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Highly varying hydro-climatological conditions, multi-party decision-making contexts, and the dynamic interconnection between water and other critical infrastructures create a wealth of challenges and opportunities for water resources planning and management. For example, reservoir operators must account for a number of time-varying drivers, such as the downstream users’ demands, short- and long-term water availability, electricity prices, and the share of power supplied by wind and solar technologies. In this context, adaptive and robust management solutions are paramount to the reliability and resilience of water resources systems. To this purpose, emerging work is focusing on the development of models and algorithms that adapt short-term decisions to newly available information, often issued in the form of weather or streamflow forecasts, or extracted from observational data collected via pervasive sensor networks, remote sensing, cyberinfrastructure, or crowdsourcing.

In this session, we solicit novel contributions related to improved multi-sectoral forecasts (e.g., water availability and demand, energy and crop prices), novel data analytics and machine learning tools for processing observational data, and real-time control solutions taking advantage of this new information. Examples include: 1) approaches for incorporating additional information within control problems; 2) methods for characterizing the effect of forecast uncertainty on the decision-making process; 3) integration of information with users’ preferences, behavioral uncertainty, and institutional setting; 4) studies on the scalability and robustness of optimal control algorithms. We welcome real-world examples on the successful application of these methods into decision-making practice.

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Convener: Stefano Galelli | Co-conveners: Paul Block, Matteo GiulianiECSECS, Joseph KasprzykECSECS, Charles RougéECSECS
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Chat time: Wednesday, 6 May 2020, 14:00–15:45

D157 |
EGU2020-12737
Victoria Vanthof and Richard Kelly

Small reservoirs represent a critical water supply to farmers across semi-arid regions. Managing these water resources is challenging because hydrological forecasting suffers from sparse rainfall measurements that do not capture highly localised rainfall accumulations. Small reservoir tank structures across South India form part of a complex ancient traditional water distribution system that has historically supplied irrigation to cropped fields during the dry-season. Despite their historical significance and the critical need for water storage in an agrarian dominated country with unpredictable rainfall, thousands of tanks have fallen into a state of disrepair with the introduction of groundwater wells and cheap electrification in the 1960s. Our current understanding of these systems lacks knowledge about the functional state of these ancient traditional water systems. This is especially critical information that is needed to rehabilitate tank structures and support water management. Previous studies suggest that functioning tanks have the potential to increase both the current water supply and support groundwater recharge. But there is little quantitative evidence to support this assertion.

To understand tank functionality, spatially explicit and temporally dynamic frequent high-resolution surface water (SW) estimates developed in a synoptic and detailed way are needed. The increased availability of high-resolution satellite imagery provides a substantial opportunity to fulfill this need through the monitoring of small inland water bodies. Monitoring tank SW from earth observation (EO) sources is constrained by their small size (5-50 ha) and rapid water drainage. To support tank monitoring during cloud-covered monsoon seasons, synthetic aperture radar (SAR) observations used in synergy with high temporal resolution visible infra-red observations is desirable.

Building from an existing surface water monitoring approach (Vanthof and Kelly, 2019), the primary aim here is to assess large-scale dynamics of tank water storage state at a basin scale. This is achieved by using multi-date and multi-sensor satellite images (Landsat-8, Sentinel-1, Sentinel-2, PlanetScope) for three years covering the northeast monsoon (Sept. – Dec.). SW observations from optical-infrared and radar observations are used to estimate tank SW areas for three monsoon seasons and converted to volumes using empirical rating curves developed for the region from Vanthof and Kelly (2019). Annually tanks were categorized by ‘tanks with water’ or ‘tanks without water’. For the ‘tanks with water’ category, an analysis was performed annually to identify spatial and temporal patterns in two indicators: temporal period of water storage and the rate of storage loss. Results show that hundreds of tanks are not able to store water despite precipitation inputs to the system. For tanks with water, further analysis reveals great variability among tanks for both indicators. As shown, this decade of EO offers exciting opportunities to apply data-driven approaches to complement more traditional physically-based hydrological understanding.  

Vanthof, V., & Kelly, R. (2019). Water storage estimation in ungauged small reservoirs with the TanDEM-X DEM and multi-source satellite observations. Remote Sens. of Environ., 235, 111437.

How to cite: Vanthof, V. and Kelly, R.: Assessing current tank storage state from multi-mission satellite observations to support water management in southern India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12737, https://doi.org/10.5194/egusphere-egu2020-12737, 2020

D158 |
EGU2020-10799
Marcelo Olivares, Eduardo Pereira, Fernanda Abarzua, Matias Gomez, and Diana Orellana

Hydropower operations are commonly prescribed as part of a grid-wide coordination process by an Independent System Operator (ISO). The scheduling problem is usually divided into two coupled problems: short- and medium-term scheduling. The medium term problem, usually within a planning horizon of a few years, takes into account uncertain inflows to every hydropower plant in the grid. This uncertainty is often represented by a scenario tree constructed from historical records. The result of this stochastic optimization problem is a set of Future Value Functions (FVF) of water in the reservoirs. These functions represent the carryover storage value, as avoided future thermal costs, for each week within the planning horizon. These FVFs are then used as a boundary condition for short-term scheduling within each week.

