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Probabilistic seasonal forecasts are important for many water-intensive activities requiring long-term planning. Among the different techniques used for seasonal forecasting, the ensemble streamflow prediction (ESP) approach has long been employed due to the singular dependence on past meteorological records. The Swedish Meteorological and Hydrological Institute is currently extending the use of long-range forecasts within its operational warning service, which requires a thorough analysis of the suitability and applicability of different methods with the national S-HYPE hydrological model. To this end, we aim to evaluate the skill of ESP forecasts over 39 493 catchments in Sweden, understand their spatio-temporal patterns, and explore the main hydrological processes driving forecast skill. We found that ESP forecasts are generally skilful for most of the country up to 3 months into the future but that large spatio-temporal variations exist. Forecasts are most skilful during the winter months in northern Sweden, except for the highly regulated hydropower-producing rivers. The relationships between forecast skill and 15 different hydrological signatures show that forecasts are most skilful for slow-reacting, baseflow-dominated catchments and least skilful for flashy catchments. Finally, we show that forecast skill patterns can be spatially clustered in seven unique regions with similar hydrological behaviour. Overall, these results contribute to identifying in which areas and seasons and how long into the future ESP hydrological forecasts provide an added value, not only for the national forecasting and warning service, but also, most importantly, for guiding decision-making in critical services such as hydropower management and risk reduction.
Marc Girons Lopez; Louise Crochemore; Ilias G. Pechlivanidis. Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden. Hydrology and Earth System Sciences 2021, 25, 1189 -1209.
AMA StyleMarc Girons Lopez, Louise Crochemore, Ilias G. Pechlivanidis. Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden. Hydrology and Earth System Sciences. 2021; 25 (3):1189-1209.
Chicago/Turabian StyleMarc Girons Lopez; Louise Crochemore; Ilias G. Pechlivanidis. 2021. "Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden." Hydrology and Earth System Sciences 25, no. 3: 1189-1209.
The functionality of a renewable electricity system in Europe depends on long-term climate variations, uneven spatiotemporal distribution of renewable energy, and constraints of storage and electric transmission. In particular, hydropower offers a large capacity for energy storage and production flexibility, but only stands for a minor part of the total energy potential. Here we explored the spatial and temporal power variance of a combined system consisting of wind-, solar- and hydropower availability for a 35-year period based on historical hydro-meteorological data from large parts of Europe. A spectral analysis of these historical time-series shows that spatiotemporal coordination within the power system can potentially contribute with a “virtual” energy storage capacity that is many times higher than the actual energy storage capacity contained in the existing hydropower reservoirs in Europe. Such virtual energy storage capacity implies reduced water storage demand, hence, indirectly contributes to reduced constraints of the food-water-energy nexus also in a wider system perspective. This study focused on the theoretical maximum potential for virtual energy storage, but the feasibility of this potential is limited by the uncertainty associated with production optimization and the meteorologic forecasts of future energy availability.
Anders Wörman; Daniela Mewes; Joakim Riml; Cintia Bertacchi-Uvo; Ilias Pechlivanidis. Virtual energy storage-gain due to spatiotemporal coordination of wind-, solar- and hydropower over Europe. 2021, 1 .
AMA StyleAnders Wörman, Daniela Mewes, Joakim Riml, Cintia Bertacchi-Uvo, Ilias Pechlivanidis. Virtual energy storage-gain due to spatiotemporal coordination of wind-, solar- and hydropower over Europe. . 2021; ():1.
Chicago/Turabian StyleAnders Wörman; Daniela Mewes; Joakim Riml; Cintia Bertacchi-Uvo; Ilias Pechlivanidis. 2021. "Virtual energy storage-gain due to spatiotemporal coordination of wind-, solar- and hydropower over Europe." , no. : 1.
The EC HYPOS (HYdro-POwer-Suite) project (https://hypos-project.eu/) has the main goal of assessing the environmental impact of existing and future hydropower systems. The project will provide a suite of data analysis applications which integrates Earth Observation (EO) technologies and hydrological modelling. These include an online Decision Support Tool (DST) for investment planning and monitoring, as well as a subscription portal combining satellite data over time, current measurements and detailed estimates for present and near future assessments. A dedicated analysis on the “blue footprint” (i.e. the amount of water used to produce a service) of reservoirs is included for addressing sustainable monitoring solutions. Such analysis comprises the evaluation of the climate change effects on reservoirs management and hydropower production. For instance, extreme weather events like short-term heavy precipitations are connected with flooding and transport of large amounts of sediments in dammed reservoirs, with critical consequences for their management. Similarly, global warming can heat the surface of water bodies and induce higher evaporation rates, thus decreasing the amount of water available for energy production.
In this study we present the first products from HYPOS project. These products are representative of what can be generated within the DST using elaboration techniques of EO data. Gridded products of water quality parameters (e.g. water turbidity, Chlorophyll-a concentration, suspended sediments concentration) are generated for the test sites of the project, which are small dammed reservoirs located in Switzerland, France, Albania and Georgia. These products are obtained using the Modular Inversion and Processing System (MIP), a sensor independent image processing chain based on radiative transfer models, which works in a multi-layer system, solving the light transfer in the atmosphere, at the water surface and inside the waterbody.
