Evaluation of satellite precipitation products for water allocation studies in the Sio-Malaba-Malakisi river basin of East Africa

Omonge, Paul; Olang, Luke O.; Herrnegger, Mathew; Schulz, Karsten

doi:10.1016/j.ejrh.2021.100983

Cited by 13 publications

(12 citation statements)

References 53 publications

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“…However, at the annual time scale, as shown in (Figure 3 and Table 4), TMPA product performed better than ARC2, GPM IMERG6, and PERSIANN products. These results are in line with prior research on the Sudanese Blue Nile basin and the East African Sio-Malaba-Malakisi river basin [23,51]. However, a study carried out in Ethiopia's Upper Blue Nile Basin contradicted the superiority of TMPA products at annual time scales [19].…”

Section: Discussionsupporting

confidence: 90%

Evaluating Current Satellite Sensors in Capturing the Spatio-temporal Rainfall Variability across North Darfur State, Sudan

Altoom,

Adam,

Ali

2023

Preprint

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Accurate rainfall measurement is vital when investigating spatial and temporal precipitation variability at different scales. However, there are many regions around the world, such as North Darfur State in Sudan, where ground-based observations are few. Satellite-based precipitation products can fill such regions' spatial and temporal rainfall data gaps. Six satellite rainfall prod-ucts, namely the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology Version 2 (ARC2.0), Climate Hazards Group In-frared Precipitation with Station Data (CHIRPS2.0), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run v 6 (GPM IMERG6), Precipitation Estima-tion from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) v3.1 were evaluated to assess their accuracy in estimating rainfall amounts variability trends in the study area. The global-based satellite rainfall products were assessed at monthly and annual time scales by applying a point-to-pixel comparison with ground-based rain gauge data for the period 2000–2019. Based on the overall statistical results at monthly and temporal yearly scales, five satellite precipitation products (TMPA, CHIRPS, GPM IMERG6, PERSIANN-CDR, and TAMSATv3.1) overestimated rainfall amounts by values ranging from 1.49% to 82.69%. In contrast, the ARC2 product underestimated rainfall amounts by values ranging from-16.9% to-20.25%. The TAMSATv3.1, CHIRPS, and TMPA performed relatively better, showing stronger correlations and higher values of Nash-Sutcliffe efficiency. This study showed that the TAMSATv3.1 and CHIRPS products could reasonably estimate rainfall amounts in the North Darfur State.

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Section: Discussionsupporting

confidence: 90%

Evaluating Current Satellite Sensors in Capturing the Spatio-temporal Rainfall Variability across North Darfur State, Sudan

Altoom,

Adam,

Ali

2023

Preprint

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“…This difference can be observed in Figure 8, in the central region of the area and in southern Maranhão on the border with the states of Tocantins, Bahia, and Piauí. [51] identified that the GPM-IMERG products tend to overestimate the amount of rainfall above 300 mm per month over the Sio-Malabo-Malakisi River basin in East Africa. [52], in the evaluation and calibration of the reduced-scale rainfall of IMERG (sub-daily), in the Shanghai metropolitan region in China, concluded that IMERG performed better at the monthly time scale, followed by the annual scale (CC = 0.89 and 0.82, respectively).…”

