Policy and practice recommendations on flood risk management in the Awash basin

In the Awash basin flooding is a frequent occurrence during the main rainy season in July and August. The changing climate patterns are intensifying extreme rainfall conditions and causing floods in unexpected locations and seasons. Added to which, narrow channel width, land cover changes and land degradation can all exacerbate flood impacts. Recent exceptional wet extremes in 2020 caused massive flooding and severe damage to property in parts of the basin. A displacement of 144,000 persons and over 5 billion Birr damage was reported in Afar Regional State alone. With support from the REACH program, the International Water Management Institute (IWMI) examined characteristics of extreme rainfall and associated drivers of flooding, comparing previous flood occurrences, and investigating non-climatic drivers including differential impacts on social groups. Key points emerging from this analysis are as follows: -Unusual rainfall in terms of location, magnitude, and timing was the major cause of flooding in 2020 -The 2020 flood was characterized by early onset, delayed recession and larger extent of floods -Although the initial trigger was extreme rainfall, many non-climate drivers contributed to and exacerbated flooding, as well as the range and intensity of impacts on communities -The impacts of flooding vary across groups within communities-Variation in type of flooding and differential impacts requires contextualized interventions Recommended action points for practitioners and policy makers: -Institutional design to facilitate coordinated management and response to floods -Collaboration on operational guidance and improvement in flood early warning -Revision of studies on the Awash based on recent science and data -Implement web-based systems for data and information sharing including “data-free-of-charge” policy for researchers and research-users -Integration of indigenous knowledge into flood risk preparedness and research -Gender-responsive interventions are critical-Inclusion and strengthening of Awash flood-related research themes in universities’ water-related program

Prediction at Ungauged Catchments through Parameter Optimization and Uncertainty Estimation to Quantify the Regional Water Balance of the Ethiopian Rift Valley Lake Basin

Quantifying uncertainties in water resource prediction in data-scarce regions is essential for resource development. We use globally available datasets of precipitation and potential evapotranspiration for the regionalization of model parameters in the data-scarce regions of Ethiopia. A regional model was developed based on 14 gauged catchments. Three possible parameter sets were tested for regionalization: (1) the best calibration parameters, (2) the best validation parameter set derived from behavioral parameters during the validation period, and (3) the stable parameter sets. Weighted multiple linear regression was applied by assigning more weight to identifiable parameters, using a novel leave-one-out cross-validation technique for evaluation and uncertainty quantification. The regionalized parameter sets were applied to the remaining 35 ungauged catchments in the Ethiopian Rift Valley Lake Basin (RVLB) to provide regional water balance estimations. The monthly calibration of the gauged catchments resulted in Nash Sutcliffe Efficiencies (NSE) ranging from 0.53 to 0.86. The regionalization approach provides acceptable regional model performances with a median NSE of 0.63. The results showed that, other than the commonly used best-calibrated parameters, the stable parameter sets provide the most robust estimates of regionalized parameters. As this approach is model-independent and the input data used are available globally, it can be applied to any other data-scarce region

Prediction at Ungauged Catchments through Parameter Optimization and Uncertainty Estimation to Quantify the Regional Water Balance of the Ethiopian Rift Valley Lake Basin

Quantifying uncertainties in water resource prediction in data-scarce regions is essential for resource development. We use globally available datasets of precipitation and potential evapotranspiration for the regionalization of model parameters in the data-scarce regions of Ethiopia. A regional model was developed based on 14 gauged catchments. Three possible parameter sets were tested for regionalization: (1) the best calibration parameters, (2) the best validation parameter set derived from behavioral parameters during the validation period, and (3) the stable parameter sets. Weighted multiple linear regression was applied by assigning more weight to identifiable parameters, using a novel leave-one-out cross-validation technique for evaluation and uncertainty quantification. The regionalized parameter sets were applied to the remaining 35 ungauged catchments in the Ethiopian Rift Valley Lake Basin (RVLB) to provide regional water balance estimations. The monthly calibration of the gauged catchments resulted in Nash Sutcliffe Efficiencies (NSE) ranging from 0.53 to 0.86. The regionalization approach provides acceptable regional model performances with a median NSE of 0.63. The results showed that, other than the commonly used best-calibrated parameters, the stable parameter sets provide the most robust estimates of regionalized parameters. As this approach is model-independent and the input data used are available globally, it can be applied to any other data-scarce region