For the assessment of the “blue footprint” of a reservoir, the water loss due to evaporation is computed by applying a consolidated mass transfer evaporation method to EO data. The resulting evaporation rates are first compared with the outputs of semi-automatic evapotranspiration EO-based models (e.g. SEBAL), and then with the estimates obtained from two different numerical models: a hydrological model (E-Hype) and a 3D hydrodynamic model (Delft3D). The key parameters influencing water evaporation rates, their behavior and the issues related to each approach are analyzed. The first comparison results are made for lake Garda, where a complete set of data is available for the production of evaporation maps.
Erica Matta; Mariano Bresciani; Claudia Giardino; Marina Amadori; Thomas Heege; Karin Schenk; Kim Knauer; Alena Bartosovar; Ilias Pechlivanidis; Marcelo Leite Ribeiro; Marina Launay; José Pedro Matos; Declan Kelleher; Nils Rüther; Kordula Valerie Anne Schwarzwälder. The HYPOS project as a support to the hydroelectric sector. 2021, 1 .
AMA StyleErica Matta, Mariano Bresciani, Claudia Giardino, Marina Amadori, Thomas Heege, Karin Schenk, Kim Knauer, Alena Bartosovar, Ilias Pechlivanidis, Marcelo Leite Ribeiro, Marina Launay, José Pedro Matos, Declan Kelleher, Nils Rüther, Kordula Valerie Anne Schwarzwälder. The HYPOS project as a support to the hydroelectric sector. . 2021; ():1.
Chicago/Turabian StyleErica Matta; Mariano Bresciani; Claudia Giardino; Marina Amadori; Thomas Heege; Karin Schenk; Kim Knauer; Alena Bartosovar; Ilias Pechlivanidis; Marcelo Leite Ribeiro; Marina Launay; José Pedro Matos; Declan Kelleher; Nils Rüther; Kordula Valerie Anne Schwarzwälder. 2021. "The HYPOS project as a support to the hydroelectric sector." , no. : 1.
Multipurpose water systems are subject to complex trade-offs among competing water uses, which could eventually have a significant potential for conflict. Hence these interlinkages should be properly identified to estimate the impact of changing allocation rules and avoid the trigger of undesirable outcomes. Concretely, forecast-based water allocation requires to assess the outputs of hydrometeorological forecasting within a sectoral context (e.g. urban, agriculture, energy) and contrast it with the current statu-quo. In this regard, stochastic hydro-economic modelling is an efficient approach to compare multipurpose water allocation rules using a common monetary unit, explicitly considering inflow uncertainty and exploiting the potential of hydrometeorological forecasting systems.
Here, we analyse the economic impacts caused by the implementation of forecast-based allocation rules on the Jucar river system in Spain. The economic revenues are calculated by combining Stochastic Dual Dynamic Programming (SDDP) with Model Predictive Control (MPC) forced with hydrometeorological forecasts. The following forecasting systems have been considered: (1) the current system operating rules forced by historical observations, (2) SMHI’s pan-European E-HYPE hydrological forecasting system forced with bias-adjusted ECMWF System 4 seasonal meteorological forecasts and post-processed using fuzzy logic to adjust forecasts to the local hydrological conditions, (3) five seasonal meteorological forecasting systems from the Copernicus Climate Change Service (ECMWF SEAS5, UKMO GloSEA5, MétéoFrance System 6, DWD GCFS and CMCC SPS3), bias-adjusted using linear scaling and further combined with locally-adjusted hydrological models, and (4) an ensemble system based on local observations of past river discharge.
Results show that the forecast-based allocation rules derived from SDDP and MPC improve the revenues obtained by the current policies forced by historical observations (which is the best scenario achievable without modifying the current operation). This indicates that combining stochastic modelling with seasonal forecasts improves water allocation performance without requiring a particular forecasting system. Although the agricultural benefits depend on the forecasting system considered, hydropower’s increases of economic returns are almost the same regardless of the forecast product. This means that hydropower revenues are mainly driven by the fact that forecast-based policies are adopted instead of using a particular forecasting service. Our results show that both uses (i.e. agriculture and hydropower) can simultaneously benefit from forecast-based operating rules, offering opportunities for collaboration to increase the regional water use efficiency.
Acknowledgements:
This study has been 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, and co-funded by the postdoctoral program of Universitat Politècnica de València (UPV)
Hector Macian-Sorribes; Patricia Marcos-Garcia; Ilias Pechlivanidis; Louise Crochemore; Manuel Pulido-Velazquez. Assessing the Water-Energy-Food nexus on the Jucar river system using hydrometeorological forecasting and stochastic hydro-economic programming. 2021, 1 .
AMA StyleHector Macian-Sorribes, Patricia Marcos-Garcia, Ilias Pechlivanidis, Louise Crochemore, Manuel Pulido-Velazquez. Assessing the Water-Energy-Food nexus on the Jucar river system using hydrometeorological forecasting and stochastic hydro-economic programming. . 2021; ():1.
Chicago/Turabian StyleHector Macian-Sorribes; Patricia Marcos-Garcia; Ilias Pechlivanidis; Louise Crochemore; Manuel Pulido-Velazquez. 2021. "Assessing the Water-Energy-Food nexus on the Jucar river system using hydrometeorological forecasting and stochastic hydro-economic programming." , no. : 1.
