Analyzing the Mara River Basin Behaviour through Rainfall-Runoff Modeling

Birundu, Anne M.; Mutua, Benedict M.

doi:10.4236/ijg.2017.89064

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…However, visual inspection shows the satisfactory performance of the model. This result agrees with the observation of Birundu & Mutua (2017) in their study that was carried out in the Mara River Basin that cuts across Kenya and Tanzania.…”

Section: Rainfall Data Comparison Bias Correction Model Calibration and Sensitivity Analysissupporting

confidence: 92%

Flood forecasting using quantitative precipitation forecasts and hydrological modeling in the Sebeya catchment, Rwanda

Assoumpta

Aja

2021

H2Open Journal

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The absence of a viable flood early warning system for the Sebeya River catchment continues to impede government efforts toward improving community preparedness, the reduction of flood impacts and relief. This paper reports on a recent study that used satellite data, quantitative precipitation forecasts and the rainfall–runoff model for short-term flood forecasting in the Sebeya catchment. The global precipitation measurement product was used as a satellite rainfall product for model calibration and validation and forecasted European Centre Medium-Range Weather Forecasts (ECMWF) rainfall products were evaluated to forecast flood. Model performance was evaluated by the visual examination of simulated hydrographs, observed hydrographs and a number of performance indicators. The real-time flow forecast assessment was conducted with respect to three different flood warning threshold levels for a 3–24-h lead time. The result for a 3-h lead time showed 72% of hits, 7.5% of false alarms and 9.5% of missed forecasts. The number of hits decreased, as the lead time increased. This study did not consider the uncertainties in observed data, and this can influence the model performance. This work provides a base for future studies to establish a viable flood early warning system in the study area and beyond.

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Section: Rainfall Data Comparison Bias Correction Model Calibration and Sensitivity Analysissupporting

confidence: 92%

Flood forecasting using quantitative precipitation forecasts and hydrological modeling in the Sebeya catchment, Rwanda

Assoumpta

Aja

2021

H2Open Journal

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Study of hydrological model for water reliability at industrial and residential area in the upper Citarum river basin, Indonesia

Cahyono,

Juliastuti,

Wijayanti

et al. 2023

Sixth International Conference of Mathematical Sciences (Icms 2022)

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Time lag effect of vegetation response to seasonal precipitation in the Mara River Basin

Feng,

Zhang,

Zhao

et al. 2023

Ecol Process

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Background Mara River Basin is an ecologically fragile area in East Africa, with a pattern of alternating wet and dry seasons shaped by periodic precipitation. Considering the regional biological traits and climatic change, the vegetation's response to seasonal variation is complicated and frequently characterized by time lags. This study analyzed the variation of the Normalized Difference Vegetation Index (NDVI) and investigated its time lag to precipitation at the monthly scale. NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect. Results The results showed that the Anomaly Vegetation Index could identify low precipitation in 2006, 2009, and 2017. The NDVI showed an increasing trend in 75% of areas of the basin, while showed a decreased significance in 3.5% of areas, mainly in savannas. As to the time lag, the 1-month lag effect dominated most months, and the spatiotemporal disparities were noticeable. Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days. Based on the time distribution of NDVI characteristic peaks, the average time lag was 35.5 days and increased with the range of seasons. Conclusions The findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020, while the trends were most obvious in the downstream related to human activities. The results could reflect the time lag of NDVI response to precipitation, and the 1-month lag effect dominated in most months with spatial heterogeneity. Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.

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Analyzing the Mara River Basin Behaviour through Rainfall-Runoff Modeling

Cited by 3 publications

References 11 publications

Flood forecasting using quantitative precipitation forecasts and hydrological modeling in the Sebeya catchment, Rwanda

Flood forecasting using quantitative precipitation forecasts and hydrological modeling in the Sebeya catchment, Rwanda

Study of hydrological model for water reliability at industrial and residential area in the upper Citarum river basin, Indonesia

Time lag effect of vegetation response to seasonal precipitation in the Mara River Basin

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