Kim, Ungtae and Jagath J. Kaluarachchi, 2009. Climate Change Impacts on Water Resources in the Upper Blue Nile River Basin, Ethiopia. Journal of the American Water Resources Association (JAWRA) 45(6):1361‐1378. Abstract:  Climate change affects water resources availability of international river basins that are vulnerable to runoff variability of upstream countries especially with increasing water demands. The upper Blue Nile River Basin is a good example because its downstream countries, Sudan and Egypt, depend solely on Nile waters for their economic development. In this study, the impacts of climate change on both hydrology and water resources operations were analyzed using the outcomes of six different general circulation models (GCMs) for the 2050s. The outcomes of these six GCMs were weighted to provide average future changes. Hydrologic sensitivity, flow statistics, a drought index, and water resources assessment indices (reliability, resiliency, and vulnerability) were used as quantitative indicators. The changes in outflows from the two proposed dams (Karadobi and Border) to downstream countries were also assessed. Given the uncertainty of different GCMs, the simulation results of the weighted scenario suggested mild increases in hydrologic variables (precipitation, temperature, potential evapotranspiration, and runoff) across the study area. The weighted scenario also showed that low‐flow statistics and the reliability of streamflows are increased and severe drought events are decreased mainly due to increased precipitation. Joint dam operation performed better than single dam operation in terms of both hydropower generation and mean annual storage without affecting the runoff volume to downstream countries, but enhancing flow characteristics and the robustness of streamflows. This study provides useful information to decision makers for the planning and management of future water resources of the study area and downstream countries.

No abstract

This work develops a methodology to project the future precipitation in large river basins under limited data and climate change while preserving the historical temporal and spatial characteristics. The computationally simple and reliable conditional generation method (CGM) is presented and applied to generate reliable monthly precipitation data in the upper Blue Nile River Basin of Ethiopia where rain‐fed agriculture is prevalent. The results showed that the temporal analysis with the CGM performs better to reproduce the historical long‐term characteristics than other methods, and the spatial analysis with the CGM reproduced the historical spatial structure accurately. A 100‐year time series analysis using the outcomes of the six general circulation models showed that precipitation changes by the 2050s (2040 through 2069) can be −7 to 28% with a mean increase of about 11%. The seasonal results showed increasing wet conditions in all seasons with changes of mean precipitation of 5, 47, and 6% for wet, dry, and mild seasons, respectively.

Despite advances in physicochemical remediation technologies, in situ bioremediation treatment based on Dehalococcoides mccartyi (Dhc) reductive dechlorination activity remains a cornerstone approach to remedy sites impacted with chlorinated ethenes. Selecting the best remedial strategy is challenging due to uncertainties and complexity associated with biological and geochemical factors influencing Dhc activity. Guidelines based on measurable biogeochemical parameters have been proposed, but contemporary efforts fall short of meaningfully integrating the available information. Extensive groundwater monitoring data sets have been collected for decades, but have not been systematically analyzed and used for developing tools to guide decision-making. In the present study, geochemical and microbial data sets collected from 35 wells at five contaminated sites were used to demonstrate that a data mining prediction model using the classification and regression tree (CART) algorithm can provide improved predictive understanding of a site's reductive dechlorination potential. The CART model successfully predicted the 3-month-ahead reductive dechlorination potential with 75.8% and 69.5% true positive rate (i.e., sensitivity) for the training set and the test set, respectively. The machine learning algorithm ranked parameters by relative importance for assessing in situ reductive dechlorination potential. The abundance of Dhc 16S rRNA genes, CH4, Fe(2+), NO3(-), NO2(-), and SO4(2-) concentrations, total organic carbon (TOC) amounts, and oxidation-reduction potential (ORP) displayed significant correlations (p < 0.01) with dechlorination potential, with NO3(-), NO2(-), and Fe(2+) concentrations exhibiting precedence over other parameters. Contrary to prior efforts, the power of data mining approaches lies in the ability to discern synergetic effects between multiple parameters that affect reductive dechlorination activity. Overall, these findings demonstrate that data mining techniques (e.g., machine learning algorithms) effectively utilize groundwater monitoring data to derive predictive understanding of contaminant degradation, and thus have great potential for improving decision-making tools. A major need for realizing the predictive capabilities of data mining approaches is a curated, open-access, up-to-date and comprehensive collection of biogeochemical groundwater monitoring data.

Abstract:Hydrologic models using water balance approaches typically use continuously observed streamflow data for calibration. Many large river basins in developing countries such as the upper Blue Nile River Basin of Ethiopia have discontinuous hydrographs that contain short continuous periods. Therefore, the efficient use of observed hydrographs for calibration of a hydrologic model is important to improve model performance. The goal of this study is to assess how limitations of continuity and duration in data affect hydrologic model calibration. A previously developed water balance model for the upper Blue Nile River basin was calibrated here using continuous and discontinuous (randomly sampled) data of different lengths. The performance of both methods was then compared each other in terms of parameter uncertainty and model efficiency. The results revealed that randomly sampled data require a shorter calibration length than continuous data to reach good model performance, about 36 and 120 months, respectively. This fact implies that discontinuous hydrographs can be useful in calibration. However, the number of high flow months included in the calibration data greatly affects model efficiency. This study suggests that randomly sampled calibration data should include at least 30% of high flow months of sufficient quality. The findings of this study will be essential to develop a hydrologic monitoring strategy for remote basins of the upper Blue Nile River basin and other similar basins where continuous observations are limited.