A review on sources of uncertainties for groundwater recharge estimates: insight into data scarce tropical, arid, and semiarid regions

Successful sustainable groundwater management requires accurate information on recharge for a given aquifer system. However, recharge estimates are usually used in relative terms rather than an absolute sense. A review of available studies on groundwater recharge estimate uncertainty as well as tools for uncertainty analysis was conducted. Nonetheless, except for the handful of studies that have conducted proper uncertainty analysis, most were inclined to implement multiple methods as an indication of the range of uncertainty. The global trend indicates that considering the significant number of methods for recharge estimation, very little has been done to assess the uncertainty of each method. Therefore, more attention should be given to the individual uncertainty analysis of selected methods as much as using multiple methods recommended for investigating uncertainty. Insight from the review indicates that, when used carefully, tracer-based analysis can be effective and coupling is required for uncertainty analysis. Furthermore, spatial uncertainty due to input data could potentially be minimized by using input data from multiple sources. Better conceptualization of the hydrogeological process can reduce the uncertainty of numerical modelling. This review is limited to widely used methods and excludes uncertainty due to inappropriate method implementation and controlled experimental uncertainties. HIGHLIGHTS The review article summarizes the uncertainties in estimating recharge using widely applied methods.; The review article highlights methods for investigating the related uncertainties.; This review highlights exertions made to quantify the uncertainty of recharge estimation globally.

Assessment of the impact of rainfall uncertainties on the groundwater recharge estimations of the Tikur-Wuha watershed, rift valley lakes basin, Ethiopia

Spatial recharge estimation uncertainty is directly proportional to uncertainty in input precipitation data Thus, the main objective of this study was to investigate the recharge uncertainty by using improved spatial rainfall observations. The physically based fully distributed hydrological model WetSpa was used to simulate 20,000 possible combinations of parameters for two model setup. The M1 model setup was developed based on the rainfall measurements obtained from rain gauge stations scattered in and around the Tikur-Wuha watershed in Ethiopia, while M2 model setup was developed using bias-corrected satellite rainfall estimates (SREs) based on Climate Hazards Group InfraRed Precipitation (CHIRP) merged with relevant ground station records. The required parameter combinations were generated using Monte Carlo simulation stratified by applying Latin Hypercube Sampling (LHS). One hundred best performing parameter combinations were selected for each model to generate spatial recharge statistics and assess the resulting uncertainty in the recharge estimates. The results revealed that enhanced spatial recharge estimates can be produced through improved CHIRP-based SREs. The long-term mean annual recharge (218.29 mm) in the Tikur-Wuha watershed was estimated. Model parameter calibration performed using discharge measurements obtained from the Wosha rain gauge station located in the subcatchment area of the Tikur-Wuha watershed had a Nash-Sutcliffe efficiency of 0.56. Seventy percent of the watershed showed a coefficient of variation (Cv) < 0.15 for M2, while 90 % of the area exhibited a Cv < 0.15 for M1. Furthermore, the study findings highlighted the importance of improving evapotranspiration data accuracy to reduce the uncertainty of recharge estimates. However, the uncontrolled irrigation water uses and the total recharge coming from the irrigation fields scattered across the Tikur-Wuha watershed were not considered in the study, which is a limitation of the study. Future studies should consider the contribution made by irrigation water to the total recharge of the watershed

Optimization of Irrigation Scheduling for Improved Irrigation Water Management in Bilate Watershed, Rift Valley, Ethiopia

