The European Center for Medium-Range Weather Forecasts (ECMWF) has recently released its most advanced reanalysis product, the ERA5 dataset. It was designed and generated with methods giving it multiple advantages over the previous release, the ERA-Interim reanalysis product. Notably, it has a finer spatial resolution, is archived at the hourly time step, uses a more advanced assimilation system, and includes more sources of data. This paper aims to evaluate the ERA5 reanalysis as a potential reference dataset for hydrological modelling by considering the ERA5 precipitation and temperatures as proxies for observations in the hydrological modelling process. This is obtained by using a semi-distributed hydrological model over basins ranging from 40km² to 6900 km² over the Upper Adige river basin in the Eastern Italian Alps. This study shows that ERA5-based precipitation product is affected by a significant bias which translates to biased runoff at all spatial scales considered in the study. We observed that ERA5 precipitation product generally overestimate low-intensity rainfall and underestimate high rainfall intensity in the region. We analysed how this affects simulation of annual max floods over the study area. The results show that flood simulations are in general surprisingly good, as they result from the combination of two cascading errors: i) overestimation of the soil moisture conditions at the start of the event and ii) the underestimation of the event forcing rainfall. Differences between ERA5 and observation datasets are mostly linked to precipitation, as temperature only marginally influences the hydrological simulation outcomes.

How to cite: Shrestha, S., Zaramella, M., Callegari, M., Greifeneder, F., and Borga, M.: Evaluation of the ERA5 reanalysis as a reference dataset for fine-scale hydrological modelling over alpine basins , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14265, https://doi.org/10.5194/egusphere-egu21-14265, 2021.

Assam has always been India’s most flood prone state due to the presence of Brahmaputra river, which is very unstable in terms of its flow direction witnessing 12 major floods from 1950 to 2012. Flooding in the basin has affected around 2.75 million of people and 0.27 million hectares of agricultural land on an average causing catastrophic damage to human life and infrastructure. In this study, we analysed all the major floods across the Brahmaputra river in the past 70 years and established the dependency within discharge and atmospheric parameters. Variable Infiltration Capacity (VIC) model was set up to simulate the flow at two stations namely Yangcun, China and Bahadurabad, Bangladesh. We  used near surface meteorological data for driving land surface modelling systems from 1901 to 2016 as input parameters to the VIC model. To avoid the discontinuity of data after 2016, we used ECMWF reanalysis (ERA5) data for the period of 2016 to 2020. After obtaining the continuous simulated discharge for 120 years, we established the relationship between the observed and simulated discharge data for which the R-squared and Nash Sutcliffe coefficient values were 0.83 and 0.78 respectively. Comparing the simulated discharge with the observed extreme discharge at various locations on the river, we apply the model to address future flood situations.

How to cite: Kale, A. and Mishra, V.: Flood forecasting system for Brahmaputra river basin , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15762, https://doi.org/10.5194/egusphere-egu21-15762, 2021.

A     recent report “The Future is Now: Science for Achieving Sustainable Development” Global Sustainable Development Report 2019 - SDG Summit’      as part of the activity of Agenda 2030 of UN, highlights the opportunity to develop Early warning system for drought, floods and other meteorological events, that by providing timely information can be used by vulnerable countries to build resilience, reduce risks and prepare more effective responses. Following the suggestion,      combining outputs from Global Circulation models, remote sensing, hydraulic models and machine learning tools,       a local scale flooding Early Warning System (EWS) is proposed for the St. Lucia island (     Caribbean). The objective of the EWS is to provide forecasts of potentially dangerous flooding phenomena at different time scale: a) 0-2 hours, nowcasting; b) 24-48 hours, short range; c) 3-10 days, middle to long range. Data used to build the model are: Geopotential Height (GPH) fields at 850 hPa and Integrated Vapor Transport (IVT) fields from European Centre for Medium-range Weather Forecasts (ECMWF) - Reanalysis v5 (ERA5); Tropical Cyclone tracks from NOAA-NHC; 18 weather stations homogeneously distributed in the island; rainfall map data from the weather radar in Saint Lucia. GPH and IVT fields were defined between 110°W - 10°W and 45°N - 10°S. The EWS is constituted by an ensemble of flooding risk forecast subsystems which is potentially applicable to Atlantic tropical and extra-tropical regions. Different approaches are used for each subsystem      to link large scale atmospheric features to local rainfall and flooding: a) Non-homogeneous Hidden Markov and Event Synchronization models to translate IVT and GPH at 850 hPa  fields (from ECMWF-Set II- Atmospheric Model Ensemble) in local      daily rainfall amount and probability of  exceedance of  a prefixed heavy rainfall threshold; b) a physical based cyclone/rainfall  model to convert      Tropical cyclone attributes – position and      maximum wind      velocity       (provided from National Hurricane Center)- in rainfall intensity spatial distribution on the island; c) a surrogate model for a  fast and accurate prediction of flooding events that is obtained from a multi-layer perceptron neural network (MLPNN), which is trained on a high-fidelity dataset relying on solution of the full two-dimensional shallow water equations with direct rainfall application.        Results show an excellent ability of the models to identify the climatic configurations that determine the occurrence of extreme events and the exceeding of threshold values ​​that generate floods. In particular, during the late hurricane season September-October-November, when is highest the probability of flood events, the EWS was able to forecast the occurrence of critical climatic configurations 86% of the times they occurred. The EWS was able to predict the exceeding of the rainfall threshold that generated floods 80% of times.

