Integrated Geospatial Analysis and Hydrological Modeling for Peak Flow and Volume Simulation in Rwanda

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

Analysis of fluctuations in vegetation dynamic over Africa using satellite data of solar-induced chlorophyll fluorescence

In Africa, vegetation is important for the protection of species habitats, maintaining local livelihoods, and the existence of wildlife. A comprehensive evaluation of vegetation dynamics using solar-induced fluorescence (SIF) is needed to acquire important information to understand the current situation of how ecosystems react to human activities and climate change, as well as for conservation planning. The research’s purpose was to detect vegetation dynamics in Africa from 2000 to 2017 using global, OCO-2-based SIF (GOSIF) and various datasets, as well as to analyze the factors influencing vegetation changes. The main findings revealed that: (1) The patterns for various vegetation in this study showed that forests experienced more vegetation expansions than croplands, grasslands, shrubland, and sparse vegetation, based on the Land Use Cover Change (LUCC) per vegetation type. (2) According to SIF, the decreasing area accounts for 29.4% of the total vegetation region while expanding area accounts for 70.6%. (3) The Hurst exponent summary exhibited that the majority of studied vegetation variations are consistent and accounted for 79.7%. (4) Based on the residual, we discovered that climatic patterns might be responsible for the greening trend of sparse vegetation and grassland. (5) The Boosted Regression Trees (BRT) showed that during the study period, Vapor pressure deficit (VPD) and the temperature had a greater impact on SIF vegetation dynamics than other factors. Our findings can aid in the development of appropriate vegetation management concepts or strategies to help in vegetation restoration in Africa

Future Climate Change Impact on the Nyabugogo Catchment Water Balance in Rwanda

Droughts and floods are common in tropical regions, including Rwanda, and are likely to be aggravated by climate change. Consequently, assessing the effects of climate change on hydrological systems has become critical. The goal of this study is to analyze the impact of climate change on the water balance in the Nyabugogo catchment by downscaling 10 global climate models (GCMs) from CMIP6 using the inverse distance weighting (IDW) method. To apply climate change signals under the Shared Socioeconomic Pathways (SSPs) (low and high emission) scenarios, the Soil and Water Assessment Tool (SWAT) model was used. For the baseline scenario, the period 1950–2014 was employed, whereas the periods 2020–2050 and 2050–2100 were used for future scenario analysis. The streamflow was projected to decrease by 7.2 and 3.49% under SSP126 in the 2020–2050 and 2050–2100 periods, respectively; under SSP585, it showed a 3.26% increase in 2020–2050 and a 4.53% decrease in 2050–2100. The average annual surface runoff was projected to decrease by 11.66 (4.40)% under SSP126 in the 2020–2050 (2050–2100) period, while an increase of 3.25% in 2020–2050 and a decline of 5.42% in 2050–2100 were expected under SSP585. Climate change is expected to have an impact on the components of the hydrological cycle (such as streamflow and surface runoff). This situation may, therefore, lead to an increase in water stress, calling for the integrated management of available water resources in order to match the increasing water demand in the study area. This study’s findings could be useful for the establishment of adaptation plans to climate change, managing water resources, and water engineering

Application of the Adapted Approach for Crop Management Factor to Assess Soil Erosion Risk in an Agricultural Area of Rwanda

Land use and land cover (LULC) management influences the severity of soil erosion risk. However, crop management (C) is one factor of the Revised Universal Soil Loss Equation (RUSLE) model that should be taken into account in its determination, as it influences soil loss rate estimations. Thus, the present study applied an adapted C-factor estimation approach (CvkA) modified from the former approach (Cvk) to assess the impact of LULC dynamics on soil erosion risk in an agricultural area of Rwanda taking the western province as a case study. The results disclosed that the formerly used Cvk was not suitable, as it tended to overestimate C-factor values compared with the values obtained from t CvkA. An approximated mean soil loss of 15.1 t ha−1 yr−1, 47.4 t ha−1 yr−1, 16.3 t ha−1 yr−1, 66.8 t ha−1 yr−1 and 15.3 t ha−1 yr−1 in 2000, 2005, 2010, 2015 and 2018, respectively, was found. The results also indicated that there was a small increase in mean annual soil loss from 15.1 t ha−1 yr−1 in 2000 to 15.3 t ha−1 yr−1 in 2018 (1.3%). Moreover, the soil erosion risk categories indicated that about 57.5%, 21.8%, 64.9%, 15.5% and 73.8% had a sustainable soil erosion rate tolerance (≤10 t ha−1 yr−1), while about 42.5%, 78.2%, 35.1%, 84.5% and 16.8% had an unsustainable mean soil erosion rate (>10 t ha−1 yr−1) in 2000, 2005, 2010, 2015 and 2018, respectively. A major portion of the area fell under the high and very high probability zones, whereas only a small portion fell under the very low, low, moderate and extremely high probability zones. Therefore, the CvkA approach presents the most suitable alternative to estimate soil loss in the western province of Rwanda with reasonable soil loss prediction results. The study area needs urgent intervention for soil conservation planning, taking into account the implementation of effective conservation practices such as terracing for soil erosion control