Chile has suffered a 10-year severe drought since 2010. Moreover, climate projections for Chile suggest an intensification of droughts in the future, in terms of both frequency and magnitude. From the water-energy nexus perspective, this phenomenon would rise energy costs and prices, and at the same time, push the electric coordinator to feed the system with less clean sources of electricity.

This work proposes and tests alternative ways to introduce plausible mega-droughts in Chile as part of the power scheduling process. We develop series representing plausible future conditions of drought and severe drought, preserving the time and spatial correlation structure of inflows. These scenarios are then used, along with historical information, to develop FVFs that take into account those severe drought scenarios. The method is tested in Chile’s main grid, represented by 624 power plants, 103 inflow points, 13 reservoirs, and 58 demand nodes.

The FVFs obtained from each alternative approach are then simulated under a wide range of futures. Results show that the introducing very severe droughts is not the best course of action, as the corresponding FVFs perform very poorly under moderately dry futures. In contrast, introducing scenarios with a moderate dry bias performs better over a wide range of future conditions, except for extremely severe droughts.   

How to cite: Olivares, M., Pereira, E., Abarzua, F., Gomez, M., and Orellana, D.: Introducing plausible mega-droughts in hydropower scheduling at the power grid level, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10799, https://doi.org/10.5194/egusphere-egu2020-10799, 2020

D159 |
EGU2020-3578
Christoph Libisch-Lehner, Harald Kling, Martin Fuchs, and Hans-Peter Nachtnebel

Hydro power assets contribute a valuable share of carbon-free energy generation worldwide. Large reservoirs are able to store energy and, combined with pump-storage capacities, they will play an important role in the future’s energy mix. In the future, the stronger integration of volatile energy sources, like solar and wind energy demands the flexibility of hydro power plants. In general, the operation of hydro power plants is a multi-stakeholder and multi-objective dynamic problem related to critical infrastructure. This requires flexible and robust reservoir operation policies, defined as closed-loop release functions where the system state is the input and turbine flows are the response of the function. Recently, Evolutionary-Multi-Objective-Direct-Policy-Search (EMODPS) yielded promising control policies for water resources systems. EMODPS is a kind of machine learning approach that relies on long records, or stochastic streamflow replicates capturing a wide range of possible conditions. A stochastic streamflow generator should actually cover all possible conditions related to the state-action-space and inflates the optimization process. Furthermore, the search procedure can implicitly identify the "most representative" states of the system and tends to approximate a better solution for these states. States that are very rarely explored but can be very important for a reliable operation have little effect on the optimized policy. In addition, artificial neuronal networks (ANN) derived from EMODPS suffer under the curse of instable sections . This is because ANN's are good at interpolating, but bad at extrapolating actions from unobserved states in the training sequence. Thus, we extend the well-known EMODPS framework by an re-optimizing approach utilizing seasonal streamflow predictions. Periodically, the reservoir policies are re-optimized based on an ensemble of streamflow predictions and the actual reservoir water levels. This adaptive policy search (APS) approach is applied to a three reservoirs cascade under Mediterranean climate, where the energy market will play an important role in the future. First results show that the hydropower operation can be improved: energy generation can slightly be increased at clearly lower cost of flood risk compared to static robust policies.

How to cite: Libisch-Lehner, C., Kling, H., Fuchs, M., and Nachtnebel, H.-P.: Seasonal streamflow forecasts fostering hydro power cascade operation applying the adaptive policy search framework, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3578, https://doi.org/10.5194/egusphere-egu2020-3578, 2020

D160 |
EGU2020-7155
Marta Zaniolo, Matteo Giuliani, Paul Block, and Andrea Castelletti

Advances in monitoring and forecasting water availability at various time and spatial scales offer a cost-effective opportunity to enhance water system flexibility and resilience. By enriching the basic information system traditionally used to design reservoir operating policies (i.e., time index and reservoir storage) with additional inputs regarding future water availability, operators can better anticipate and prepare for the onset of extreme hydrologic conditions (wet or dry years). Numerous candidate hydro-meteorological variables and forecasts may potentially be included in the operation design, however, and the best input set for a given problem is not always evident a priori. Additionally, for multi-purpose systems, the most appropriate information set and policy shape likely changes according to the objective tradeoff. 
In this work, we contribute a novel Machine Learning approach to link an Input Variable Selection routine with a multi-objective Direct Policy Search framework in order to retrieve the best policy input set online (i.e., while learning the policy) and Pareto-dynamically. The selected policy search routine is the Neuro-Evolutionary Multi-Objective Direct Policy Search (NEMODPS) which generates flexible policy shapes adaptive to changes in the policy input set. This approach is demonstrated for the lower Omo River basin, in southern Ethiopia, where regulation of the recently constructed Gibe III megadam must strike a balance between hydroelectricity generation, large scale irrigation, and ecosystem services downstream.
We develop a dataset of candidate policy inputs comprising streamflow and precipitation forecasts at multiple spatial and temporal scales, from days to months ahead. Long term (season-ahead) forecasts are conditioned on well-recognized climatic oscillations in the region. Specifically, Artificial Intelligence tools are used to detect relevant anomalies in gridded global climatic datasets of sea-surface temperature, sea-level pressure and geopotential height, which are used as predictors for a multi-variate non-linear forecast model.  Moreover, we analyze how varying objectives – and tradeoffs therein – benefit from different information.
Results suggest that informing water system operations with appropriate information can reduce conflicts between water uses, especially in extreme years when a basic policy is particularly inefficient.