How to cite: Cioffi, F., Conticello, F. R., Giannini, M., Lapini, T., Pirozzoli, S., Scotti, V., Telesca, V., and Tieghi, L.: A downscaling model system for early warning flooding forecast, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11163, https://doi.org/10.5194/egusphere-egu21-11163, 2021.

Earth Observations (EO) have become popular in hydrology because they provide information in locations where direct measurements are either unavailable or prohibitively expensive to make. Recent scientific advances have enabled the assimilation of EOs into hydrological models to improve the estimation of initial states and fluxes which can further lead to improved  forecasting of different variables. When assimilated, the data exert additional controls on the quality of the forecasts; it is hence important to apportion the effects according to model forcings and the assimilated datasets. Here,  we investigate the hydrological response and seasonal predictions over the snowmelt driven Umeälven catchment in northern Sweden. The HYPE hydrological model is driven  by two meteorological forcings: (i) a downscaled GCM meteorological product based on the bias-adjusted ECMWF SEAS5 seasonal forecasts, and (ii) historical meteorological data based on the Extended Streamflow Prediction (ESP) technique. Six datasets are assimilated consisting of four EO products (fractional snow cover, snow water equivalent, and the actual and potential evapotranspiration) and two in-situ measurements (discharge and reservoir inflow). We finally assess the impacts of the meteorological forcing data and the assimilated EO and in-situ data on the quality of streamflow and reservoir inflow seasonal forecasting skill for the period 2001-2015. The results show that all assimilations generally improve the skill but the improvement varies depending on the season and assimilated variable. The lead times until when the data assimilations influence the forecast quality are also different for different datasets and seasons; as an example, the impact from assimilating snow water equivalent persists for more than 20 weeks during the spring. We finally show that the assimilated datasets exert more control on the forecasting skill than the meteorological forcing data, highlighting the importance of initial hydrological conditions for this snow-dominated river system.

How to cite: Musuuza, J. L., Crochemore, L., and Pechlivanidis, I. G.: What is the impact of earth observation and in-situ data assimilation on seasonal hydrological forecast quality?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5060, https://doi.org/10.5194/egusphere-egu21-5060, 2021.

The European Center for Medium-Range Weather Forecasts (ECMWF) has presented in 2017 its latest seasonal forecasting system, SEAS5, available at 1° spatial resolution and daily timestep. More recently, in 2019, the ERA5 reanalysis dataset was released, replacing ERA Interim in providing climatic variables at a finer spatial and temporal resolution (30 km and hourly respectively). The use of such numerical weather predictions and re-analysis data has increased following the need for skills in planning water resources and preventing hydrogeological risk, as demanded by policy makers, energy stakeholders and public authorities. In this work, we apply at a sub-seasonal timescale the ECMWF-SEAS5 hindcast dataset to assess its prediction skills in the upper Adige river basin in the Eastern Italian Alps. The classical Extended Streamflow Prediction (ESP) framework was designated as a benchmark to assess ECMWF scores over the reference, a model simulation calibrated and validated on the runoff observed from 16 sub-basins and size spanning from 50 to 6900 km². Before application, ECMWF was downscaled and adjusted to the ERA5 re-analysis data by means of a Quantile Mapping (QM) technique. The analysis was conducted over 23 hindcast years from 1993 to 2016 exploiting the semi-distributed basin-scale hydrological model (ICHYMOD). We showed that the sub seasonal QPF-based forecasts have advantages over the ESP, although, generally their skill deteriorates in lead times after day 15. Moreover, ECMWF predictions better perform during early-spring snowmelt and late summer. During late spring and early summer, the forecast skills of the two frameworks vary from basin to basin depending on specific features and lead times.  

How to cite: Zaramella, M., Shtrestha, S., Callegari, M., Crespi, A., Greifeneder, F., and Borga, M.: Sub-seasonal streamflow predictions by combining numerical weather models and re-analysis data in alpine catchments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14138, https://doi.org/10.5194/egusphere-egu21-14138, 2021.

Soil moisture is highly variable in space and time. Climate change is expected to increase the variation in precipitation that may cause more frequent extremes in soil moisture. This will have major impacts on agriculture and infrastructure. Hence, forecasting can help mitigate the impacts of soil moisture extremes by providing warning about upcoming extreme events. Accurate soil moisture forecasting will provide policymakers, farmers and other stakeholders more reliable information on crop yield potential and flood risk to improve decision making.  Real-time soil moisture monitoring and forecasting can be accomplished by utilizing a numerical modelling approach that consolidates various sources of weather and hydrological data to simulate soil moisture levels. Soil water movement is difficult to describe numerically for fine-textured soils. Additionally, soil water behaviour during freeze/thaw events are generally weakly described by numerical tools. This study addresses both problems and evaluates how soil moisture can be forecasted under the hydrologically challenging conditions of the Red River Valley using the Brunkild catchment within the Red River basin.  The Brunkild catchment represents a highly variable landscape cross-section that includes heavy clay soils of the Red River Valley through to the coarse-textured soils of the adjacent escarpment. Soil moisture levels were continuously monitored from June – August 2020 using Sentek sensors which were installed at depths of 10 to 90 cm with 10 cm spacing, and with POGO sensors that were used to manually measure surface soil moisture levels at monthly intervals from June to August 2020. Climate variables were obtained from the RISMA (Real-time In-situ Soil Monitoring for Agriculture) stations present inside the catchment.  In addition to soil moisture data, surface water flow and groundwater data will also be used to aid with calibration and validation of a fully-integrated HydroGeoSphere (HGS) surface water – groundwater model of the catchment. Preliminary results using MERRA 2 data as climate forcing showed a strong fit for all observations in sandy soils and a good fit for all observation in clay. The next simulations will use the observed weather data. The model will be recalibrated and then being used to forecast soil moisture in the Brunkild catchment for the coming 14 days for the 2021 growing season.

