of Morocco Using Multifactorial Approach

Drainage Network Patterns Determinism: A Comparison in Arid, Semi-Arid and Semi-Humid Area of Morocco Using Multifactorial Approach

New Insights Into Error Decomposition for Precipitation Products

Dynamics of Green and Blue Water Supply Stress Index Across Major Global Cropland Basins

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