The ability to adequately and continually assess the hydrological catchment response to extreme rainfall events in a timely manner is a prerequisite component in flood-forecasting and mitigation initiatives. Owing to the scarcity of data, this particular subject has captured less attention in Rwanda. However, semi-distributed hydrological models have become standard tools used to investigate hydrological processes in data-scarce regions. Thus, this study aimed to develop a hydrological modeling system for the Nyabarongo River catchment in Rwanda, and assess its hydrological response to rainfall events through discharged flow and volume simulation. Initially, the terrain Digital Elevation Model (DEM) was pre-processed using a geospatial tool (HEC-GeoHMS) for catchment delineation and the generation of input physiographic parameters was applied for hydrological modeling system (HEC-HMS) setup. The model was then calibrated and validated at the outlet using sixteen events extracted from daily hydro-meteorological data (rainfall and flow) for the rainy seasons of the country. More than in other events, the 15th, 9th, 13th and 5th events showed high peak flows with simulated values of 177.7 m3s−1, 171.7 m3s−1, 169.9 m3s−1, and 166.9 m3s−1, respectively. The flow fluctuations exhibited a notable relation to rainfall variations following long and short rainy seasons. Comparing the observed and simulated hydrographs, the findings also unveiled the ability of the model to simulate the discharged flow and volume of the Nyabarongo catchment very well. The evaluated model’s performance exposed a high mean Nash Sutcliffe Efficiency (NSE) of 81.4% and 84.6%, with correlation coefficients (R2) of 88.4% and 89.8% in calibration and validation, respectively. The relative errors for the peak flow (5.5% and 7.7%) and volume (3.8% and 4.6%) were within the acceptable range for calibration and validation, respectively. Generally, HEC-HMS findings provided a satisfactory computing proficiency and necessitated fewer data inputs for hydrological simulation under changing rainfall patterns in the Nyabarongo River catchment. This study provides an understanding and deepening of the knowledge of river flow mechanisms, which can assist in establishing systems for river monitoring and early flood warning in Rwanda.
AbstractIncreasing global warming has led to the incremental retreat of glaciers, which in turn affects the water supply of the rivers dependent on glacier melts. This is further affected by the increases in flooding that is attributable to heavy rains during the snowmelt season. An accurate estimation of streamflow is important for water resources planning and management. Therefore, this paper focuses on improving the streamflow forecast for Kaidu River Basin, situated at the north fringe of Yanqi basin on the south slope of the Tianshan Mountains in Xinjiang, China. The interannual and decadal scale oceanic-atmospheric oscillations, i.e., Pacific decadal oscillation (PDO), North Atlantic oscillation (NAO), Atlantic multidecadal oscillation (AMO), and El Nino–southern oscillation (ENSO), are used to generate streamflow volumes for the peak season (April–October) and the water year, which is from October of the previous year to September of the current year for a period from 1955–2006. A data-driven model...
Abstract Mountain snow is a fundamental freshwater supply in the arid regions. Climate warming alters the timing of snowmelt and shortens the snow cover duration, which greatly influences the regional climate and water management. However, a reliable estimation of snow mass in the Tianshan Mountains (TS) is still unclear due to the scarcity of extensive continuous surface observations and a complex spatial heterogeneity. Therefore, a long‐time snow simulation from 1982 to 2018 was performed in WRF/Noah‐MP to quantify snow mass in the TS, forced by ERA5 reanalysis data and real‐time updated leaf area index and green vegetation fraction. Meanwhile, March snow mass (close to the annual peak snow mass), snow cover fraction (SCF), and their associated trends were investigated in the TS. The results indicated a good accuracy of the simulated snow water equivalent (root mean square error [RMSE]: 7.82 mm/day) with a slight overestimation (2.84 mm/day). Compared with ERA5 data set, the RMSE and mean bias of the daily snow depth from WRF/Noah‐MP downscaling were significantly reduced by 95.74% and 93.02%, respectively. The climatological March snow mass measured 97.85 (±16.60) Gt in the TS and exhibited a negligible tendency during the study period. The total precipitation during the cold season controlled the variations of March snow mass. The increased precipitation in the high‐altitude regions contributed to an extensive snow mass, which could offset the loss in the TS lowland. In contrast, rapidly rising air temperature caused a significant reduction of March SCF, particularly in the Southern TS.
An original modeling framework for the assessment of climate variation and change impacts on the performance of a complex flood protection system has been developed for the city of Winnipeg in the Red River basin, Manitoba, Canada. The modeling framework allows for the evaluation of different climate change scenarios generated by the global climate models. Temperature and precipitation are used as the main factors affecting flood flow generation. The main contribution of the reported work is the use of a system dynamics modeling and simulation approach in the development of a system performance assessment model. The assessment-modeling framework is based on flood flows, capacity of flood control structures, and failure flow levels at different locations in the basin. The results of this study (shown only to illustrate the methodology) indicate that the capacity of the existing Red River flood protection system is sufficient to accommodate future climate variability and change.
Spatial variation of water quality in rivers is a function of the surrounding environment and land, the reason why water indices are important to reduce the bulk of information into a simplified and understandable manner for specific purposes.This study aimed at assessing the spatial distribution of water quality of 23 Rwandan rivers that drain into the Lake Kivu by using the National Sanitation Foundation Water Quality Index (NSFWQI) and the River Pollution Index (RPI).The study collected field data and analyzed the parameters of the NSFWQI and RPI including suspended solids, turbidity, biological oxygen demand, nitrate, temperature, total phosphorus, pH, fecal coliform and dissolved oxygen.For gathering details related to entities adjacent to rivers, land use and land cover, topography and rainfall have been analyzed.The results showed that good water quality (negligibly polluted) was located in areas dominated by forestland while bad and very bad (39%, 26%) classes of rivers (severely polluted) were influenced by the dominance of farmland.Moreover, 22% of rivers in medium class were equivalent to 26% moderately polluted due to the disturbance of other land use types and other factors such as slope and tropical rainfall.
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