How to cite: Shankara Mahadevan, K. P., Holländer, H., Bullock, P., Frey, S., and Ojo, T.: Physical-based hydrological modelling for real-time forecasting of soil moisture in a mesoscale catchment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13695, https://doi.org/10.5194/egusphere-egu21-13695, 2021.

In many parts of the world, surface water and groundwater are used complementarily to supply agricultural production and to meet urban water demands. Conjunctive management of these water resources requires balancing of the different characteristics of surface water and groundwater with respect to availability, quality and cost of supply. Ensemble forecasts of surface water and groundwater availability can inform management decisions but require explicit representation of the complex processes controlling surface and groundwater interactions. While many methods and operational services exist that provide independent forecasts for surface and groundwater availability, to our knowledge no approaches for coupled forecasting have been developed yet.

In this presentation we introduce an approach that generates coupled forecasts of surface water and groundwater availability. It extends the Forecast Guided Stochastic Scenarios (FoGSS) (Bennett et al., 2016) approach to forecast groundwater level at specified locations, in addition to streamflow totals, to lead times of 12 months at monthly time steps. We adapt a conceptual hydrological model to improve predictions of streamflow and, as a by-product, groundwater level. We then apply independent error models to streamflow and groundwater level to reduce bias, update predictions using recent observations and quantify residual uncertainty. Ensemble streamflow and groundwater forecasts are generated by forcing the hydrological and error models with ensemble rainfall forecasts generated by post-processing ECMWF System 5 outputs. The skill, bias and reliability of the rainfall, streamflow and groundwater level forecasts were assessed for a case-study catchment in South-East Queensland, Australia. We find that skill of forecasts is dependent on the forecast issue month and lead time, with groundwater level forecasts displaying significant skill to lead times of 12 months, while streamflow forecast skill rarely persists beyond 3 months.  We conclude by describing opportunities to improve forecast skill and some of the challenges that may be faced in the operational delivery of water resource forecasts in real-time.

Reference

Bennett, J. C., Wang, Q. J., Li, M., Robertson, D. E., and Schepen, A.: Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model, Water Resources Research, 52, 8238-8259, 10.1002/2016WR019193, 2016.

How to cite: Robertson, D., Fu, G., Barron, O., Hodgson, G., and Schepen, A.: Coherent intra-annual ensemble forecasts of surface and groundwater availability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13483, https://doi.org/10.5194/egusphere-egu21-13483, 2021.

Groundwater level monitoring is an important way for water resource managers to obtain information on the state of the groundwater system and make informed decisions. In many countries around Europe the right to abstract groundwater (e.g., for drinking water or irrigation purposes) is bound to observed groundwater levels. In particular during and after periods of drought such rights to abstract groundwater may be temporarily denied. As climate change is expected to increase the frequency and intensity of hydrological extremes, severe drought events become more likely, potentially increasing the gap between groundwater demand and supply. An early warning system of a potential groundwater drought could help water managers make informed decisions in advance, to try and counteract the effects of drought. In this study we investigate the use of seasonal forecasts from the ECMWF SEAS5 system to forecast groundwater levels around Europe. The groundwater levels are simulated using a non-linear time series model using impulse response functions as implemented in Pastas (https://github.com/pastas/pastas). Forecasts are compared to groundwater level simulations based on historic meteorological data from the E-OBS database. The methods are tested on 10 long-term (30 years) groundwater level time series. The use of the Standardized Groundwater Index (SGI) is tested to assess the forecast quality and communicate results with decision makers. Bias-correction of the SEAS5 forecasts is found to be necessary to forecast groundwater levels at this local scale. Preliminary results show that the forecast quality depends on the memory effect of the groundwater system, which can be characterized by the auto-correlation of the time series. In addition, it is found that the groundwater levels forecasts have smaller ranges in spring then in the winter months. This may be explained by the fact that groundwater levels in spring are more dependent on evaporation than on precipitation and that forecast of the first are better than those of the latter. The results from this study may be used to improve early warning systems that forecast groundwater droughts.

How to cite: Collenteur, R. and Birk, S.: Seasonal forecasting of groundwater levels in Europe using Pastas time series models and ECMWF SEAS5 forecasts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3902, https://doi.org/10.5194/egusphere-egu21-3902, 2021.

Inflow forecasts play an essential role in the management of hydropower reservoirs. Forecasts help operators to mitigate flood risks, meet environmental requirements, and maximise the value of power generated. In Scotland, operational inflow forecasts for hydropower facilities are typically limited in range to 2 weeks ahead, which marks the predictability barrier of deterministic weather forecasts. Extending the horizon of these forecasts may allow operators to take more proactive responses to risks of adverse weather conditions, thereby improving water management and increasing profits.