How to cite: Zaniolo, M., Giuliani, M., Block, P., and Castelletti, A.: Dynamic retrieval of informative inputs for multi-sector reservoir policy design with diverse spatio-temporal objective scales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7155, https://doi.org/10.5194/egusphere-egu2020-7155, 2020

D161 |
EGU2020-7578
Henrique Moreno Dumont Goulart, Matteo Giuliani, Jonathan Herman, Scott Steinschneider, and Andrea Castelletti

Climate change is expected to increase the variability of hydrological regimes, generating more recurrent and intense floods and droughts. This trend will very likely diminish the resilience of reservoir systems in supplying water, controlling floods, and generating energy. While forecast information has proven valuable for improving water systems operations under stationary hydroclimatic conditions, little is known about its potential value in more variable regimes and its capacity in mitigating the increased risks. In this work, we propose a framework to quantify the future operational value of forecast information under different climate change projections. Specifically, a stochastic model replicating observed forecast error is calibrated over a hindcast dataset from the Subseasonal to Seasonal (S2S) prediction project and used to generate synthetic forecasts for future hydrologic scenarios. Then, a policy search routine is used to design optimal operating policies informed by the forecast information. The forecast operational value is quantified by comparing the performance of these policies against a baseline solution not informed by any forecast and an upper bound solution which uses perfect knowledge of the future. This experiment is performed on a case study of Folsom Reservoir, California. Results indicate that the use of forecasts can improve future operations both in terms of water supply and flood control. We assess the forecast value in two distinct forms: the absolute value, which is the total gain generated by the use of forecast information and aligns with the provider point of view, and the relative value, which measures the gain with respect to the no-forecast case and relates to the reservoir operator perspective. The absolute value of forecasts is projected to increase for all selected scenarios. Conversely, projected relative forecast value depends on the nature of the climate scenario, increasing in wet scenarios while decreasing in dry scenarios. This experiment suggests that risks associated with increasing precipitation variability on seasonal to interannual timescales can be at least partially mitigated by the use of short-term forecasts. Future work will consider the potential for the forecast error structure to change over time as a result of climate change and improved weather models.

How to cite: Moreno Dumont Goulart, H., Giuliani, M., Herman, J., Steinschneider, S., and Castelletti, A.: Assessing the operational value of short-term forecast information under climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7578, https://doi.org/10.5194/egusphere-egu2020-7578, 2020

D162 |
EGU2020-20981
Tingju Zhu, Guilherme Marques, Josué Medellin-Azuara, and Jay Lund

Advances in probabilistic seasonal flow forecasts sparked renewed interests to improve water management, through explicit incorporation of forecasts and forecast uncertainties into decision-making. Here, we develop a three-stage stochastic programming model to optimize integrated agricultural and urban water management decisions by directly considering probabilistic seasonal flow forecasts. The model represents urban water users which make short-term and long-term water conservation choices to maximize supply reliability and minimize conservation costs; it also represents irrigators which optimize land and water allocations to annual and perennial crops to maximize farm revenue, besides water transfers between agricultural and urban uses. Long-term urban conservation measures, areas of perennial crops, and capital investments in onfarm irrigation are considered in the first stage; annual crop areas, which depend on forecasted flows, are considered in the second stage; and reductions of irrigated annual and perennial crop areas due to water scarcity, conjunctive use operations, and water transfers informed by realized hydrologic year types are considered in the third stage. The temporal hierarchy of these decisions intends to approximate actual decision-making process by simultaneously considering long- and short-term decisions, forecasts, and forecasting skills. This paper provides a framework for quantifying the value of probabilistic forecasting information and forecasting skills, for managing complex regional water systems, including agricultural and urban water uses, water transfers, and conjunctive use of surface water and groundwater.

How to cite: Zhu, T., Marques, G., Medellin-Azuara, J., and Lund, J.: Water Management and Transfers Optimization with Probabilistic Seasonal Forecasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20981, https://doi.org/10.5194/egusphere-egu2020-20981, 2020

D163 |
EGU2020-3693
| solicited
Nick van de Giesen, Frank Annor, Rick Hagenaars, Petra Izeboud, Vivaoliva Shoo, Patricia Trambauer, Shristi Vaidya, Jurjen Wagemaker, and Hessel Winsemius

Dar es Salaam is subject to regular flooding, especially from the Msimbazi River, causing tens of deaths and over $100 million damages per year. Dar es Salaam is not an exceptional case, as many cities in the Global South face rapid urban expansion, which causes increased impermeable services, clogging of drains by sediment and solid waste, as well as encroachment of the floodplains. Although in the long term, structural measures are needed, much is to be gained short term by a flood early warning system that aims to increase situational awareness and optimise allocation of resources during and after floods. The Community Water Watch project, which contributes to the Tanzania Urban Resilience Program, addresses these aspects through a mixture of data streams.

