The Impact of River Regulation on Downstream Socio-Hydrologic Systems in Ethiopia

In recent years, a renewed interest in large-scale hydraulic interventions has developed, frequently justified by the premise of making the agricultural and energy sectors climate-resilient. Despite this important climate effort, hydraulic interventions are controversial and have far-reaching impacts on river-dependent communities and the environment. Drawing on gis analyses of remote sensing images and qualitative and quantitative empirical evidence from the field, this PhD dissertation focused on the impact of two large dams and one inter-basin water transfer (ibwt) on downstream socio-hydrologic systems (coupled human-water systems) in Ethiopia. The results indicated that (i) downstream hydrogeomorphic systems drastically altered after the implementation of the hydraulic interventions, (ii) small-scale farmer-led irrigation systems more efficiently increased crop productivities than several large-scale irrigation projects, (iii) the newly induced hydrologic regimes strongly altered downstream social interactions due to impeded river crossing and (iv) ill-prepared land redistributions and resettlements left thousands of households with a high risk of impoverishment.

Non-linear Routing Scheme at Grid Cell Level for Large Scale Hydrologic Models: A Review

New tools and concepts in the form of mathematical models, remote sensing and Geographic Information System (GIS), communication and telemetering have been developed for the complex hydrologic systems that permit a different analysis of processes and allow watershed to be considered as an integrated planning and management unit. Hydrological characteristics can be generated through spatial analysis, and ready for input into a distributed hydrologic models to define adequately the hydrological response of a watershed that can be related back to the specific environmental, climatic, and geomorphic conditions. In the present paper, some recent development in hydrologic modeling will be reviewed with recognition of the role of horizontal routing scheme in large scale hydrologic modeling. Among others, these developments indicated the needs of alternative horizontal routing models at grid scale level that can be coupled to land surface parameterization schemes that presently still employed the linear routing model. Non-linear routing scheme will be presented and discussed in this paper as possible extension.

Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?

We confirm that energy dissipation weighting provides the most accurate approach to determining the effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained to infer the energy dissipation weighting pattern from an image of the K distribution, although it was less accurate for cases with highly localized structures that controlled flow. Furthermore, the UNET architecture learned to infer the energy dissipation weighting even if it was not trained directly on this information. However, the weights were represented within the UNET in a way that was not immediately interpretable by a human user. This reiterates the idea that even if ML/DL algorithms are trained to make some hydrologic predictions accurately, they must be designed and trained to provide each user-required output if their results are to be used to improve our understanding of hydrologic systems.

A Hydro-Economic Methodology for the Food-Energy-Water Nexus: Valuation and Optimization of Water Resources

Growing global water stress caused by the combined effects of growing populations, increasing economic development, and climate change elevates the importance of managing and allocating water resources in ways that are economically efficient and that account for interdependencies between food production, energy generation, and water networks—often referred to as the “food-energy-water (FEW) nexus.” To support these objectives, this report outlines a replicable hydro-economic methodology for assessing the value of water resources in alternative uses across the FEW nexus–including for agriculture, energy production, and human consumption—and maximizing the benefits of these resources through optimization analysis. The report’s goal is to define the core elements of an integrated systems-based modeling approach that is generalizable, flexible, and geographically portable for a range of FEW nexus applications. The report includes a detailed conceptual framework for assessing the economic value of water across the FEW nexus and a modeling framework that explicitly represents the connections and feedbacks between hydrologic systems (e.g., river and stream networks) and economic systems (e.g., food and energy production). The modeling components are described with examples from existing studies and applications. The report concludes with a discussion of current limitations and potential extensions of the hydro-economic methodology.

Review of the most significant caves in Montenegro

The most significant caves in Montenegro were distinguished in response to their physical-geographic, biological, archeological and morphometric characteristics (length and depth). Caves distribute in four distinctive regions: coastal karst, karst plateau (relict valley system), fluvial karst (recent hydrologic systems) and the high mountainous karst area. The most outstanding within the last, due to abundances of the major caves with depths from a few hundred meters to 1,162 m, are four mountain regions: Mt. Durmitor, Mt. Lovćen-Orjen, Mt. Maganik and Mt. Bjelič.

Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?

We confirm that energy dissipation weighting provides the most accurate approach to determining the effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained to infer the energy dissipation weighting pattern from an image of the K distribution with high fidelity, although it was less accurate for cases with highly localized structures that controlled flow. Furthermore, the UNET architecture learned to infer the energy dissipation weighting even if it was not trained on this information directly. However, the weights were represented within the UNET in a way that was not immediately interpretable by a human user. This reiterates the idea that even if ML/DL algorithms are trained to make some hydrologic predictions accurately, they must be designed and trained to provide each user-required output if their results are to be used to improve our understanding of hydrologic systems most effectively.

An ensemble of deep learning models with data assimilation for hydrologic forecasting

<p>We present an approach to combine two data-centric approaches, data assimilation (DA) and deep learning (DL), from the perspective of hydrologic forecasting. DA is a statistical approach based on Bayesian filtering to produce optimal states and/or parameters of a dynamic model using observations. By extracting information from both model and observational data, DA improves not only the performance of numerical modeling, but also understanding of uncertainties in predictions. While DA complements information gaps in model and observational data, DL constructs a new modeling system by extracting and abstracting information solely from data without relying on the conventional knowledge of hydrologic systems. In a new approach, an ensemble of deep learning models can be updated by real-time data assimilation when a new observation becomes available. In the presentation, we will focus on discussing the potentials of combining two data-centric approaches.</p><p> </p>