Section: Resultsmentioning

confidence: 99%

Evaluation of the IMERG Product in the MATOPIBA Region, Brazil

Araujo,

Duarte,

Barreiros

et al. 2024

IJLERA

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Managing water resources around the world requires challenges imposed by natural and anthropogenic processes. Having knowledge about water resources, whether qualitative or quantitative, almost always depends on monitoring rainfall stations for their temporal and spatial understanding. Monitoring dry and wet seasons in a country or region is a difficult problem, especially when the network of meteorological stations is sparse or does not cover the entire area. The Integrated Multisatellite Retrieval of Global Precipitation Measurements (IMERG) produces and provides a source of high-resolution spatial and temporal precipitation estimates obtained by satellites and has been widely used since its launch in 2014, providing global precipitation data since 2000. In the present study, the performance of the GPM (Global Precipitation Measurement) product (IMERG-final run) was evaluated in the MATOPIBA region, located in the northern and northeastern regions of Brazil, from October 2000 to September 2021. The monthly and annual IMERG estimates were compared against the rainfall data from 197 ground stations in the study region belonging to the Agência Nacional das Águas (ANA). The metrics used for this study's analysis of IMERG's performance in detecting rainfall were both quantitative and qualitative. The IMERG estimates overestimated those of the rain gauges by about 13%. However, the performance result was quite satisfactory for the estimation of rainfall events, both monthly and annually, that is, excellent correlation with their spatial and temporal distribution as well as their volume. It was concluded that this product is a satisfactory source of alternative rainfall data, especially in areas without a dense network of rain gauges, such as the study region.

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“…CHIRPS dataset has been tested and proven to be one of the best datasets for the estimation of daily, monthly and seasonal rainfall in different climatic conditions as compared to other satellite datasets such as TRMM, TAMSATv3, PERSIANN-CDR, GPM-3IMERG6 and CMORPH-CPC (de Moraes Cordeiro & Blanco, 2021;Omonge et al, 2022;Mulungu & Mukama, 2022;Taye et al, 2023). The CHIRPS dataset has been validated and compared with another satellite dataset in Southern and Eastern Africa and proved to provide best-fit estimates (Ayehu et al, 2018;Goshime et al, 2019;Taye et al, 2023).…”

Section: Chirps Datasetmentioning

confidence: 99%

Usangu rainfall variability analysis: Unveiling spatio-temporal trend, distribution and concentration of rainfall in a semi-arid region

Kavishe,

Mulungu,

Alexander

2024

Preprint

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Rainfall unpredictability in semi-arid climates affects the quality and quantity of water available for socioeconomic activities, ecological maintenance and developmental projects. Understanding rainfall characteristics such as trend, distribution, and concentration is important for managing water resources and planning to mitigate impacts from rainfall uncertainty. This study was conducted in the Usangu catchment located in a semi-arid environment with three competing users agricultural land upstream, and the National Park and hydropower plant. A multidisciplinary approach that integrated statistical methods, hydrological analysis and geographic information system (GIS) tools was used to unveil a spatio-temporal trend and variability of annual rainfall in Usangu. The results indicated a significant trend for all stations except Mafinga station. The highest decrease was at Kimani (S=-6.13) and the highest increase at Matamba (S = 2.93). There is a distinct distribution of rainfall between the highlands (800-1,200 mm/year) and lowlands (500–800 mm/year). Rainfall is concentrated in short periods indicated by higher precipitation concentration index (PCI) values ranging from (18.8 to 23.6). There is less dispersion of rainfall at station N based on the coefficient of variation (CV = 17.1%) and high dispersion at Kimani (CV = 33.6%) but the majority of the catchment experienced consistent rainfall patterns. According to the standardised anomaly index (SAI), oftentimes the catchment experienced normal conditions (32 years out of 42 years) which accounts for 77%. Also, extreme drought conditions are more dominant in the lowlands than in the highlands. The results of this study are useful for long-term planning of water resources utilization in Usangu.

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Evaluation of satellite precipitation products for water allocation studies in the Sio-Malaba-Malakisi river basin of East Africa

Cited by 13 publications

References 53 publications

Evaluating Current Satellite Sensors in Capturing the Spatio-temporal Rainfall Variability across North Darfur State, Sudan

Evaluating Current Satellite Sensors in Capturing the Spatio-temporal Rainfall Variability across North Darfur State, Sudan

Evaluation of the IMERG Product in the MATOPIBA Region, Brazil

Usangu rainfall variability analysis: Unveiling spatio-temporal trend, distribution and concentration of rainfall in a semi-arid region

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