In an online dashboard, these three data streams come together to create meaningful information. First, a dense network of TAHMO weather stations and two hydrological stations report in near-real time the atmospheric input and state of the system. Second, a hydraulic model uses this information to provide forecasts in terms of discharge, flood levels, and flood extents. Finally, social media platforms, such as Twitter, Telegram, WhatsApp, and JAMII Forums, are continuously searched for texts and photos concerning flooding to provide an overview of flood impacts and ways in which people are dealing with them. Tailor-made dashboards have been built to cater to different users such as the Tanzania Red Cross Society and the local transportation company DART. Due to the intense co-creation processes during the design of the system, these dashboards have already produced actionable information that has prevented damages and possibly has saved lives. The solution is very scalable to any city dealing with similar flood problems.

How to cite: van de Giesen, N., Annor, F., Hagenaars, R., Izeboud, P., Shoo, V., Trambauer, P., Vaidya, S., Wagemaker, J., and Winsemius, H.: Community Water Watch: Measurements, Forecasts and Impacts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3693, https://doi.org/10.5194/egusphere-egu2020-3693, 2020

D164 |
EGU2020-1992
Anqi Tan and Senlin Chen

Discrete differential dynamic programming algorithm is widely used in reservoir power generation dispatching, but the problem of "dimensional disaster" still exists, and there are different degrees of limitations such as premature convergence and uncertainty of convergence. In the existing monographs and literature, there is little research on the algorithm itself. The iterative solution convergence conditions, initial parameters, and initial trajectory selection of the mathematical model for reservoir power generation scheduling optimization have important effects on the iterative process and results. The convergence conditions directly determine when the iterative process converges and its calculation results. In this paper, the solution convergence conditions are studied. Based on the calculation results of the mathematical model of reservoir power generation scheduling optimization, the method of iteratively solving the convergence conditions when different state quantities are used as control factors is systematically studied. Shuibuya Hydropower Station Scheduling results show that using this method to determine the termination step size can shorten the calculation time and obtain an optimization result close to the ideal value, avoid the randomness of the convergence process of the iterative solution, and improve the accuracy of the DDDP algorithm and the efficiency of the target value.

How to cite: Tan, A. and Chen, S.: Research on the Convergence Condition of Iterative Solution for the Mathematical Model of Reservoir Power Generation Optimization, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1992, https://doi.org/10.5194/egusphere-egu2020-1992, 2020

D165 |
EGU2020-3215
Xinhai Zhang

Ensuring the ecological flow of the Weihe River is a basic requirement for strengthening water resources management and ecological protection and restoration in Weihe River Basin and is of great significance for ensuring the safety of water resources and ecological safety in Weihe River Basin. This study selects three major control cross-sections of Beidao, Linjiacun, and Huaxian for ecological flow protection research. In this paper, the existing results of the main control cross-sections were summarized, the Tennant method and the most withered month average flow method were applied to analyze and calculate the ecological base flow. Then, the flow data from 1980 to 2016 are applied to analyze the degree of ecological flow guarantee. Based on the changes in the Weihe River runoff and the development of water resources, the rationality and accessibility of the ecological flow were demonstrated, and the ecological base flow indicators of Beidao, Linjiacun and Huaxian cross-sections were comprehensively determined to be 2m3/s, 5m3/s, and 12m3/s, respectively. Furthermore, the current status of ecological security in the Weihe River Basin was analyzed in depth. It is clear that there were problems in the Weihe River Basin, such as strong water demand, the high pressure of water uses for life, production and ecology during dry years, difficult guarantee of ecological flow, incomplete ecological flow guarantee working mechanism, etc. Based on the analysis, the suggestions were proposed from the perspectives of enhancing the organization and leadership, intensifying the unified allocation of water resources in the Weihe River, strengthening the capacity of water regime monitoring, establishing an early warning system for ecological flow, strictly controlling water withdrawal, and reinforcing supervision and assessment. Then the countermeasure system of ecological flow guarantee was established.

How to cite: Zhang, X.: Study on Countermeasures System of Ecological Flow Guarantee in Weihe River Basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3215, https://doi.org/10.5194/egusphere-egu2020-3215, 2020

D166 |
EGU2020-5766
Aditya Kapoor and Deepak Kashyap

Present study aims at planning of agricultural groundwater development in Bist interbasin (India) that is experiencing excessive water table decline due to intense agricultural groundwater pumping. The interbasin covering an area of 8040 Km2 comprises an alluvial unconfined aquifer that is hydraulically connected to two major perennial rivers viz., Satluj and Beas.  A finite difference based distributed flow model of the study area was calibrated for transmissivity geostatistically on the basis of discrete point data at 15 points, and head fields available at 6 discrete times. The deficit of transmissivity data was overcome by invoking the Pilot point approach wherein transmissivity at additional artificial points are estimated by the least squares approach. The calibrated model was oriented towards the agricultural objective by correlating its sink term with crop areas. The model was used to simulate the long-term stabilized head and the depth fields corresponding to prevailing cropping pattern. The simulation indicates large and unsustainable water table decline. The impact of various moderated cropping patterns on the water table decline was subsequently simulated. It was concluded that replacement of some fraction of area under water intensive rice crop by maize crop area may stabilize the water table depths to acceptable limit.