This study outlines a method of producing skilful probabilistic inflow forecasts for hydropower reservoirs on sub-seasonal timescales (up to 6-weeks ahead), directly from Numerical Weather Prediction (NWP) model output. Using a case study site of a large hydropower reservoir in the Scottish Highlands, we use the European Centre for Medium-range Weather Forecasting (ECMWF) extended-range forecast to create probabilistic inflow forecasts for the reservoir. Inflow forecasts are derived by training a linear regression model for the observed inflow onto the NWP precipitation, and subsequently applying post-processing techniques from Ensemble Model Output Statistics.

We show that the inflow forecasts hold fair skill relative to climatology up to six weeks ahead. Average inflow forecasts for the period 1-35 days ahead hold good skill relative to climatology, and are comparably skilful to an average inflow forecast for the period 8-14 days ahead. Forecasts are more skilful in winter than summer, which is consistent with physical teleconnections from the tropics that operate on sub-seasonal timescales.

We further apply a stylised cost model that demonstrates the potential value of these forecasts through improved water management. The stylised cost model indicates that the sub-seasonal probabilistic inflow forecast are sufficiently reliable to improve decision making and deliver added value across all forecast horizons up to six weeks ahead, relative to climatological or deterministic forecasts.

How to cite: Graham, R. M., Browell, J., Bertram, D., and White, C. J.: Developing a Sub-seasonal Forecasting System for Hydropower Reservoirs in Scotland , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7252, https://doi.org/10.5194/egusphere-egu21-7252, 2021.

The Copernicus Climate Change Service aims at facilitating the emergence of a downstream market of climate services with the ultimate goal of supporting the development of a climate-smart society. Central to this vision is the free and unrestricted distribution of high-quality climate data through the Climate Data Store [1], with seasonal meteorological predictions among them. Within this unique and challenging framework, ULYSSES [2] will provide the first "seamless'' multi-model hydrological seasonal prediction system, with a global coverage at a spatial resolution of 0.1° The ULYSSES modeling chain is based on the successfully tested EDgE proof of concept [3] using four state-of-the-art hydrological models (Jules, HTESSEL, mHM, and PCR-GLOBWB). A unique feature of this production chain consists of using the same land surface datasets (e.g. DEM, soil properties) with identical spatio-temporal resolutions and forecast inputs for all HMs, and the same river routing scheme (i.e., the multi-scale routing model mRM).

The initial conditions of the production chain will be based on ERA5-Land dataset and the seasonal forecasts will be driven by a 25-member ensemble generated by the ECMWF-SEAS5 model. ULYSSES aims at generating six essential hydrological variables: snow-water equivalent, snowmelt, evapotranspiration, soil moisture, total runoff, and streamflow with a lead-time of up to six months.  The seasonal forecast was verified at 250+ gauges distributred in all continents during the hind-casting period from 1993 to 2019. The operational forecasting period —in testing phase— started in January 2021 and be extended through until July 2021.  The first operational ULYSSES forecast will be made available by the 10th of each month starting in January 2021.

All input data sets (ERA5-Land), seasonal forecasts (SEAS5) and ULYSSES outputs will be made available in the Copernicus Climate Data Store [1] and will be open access. We aim to engage institutions and researchers around the world that are willing to evaluate the forecasts model performance, with the aim of improving the system in the future. In this talk, the modelling chain concept, model setup and verification of initial results will be presented.

• [1] https://cds.climate.copernicus.eu
• [2] https://www.ufz.de/ulysses
• [3] https://doi.org/10.1175/BAMS-D-17-0274.1

How to cite: Samaniego, L., Thober, S., Kelbling, M., Schweppe, R., Rakovec, O., Shrestha, P., Martinez-de la Torre, A., Blyth, E. M., Smith, K. A., Rees, G., Fry, M., Sutanudjaja, E., Wanders, N., Bierkens, M. F., and van Beek, R.: ULYSSES: a system for global multi-model hydrological seasonal predictions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2340, https://doi.org/10.5194/egusphere-egu21-2340, 2021.

The increasing availability and reliability of river flow forecasts has coincided with forecast-guided operating arrangements that seek to enhance the benefits of water reservoir operations. In the western United States, where winter snowpack depth indicates spring and summer flow, water release decisions may be informed with estimates of incoming water out to weeks and months ahead. Understanding how such medium to long-range forecasts affect the spatial and temporal distribution of water availability at across large basins and regions is necessary to accurately drive and constrain expansive, water-dependent system simulations, such grid-scale electrical power dispatch. This presentation will encompass recent research activities aimed at characterizing the use of forecasts and simulating their effects in large scale hydrological and water management models. Results show that forecasts must be included in reservoir models to accurately reproduce release decisions. Implementing these operations in large-scale hydrological models remains a significant challenge to be tackled by the community. Problems of simulated streamflow bias and lack of accurate data describing water withdrawal and consumption means must be addressed to harness realistic, forecast-based operations in large-scale water management models.

How to cite: Turner, S. and Voisin, N.: Understanding uses and implications of water forecasting in multisectoral research, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-920, https://doi.org/10.5194/egusphere-egu21-920, 2021.

