Evaluation of Climate Hazards Group InfraRed Precipitation Station (CHIRPS) <scp>satellite‐based</scp> rainfall estimates over Finchaa and Neshe Watersheds, Ethiopia

Geleta, Chala Daba; Deressa, Tesema Adamu

doi:10.1002/eng2.12338

Cited by 25 publications

(9 citation statements)

References 53 publications

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“…As well as rainfall data within a period of 10 years to produce river discharge data. The data used to obtain the data are described as follows (a) and (b): (Geleta and Deressa, 2021). The rainfall data used has gone through an interpolation process in GEE to cover the study area.…”

Section: Methodsmentioning

confidence: 99%

“…CHIRPS itself provides daily rainfall data at 0.05° spatial resolution since 1981 to the present, and users can use it for free. CHIRPS data shows good potential accuracy for use in meteorological studies and research related to water resources(Geleta and Deressa, 2021). The rainfall data used has gone through an interpolation process in GEE to cover the study area.…”

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confidence: 99%

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2d Simulation of Design Discharge in Flood Hazard Spatial Analysis Using Hec-Ras, (Case Study Mata Allo Sub-Watershed, Enrekang, Indonesia)

SIDENG¹,

UPU²,

HARIS³

et al. 2023

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Indonesia is one of the countries frequently hit by hydrometeorological disasters. Flood is one of the disasters that often occurs in Indonesia. Especially for the Enrekang area which is traversed by the Mata Allo River. With topographical characteristics and land use that has the potential to cause flooding and high rainfall. It will harm activities in the watershed area. In reducing the impact that occurs, it is necessary to identify and map flood-prone areas as initial information on flood control. This study aims to determine the distribution and level of vulnerability to flooding in the Mata Allo sub-watershed using 2D HEC-RAS simulations. By utilizing annual rainfall data, the Nakayasu HSU method is applied to generate design discharge values to be used in flood simulations. The simulation results show that there are five classes of flood hazard levels which show the uniformity of flood vulnerability levels in each return period. The average level of vulnerability is in the very low range of ±9.60 Ha, in the low range of ±8.8 Ha, medium vulnerability is in the range of ±13.63 Ha, high vulnerability is ±23.58 Ha, and extreme vulnerability is ± 25.71 Ha. The area that is mapped is an area that is often affected when a flood occurs. As a result, the use of this approach can provide an overview of the distribution of flood-prone areas.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

2d Simulation of Design Discharge in Flood Hazard Spatial Analysis Using Hec-Ras, (Case Study Mata Allo Sub-Watershed, Enrekang, Indonesia)

SIDENG¹,

UPU²,

HARIS³

et al. 2023

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“…The product is available at the Climate Hazards Group (https://www.chc.ucsb.edu/data/chirps/) on daily, 10-day, and monthly timescales from the 1981-near present. Due to its availability at high spatial and temporal resolution, CHIRPS is widely used in hydrological studies (Luo et al, 2019;Gebrechorkos et al, 2020;Geleta and Deressa, 2021;Wang et al, 2021;Opere et al, 2022;Day and Howarth, 2019;Gebrechorkos et al, 2019) and modelling of hydrological extremes such as droughts and floods (Chen et al, 2020;Mianabadi et al, 2022;Peng et al, 2020).…”

Section: Input Global and Quasi-global Precipitation Datasetsmentioning

confidence: 99%

Global scale evaluation of precipitation datasets for hydrological modelling

Gebrechorkos,

Leyland,

Dadson

et al. 2023

Preprint

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Abstract. Precipitation is the most important driver of the hydrological cycle but is challenging to estimate over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (ERA5 global reanalysis (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERCCDR)) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against more than 1800 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly and daily time scales, MSWEP followed by ERA5 demonstrated a higher CC and KGE than other datasets for more than 50 % of the stations. Whilst, ERA5 was the second-highest performing dataset, it showed the highest error and bias in about 20 % of the stations. The PERCCDR is the least well performing dataset with large bias (percentage of bias up to 99 %) and errors (normalised root mean square error up to 247 %) with a higher KGE and CC than the other products in less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

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“…While the cultivated land cover has continually increased over the years, the grasslands and wetlands have dwindled (Senbeta, 2018). The cultivated land is covered by various crops cultivated by smallholder households including teff, wheat, barley, sorghum, maize, millet, oats, lentils, beans, peas, sesame, vegetables, and fruits (Geleta & Deressa, 2021). The topography of the Amerti-Neshe catchment varies considerably from lowlands as low as 902 m a.s.l.…”

Section: Study Areamentioning

confidence: 99%

Impact of Hydropower Dam on Household Water Security: Evidence from Amerti-Neshe Reservoir in Northwestern Ethiopia

Jima

Guta²,

Senbeta³

et al. 2022

AJMSS

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Understanding the impact of hydropower dam construction on adjacent local community water security is critical for identifying factors that influence water security as well as improving water supplies in rural areas. This study analyzes the impact of hydropower dam construction on the water security of households around the Amerti and Neshe dams in Northwestern Ethiopia. A multistage sampling procedure was followed to select 485 households (268 affected and 217 non-affected) for our analysis. A principal component analysis was applied to five dimensions of water security (access, utilization, affordability, quality, and quantity) and then the households water security index (HWSI) was constructed. An endogenous switching regression model was applied to analyze the impact of dam construction on the water security status at the household level in the study area. Our findings revealed that there is a significant mean difference in HWSI between affected and non-affected households regarding water access and quality. The average treatment effect indicates that the construction of the two dams has had a negative impact on household water security, reducing it by 12%. As a result, government and other stakeholders should consider relocating affected households to better areas in order to mitigate such negative effects on household water security.

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Evaluation of Climate Hazards Group InfraRed Precipitation Station (CHIRPS) satellite‐based rainfall estimates over Finchaa and Neshe Watersheds, Ethiopia

Cited by 25 publications

References 53 publications

2d Simulation of Design Discharge in Flood Hazard Spatial Analysis Using Hec-Ras, (Case Study Mata Allo Sub-Watershed, Enrekang, Indonesia)

2d Simulation of Design Discharge in Flood Hazard Spatial Analysis Using Hec-Ras, (Case Study Mata Allo Sub-Watershed, Enrekang, Indonesia)

Global scale evaluation of precipitation datasets for hydrological modelling

Impact of Hydropower Dam on Household Water Security: Evidence from Amerti-Neshe Reservoir in Northwestern Ethiopia

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