Abstract. The main objective of this study was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data from the Global Land Evaporation Amsterdam Model (GLEAM_v3.0a) and from the Moderate Resolution Imaging Spectroradiometer Global Evaporation (MOD16) for the Ogun River Basin (20 292 km2) located in southwestern Nigeria. Three potential evapotranspiration (PET) equations (Hargreaves, Priestley–Taylor and Penman–Monteith) were used for the SWAT simulation of AET. The reference simulations were the three AET variables simulated with SWAT before model calibration took place. The sequential uncertainty fitting technique (SUFI-2) was used for the SWAT model sensitivity analysis, calibration, validation and uncertainty analysis. The GLEAM_v3.0a and MOD16 products were subsequently used to calibrate the three SWAT-simulated AET variables, thereby obtaining six calibrations–validations at a monthly timescale. The model performance for the three SWAT model runs was evaluated for each of the 53 subbasins against the GLEAM_v3.0a and MOD16 products, which enabled the best model run with the highest-performing satellite-based AET product to be chosen. A verification of the simulated AET variable was carried out by (i) comparing the simulated AET of the calibrated model to GLEAM_v3.0b AET, which is a product that has different forcing data than the version of GLEAM used for the calibration, and (ii) assessing the long-term average annual and average monthly water balances at the outlet of the watershed. Overall, the SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool. The 95 % uncertainty of the SWAT-simulated variable bracketed most of the satellite-based AET data in each subbasin. A validation of the simulated soil moisture dynamics for GS1 was carried out using satellite-retrieved soil moisture data, which revealed good agreement. The SWAT model (GS1) also captured the seasonal variability of the water balance components at the outlet of the watershed. This study demonstrated the potential to use remotely sensed evapotranspiration data for hydrological model calibration and validation in a sparsely gauged large river basin with reasonable accuracy. The novelty of the study is the use of these freely available satellite-derived AET datasets to effectively calibrate and validate an eco-hydrological model for a data-scarce catchment.

In the Ethiopian Highlands, stone bunds (SBs) are a common practice for soil and water conservation, influencing runoff and erosion processes from sloped agricultural areas. The objective of this study was to investigate how SBs affect spatiotemporal relationships of these processes to better understand their impacts on soil water development at the smallholder farmer's field level. Study area was the Gumara‐Maksegnit Watershed in northern Ethiopia, where two representative transects were investigated: One transect crossed a 71 m‐long field intersected by 2 SBs traced along the contour. The second transect crossed a similar hillslope without conservation structures at a length of 55 m representing baseline (untreated) conditions (no stone bund). During the rainy season of 2012, bulk density and volumetric water content were monitored, and tension disc infiltrometer experiments were performed to determine the saturated hydraulic conductivity and to derive soil water retention characteristics. Our observations show that SB decreased significantly soil bulk density in center and lower zones of SB transect compared with no stone bund. No temporal change was observed. Results targeting the surface soil moisture indicate that infiltration was higher with SB and happened earlier in the rainy season in the zones around the SBs. Saturated hydraulic conductivity was positively affected by SB and increased significantly. Improved soil hydrology by SB fields may increase crop yields by higher soil water contents but also by extending the growing season after the rainy season. Therefore, SBs are a successful measure to establish climate‐resilient agriculture in the Ethiopian Highlands.

Abstract. Environmental modeling studies aim to infer the impacts on environmental variables that are caused by natural and human-induced changes in environmental systems. Changes in environmental systems are typically implemented as discrete scenarios in environmental models to simulate environmental variables under changing conditions. The scenario development of a model input usually involves several data sources and perhaps other models, which are potential sources of uncertainty. The setup and the parametrization of the implemented environmental model are additional sources of uncertainty for the simulation of environmental variables. Yet to draw well-informed conclusions from the model simulations it is essential to identify the dominant sources of uncertainty. In impact studies in two Austrian catchments the eco-hydrological model Soil and Water Assessment Tool (SWAT) was applied to simulate discharge and nitrate-nitrogen (NO3--N) loads under future changing conditions. For both catchments the SWAT model was set up with different spatial aggregations. Non-unique model parameter sets were identified that adequately reproduced observations of discharge and NO3--N loads. We developed scenarios of future changes for land use, point source emissions, and climate and implemented the scenario realizations in the different SWAT model setups with different model parametrizations, which resulted in 7000 combinations of scenarios and model setups for both catchments. With all model combinations we simulated daily discharge and NO3--N loads at the catchment outlets. The analysis of the 7000 generated model combinations of both case studies had two main goals: (i) to identify the dominant controls on the simulation of discharge and NO3--N loads in the two case studies and (ii) to assess how the considered inputs control the simulation of discharge and NO3--N loads. To assess the impact of the input scenarios, the model setup, and the parametrization on the simulation of discharge and NO3--N loads, we employed methods of global sensitivity analysis (GSA). The uncertainties in the simulation of discharge and NO3--N loads that resulted from the 7000 SWAT model combinations were evaluated visually. We present approaches for the visualization of the simulation uncertainties that support the diagnosis of how the analyzed inputs affected the simulation of discharge and NO3--N loads. Based on the GSA we identified climate change and the model parametrization as being the most influential model inputs for the simulation of discharge and NO3--N loads in both case studies. In contrast, the impact of the model setup on the simulation of discharge and NO3--N loads was low, and the changes in land use and point source emissions were found to have the lowest impact on the simulated discharge and NO3--N loads. The visual analysis of the uncertainty bands illustrated that the deviations in precipitation of the different climate scenarios to historic records dominated the changes in simulation outputs, while the differences in air temperature showed no considerable impact.

Abstract. The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C, and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale is very limited due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challenges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 972 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on the administrative level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude, particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large-scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small-scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.

comparing the simulated AET of the calibrated model to GLEAM_v3.0b AET, this is a product that has a different forcing 30 data to version of GLEAM used for the calibration, and (ii) assessing the long-term average annual and average monthly water balances at the outlet of the watershed. Overall, the SWAT model structure composed of Hargreaves PET equation and calibrated using the GLEAM_v3.0a data performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool. The 95% uncertainty of the SWAT simulated variable bracketed most