How to cite: Kapoor, A. and Kashyap, D.: Model Assisted Planning of Agricultural Groundwater Development in Bist Doab Interbasin (India), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5766, https://doi.org/10.5194/egusphere-egu2020-5766, 2020

D167 |
EGU2020-6869
Aristeidis Koutroulis, Manolis Grillakis, and Ioannis Tsanis

Seasonal forecasts can be interfaced with practical applications to improve and increase the operational capabilities of several targeted sectors, as for example by reducing weather related risks such as freshwater stress in agriculture. In this study we present the co-design, development and evaluation of a local scale demonstrator that brings seasonal hydro-meteorological forecasts in local water management of an intensively cultivated watershed in the island of Crete, Greece. The information included in the development of the Drought Decision Support System (DDSS) was identified during an initial user survey, in which seasonal climate predictions were rated as the most prominent from a number of information regarding weather and climate (change). The DDSS consists of three pillars of information (i) specific sources and guidance for weather and climate information not familiar to the local users, (ii) a demonstrator of seasonal forecasting of reservoir inflow and (iii) locally adjusted seasonal forecast information for precipitation over the watershed. Seasonal forecasts are demonstrated in a user-friendly probabilistic form against climatic conditions based on observations. The usefulness, the accessibility of information, as well as the barriers which currently hamper the deployment of climate services in the in the decision-making process were examined through an evaluation survey.

How to cite: Koutroulis, A., Grillakis, M., and Tsanis, I.: Introducing seasonal hydro-meteorological forecasts in local water management. A local scale demonstrator for an intensively cultivated watershed in the island of Crete, Greece., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6869, https://doi.org/10.5194/egusphere-egu2020-6869, 2020

D168 |
EGU2020-6877
Thanh Duc Dang, AFM Kamal Chowdhury, Paul Block, and Stefano Galelli

The ASEAN economic growth is one of the main factors driving the development of hydropower dams in the lower Mekong River Basin. Recent studies show that the performance of these infrastructures is uncertain and largely affected by both seasonal and inter-annual water availability. During El Niño years, for example, weaker monsoon rainfalls reduce the amount of available hydropower, which must be offset by a deeper reliance on fossil fuels. A potential solution to this problem stands in the idea of informing hydropower operations with seasonal hydro-meteorological forecasts. Here, we explore the value of forecasts through a computational framework consisting of three components. First, we use principal-component regression to predict seasonal cumulated precipitation at multiple sites within the Mekong basin. Second, we harness the information contained in the forecasts to optimize both firm and annual hydropower production of each dam; a result attained by coupling the Variable Infiltration Capacity hydrologic model with a Multi-Objective Evolutionary Algorithm. Third, we use the power system model PowNet to simulate the energy mix of Thailand and Laos, and thereby evaluate the forecast value in terms of reduced CO2 emissions and energy production costs. Modelling results for the period 1995-2004 show that the use of seasonal forecasts reduces annual operating costs and CO2 emissions by at least 10 million USD and 20 million tons, respectively.

How to cite: Dang, T. D., Chowdhury, A. K., Block, P., and Galelli, S.: Forecast-informed operation of transboundary water-energy systems: a case study in the lower Mekong River Basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6877, https://doi.org/10.5194/egusphere-egu2020-6877, 2020

D169 |
EGU2020-8033
Majed Khadem, Richard Dawson, and Claire Walsh

Uneven distribution of water resources in the face of climate change and population growth is imposing increasing threats to communities as well as challenging decision-makers. Inter-basin water transfer (IBT) schemes have been appreciated as one of the common approaches to tackle this issue. This work presents a framework for climate impact assessment and feasibility study for IBTs. The framework investigates negative impacts of IBTs on the donor and receiving bodies. This is done by calculating hydrological drought risk and environmental risks to freshwater habitats under 1200 future climatic scenarios and two different transfer scenarios. 2.2 Km resolution time-series from UK’s Met Office most recent climate projection (UKCP18) is used as the input scenario and a water resources model developed at Newcastle University is implemented to determine allocation and calculate the above risk factors. This work considers transferring raw water from England’s water-rich North East to its water-stressed South East as the case study. This case was chosen because England, with no major IBT scheme, is experiencing challenges from more frequent climate change and increasing demand for water in London. Additionally, organisations such as National Infrastructure Commission (NIC) and Environment Agency (EA) have encouraged England’s water companies to consider IBT as one of the options to improve water supply resilience. In this study, we assess schemes to transfer water using the existing infrastructures of water companies located from North East to South East of England to minimise costs and environmental impacts. Results suggest that, under a wide range of future scenarios, meeting London’s annual water shortage through transfers from the North East during wet season of each year not only increases London’s water supply resilience but also boosts flood resilience in the North East donor basin while still meeting environmental requirements.

