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While the availability of "big data" on biophysical parameters through citizen science and/or from public/private sources is expected to help in addressing data scarcity issues, there is little understanding of whether and/or how such data will improve watershed simulations. This research aimed to evaluate whether improvements in resolutions of Digital Elevation Model (DEM) and soil data will enhance streamflow and sediment yield simulations and thereby improve soil and water management decisions. The study was conducted in two different-sized watersheds (Anjeni and Gilgel Abay with ~ 1 km2 and ~ 1655 km2 area, respectively) in the Upper Blue Nile basin in Ethiopia. Effects of DEM and soil data resolutions on streamflow and sediment yield were evaluated using the Soil and Water Assessment Tool (SWAT). The results showed that the effect of DEM and soil data resolution on streamflow and sediment yield simulation was scale dependent finer resolution DEM and soil datasets improved streamflow and sediment yield simulations in the smaller Anjeni watershed, whereas DEM resolution had no effect in the bigger Gilgel Abay watershed. Small watersheds are often used to understand watershed processes, and thus the use of finer-resolution spatial data for watershed simulations could result in better results. Findings from the smaller Anjeni watershed suggested that the combined use of finer resolution DEM and soil data could potentially improve sediment yield simulations although the lack of observed sediment yield data did not allow verification of this at the larger Gilgel Abay watershed. [ABSTRACT FROM AUTHOR]

Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R2 of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R2 of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R2 of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making. [ABSTRACT FROM AUTHOR]

Climate extreme events have been observed more frequently since the 1970s throughout Ethiopia, which adversely affects the socio-economic development of the country, as its economy depends on agriculture, which, in turn, relies heavily on annual and seasonal rainfall. Climate extremes studies conducted in Ethiopia are mainly limited to a specific location or watershed, making it difficult to have insights at the national level. The present study thus aims to examine the observed climate extreme events in Ethiopia at both station and agro-ecological zone (AEZ) levels. Daily rainfall and temperature data for 47 and 37 stations, respectively (1986 up to 2020), were obtained from the National Meteorology Agency (NMA). The Modified Mann–Kendall (MMK) trend test and the Theil–Sen slope estimator were employed to estimate the trends in rainfall and temperature extremes. This study examines trends of 13 temperature and 10 rainfall extreme indices using RClimDex in R software. The results revealed that most of the extreme rainfall indices showed a positive trend in the majority of the climate stations. For example, an increase in consecutive dry days (CDD), very heavy rainfall days (R20), number of heavy rainfall days (R10) and consecutive wet days (CWD) were exhibited in most climate stations. In relation to AEZs, the greater number of extreme rainfall indices illustrated an upward trend in cool and sub-humid, cool and humid, and cool and moist AEZs, a declining trend in hot arid AEZ, and equal proportions of increasing and decreasing trends in warm semi-arid AEZs. Concerning extreme temperature indices, the result indicated an increasing trend of warm temperature extreme indices and a downward trend of cold temperature extreme indices in most of the climate stations, indicating the overall warming and dryness trends in the country. With reference to AEZs, an overall warming was exhibited in all AEZs, except in the hot arid AEZ. The observed trends in the rainfall and temperature extremes will have tremendous direct and indirect impacts on agriculture, water resources, health, and other sectors in the country. Therefore, the findings suggest the need for identifying and developing climate change adaptation strategies to minimize the ill effects of these extreme climate events on the social, economic, and developmental sectors. [ABSTRACT FROM AUTHOR]

Soil erosion and subsequent land degradation undermine efforts to ensure food security and environmental sustainability in Ethiopia. The government of Ethiopia has implemented extensive soil and water conservation (SWC) programs in severely degraded and food-insecure areas of the country, in some cases integrated with subsequent or parallel irrigation development. However, the effectiveness of these interventions has not been extensively evaluated. This study, therefore, evaluates the performance and impacts of SWC practices in terms of improving vegetation greenness and reducing soil erosion in Feresmay watershed in Ethiopia. Long- term Landsat-based Normalised Difference Vegetation Index (NDVI), Revised Universal Soil Loss Equation (RUSLE), and Soil and Water Assessment Tool (SWAT) were used for change-detection analysis before and after the implementation of various SWC interventions. The results revealed the positive impacts of SWC interventions in improving the vegetation greenness and soil erosion reduction although the outcome varied by intervention. Increased vegetation green-ness was observed largely in areas where area closure with catchment treatment (ACCT) and impacts of irrigation (IRR) interventions are dominant, while relatively little impact was observed at the watershed level analysis. Although these interventions helped to reduce soil loss, the results highlighted the need for more SWC interventions to minimise further soil loss. [ABSTRACT FROM AUTHOR]