The hydrological forecasting on seasonal (up to 7 months ahead) timescales is needed for decision-making in the hydropower sector. Being one of the vital influencing factors on hydro-production, a lot of development in dynamical forecasting at seasonal timescales has been done recently. However, the forecast bias still remains in different variables and consequently the skill of corresponding streamflow forecasts varies from month to month.
This study aims to explore the potential for “pattern-based” seasonal hydrological forecasts that make use of hydrological weather regimes and teleconnection indices to improve forecast skill. The work is built on the hypothesis that hydrological weather regimes and teleconnection indices can be used to select analogue years (setting an ensemble) from a record of historical precipitation and temperature data with which to force a hydrological model to generate tailored seasonal forecasts of reservoir inflows. The hydrological weather regimes have been classified based on the concept of fuzzy sets using the anomalies of daily mean sea level pressure from reanalysis data (i.e., ERA-Interim). Precipitation records, measured in the Umeälven river basin during 1981-2016 are used as local observations to optimize each fuzzy rule that describes a type of “average” variability of local climate in terms of the frequency and magnitude of precipitation events. The teleconnection indices are compiled from the Climate Prediction Center, which describe global atmospheric variability. The methodology has been applied to 84 sub-catchments across seven of the most important hydropower producing river systems in Northern Sweden. However, the performance for the Umeälven river system is of particular interest here.
Comparing to the traditional Ensemble Streamflow Prediction (ESP) method, the “pattern-based” seasonal hydrological forecasting shows a marked improvement, which is likely due to the weighted analogue-ESP approach as well as the selected analogues using the large-scale climate information described by hydrological weather regimes and teleconnection indices. The general performance of the two different approaches for selecting the analogues are similar; however, occasionally there are large differences in both the best analysis lead times and the spread of skill across the sub-catchments suggesting that those results are achieved using analogues based on different physical processes.
Wei Yang; Kean Foster; Ilias G. Pechlivanidis. Enhancement of seasonal hydrological forecasting with “pattern-based” large-scale climatology . 2021, 1 .
AMA StyleWei Yang, Kean Foster, Ilias G. Pechlivanidis. Enhancement of seasonal hydrological forecasting with “pattern-based” large-scale climatology . . 2021; ():1.
Chicago/Turabian StyleWei Yang; Kean Foster; Ilias G. Pechlivanidis. 2021. "Enhancement of seasonal hydrological forecasting with “pattern-based” large-scale climatology ." , no. : 1.
Production of a large ensemble of climate simulations suitable for impact assessments is an attempt to enhance our knowledge about the associated uncertainties in future projections. However, the actual quantification of the change in the climate and its impact relies on the ensemble of models selected, particularly given the wide availability of climatic simulations from various initiatives, i.e. CMIP5, CORDEX.
Here, we hypothesize that historical streamflow observations contain valuable information to investigate practices for the selection of climate model ensembles. We apply eight selection methods (based on democracy, diversity of GCM, diversity of RCM, maximum information minimum redundancy, best performing hindcasted climate depiction, best performing hydrological model, simple climate model averaging and reliable ensemble average) to subset an ensemble available from 16 combinations of Euro-CORDEX GCM-RCM by comparing observed to simulated streamflow shift of the Danube from a reference period (1960–1989) to an evaluation period (1990–2014). Simulations are carried out with the well-performing Upper Danube COSERO hydrological model, spanning a calibration and evaluation period of more than 100 years. Comparison against no selection shows that an informed selection of ensemble members improves the quantification of climate change impacts where methods that maintain the diversity and information content of the full ensemble are favourable. In addition, the method followed allows the assessment which individual climate models perform best, where only three of 16 models were able to correctly reproduce the direction of streamflow change in each season.
Prior to carrying out climate impact assessments, we propose splitting the long-term historical data and using it to test climate model performance, sub-selection methods, and their agreement in reproducing the indicator of interest, which further provide the expectable benchmark of near- and far-future impact assessments. This test can further be applied in multi-basin experiments to obtain a better understanding of uncertainty propagation and uncertainty reduction in hydrological impact studies.
Jens Kiesel; Philipp Stanzel; Harald Kling; Nicola Fohrer; Sonja C. Jähnig; Ilias Pechlivanidis. The more is not the merrier – an informed selection of climate model ensembles can enhance the quantification of hydrological change. 2021, 1 .
AMA StyleJens Kiesel, Philipp Stanzel, Harald Kling, Nicola Fohrer, Sonja C. Jähnig, Ilias Pechlivanidis. The more is not the merrier – an informed selection of climate model ensembles can enhance the quantification of hydrological change. . 2021; ():1.
Chicago/Turabian StyleJens Kiesel; Philipp Stanzel; Harald Kling; Nicola Fohrer; Sonja C. Jähnig; Ilias Pechlivanidis. 2021. "The more is not the merrier – an informed selection of climate model ensembles can enhance the quantification of hydrological change." , no. : 1.
The Operational Water Service of C3S (developed by the Swedish Meteorological and Hydrological Institute (SMHI)) aims to help a broad range of water managers with water allocation, flood management, ecological status and industrial water use, to adapt their strategies in order to adapt to climate variability and change. The aim is to speed up the workflow in climate-change adaptation by using seasonal hydrological forecasts and climate-impact indicators. This is done by offering an interactive web application with refined data, guidance and practical showcases to water managers across Europe. Policy makers will find a comprehensive overview for Europe with key messages and consultants can use the service for developing climate impact assessments and adaptation strategies.