How to cite: Khadem, M., Dawson, R., and Walsh, C.: Towards a resilient water future via inter-basin water transfer: climate impact assessment and feasibility study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8033, https://doi.org/10.5194/egusphere-egu2020-8033, 2020

D170 |
EGU2020-9720
Yousra Saoudi, Louise Crochemore, Ilias Pechlivanidis, and Matteo Giuliani

The recent advances in the skill of hydroclimatic services are motivating the need for quantifying their value in informing decisions. State-of-the-art forecasts proved to be skillful over seasonal and longer time scales especially in regions where climate teleconnections, such as El Nino Southern Oscillation, or particular hydrological characteristics, such as snow- and/or baseflow-dominance, enable predictability over such long lead times. Recent studies have investigated the value of seasonal streamflow forecasts in informing the operations of water systems in order to improve reservoir management strategies. However, how to best inform the operations of hydropower systems is still an open question because hydropower reservoir operations benefit from hydroclimatic services over a broad range of time scales, from short-term to seasonal and decadal time horizons, for combining daily and sub-daily operational decisions with strategic planning on the medium- to long- term.

In this work, we propose a machine-learning based framework to quantify the value of hydroclimatic services as their contribution to increasing the hydropower production of the Grand Ethiopian Renaissance Dam (GERD) in Ethiopia. The GERD, with an installed capacity of more than 6,000 MW is considered the largest hydroelectric power plant in Africa and the seventh largest in the world. Its construction is part of the strategic hydropower development plan in Ethiopia that aims to serve the growing domestic and foreign electricity demands. The quantification of the forecasts value relies on the Information Selection Assessment framework, which is applied to a service based on bias adjusted ECMWF SEAS5 seasonal forecasts used as input to the World-wide HYPE hydrological model. First, we evaluate the expected value of perfect information as the potential maximum improvement of a baseline operating policy relying on a basic information with respect to an ideal operating policy designed under the assumption of perfect knowledge of future conditions. Second, we select the most informative lead times of inflow forecast by employing input variable selection techniques, namely the Iterative Input Selection algorithm. Finally, we assess the expected value of sample Information as the performance improvement that could be achieved when the inflow forecast for the selected lead time is used to inform operational decisions. In addition, we analyze the potential value of forecast information under different future climate scenarios.

Preliminary results show that the maximum space for increasing the hydropower production of the GERD baseline operations not informed by any forecast is relatively small. This potential gain becomes larger when we focus on the performance during the heavy rainy season from June to September (Kiremt season), making room for the uptake of forecast information. The added production obtained with the forecast-informed operations of the GERD may represent an additional option in the current negotiations about the dam impacts on the downstream countries.

 

How to cite: Saoudi, Y., Crochemore, L., Pechlivanidis, I., and Giuliani, M.: Assessing the value of hydroclimatic services for hydropower megadams: the case of the Grand Ethiopian Renaissance Dam, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9720, https://doi.org/10.5194/egusphere-egu2020-9720, 2020

How to cite: Saoudi, Y., Crochemore, L., Pechlivanidis, I., and Giuliani, M.: Assessing the value of hydroclimatic services for hydropower megadams: the case of the Grand Ethiopian Renaissance Dam, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9720, https://doi.org/10.5194/egusphere-egu2020-9720, 2020

How to cite: Saoudi, Y., Crochemore, L., Pechlivanidis, I., and Giuliani, M.: Assessing the value of hydroclimatic services for hydropower megadams: the case of the Grand Ethiopian Renaissance Dam, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9720, https://doi.org/10.5194/egusphere-egu2020-9720, 2020

D171 |
EGU2020-11713
Giacomo Trombetta, Andrea Castelletti, Matteo Giuliani, Marta Zaniolo, and Paul Block

Transboundary river basins worldwide are commonly managed by unique, institutionally independent decision makers and characterized by multiple stakeholders with conflicting interests, including distribution, co-management, and use of water resources across sectors and among countries. This competition is expected to exacerbate in the future due to climate change induced water scarcity, increasing demand, and the development of infrastructure, which is often criticized for potentially jeopardizing downstream security by affecting water supply, irrigation, and energy production. 

The Nile River basin is an emblematic transboundary basin, encompassing 11 countries and home to one-third of the African population. The largest fraction of Nile River streamflow originates in Ethiopia and is conveyed into the system via the Blue Nile. However, the larger water users have historically been downstream, in particular Egypt, where the High Aswan Dam (HAD) constitutes the backbone of Egyptian electricity supply and enables the irrigation of vast agricultural districts. This geographic disparity between water origination and consumption provides both the potential for conflict and the rationale for cooperation. Currently, the ongoing construction of the soon-to-be largest dam in Africa, the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile, is highly debated given concerns rising from how it will affect water supply and power generation in downstream countries. However, GERD may represent a response to the frequent regional power shortages, foster economic development, and represents a unique opportunity for cooperation between riparian countries from which all parties can benefit.