The evaluation of inflow forecast quality and value is essential in hydroelectric reservoir management. Forecast value can be quantified by the economic gains obtained when optimizing hydroelectric reservoir operations informed by weather and hydrological forecasts. This study [1] investigates the impact of 7-day streamflow forecasts on the optimal management of hydroelectric reservoirs and the associated economic gains. Flows from ten catchments in France are synthetically generated over a 4-year period to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts define the inflows to ten hydroelectric reservoirs, which are conceptually parametrized. Each reservoir is associated to a downstream power plant with yield 1 which produces electricity valued with a price signal. The system is modelled using linear programming. Relationships between forecast quality and economic value (hydropower revenue) show that forecasts with a recurrent positive bias (overestimation) and low accuracy generate the highest economic losses when compared to the reference management system where forecasts are equal to observed inflows. The smallest losses are observed for forecast systems with under-dispersion reliability bias, while forecast systems with negative bias (underestimation) show intermediate losses. Overall, the losses (which amount to millions of Euros) represent approximately 1% to 3% of the revenue over the study period. Besides revenue, the forecast quality also impacts spillage, stock evolution, production hours and production rates. For instance, forecasting systems that present a positive bias result in a tendency of operations to keep the storage at lower levels so that the reservoir can be able to handle the high volumes expected. This impacts the optimal placement of production at the best hours (i.e. when prices are higher) and the opportunity to produce electricity at higher production rates. Our study showed that when using biased forecasting systems, hydropower production is not only planned during more hours at lower rates but also at hours with lower median prices of electricity. The modelling approaches adopted in our study are certainly far from representing all the complexity of hydropower management under uncertainty. However, they proved to be adapted to obtaining the first orders of magnitude of the value of inflow forecasts in elementary situations.

[1] https://doi.org/10.5194/hess-2020-410

How to cite: Ramos, M.-H., Cassagnole, M., Zalachori, I., Thirel, G., Garçon, R., Gailhard, J., and Ouillon, T.: Understanding how hydrological forecast quality impacts the management of hydroelectric reservoirs, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14532, https://doi.org/10.5194/egusphere-egu21-14532, 2021.

Given the wide range of institutional and market contexts in which hydroelectric dams are operated, determining the value added from improvements in hydrologic forecasts is a challenge. Many previous examples of hydrologic forecasts being used to optimize hydropower production strategies at dams focus on a single reservoir system or watershed, with a key assumption that the marginal value of hydropower production is exogenously-defined (dams are ‘price takers’ in markets for electricity that exhibit no market power). In some cases, this may accurately reflect current institutional boundaries and decision making processes. However, with increased attention being paid to how more coordinated grid management strategies, including management of hydropower assets, could facilitate deep integration of renewable energy, it is critical to understand how the use of improved hydrologic forecasts could produce wider grid-scale benefits, including  lower costs and emissions. In this study, we quantify the value of streamflow forecasts to a centralized power system operator in charge of coordinating sub-weekly operations of hydropower assets, using the Western U.S. as a case study. We propagate flow forecasts through realistic models of reservoir operations and models of bulk power systems/wholesale electricity markets. Our results shed light on how the value of flow forecasts to grid operations can vary across regions and power systems. They also highlight the potential for conflicts between firm-specific objectives (profit maximization) and system-wide objectives (minimization of costs and emissions) when determining value added from hydrologic forecasts.  

How to cite: Kern, J., Voisin, N., Turner, S., Yan, H., and Oikonomou, K.: Valuing Streamflow Forecasts in Centrally Controlled Power Systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1406, https://doi.org/10.5194/egusphere-egu21-1406, 2021.

Ensuring the resilience and security of water supply will be one of the most significant future challenges facing water utilities worldwide given potential impacts of climate change and population growth. The development of new water resource options is costly, hence the need to develop techniques to maximize the potential and resilience of current water resource assets without compromising environmental regulations. The availability of real-time meteorological and hydrological data combined with real-time forecasting techniques provide a potential to increase water abstraction volumes without compromising environmental regulations and reduce operational costs. This paper presents an approach for managing surface water abstraction utilizing real-time flow forecasting techniques, coupled with a water resource model and Genetic Algorithm optimization. To evaluate this approach, a retrospective analysis of a historical period 2017/2018 is conducted, comparing historic water abstractions, reservoir water level data, flows downstream abstraction point and energy costs at a case study abstraction site within a catchment in the UK with corresponding simulations based on forecasted flows. Simulation results show that on average 25 Ml/day of additional water could have been abstracted using the forecasting led scheme, without compromising environmental regulations. The results show that rapid declines in reservoir levels during low flow periods can be avoided and energy costs can be significantly reduced (by approximately £ 0.35M / annum) using the proposed approach. This study demonstrates the benefits of utilizing real-time flow forecasting and flexible water pumping schedules to maximize the value of existing surface water resources, in some cases this may reduce the need for significant investment to increase the resilience of supply. Further work will seek to extend the approach to enable optimization of pumping and water release operations in multi reservoir systems.

How to cite: Yassin, M., Maher, K., Speight, V., and Shucksmith, J.: Optimizing surface water pumping operations utilizing hydrological forecasting and a genetic algorithm, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-844, https://doi.org/10.5194/egusphere-egu21-844, 2021.