The availability of water for agricultural production is under threat from climate change and rising demands from various sectors. In this paper, a simulation-optimization model for optimizing the irrigation schedule in the Bilate watershed was developed, to save irrigation water and maximize the yield of deficit irrigation. The model integrated the Soil and Water Assessment Tool (SWAT) and an irrigation-scheduling optimization model. The SWAT model was used to simulate crop yield and evapotranspiration. The Jensen crop-water-production function was applied to solve potato and wheat irrigation-scheduling-optimization problems. Results showed that the model can be applied to manage the complicated simulation-optimization irrigation-scheduling problems for potato and wheat. The optimization result indicated that optimizing irrigation-scheduling based on moisture-stress-sensitivity levels can save up to 25.6% of irrigation water in the study area, with insignificant yield-reduction. Furthermore, optimizing deficit-irrigation-scheduling based on moisture-stress-sensitivity levels can maximize the yield of potato and wheat by up to 25% and 34%, respectively. The model developed in this study can provide technical support for effective irrigation-scheduling to save irrigation water and maximize yield production

Evaluation of a multi-staged bias correction approach on CHIRP and CHIRPS rainfall product: a case study of the Lake Hawassa watershed

A promising future development area to improve the accuracy of satellite rainfall estimates (SREs) is accessing merits from different sources of data through combining algorithms. The main objective of this study is to assess the accuracy and importance of the fused multistage approach of bias correction. Accordingly, two versions of resampled and spatially bias-corrected Climate Hazards Group Infrared Precipitation (CHIRP) estimates were merged with ground measurements using a conditional merging procedure. Results of applied performance measures (i.e. seven) on corrected and merged CHIRP SREs show that the Percent of Detection (POD) and Percent Volume Error (PVE) have improved. Depending on the combination of coupled stations for validation, up to 70 and 50% PVE improvement was achieved at some stations for wet and dry periods, respectively. Moreover, the bias-corrected and conditionally merged CHIRP SREs have outperformed the estimates by resampling CHIRP with station dataset (CHIRPS) over the sparsely populated western part of the watershed. However, the devised method was limited in considering dry-day events during bias correction, which in turn has affected the performance of the bias correction of the CHIRPS product. Finally, future research should concentrate on such methods of fusing to understand the benefits of various approaches and produce more precise rainfall records. HIGHLIGHTS The research provides a fused multi-staged approach for reducing errors in CHIRP and CHIRPS satellite rainfall estimates.; Application of parametric QM for spatial bias correction followed by conditional merging improves the quality of CHIRP SREs.; Bias-corrected and conditionally merged CHIRP estimate outperforms the estimates by CHIRPS.; Incorporating additional ground station records improves the estimates of SREs.

Advances in water resources research in the Upper Blue Nile basin and the way forward: A review

The Upper Blue Nile basin is considered as the lifeline for ∼250 million people and contributes ∼50 Gm3/year of water to the Nile River. Poor land management practices in the Ethiopian highlands have caused a significant amount of soil erosion, thereby threatening the productivity of the Ethiopian agricultural system, degrading the health of the aquatic ecosystem, and shortening the life of downstream reservoirs. The Upper Blue Nile basin, because of limited research and availability of data, has been considered as the “great unknown.” In the recent past, however, more research has been published. Nonetheless, there is no state-of-the-art review that presents research achievements, gaps and future directions. Hence, this paper aims to bridge this gap by reviewing the advances in water resources research in the basin while highlighting research needs and future directions. We report that there have been several research projects that try to understand the biogeochemical processes by collecting information on runoff, groundwater recharge, sediment transport, and tracers. Different types of hydrological models have been applied. Most of the earlier research used simple conceptual and statistical approaches for trend analysis and water balance estimations, mainly using rainfall and evapotranspiration data. More recent research has been using advanced semi-physically/physically based distributed hydrological models using high-resolution temporal and spatial data for diverse applications. We identified several research gaps and provided recommendations to address them. While we have witnessed advances in water resources research in the basin, we also foresee opportunities for further advancement. Incorporating the research findings into policy and practice will significantly benefit the development and transformation agenda of the Ethiopian government