In this work we explore how varying levels of cooperation among the riparian countries, from individualistic behavior to full cooperation, might impact hydropower production and irrigated agriculture in the Nile River basin. We use an Evolutionary Multi-Objective Direct Policy Search approach to design optimal operation of a three-dimensional reservoir system, including GERD (Ethiopia), HAD (Egypt), and Merowe Dam (Sudan), under historical hydro-climatic conditions and under different cooperation levels, assuming the capacity of re-optimization of the High Aswan Dam and the Merowe Dam. Expected results may illustrate the benefits of implementing a centralized rather than an individualistic strategy, highlighting the value of full information exchange and of basin-wide cooperation.

How to cite: Trombetta, G., Castelletti, A., Giuliani, M., Zaniolo, M., and Block, P.: From individualistic behavior to full cooperation: optimal management policy design under varying cooperation levels in the Nile River basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11713, https://doi.org/10.5194/egusphere-egu2020-11713, 2020

D172 |
EGU2020-12440
Jia Yi Ng, Donghoon Lee, Stefano Galelli, and Paul Block

Season-ahead hydro-climatological forecasts are a useful source of information for hydropower operators: at the onset of a flooding season, for example, predictive information on the timing and magnitude of the inflow volume can help operators schedule the release trajectory, decide on the amount of volume to store, and therefore maximize hydropower production. Intuitively, the forecast value varies not only with predictive accuracy, or skill, but also with the reservoir design specifications. Characterizing and explaining the relationship between skill, design specifications, and value is thus a necessary step towards a more informed and effective use of seasonal forecasts. To investigate the nature of this relationship, we modeled 1,593 hydropower reservoirs, for which we developed 3-month ahead monthly inflow forecasts—based on a principal component linear regression model. Our results show that more than half of the dams could benefit from forecasts, averaging a 6.56% annual increase in hydropower production. We also found that forecast value is largely controlled by reservoir design specifications; specifically, we found that reservoirs with small storage capacity (relative to inflow) and large inflow volumes (relative to turbine capacity) have better chances of benefitting from accurate forecasts. With this information, we classify and map each dam on the basis of its potential to increase hydropower production.

How to cite: Ng, J. Y., Lee, D., Galelli, S., and Block, P.: Benefits and limits of season-ahead forecasts for hydropower production: a global analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12440, https://doi.org/10.5194/egusphere-egu2020-12440, 2020

D173 |
EGU2020-14078
Mojca Sraj, Mira Kobold, Sašo Petan, Nejc Bezak, and Mitja Brilly

The Danube River basin is the most international river basin in the world with many large tributaries having catchments in 19 countries. Since frequency of floods in the Danube River basin increased in the last decades, the need for a more effective and harmonized regional and cross-border cooperation in the field of flood and ice forecasting arises. The need for enhanced cooperation in flood protection was officially recognized in various international and interregional policy documents, therefore DAREFFORT project was initiated under the Interreg Danube Transnational Programme to identify the state of the art of flood and ice forecasting techniques and raise awareness among the countries about the basic problems of flood and ice forecasting (e.g. the lack of a unified data exchange at the catchment level) and to help implement the Danube Flood Risk Management Plan in line with the Flood Risk Directive.

The main aim of the DAREFFORT project is to give a comprehensive overview about the complex national flood and ice forecasting systems and to eliminate the shortcomings of the existing forecasting practices as well as to improve the exchange and availability of hydrological and meteorological data between the participating countries with establishment of the Danube Hydrological Information System (Danube HIS). In order to achieve this goal, national reports on the status quo of the Danube regional flood and ice forecasting system and methodologies as well as a detailed questionnaire were prepared by all project partner countries. Information about the countries’ hydrological and meteorological data availability, recording methods and coverage with the monitoring networks, codings and national database system, data flow, forecasting time intervals and accuracy, response times, cross-border issues and data dissemination etc. was covered in the questionnaire. The evaluation of 12 national reports and results of questionnaires showed a comprehensive overview of flood and ice forecasting systems and methodologies in the Danube River basin.

The gathered information about national flood and ice forecasting practices and the acquired knowledge through the project implementation process will result into an international policy proposal for a harmonized data exchange protocol, including the sufficient quantity, quality, and format of the data exchange.

How to cite: Sraj, M., Kobold, M., Petan, S., Bezak, N., and Brilly, M.: Review of flood and ice forecasting systems and methodologies in the Danube River countries, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14078, https://doi.org/10.5194/egusphere-egu2020-14078, 2020

D174 |
EGU2020-15835
Andrea Castelletti, Federica Bertoni, Matteo Giuliani, and Patrick Reed

There is a large body of recent research that is capitalizing on the improved skill of state-of-the-art hydroclimatic services for investigating their value in informing water reservoir operations. Yet, the potential value of these services in informing infrastructure design is still unexplored. In this work, we investigate the added value of hydroclimatic services in the planning of water reservoirs, composed of the joint design of the infrastructure’s size and its operations informed by streamflow forecasts. We demonstrate the potential of our approach through an ex-post design analysis of the Kariba dam in the Zambezi river basin, which is the largest man-made reservoir in Africa. The reservoir is operated for hydropower production and irrigation supply. Specifically, we search for flexible operating policies informed by streamflow forecasts that allow the design of smaller and less costly reservoirs with respect to solutions that do not rely on forecast information. This requires selecting the most informative forecast lead times to use in the dam design phase, which depends on both infrastructural reservoir characteristics and tradeoffs across performance objectives. After estimating the value of perfect forecasts, we analyze its sensitivity with respect to using imperfect synthetic forecasts characterized by different biases. The results show that informing the infrastructure design with perfect streamflow forecasts allows reducing capital costs by 20% with respect to a baseline solution not informed by any forecast, while maintaining the same performance in terms of hydropower production and water supply. Forecast overestimation results in the most critical synthetic forecast bias, reducing their value by 8%. Moreover, our analysis show that forecast value is highly sensitive to reservoir size and operational tradeoffs, ultimately representing a valuable tool for supporting the ongoing planning of 3,700 major dams worldwide.