The development of the current operational climate service for water management is based on the experience from two previous proof-of-concepts and will also be aligned with the hydrological model system of the Copernicus Emergency Management Service (CEMS). The service is uses data from the Climate Data Store and the operational hydrological seasonal forecasting system runs entirely in the European Centre for Medium range Weather Forecasts (ECMWF) technical environment, although developed by SMHI.
The operational Water Service of C3S will be launched during the spring of 2021, and a series of activities and user interactions will be organised to ensure that the applications developed for the service fulfil the users’ needs. Here, we present the development process of the operational service and key outcomes from co-design interactions and resulting applications. The key issues identified by the user community were: i) clear visualisation and graphical representation of skill in seasonal forecasts and confidence in climate projections, ii) need of detailed documentation and process transparency in hydrological models and production of data, iii) user guidance and tutorials are needed for better understanding of the applications, and iv) workflows and scripts for indicator production in new applications for developers of information systems.
Christiana Photiadou; Peter Berg; Denica Bozhinova; Anna Eronn; Fulco Ludwig; Maria del Pozo Garcia del Pozo Garcia; Ilias Pechlivanidis. Operational Water Service for Copernicus Climate Change Service: development at European scale. 2021, 1 .
AMA StyleChristiana Photiadou, Peter Berg, Denica Bozhinova, Anna Eronn, Fulco Ludwig, Maria del Pozo Garcia del Pozo Garcia, Ilias Pechlivanidis. Operational Water Service for Copernicus Climate Change Service: development at European scale. . 2021; ():1.
Chicago/Turabian StyleChristiana Photiadou; Peter Berg; Denica Bozhinova; Anna Eronn; Fulco Ludwig; Maria del Pozo Garcia del Pozo Garcia; Ilias Pechlivanidis. 2021. "Operational Water Service for Copernicus Climate Change Service: development at European scale." , no. : 1.
Robust information of hydrometeorological extremes is important for effective risk management, mitigation and adaptation measures by public authorities, civil and engineers dealing for example with water management. Typically, return values of certain variables, such as extreme precipitation and river discharge, are of particular interest and are modelled statistically using Extreme Value Theory (EVT). However, the estimation of these rare events based on extreme value analysis are affected by short observational data records leading to large uncertainties.
In order to overcome this limitation, we propose to use the latest seasonal meteorological prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) and seasonal hydrological forecasts generated with the pan-European E-HYPE model of the original period 1993-2015 and to extend the dataset to longer synthetic time series by pooling single forecast months to surrogate years. To ensure an independent dataset, the seasonal forecast skill is assessed in advance and months (and lead months) with positive skill are excluded. In this study, we simplify the method and work with samples of 6- and 4-month forecasts (instead of the full 7-month forecasts) depending on the statistical independency of the variables. It enables the record to be extended from the original 23 years to 3450 and 2300 surrogate years for the 6- and 4-month forecasts respectively.
Furthermore, we investigate the robustness of estimated 50- and 100-year return values for extreme precipitation and river discharge using 1-year block maxima that are fitted to the Generalized Extreme Value distribution. Surrogate sets of pooled years are randomly constructed using the Monte-Carlo approach and different sample sizes are chosen. This analysis reveals a considerable reduction in the uncertainty of all return period estimations for both variables for selected locations across Europe using a sample size of 500 years. This highlights the potential in using the ensembles of meteorological and hydrological seasonal forecasts to obtain timeseries of sufficient length and minimize the uncertainty in the extreme value analysis.
Katharina Klehmet; Peter Berg; Denica Bozhinova; Louise Crochemore; Ilias Pechlivanidis; Christiana Photiadou; Wei Yang. Robustness of precipitation and river discharge extremes in the surrogate world of seasonal forecasts. 2021, 1 .
AMA StyleKatharina Klehmet, Peter Berg, Denica Bozhinova, Louise Crochemore, Ilias Pechlivanidis, Christiana Photiadou, Wei Yang. Robustness of precipitation and river discharge extremes in the surrogate world of seasonal forecasts. . 2021; ():1.
Chicago/Turabian StyleKatharina Klehmet; Peter Berg; Denica Bozhinova; Louise Crochemore; Ilias Pechlivanidis; Christiana Photiadou; Wei Yang. 2021. "Robustness of precipitation and river discharge extremes in the surrogate world of seasonal forecasts." , no. : 1.
The Standardized Precipitation Index (SPI) is one of the most popular indices for characterizing the meteorological drought on a range of time scales. To date, SPI has been thoroughly used to monitor and predict drought in the precipitation signal and to further support early warning and climate services. While many studies focus on the performance improvement of drought models, there is to our knowledge no reference around the correct computation of SPI in a drought forecasting setting. As SPI is typically computed on the entire data set, prior to model-validation, bias is introduced to both the training and validation sets. This stems from the fact that the distribution parameters of the index are estimated using observations from the validation and test sets leading to information leakage. Here, we propose a modified calculation of SPI oriented for forecasting applications by measuring the bias introduced to the SPI values in the training set. Moreover, we propose the best practice for calculating the SPI during model-validation and encapsulate these in a drought forecasting framework. The proposed framework is further demonstrated using a 50-year data set from Sweden. Our findings suggest that the amount of bias introduced to the training sets increases with increased SPI scale, significantly affecting in some cases more than 80% of the available basins.
