Evaluating the effects of DEM and soil data resolution on streamflow and sediment yield simulations in the Upper Blue Nile basin.

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.

Impacts of soil and water conservation measures on soil physicochemical properties in the Jibgedel Watershed, Ethiopia.

Soil erosion significantly affects agricultural production. Soil and Water Conservation (SWC) measures have been constructed to reduce soil loss. However, the impact of SWC measures on physicochemical soil properties has rarely been investigated in most parts of Ethiopia. Therefore, this study was designed to evaluate the effects of SWC measures on selected soil physicochemical properties in the Jibgedel watershed, West Gojjam zone, Ethiopia. The study also assessed the farmers' perception of the benefits and impacts of SWC measures. Composite and core soil samples were taken at a depth of 0 to 20 cm from four farmlands with SWC measures (soil bund, stone bund, and soil bund with sesbania tree) and without SWC measures in three replications. Results have shown that employing SWC measures in the farmland significantly improved most of the physicochemical properties of the soil compared to farmland without SWC measures. Bulk density from soil bund with and without sesbania trees was significantly lower than stone bund and untreated farmland. Soil organic carbon, total nitrogen, electrical conductivity, and available phosphorus from soil bund with sesbania tree were significantly higher than other treatments. The result also revealed that most farmers perceived that the implemented SWC measures improved soil fertility and crop yield. SWC measures are easier to adopt for integrated watershed management when farmers are well-versed in them.

Evaluating the Effectiveness of Best Management Practices On Soil Erosion Reduction Using the SWAT Model: for the Case of Gumara Watershed, Abbay (Upper Blue Nile) Basin.

This study was conducted to evaluate the effectiveness of best management practices (BMPs) to reduce soil erosion in Gumara watershed of the Abbay (Upper Blue Nile) Basin using the Soil and Water Assessment Tool (SWAT) model. The model was calibrated (1995-2002) and validated (2003-2007) using the SWAT-CUP based on observed streamflow and sediment yield data at the watershed outlet. The study evaluated four individual BMP Scenarios; namely, filter strips (FS), stone/soil bunds (SSB), grassed waterways (GW) and reforestation of croplands (RC), and three blended BMP Scenarios, which combines individual BMPS of FS and RC (FS & RC), GW and RC (GW & RC), and SSB and GW (SSB & GW). Mean annual sediment yield at the baseline conditions was estimated at 19.7 t ha-1yr-1, which was reduced by 13.7, 30.5, 16.2 and 25.9% in the FS, SSB, GW, and RC Scenarios, respectively at the watershed scale. The highest reduction efficiency of 34% was achieved through the implementations of the SSB & GW Scenario. The GW & RC, and FS & RC Scenarios reduced the baseline sediment yield by 32% and 29.9%, respectively. The study therefore concluded that the combined Scenarios mainly SSB & GW, and GW & RC can be applied to reduce the high soil erosion in the Gumera watershed, and similar agro-ecological watersheds in Ethiopia. In cases where applying the combined scenarios is not possible, the SSB Scenario can yield significant soil erosion reduction.

Evaluating potential impacts of land management practices on soil erosion in the Gilgel Abay watershed, upper Blue Nile basin.

Assessing the potential impacts of different land management practices helps to identify and implement sustainable watershed management measures. This study aims to assess a change in soil erosion rate under different land management practices in the Gilgel Abay watershed of the upper Blue Nile basin, Ethiopia. The Revised Universal Soil Loss Equation (RUSLE) model that was adapted to the Ethiopian highlands context was employed to estimate the rate of soil erosion. The impact of land management practices on soil erosion was estimated for three scenarios, which were baseline, intensive cultivation, and extensive cultivation scenarios. At the baseline scenario, the mean annual soil erosion was estimated at ~32.8 t ha-1yr-1, which is equivalent to a loss of ~13.66 Mt yr-1 from the entire watershed. While the rate of soil erosion reduced to ~11.3 t ha-1yr-1 during the implementation of intensive cultivation management practice, which reduced the total soil loss in the watershed by 65%. On the other hand, under the extensive cultivation scenario, the mean annual soil erosion rate increased to ~34.4 t ha-1yr-1. The findings suggest that implementing agricultural intensification management practices can significantly reduce soil erosion in the watershed.

Evaluating satellite-based evapotranspiration estimates for hydrological applications in data-scarce regions: A case in Ethiopia.

Water resource development opens up opportunities for improving smallholder farmer livelihoods in sub-Saharan Africa; however, implementation of water resource interventions to ensure sustainability hinges on the availability of sufficient quantity and quality data for monitoring, analysis and planning. Such data is often acquired through instrumentation of water resources (e.g. stream flow monitoring) or the use of hydrological models. In sub-Saharan Africa, data scarcity has limited the ability to monitor and make appropriate decisions for water resource allocation and use. Data derived from remote sensing has been considered a viable option to fill this gap; however, there is limited research in the region that evaluate the quality of the remotely sensed based datasets. This study evaluated actual evapotranspiration (AET) estimates derived from Advanced Very High Resolution Radiometer (AVHRR AET) images and Moderate Resolution Imaging Spectrometer (MOD16 AET) images using estimates from a grid-based Soil and Water Assessment Tool (SWAT). The SWAT model was set up for the entire country of Ethiopia, and calibrated and validated using observed streamflow at several meso-scale watersheds in which satisfactory model performance was obtained. AET estimates from the calibrated and validated SWAT model were then used to evaluate remotely sensed based AET for three landscapes. The AVHRR AET better agreed with the SWAT-simulated AET than the MOD16 AET, although the AVHRR AET overestimated the SWAT-simulated AET in all of the landscapes. Both remotely sensed AET products showed better agreement with the SWAT-simulated AET over agriculture dominated landscapes compared to grassland and forest dominated landscapes. The findings of the study suggest that remotely sensed based AET may help to fine-tune hydrological models in agricultural landscapes in data-scarce regions to improve studies on the impacts of water management interventions aiming to ensure environmental sustainability while enhancing agricultural production, and household income and nutrition.

Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia.

The Weather Research and Forecasting (WRF) model is one of the regional climate models for dynamically downscaling climate variables at finer spatial and temporal scales. The objective of this study was to evaluate the performance of WRF model for simulating temperature and rainfall over Lake Tana basin in Ethiopia. The WRF model was configured for six experimental setups using three land surface models (LSMs): Noah, RUC and TD; and two land use datasets: USGS and updated New Land Use (NLU). The performances of WRF configurations were assessed by comparing simulated and observed data from March to August 2015. The result showed that temperature and rainfall simulations were sensitive to LSM and land use data choice. The combination of NLU with RUC and TD produced very small cold bias (0.27 °C) and warm bias (0.20 °C) for 2m maximum temperature (Tmax) and 2m minimum temperature (Tmin), respectively. WRF model with RUC and NLU captured well the observed spatial and temporal variability of Tmax, while TD and NLU for Tmin. Moreover, rainfall simulation was better with NLU; especially NLU and Noah configuration produced the smallest mean bias (2.39 mm/day) and root mean square error (6.6 mm/day). All the WRF experiments overestimated light and heavy rainfall events. Overall, findings showed that the application of updated land use data substantially improved the WRF model performance in simulating temperature and rainfall. The study would provide valuable support for identifying suitable LSM and land use data that can accurately predict the climate variables in the Blue Nile basin.