The Requena-Utiel aquifer in the Jucar River Basin (Mediterranean Spain) is mined mainly for the irrigation of vineyards (Denominación de Origen Utiel-Requena), and some olive and nut trees. It has been recently declared as in bad quantitative status by the Jucar River Basin Agency (Confederación Hidrográfica del Júcar, CHJ). Among the measures taken to control water abstraction, a pumping cap for the irrigation season (May-September) has been agreed between the CHJ and the groundwater user association. This limit depends on the cumulative precipitation from December to April (classifying the year in wet, normal or dry), although that irrigation amount is in any case below the crop requirements. Consequently, predicting the type of year beforehand is a piece of valuable information for the water users in order to optimally schedule groundwater pumping and foresee crop production.

This study analyses the ability of seasonal meteorological forecasts from the Copernicus Climate Change Service (C3S) to anticipate the type of year in the agricultural areas of the Requena Utiel aquifer considering different periods ahead. The following seasonal forecasting services were used: ECMWF SEAS5, UKMO GloSEA5, MétéoFrance System, DWD GCFS, and CMCC SPS. Seasonal forecasts issued between November 1^st and April 1^st were downloaded and post-processed using a month-dependent linear scaling against historical records. Once post-processed, the skill of seasonal forecasts to predict the type of year has been evaluated for the 1995-2015 period, depending on the anticipation time.

Results show that, on a broader view, the type of year cannot be safely anticipated before April 1^st. However, we have identified that, for particular types of year and forecasting services, the anticipation time can be enlarged (e.g predicting wet years in December). Furthermore, we have found a direct relationship between the strength of the signal (number of ensemble members that predict the same type of year) and the forecasting skill, meaning that seasonal forecasts showing a strong signal, if properly identified, could offer valuable information months in advance to the beginning of the irrigation season.

Acknowledgements:

This study has received funding from the eGROUNDWATER project (GA n. 1921), part of the PRIMA programme supported by the European Union’s Horizon 2020 research and innovation programme. It has been also supported by the ADAPTAMED project (RTI2018-101483-B-I00), funded by the Ministerio de Economia y Competitividad (MINECO) of Spain and with EU FEDER funds.

How to cite: Rubio-Martin, A., Macian-Sorribes, H., Lopez-Perez, E., Garcia-Prats, A., Manzano-Juarez, J., Jimenez-Bello, M. A., and Pulido-Velazquez, M.: Forecasting groundwater pumping cap in an overexploited Mediterranean aquifer using seasonal meteorological forecasts from Copernicus Climate Change Service, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12273, https://doi.org/10.5194/egusphere-egu21-12273, 2021.

An emerging literature evaluates the water management benefits of hydroclimatic forecasts with timescales of a few days to several months ahead. These studies rely on existing forecast products, which they compare to one another and to baseline scenarios, such as perfect forecast or usual climate or streamflow conditions. Results compare the different products and baselines, both in terms of forecast skill and in terms of value for water management. Yet, the means to systematically explore the link between forecast skill and value (e.g., in terms of water supply reliability or hydropower production) are hampered by the lack of techniques to generate synthetic forecasts that 1) are realistic in that they present similar statistical properties to existing products, and 2) foster productive two-ways conversations between the analysts and academics who propose new products and those who use them to inform decision-making, so they can determine where to focus further product development efforts.

This work proposes a methodology for generating forecasts from an existing product and existing hydroclimatic records (rainfall, temperature, streamflow…). It perfects and extends a recent synthetic forecast generation technique that deterministically generates a forecast for a point in the future with the desired bias and accuracy, using a linear combination of the quantity to predict with a predictor. It perfects it by proposing a methodology to generate a family of forecasts with desired skill and bias, for several of the most common skill measures, including mean absolute error and (root) mean square error. Generated synthetic forecasts are therefore based on existing products and retain their statistical properties while presenting improved skill. The skill improvement can apply to the whole forecast or only to targeted conditions, e.g., drought or flood conditions, or forecasts during and for a certain period of the year. This opens the doors to systematic exploration of the benefits of marginal forecast improvements. The technique is also extended to ensemble (or probabilistic) forecasts, to allow for generating synthetic ensembles with targeted improvements to the continuous ranked probability skill score (CRPSS).

How to cite: Rougé, C.: Generating families of synthetic forecasts of different skills from an existing forecast product, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12367, https://doi.org/10.5194/egusphere-egu21-12367, 2021.

Forecast informed operations hold great promise as a soft pathway to improve water resources system performance. Generating synthetic forecasts of hydro-meteorological variables is crucial for robust validation of this approach, as advanced numerical weather prediction hindcasts have a limited timespan (10-40 years) that is insufficient for assessing risk related to forecast-informed operations during extreme events. We develop a generalized error model for synthetic forecast generation that is applicable to a range of forecasted variables used in water resources management. The approach samples from the distribution of forecast errors over the available hindcast period and adds them to long records of observed data to generate synthetic forecasts. The approach utilizes the flexible Skew Generalized Error Distribution (SGED) to model marginal distributions of forecast errors that can exhibit heteroskedastic, auto-correlated, and non-Gaussian behavior. An empirical copula is used to capture covariance between variables and forecast lead times and across space. We demonstrate the method for medium-range forecasts across Northern California in two case studies for 1) streamflow and 2) temperature and precipitation, which are based on hindcasts from operational CONUS hydrologic and meteorological forecast models. The case studies highlight the flexibility of the model and its ability to emulate space-time structures in forecasts at scales critical for flood management. The proposed method is generalizable to other locations and computationally efficient, enabling fast generation of long synthetic forecast ensembles that are appropriate for the design and testing of forecast informed policy or characterization of forecast uncertainty for water resources risk analysis.