Konstantinos Mammas; Demetris F. Lekkas; Ilias Pechllivanidis. A framework to quantify bias for improved drought forecasting . 2021, 1 .
AMA StyleKonstantinos Mammas, Demetris F. Lekkas, Ilias Pechllivanidis. A framework to quantify bias for improved drought forecasting . . 2021; ():1.
Chicago/Turabian StyleKonstantinos Mammas; Demetris F. Lekkas; Ilias Pechllivanidis. 2021. "A framework to quantify bias for improved drought forecasting ." , no. : 1.
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.
Jude Lubega Musuuza; Louise Crochemore; Ilias G. Pechlivanidis. What is the impact of earth observation and in-situ data assimilation on seasonal hydrological forecast quality? 2021, 1 .
AMA StyleJude Lubega Musuuza, Louise Crochemore, Ilias G. Pechlivanidis. What is the impact of earth observation and in-situ data assimilation on seasonal hydrological forecast quality? . 2021; ():1.
Chicago/Turabian StyleJude Lubega Musuuza; Louise Crochemore; Ilias G. Pechlivanidis. 2021. "What is the impact of earth observation and in-situ data assimilation on seasonal hydrological forecast quality?" , no. : 1.
The scientific community has made significant progress towards improving the skill of hydrological forecasts; however, most investigations have normally been conducted at single or in a limited number of catchments. Such an approach is indeed valuable for detailed process investigation and therefore to understand the local conditions that affect forecast skill, but it is limited when it comes to scaling up the underlying hydrometeorological hypotheses. To advance knowledge on the drivers that control the quality and skill of hydrological forecasts, much can be gained by comparative analyses and from the availability of statistically significant samples. Large-scale modelling (at national, continental or global scales) can complement the in-depth knowledge from single catchment modelling by encompassing many river systems that represent a breadth of physiographic and climatic conditions. In addition to large sample sizes which cover a gradient in terms of climatology, scale and hydrological regime, the use of machine learning techniques can contribute to the identification of emerging spatiotemporal patterns leading to forecast skill attribution to different regional physiographic characteristics.
Here, we draw on two seasonal hydrological forecast skill investigations that were conducted at the national and continental scales, providing results for more than 36,000 basins in Sweden and Europe. Due to the large generated samples, we are capable of demonstrating that the quality of seasonal streamflow forecasts can be clustered and regionalized, based on a priori knowledge of the local hydroclimatic conditions. We show that the quality of seasonal streamflow forecasts is linked to physiographic and hydroclimatic descriptors, and that the relative importance of these descriptors varies with initialization month and lead time. In our samples, hydrological similarity, temperature, precipitation, evaporative index, and precipitation forecast biases are strongly linked to the quality of streamflow forecasts. This way, while seasonal river flow can generally be well predicted in river systems with slow hydrological responses, predictability tends to be poor in cold and semiarid climates in which river systems respond immediately to precipitation signals.
Ilias Pechlivanidis; Louise Crochemore; Marc Girons Lopez. Why is large sample hydrology important in hydrological forecasting? . 2021, 1 .
AMA StyleIlias Pechlivanidis, Louise Crochemore, Marc Girons Lopez. Why is large sample hydrology important in hydrological forecasting? . . 2021; ():1.
Chicago/Turabian StyleIlias Pechlivanidis; Louise Crochemore; Marc Girons Lopez. 2021. "Why is large sample hydrology important in hydrological forecasting? ." , no. : 1.
Recent improvements in initialization procedures and representation of large-scale hydrometeorological processes have contributed to advancing the accuracy of hydroclimatic forecasts, which are progressively more skillful over seasonal and longer timescales. These forecasts are potentially valuable for informing strategic multisector decisions, including irrigated agriculture, for which they can improve crop choices and irrigation scheduling. In this operational context, the accuracy associated with the forecast system setup does not necessarily yield proportional marginal benefit, as this is also affected by how forecasts are employed by end users. This paper aims at quantifying the value of hydroclimatic forecasts in terms of potential economic benefit to the end users, which allows for the inference of a relation between gains in forecast skill and gains in end user profit. We also explore the sensitivity of this benefit to both forecast system setup and end user behavioral factors. These analyses are supported by an evaluation framework demonstrated on the Lake Como system (Italy), a regulated lake operated for flood protection and irrigation supply. Our framework relies on an integrated modeling chain composed of three building blocks: bias-adjusted seasonal meteorological forecasts are used as input to the continentally calibrated E-HYPE hydrological model; predicted lake inflows are used for conditioning the daily lake operations; and the resulting lake releases feed an agricultural model to estimate the net profit of the farmers in a downstream irrigation district. Results suggest that despite the gain in average conditions being negligible, informing the operations of Lake Como based on seasonal hydrological forecasts during intense drought episodes allows about 15 % of the farmers' profit to be gained with respect to a baseline solution not informed by any forecast. Moreover, our analysis suggests that behavioral factors capturing different perceptions of risk and uncertainty significantly impact the quantification of the benefit to the end users, whereby the estimated forecast value is potentially undermined by different levels of end user risk aversion. Lastly, our results show an intricate skill-to-value relation modulated by the underlying hydrologic conditions, which is well aligned over an exponential function in dry years, while the gains in profit are almost insensitive to the improvements in forecast skill in wet years.