This study investigates multi-dimensional impacts of adopting new technology in agriculture at the farm/village and watershed scale in sub-Saharan Africa using the Integrated Decision Support System (IDSS). Application of IDSS as an integrated modeling tool helps solve complex issues in agricultural systems by simultaneously assessing production, environmental, economic, and nutritional consequences of adopting agricultural technologies for sustainable increases in food production and use of scarce natural resources. The IDSS approach was applied to the Amhara region of Ethiopia, where the scarcity of resources and agro-environmental consequences are critical to agricultural productivity of small farm, to analyze the impacts of alternative agricultural technology interventions. Results show significant improvements in family income and nutrition, achieved through the adoption of irrigation technologies, proper use of fertilizer, and improved seed varieties while preserving environmental indicators in terms of soil erosion and sediment loadings. These pilot studies demonstrate the usefulness of the IDSS approach as a tool that can be used to predict and evaluate the economic and environmental consequences of adopting new agricultural technologies that aim to improve the livelihoods of subsistence farmers. [ABSTRACT FROM AUTHOR]

Sub-Saharan Africa has long been beset with food insecurity. Investment in small-scale irrigation may provide a solution to address the challenge by extending crop production into the dry season. We present an agent-based modeling system to assess the potential of dry-season small-irrigation development in Sub-Saharan Africa with an application to Ethiopia. We identify significant potential for investing in dry-season small-scale irrigation in Ethiopia and map geographic domains with highest investment opportunities. • An agent-based model is proposed to map dry-season small-scale irrigation potential. • The model considers both biophysical and economic constraints. • The model is applied to assess irrigation potential in Ethiopia. • Irrigation potential in Ethiopia about 1 million ha by 2030 is identified. [ABSTRACT FROM AUTHOR]

• Field level and watershed data were collected on 36 households growing vegetables. • The runoff generating and groundwater recharging areas were identified. • The optimal water requirement of onion and tomato were estimated. • The optimal fertilizer requirement of onion and tomato were estimated. • The shallowground water is not sufficient to meet vegetable water demand. The vast majority of farmers in sub-Saharan Africa depend on rainfed agriculture for food production and livelihood. Various factors including but not limited to rainfall variability, land degradation, and low soil fertility constrain agricultural productivity in the region. The objectives of this study were to 1) estimate the water resources potential to sustain small-scale irrigation (SSI) in Ethiopia during the dry season so as to expand food supply by growing vegetables, and 2) understand the gaps and constraints of vegetable production. The case studies were conducted in the Robit and Dangishta watersheds of the Upper Blue Nile Basin, Ethiopia. To document farmers' cropping practices, field-level data were collected from 36 households who had been cultivating tomato (Solanum lycopersicum L.) and onion (Allium cepa L.) during the dry season (November – April). Two components of the Integrated Decision Support System (IDSS) - the Soil and Water Assessment Tool (SWAT) and Agricultural Policy Environmental eXtender (APEX) – were respectively used to assess impacts of SSI at the watershed and field-scale levels. Results suggest that there is a substantial amount of surface runoff and shallow groundwater recharge at the watershed scale. The field-scale analysis in the Robit watershed indicated that optimal tomato yield could be obtained with 500 mm of water and 200 to 250 kg/ha of urea applied with 50 kg/ha of diammonium phosphate (DAP). In Dangishta, optimum onion yield can be obtained with 400 mm of water and 120 to 180 kg/ha of urea applied with 50 kg/ha of DAP. The field-scale simulation indicated that the average shallow groundwater recharge (after accounting for other groundwater users such as household and livestock use) was not sufficient to meet tomato and onion water demand in the dry season (October to April). The field-scale analysis also indicated that soil evaporation attributed a significant proportion of evapotranspiration (60% for onion and 40% for tomato). Use of mulching or other soil and water conservation interventions could optimize irrigation water for vegetable production by reducing soil evaporation and thereby increasing water availability in the crop root zone. [ABSTRACT FROM AUTHOR]

While the availability of "big data" on biophysical parameters through citizen science and/or from public/private sources is expected to help in addressing data scarcity issues, there is little understanding of whether and/or how such data will improve watershed simulations. This research aimed to evaluate whether improvements in resolutions of Digital Elevation Model (DEM) and soil data will enhance streamflow and sediment yield simulations and thereby improve soil and water management decisions. The study was conducted in two different-sized watersheds (Anjeni and Gilgel Abay with ~ 1 km 2 and ~ 1655 km 2 area, respectively) in the Upper Blue Nile basin in Ethiopia. Effects of DEM and soil data resolutions on streamflow and sediment yield were evaluated using the Soil and Water Assessment Tool (SWAT). The results showed that the effect of DEM and soil data resolution on streamflow and sediment yield simulation was scale dependent finer resolution DEM and soil datasets improved streamflow and sediment yield simulations in the smaller Anjeni watershed, whereas DEM resolution had no effect in the bigger Gilgel Abay watershed. Small watersheds are often used to understand watershed processes, and thus the use of finer-resolution spatial data for watershed simulations could result in better results. Findings from the smaller Anjeni watershed suggested that the combined use of finer resolution DEM and soil data could potentially improve sediment yield simulations although the lack of observed sediment yield data did not allow verification of this at the larger Gilgel Abay watershed., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)