How to cite: Brodeur, Z. and Steinschneider, S.: A generalized approach to generate synthetic short-to-medium range hydro-meteorological forecasts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5852, https://doi.org/10.5194/egusphere-egu21-5852, 2021.

According to the Water Law of the Republic of Slovenia anyone who wants to use water resources in addition to the general use (e.g. drinking, swimming and other recreational uses) needs to acquire a water right. Approving the water rights is in principal based on two conditions: (i) the discharge downstream from the withdrawal should be at least equal to ecologically acceptable flow and (ii) the withdrawal of the water should not influence the natural conditions or other already present uses and needs of water resources. Through the development of the system’s basis we have focused on the first condition, the hydrologically available water resources.

The information about the amount of the water in a river or a stream is provided by the discharge measurements, performed at locations of the water gauging stations by the Slovenian Environmental agency (ARSO). However, when granting a water permit, the location of certain water withdrawal can be anywhere along the watercourse. Therefore, we have tested seven advanced statistical models to connect characteristic discharges (mean and mean minimal discharge for a selected 30-year period) at measured points with attribute data describing the properties of the corresponding catchment. The 49 attribute values were gathered through analysis of spatial data in GIS environment and provided information about precipitation, temperature, geological structure, land use and water use in the area. According to the comparison of models performance we have selected the neural networks. They were used to estimate characteristic discharges in 340 selected points on the water courses all over the country. These values provide the basis for calculation of the ecologically acceptable flow and the amount of discharge available for use under certain terms. In addition, for the selected points (340 points) also the natural (reference) discharge was estimated as a discharge which would be observed without any water use present upstream. Simulations of natural discharge situation were performed for various scenarios and multiple selected test cases. The estimated differences between natural and measured discharge for selected points was than generalized for all the other points using the hierarchical clustering approach. So far the basic information about the amount of water available for use on watercourses with basins larger than 10 km² was estimated. However, the project is ongoing with the focus on improving the models, including the complex interactions between surface waters and groundwaters, and taking into account the vulnerability of the natural environment and ecosystem services they provide as well as sectoral needs.

How to cite: Šantl, S., Rojnik, A., Javornik, L., Rozman, D., and Zabret, K.: Decision support system for evaluation of available surface water resources for use in Slovenia – development of the basis , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8281, https://doi.org/10.5194/egusphere-egu21-8281, 2021.

During the past three decades, the over-growing population of the south-east Asian countries is becoming a threat to the available potential water sources. This region also includes many developed as well as developing countries including Korea, China, Japan and India, and the higher rate of GDP growth also enhanced the living standard of people. As a result, the water scarcity has been increasing in various mega-cities of the region. Here, we present the assessment of grid-scale domestic water demand of each country by evaluating the gridded Domestic Structural Water Intensity (DSWI) over a period of 1995-2015 at a spatial resolution of 0.5°. We estimated yearly grid-scale DSWI with using the past economic development based on GDP and the population. Considering the gridded water demand, we assessed the vulnerability of major river basins of the region and the few cities having more than one million population. A few mega-cities in India located in arid and semi-arid regions of the river basins are already experiencing water stress. Developing such gridded dataset will give a better shape to project the future water demand by integrating the datasets within a water demand module of any land surface models.

Acknowledgements

This work was supported by a grant from the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (2020R1A2C2007670).

How to cite: Panda, M. R. and Kim, Y.: Estimating Gridded Domestic Water Demand and Assessing its Vulnerability over South-East Asian Countries, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7630, https://doi.org/10.5194/egusphere-egu21-7630, 2021.

Flooding due to sudden release from a hydropower dam during monsoon is becoming a serious concern for downstream locality, especially when there is lack of coordination between the dam authority and the Disaster Management Authority (DMA) at downstream. For hilly river, a disastrous flash flood is generally caused by short duration high intensity precipitation and a pondage hydropower project cannot attenuate such flood. Generally, reservoir simulation/optimization for a hydropower project is carried out on monthly, ten-daily or at best on daily basis to determine the best operating policy and to analyze impact of such operation on the flow scenario and therefore, in conventional analysis such flash flood event goes un-noticed. A detailed investigation of the downstream flooding is required before the construction of any hydropower project with at least on hourly basis to get insight into the impact of such inflow at downstream. Non-availability of short duration precipitation/flow data in interior project area, particularly in developing country hinder such analysis. Need and scope of such analysis is demonstrated by using a typical flow hydrograph of 48 hours, having two flood peaks, as inflow to the Lower Subansiri Hydroelectric Project (LSHP). The project is located in the Subansiri River, the largest tributary of the Brahmaputra River in India. Two operating policies; i) Standard Operating Policy (SOP) and ii) Dynamic Programming (DP) generated operating policy have been tested and both the polices have generated similar hourly flow time series of total reservoir outflow (spill + Release). These reservoir operation models have been coupled with the hydrodynamic model to route the hourly reservoir outflow from LSHP to a flood prone area located 13Km downstream of it. Post dam flood scenario thus generated is compared with the pre dam flood scenario by routing the same inflow hydrograph without considering the dam. As the river has an embankment, and flooding occurs only when the embankment fails, a specified water level at the downstream section has been considered as critical for flooding for the purpose of a comparative study.  For the considered inflow hydrograph, it is observed that the flood magnitude is not increased by the action of dam operation rather peaks get slightly attenuated. However, in natural condition without dam, flood rises gradually providing prior information to the locality and providing sufficient time for completing pre-disaster actions based on experience. With inclusion of dam, peak flow rises vary rapidly from a very low flow without showing any indication of flood beforehand and thus flood becomes more disastrous. Sudden fluctuation of water level can also cause failure of river bank and progressive bank failure can eventually cause the embankment to fail. The analysis has shown the possible impact of hydel project with more clarity to help disaster manager prepare mitigation measures in an informed way.