Matteo Giuliani; Louise Crochemore; Ilias Pechlivanidis; Andrea Castelletti. From skill to value: isolating the influence of end user behavior on seasonal forecast assessment. Hydrology and Earth System Sciences 2020, 24, 5891 -5902.
AMA StyleMatteo Giuliani, Louise Crochemore, Ilias Pechlivanidis, Andrea Castelletti. From skill to value: isolating the influence of end user behavior on seasonal forecast assessment. Hydrology and Earth System Sciences. 2020; 24 (12):5891-5902.
Chicago/Turabian StyleMatteo Giuliani; Louise Crochemore; Ilias Pechlivanidis; Andrea Castelletti. 2020. "From skill to value: isolating the influence of end user behavior on seasonal forecast assessment." Hydrology and Earth System Sciences 24, no. 12: 5891-5902.
Probabilistic seasonal forecasts are important for many water-intensive activities requiring long-term planning. Among the different techniques used for seasonal forecasting, the Ensemble Streamflow Prediction (ESP) approach has long been employed due to the singular dependence on past meteorological records. The Swedish Meteorological and Hydrological Institute is currently extending the use of long-range forecasts within its operational warning service, which requires a thorough analysis of the suitability and applicability of different methods with the national S-HYPE hydrological model. To this end, we aim to evaluate the skill of ESP forecasts over 39,493 catchments in Sweden, understand their spatiotemporal patterns, and explore the main hydrological processes driving forecast skill. We found that ESP forecasts are generally skilful for most of the country up to 3 months into the future but that large spatiotemporal variations exist. Forecasts are most skilful during the winter months in northern Sweden, except for the highly-regulated hydropower-producing rivers. The relationships between forecast skill and 15 different hydrological signatures show that forecasts are most skilful for slowly-reacting, baseflow-dominated catchments and least skilful for flashy catchments. Finally, we show that forecast skill patterns can be spatially clustered in 7 unique regions with similar hydrological behaviour. Overall, these results contribute to identify in which areas, seasons, and how long into the future ESP hydrological forecasts provide an added value, not only for the national forecasting and warning service but, most importantly, to guide decision-making in critical services such as hydropower management and risk reduction.
Marc Girons Lopez; Louise Crochemore; Ilias G. Pechlivanidis. Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden. 2020, 2020, 1 -27.
AMA StyleMarc Girons Lopez, Louise Crochemore, Ilias G. Pechlivanidis. Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden. . 2020; 2020 ():1-27.
Chicago/Turabian StyleMarc Girons Lopez; Louise Crochemore; Ilias G. Pechlivanidis. 2020. "Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden." 2020, no. : 1-27.
The assessment of climate change and its impact relies on the ensemble of models available and/or sub-selected. However, an assessment of the validity of simulated climate change impacts is not straightforward because historical data is commonly used for bias-adjustment, to select ensemble members or to define a baseline against which impacts are compared—and, naturally, there are no observations to evaluate future projections. We hypothesize that historical streamflow observations contain valuable information to investigate practices for the selection of model ensembles. The Danube River at Vienna is used as a case study, with EURO-CORDEX climate simulations driving the COSERO hydrological model. For each selection method, we compare observed to simulated streamflow shift from the reference period (1960–1989) to the evaluation period (1990–2014). Comparison against no selection shows that an informed selection of ensemble members improves the quantification of climate change impacts. However, the selection method matters, with model selection based on hindcasted climate or streamflow alone is misleading, while methods that maintain the diversity and information content of the full ensemble are favorable. Prior to carrying out climate impact assessments, we propose splitting the long-term historical data and using it to test climate model performance, sub-selection methods, and their agreement in reproducing the indicator of interest, which further provide the expectable benchmark of near- and far-future impact assessments. This test is well-suited to be applied in multi-basin experiments to obtain better understanding of uncertainty propagation and more universal recommendations regarding uncertainty reduction in hydrological impact studies.
Jens Kiesel; Philipp Stanzel; Harald Kling; Nicola Fohrer; Sonja C. Jähnig; Ilias Pechlivanidis. Streamflow-based evaluation of climate model sub-selection methods. Climatic Change 2020, 1 -19.
AMA StyleJens Kiesel, Philipp Stanzel, Harald Kling, Nicola Fohrer, Sonja C. Jähnig, Ilias Pechlivanidis. Streamflow-based evaluation of climate model sub-selection methods. Climatic Change. 2020; ():1-19.
Chicago/Turabian StyleJens Kiesel; Philipp Stanzel; Harald Kling; Nicola Fohrer; Sonja C. Jähnig; Ilias Pechlivanidis. 2020. "Streamflow-based evaluation of climate model sub-selection methods." Climatic Change , no. : 1-19.
