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Evaluation the performance of several gridded precipitation products over the highland region of Yemen for water resources management

Evaluation the performance of several gridded precipitation products over the highland region of Yemen for water resources management
Evaluation the performance of several gridded precipitation products over the highland region of Yemen for water resources management
Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.
Climate change, Highland region, Precipitation gridded products, Rainfall estimates, Yemen
2072-4292
1-23
AL-Falahi, Ali Hamoud
89c616ad-4fc1-40af-9b6a-0e100a122114
Saddique, Naeem
3976c070-76f2-4473-840a-2eb0ac980f53
Spank, Uwe
fe987aba-8323-47b3-9716-67e128c06118
Gebrechorkos, Solomon
ff77f8a3-b6ef-4cfd-aebd-a003bf3947a5
Bernhofer, Christian
155c7933-3b1e-453f-b492-9890dff85513
AL-Falahi, Ali Hamoud
89c616ad-4fc1-40af-9b6a-0e100a122114
Saddique, Naeem
3976c070-76f2-4473-840a-2eb0ac980f53
Spank, Uwe
fe987aba-8323-47b3-9716-67e128c06118
Gebrechorkos, Solomon
ff77f8a3-b6ef-4cfd-aebd-a003bf3947a5
Bernhofer, Christian
155c7933-3b1e-453f-b492-9890dff85513

AL-Falahi, Ali Hamoud, Saddique, Naeem, Spank, Uwe, Gebrechorkos, Solomon and Bernhofer, Christian (2020) Evaluation the performance of several gridded precipitation products over the highland region of Yemen for water resources management. Remote Sensing, 12 (18), 1-23, [2984]. (doi:10.3390/rs12182984).

Record type: Article

Abstract

Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.

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Accepted/In Press date: 11 September 2020
e-pub ahead of print date: 14 September 2020
Published date: September 2020
Keywords: Climate change, Highland region, Precipitation gridded products, Rainfall estimates, Yemen

Identifiers

Local EPrints ID: 443833
URI: http://eprints.soton.ac.uk/id/eprint/443833
ISSN: 2072-4292
PURE UUID: 4cd18c00-e2fe-4d86-aeed-102ef81ed78e
ORCID for Solomon Gebrechorkos: ORCID iD orcid.org/0000-0001-7498-0695

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Date deposited: 14 Sep 2020 16:36
Last modified: 23 Jul 2022 02:24

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Contributors

Author: Ali Hamoud AL-Falahi
Author: Naeem Saddique
Author: Uwe Spank
Author: Christian Bernhofer

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