How to cite: Castelletti, A., Bertoni, F., Giuliani, M., and Reed, P.: Informed water infrastructure design: improving coupled dam sizing and operation by streamflow forecasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15835, https://doi.org/10.5194/egusphere-egu2020-15835, 2020

D175 |
EGU2020-17461
Bin Liang, Matteo Giuliani, Liping Zhang, Senlin Chen, and Andrea Castelletti

Although being one of the most important approaches to design optimal water reservoir operating policies, the Stochastic Dynamic Programming is challenged by the three curses of dimensionality, modeling, and multiple objectives that make it unsuitable in most practical applications. Increased hydrological variability induced by climate change and human activities further challenges the control of hydraulic infrastructures calling for more flexible and efficient approaches to operation design. Tree-based fitted Q-iteration (FQI) is a value-based, offline and batch mode reinforcement learning method, which employs the principles of continuous approximation of value function through non parametric randomized ensemble of regression tree, i.e. Extremely Randomized Tree. So far FQI has been used for relatively simple systems, including one dam and several state variables, and looking at historical hydrology. In this work, we explore the potential for FQI to design reservoir network operation under varying hydro-climatological conditions. The approach is demonstrated on a real-world case study concerning the optimal operation of a network of three water reservoirs in the Qingjiang River basin, China. Preliminary results show that the computational efficiency and performance of the policies derived by FQI are all satisfactory compare to traditional Stochastic Dynamic Programming, and the advantages in terms of computational efficiency and policies performance become more relevant when evaluated considering uncertain hydro-climatological and socio-economic conditions that requires using more information for conditioning the control policy. 

How to cite: Liang, B., Giuliani, M., Zhang, L., Chen, S., and Castelletti, A.: Fitted Q-iteration for optimal water reservoir network operation under varying hydro-climatic conditions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17461, https://doi.org/10.5194/egusphere-egu2020-17461, 2020

D176 |
EGU2020-22257
Guang Yang and Paul Block

Incorporating streamflow forecasts into reservoir management can often lead to improved operational efficiency. Large-scale climate variables and indices – in addition to local hydrologic variables – may also provide valuable information for reservoir operations given their limitate relationship with streamflow. A new tree-based machine learning approach for updating reservoir operating rules conditioned on large-scale climate indices is proposed by selecting the most suitable reservoir decision-making pattern for each year. Multiple types of reservoir operating rules can be extracted from the historical streamflow data with different hydrological (e.g., wet and dry) conditions. Their performance can be recorded and correlated with climate indices by using a decision-tree classification model, and then the rules with the best performance conditioned on a given climate index value can be selected for reservoir operations. A case study of reservoir operations for the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile River demonstrates that the proposed tree-based reservoir operation framework can accurately select suitable decision-making rules both for normal and forecast-informed reservoir operations. Notably, incorporating May Nino 4.0 values into GERD reservoir operations can increase power generation during flood seasons, especially in extreme years.

How to cite: Yang, G. and Block, P.: A forecast-informed reservoir operation framework incorporating climate indices, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22257, https://doi.org/10.5194/egusphere-egu2020-22257, 2020

D177 |
EGU2020-22258
Paul Block and Donghoon Lee

Globally, many floods occur in developing or tropical regions where the impact on public health is substantial, including death and injury, endemic disease, and so on. Although these flood impacts on public health have been investigated, integrated management of floods and flood-induced health risks is technically and institutionally limited. Specifically, while the use of climatic and hydrologic forecasts for disaster management has been highlighted, analogous predictions for forecasting the magnitude and impact of health risks are lacking, as is the infrastructure for health early warning systems, particularly in developing countries. To address this critical need, we develop flood forecasts and a flood-health vulnerability and risk prediction model for Peru based on geographic, demographic, socio-economic, health vulnerability indicators, and decision weights from national agencies. This model estimates an integrated flood-health vulnerability index with the purpose of identifying measures to reduce potential vulnerability/risk and enhance the capacity to act proactively and efficiently to minimize impacts. These spatially explicit impacts (e.g., damages) can be utilized by international and local disaster management agencies to improve their existing disaster management strategies. Once this approach is systematically linked with the global and local flood forecast systems, it can provide the groundwork for a future multi-sectoral (flood and health) risk warning system.

How to cite: Block, P. and Lee, D.: Predicting floods and flood-health vulnerability to support pre-disaster management in Peru, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22258, https://doi.org/10.5194/egusphere-egu2020-22258, 2020