Recent technological advances in representation of processes in numerical climate models have led to skilful predictions, which can consequently increase the confidence of hydrological predictions and usability of hydro‐climatic services. Given that many water‐related stakeholders are affected by seasonal hydrological variations, there is a need to manage such variations to their advantage through better understanding of the drivers that influence hydrological predictability. Here we analyse the seasonal forecasts of streamflow volumes across about 35400 basins in Europe, which lie along a strong gradient in terms of climatology, scale and hydrological regime. We then link the seasonal volumetric errors to various physiographic‐hydro‐climatic descriptors and meteorological biases in order to identify the key drivers controlling predictability. Streamflow volumes over Europe are well predicted, yet with some geographic and seasonal variability; however, the predictability deteriorates with increasing lead time particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of descriptors, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin's hydrological regime, with limited predictability in relatively flashy basins. On the contrary, snow and/or baseflow dominated regions with long recessions show high streamflow predictability. Finally, climatology and precipitation forecast biases are also related to streamflow predictability, highlighting the importance of developing robust bias‐adjustment methods. Overall, this investigation shows that the seasonal streamflow predictability can be clustered, and hence regionalised, based on a priori knowledge of local hydro‐climatic conditions.
I. G. Pechlivanidis; L. Crochemore; J. Rosberg; T. Bosshard. What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? Water Resources Research 2020, 56, 1 .
AMA StyleI. G. Pechlivanidis, L. Crochemore, J. Rosberg, T. Bosshard. What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? Water Resources Research. 2020; 56 (6):1.
Chicago/Turabian StyleI. G. Pechlivanidis; L. Crochemore; J. Rosberg; T. Bosshard. 2020. "What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts?" Water Resources Research 56, no. 6: 1.
The operation feasibility of small hydropower plants in mountainous sites is subjected to the run-of-river flow, which is also dependent on a high variability in precipitation and snow cover. Moreover, the management of this kind of system has to be performed with some particular operation conditions of the plant (e.g., turbine minimum and maximum discharge) but also some environmental flow requirements. In this context, a technological climate service is conceived in a tight connection with end users, perfectly answering the needs of the management of small hydropower systems in a pilot area, and providing a forecast of the river streamflow together with other operation data. This paper presents an overview of the service but also a set of lessons learnt related to the features, requirements, and considerations to bear in mind from the point of view of climate service developers. In addition, the outcomes give insight into how this kind of service could change the traditional management (normally based on past experience), providing a probability range of the future river flow based on future weather scenarios according to the range of future weather possibilities. This highlights the utility of the co-generation process to implement climate services for water and energy fields but also that seasonal climate forecasting could improve the business as usual of this kind of facility.
Eva Contreras; Javier Herrero; Louise Crochemore; Ilias Pechlivanidis; Christiana Photiadou; Cristina Aguilar; María José Polo. Advances in the Definition of Needs and Specifications for a Climate Service Tool Aimed at Small Hydropower Plants’ Operation and Management. Energies 2020, 13, 1827 .
AMA StyleEva Contreras, Javier Herrero, Louise Crochemore, Ilias Pechlivanidis, Christiana Photiadou, Cristina Aguilar, María José Polo. Advances in the Definition of Needs and Specifications for a Climate Service Tool Aimed at Small Hydropower Plants’ Operation and Management. Energies. 2020; 13 (7):1827.
Chicago/Turabian StyleEva Contreras; Javier Herrero; Louise Crochemore; Ilias Pechlivanidis; Christiana Photiadou; Cristina Aguilar; María José Polo. 2020. "Advances in the Definition of Needs and Specifications for a Climate Service Tool Aimed at Small Hydropower Plants’ Operation and Management." Energies 13, no. 7: 1827.
Addressing the user needs at the local and large scales remains an ongoing scientific and operational effort to the various hydro-climatic service providers. The evolution of hydro-climatic services has received high attention, particularly given the recent scientific and computational advancements that have led to skillful meteorological forecasts at time horizons from sub-seasonal (up to 6 weeks ahead) to seasonal (up to a year ahead). Sub-seasonal to seasonal (S2S) forecasts have great potential for user groups that are affected by climatic variations and that could manage such variations to their advantage through better predictions. Therefore the Swedish Meteorological and Hydrological Institute co-developed with users from the water-related sectors a demonstrator interface to communicate the ensemble of pan-European and global hydro-climatic indicators at the catchment scale.
Here we present these operational hydro-climatic services for the long time horizons, and focus on the setup, the implementation and the challenges. The provided hydro-climatic forecasts are based on the bias-adjusted meteorological forecasts from ECMWF (i.e. daily precipitation and daily mean, maximum and minimum temperature) and the pan-European E-HYPE and global WW-HYPE hydrological models (http://hypeweb.smhi.se/). The forecasts are updated frequently when the newly initialised forecasts become available. Hydro-climatic information for variables such as river flow, water discharge, actual and potential evapotranspiration, soil water content, precipitation and temperature is presented as maps and graphs, for both climatology and forecast period. The service provides also the option to download the forecast information (catchment scale) including also the metadata and forecast skill information. The map shows the anomaly for each catchment and lead time using as reference either the catchment’s normal conditions (based on terciles) or extremes (10th and 90th percentiles) for the period of interest. To overcome misinterpretation of the forecasted information, we set as default the option to the user to mask the catchments in which forecasts have no skill (based on re-forecast analysis); meaning that climatology is more predictive than ECMWF forecasts. The graphs display the median and different percentiles of the ensemble of forecasts, and the high and low thresholds of the normal and extreme conditions for the period of interest.
Keywords
Seasonal hydro-meteorological forecasting, Copernicus C3S, global climate services
Thomas Bosshard; Berit Arheimer; Louise Crochemore; Frida Gyllensvärd; Ilias Pechlivanidis; Christiana Photiadou. Continental and global hydro-climatic forecasting services to address user needs for the water-related sectors. 2020, 1 .