How to cite: Devi, D., Baruah, A., and Sarma, A. K.: Characterizing Dam Induced Flood at Downstream of a Hydel Project , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8757, https://doi.org/10.5194/egusphere-egu21-8757, 2021.

With more than 200 dams currently under construction in the Sub-Saharan region, hydropower is expected to dominate the African renewable energy market in the coming decades. Even though the construction of new dams has been widely recognized as a key factor in promoting energy security, damming rivers also augments the volume of stagnant water, inevitably enhancing the transmission of malaria by creating new vector breeding habitats. The interdependence between large dams and malaria transmission constitutes an extremely critical public health challenge in Africa. Nowadays, managing drawdown rates into reservoir operation as a malaria control measure appears a viable solution to reduce the spread of the virus near large reservoirs, notwithstanding undesirable outcomes in terms of hydropower generation. In this regard, recent technological developments in the field of floating solar photovoltaic installations open the path for flexible hydropower operation by boosting photovoltaic energy generation using the same electricity transmission infrastructure. The aim of this study is to propose an integrated framework, where the optimal floating solar sizing and reservoir operations are jointly designed for minimizing malaria diffusion without compromising the ability of the energy sector to fulfill energy demands. The framework employs Evolutionary Multiobjective Direct Policy Search into a novel approach to floating solar photovoltaic size planning, which internalizes the operation design problem. The potential of the proposed framework is tested in the Zambezi river basin, where the Kariba dam is mainly operated for hydropower production, with considerable negative health effects in the proximity of the reservoir. Numerical results show that design alternatives coupling reservoir operation with floating solar photovoltaic largely dominates pure management solutions in terms of malaria spread and energy generation. Besides, the relatively limited (from 0.2 to 1.5% of the total lake area) optimal extent of the photovoltaic plant highlights the potential economic benefits of increasing the penetration of this technology in Sub-Saharan Africa, with capital costs balanced by boosted energy income within the first seven years from the initial investment.

How to cite: Amaranto, A. and Castelletti, A.: Joint design of floating solar plant and dam operating policies for waterborne epidemics control: an assessment on the Kariba dam, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2825, https://doi.org/10.5194/egusphere-egu21-2825, 2021.

Urban land expansion is expected for our changing world, which unmitigated will result in increased flooding and nutrient exports that already wreak havoc on the wellbeing of coupled human-natural systems worldwide. Reforestation of urbanized catchments is one green infrastructure strategy to reduce stormwater volumes and nutrient exports. Reforestation designs must balance the benefits of flood flow reduction against the costs of implementation and the chance to exacerbate droughts via reduction in recharge that supplies low flows. Optimal locations and numbers of trees depend on the spatial distribution of runoff and streamflow in a catchment; however, calibration data are often only available at the catchment outlet. Equifinal model parameterizations for the outlet can result in uncertainty in the locations and magnitudes of streamflows across the catchment, which can lead to different optimal reforestation designs for different parameterizations.

Multi-objective robust optimization (MORO) has been proposed to discover reforestation designs that are robust to such parametric model uncertainty. However, it has not been shown that this actually results in better decisions than optimizing to a single, most likely parameter set, which would be less computationally expensive. In this work, the utility of MORO is assessed by comparing reforestation designs optimized using these two approaches with reforestation designs optimized to a synthetic true set of hydrologic model parameters. The spatially-distributed RHESSys ecohydrological model is employed for this study of a suburban-forested catchment in Baltimore County, Maryland, USA. Calibration of the model’s critical parameters is completed using a Bayesian framework to estimate the joint posterior distribution of the parameters. The Bayesian framework estimates the probability that different parameterizations generated the synthetic streamflow data, allowing the MORO process to evaluate reforestation portfolios across a probability-weighted sample of parameter sets in search of solutions that are robust to this uncertainty.

Reforestation portfolios are designed to minimize flooding, low flow intensity, and construction costs (number of trees). Comparing the Pareto front obtained from using MORO with the Pareto fronts obtained from optimizing to the estimated maximum a posteriori (MAP) parameter set and the synthetic true parameter set, we find that MORO solutions are closer to the synthetic solutions than are MAP solutions. This illustrates the value of considering parametric uncertainty in designing robust water systems despite the additional computational cost.

How to cite: Smith, J., Lin, L., Quinn, J., and Band, L.: Multi-objective Optimization of Catchment Reforestation Robust to Uncertainty in Bayesian-Calibrated Watershed Model Parameters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6234, https://doi.org/10.5194/egusphere-egu21-6234, 2021.