AMA StyleThomas Bosshard, Berit Arheimer, Louise Crochemore, Frida Gyllensvärd, Ilias Pechlivanidis, Christiana Photiadou. Continental and global hydro-climatic forecasting services to address user needs for the water-related sectors. . 2020; ():1.
Chicago/Turabian StyleThomas Bosshard; Berit Arheimer; Louise Crochemore; Frida Gyllensvärd; Ilias Pechlivanidis; Christiana Photiadou. 2020. "Continental and global hydro-climatic forecasting services to address user needs for the water-related sectors." , no. : 1.
During dry spells, a large part of the Netherlands depends on water from the IJssel lake, a large surface water reservoir. Water is extracted for a number of purposes, such as irrigation, water quality, shipping and drinking water. Besides local precipitation, the main source of water flowing into the lake is the river IJssel; a distributary of the Rhine. To keep water available for extraction by the surrounding regions, lake levels cannot be allowed to fall more than about 20 cm under the regular summer maintenance level. Prior to the onset of a drought, therefore, it might be desirable to raise lake levels to maintain sufficient water availability during the dry spell. For adequate management of the reservoir, therefore, long-range forecasting of precipitation and river discharge would be extremely helpful. However, meteorological forecast skill is known to be nearly absent for lead times longer than about a month in northwestern Europe. The land surface contains a number of components that may increase forecast skill for Rhine river discharge; examples are the amount of snow in the Alps, groundwater, and soil moisture. We investigate to what extent this is the case and whether the forecast skill of Rhine river discharge forecasts increases with increasing detail in the land surface parameterization of the initial conditions. We collected streamflow reforecasts from various sources: ECMWF SEAS5, EFAS, SMHI-HYPE and a high-resolution distributed hydrological model (WFLOW), forced by ECMWF SEAS5 meteorological forecasts.
Bart Van Den Hurk; Ruud Hurkmans; Fredrik Wetterhal; Ilias Pechlivanidis; Albrecht Weerts. Added seasonal forecasting skill from land surface parameterization detail. 2020, 1 .
AMA StyleBart Van Den Hurk, Ruud Hurkmans, Fredrik Wetterhal, Ilias Pechlivanidis, Albrecht Weerts. Added seasonal forecasting skill from land surface parameterization detail. . 2020; ():1.
Chicago/Turabian StyleBart Van Den Hurk; Ruud Hurkmans; Fredrik Wetterhal; Ilias Pechlivanidis; Albrecht Weerts. 2020. "Added seasonal forecasting skill from land surface parameterization detail." , no. : 1.
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.
Yousra Saoudi; Louise Crochemore; Ilias Pechlivanidis; Matteo Giuliani. Assessing the value of hydroclimatic services for hydropower megadams: the case of the Grand Ethiopian Renaissance Dam. 2020, 1 .
AMA StyleYousra Saoudi, Louise Crochemore, Ilias Pechlivanidis, Matteo Giuliani. Assessing the value of hydroclimatic services for hydropower megadams: the case of the Grand Ethiopian Renaissance Dam. . 2020; ():1.
Chicago/Turabian StyleYousra Saoudi; Louise Crochemore; Ilias Pechlivanidis; Matteo Giuliani. 2020. "Assessing the value of hydroclimatic services for hydropower megadams: the case of the Grand Ethiopian Renaissance Dam." , no. : 1.
Streamflow information for the months ahead is of great value to existing decision-making practices, particularly to those affected by the vagaries of the climate and who would benefit from better understanding and managing climate-related risks. Despite the large effort, there is still limited knowledge of the key drivers controlling the quality of the seasonal streamflow forecasts. In this investigation, we show that the seasonal streamflow predictability can be clustered, and hence regionalised, based on a priori knowledge of local hydro-climatic conditions. To reach these conclusions we analyse the seasonal forecasts of streamflow volumes across about 35400 basins in Europe, which vary in terms of climatology, scale and hydrological regime. We then link the forecast quality to various descriptors including physiography, hydro-climatic characteristics and meteorological biases. This allows the identification of the key drivers along a strong hydro-climatic gradient. Results show that, as expected, the seasonal streamflow predictability varies geographically and seasonally with acceptable values for the first lead months. In addition, the predictability deteriorates with increasing lead months particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of drivers, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin’s hydrological regime, with quickly reacting basins (of low river memory) showing limited predictability. On the contrary, snow and/or baseflow dominated regions with long recessions (and hence high river memory) show high streamflow predictability. Finally, climatology and precipitation biases are also strongly related to streamflow predictability, highlighting the importance of developing robust bias-adjustment methods.
Ilias Pechlivanidis; Louise Crochemore; Thomas Bosshard. Seasonal streamflow forecasting - Which are the drivers controlling the forecast quality? 2020, 1 .
AMA StyleIlias Pechlivanidis, Louise Crochemore, Thomas Bosshard. Seasonal streamflow forecasting - Which are the drivers controlling the forecast quality? . 2020; ():1.
Chicago/Turabian StyleIlias Pechlivanidis; Louise Crochemore; Thomas Bosshard. 2020. "Seasonal streamflow forecasting - Which are the drivers controlling the forecast quality?" , no. : 1.