Informed water infrastructure design: improving coupled dam sizing and operation by streamflow forecasts

2020 ◽  
Author(s):  
Andrea Castelletti ◽  
Federica Bertoni ◽  
Matteo Giuliani ◽  
Patrick Reed
Keyword(s):  
Large Body ◽  
Water Reservoir ◽  
Added Value ◽  
Lead Times ◽  
Forecast Bias ◽  
Capital Costs ◽  
Ex Post ◽  
Infrastructure Design ◽  
Streamflow Forecasts ◽  
Hydropower Production

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

Valuing Streamflow Forecasts in Centrally Controlled Power Systems

2021 ◽  
Author(s):  
Jordan Kern ◽  
Nathalie Voisin ◽  
Sean Turner ◽  
Hongxiang Yan ◽  
Konstantinos Oikonomou
Keyword(s):  
Power Systems ◽  
Electricity Markets ◽  
Management Strategies ◽  
Profit Maximization ◽  
Value Added ◽  
Marginal Value ◽  
Wide Range ◽  
Streamflow Forecasts ◽  
Hydropower Production ◽  
Bulk Power

<p>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.  </p>

scholarly journals The suitability of remotely sensed soil moisture for improving operational flood forecasting

2014 ◽  
Vol 18 (6) ◽  
pp. 2343-2357 ◽  
Author(s):  
N. Wanders ◽  
D. Karssenberg ◽  
A. de Roo ◽  
S. M. de Jong ◽  
M. F. P. Bierkens
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Remotely Sensed ◽  
Added Value ◽  
Lead Times ◽  
Validation Data ◽  
Data Set ◽  
Discharge Data

Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

scholarly journals Probabilistic streamflow forecasts based on hydrologic persistence and large-scale climate signals in central Texas

2010 ◽  
Vol 13 (4) ◽  
pp. 760-774 ◽  
Author(s):  
Wenge Wei ◽  
David W. Watkins
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
River System ◽  
Forecast Model ◽  
Climate Indices ◽  
Lead Times ◽  
Forecast Performance ◽  
Streamflow Forecasts ◽  
Central Texas ◽  
Multiple Predictor

Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking to provide reliable water supplies and maximize system benefits. In this study, streamflow autocorrelation and large-scale climate information are used to generate probabilistic streamflow forecasts for the Lower Colorado River system in central Texas. A number of potential predictors are evaluated for forecasting flows in various seasons, including large-scale climate indices related to the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO) and others. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. An ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distribution-oriented metrics, and implications for decision making are discussed.

National primary drinking water regulation for arsenic: A retrospective assessment of costs

2014 ◽  
Vol 5 (2) ◽  
pp. 259-284 ◽  
Author(s):  
Cynthia Morgan ◽  
Nathalie B. Simon
Keyword(s):  
Drinking Water ◽  
Mitigation Strategies ◽  
Design Flow ◽  
Cost Data ◽  
Cost Information ◽  
Capital Costs ◽  
Retrospective Assessment ◽  
Ex Post ◽  
Treatment Technologies ◽  
Data Yield

Abstract:This paper compares EPA’s ex ante cost analysis of the 2001 maximum contaminant limit (MCL) for Arsenic in Drinking Water to an ex post assessment of the costs. Because comprehensive cost information for installed treatment technologies or other mitigation strategies pursued by water systems to meet the new standard is not available, this case study relies upon ex post cost data from EPA Demonstration Projects, capturing a total of 50 systems across the US. Information shared by several states and independent associations on the types (but not costs) of treatment technologies used by systems is also summarized. Comparisons of predicted costs to realized costs using our limited data yield mixed results. Plotting the capital cost data from the Demonstration Projects against the cost curves for the compliance technologies recommended for smaller systems, we find that the EPA methodology overestimated capital costs in most cases, especially as the size of the system increases (as measured by the design flow rate).

scholarly journals Global meteorological drought – Part 2: Seasonal forecasts

2014 ◽  
Vol 18 (7) ◽  
pp. 2669-2678 ◽  
Author(s):  
E. Dutra ◽  
W. Pozzi ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
L. Magnusson ◽  
...  
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Added Value ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Global Precipitation Climatology Centre ◽  
The World

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two data sets as initial conditions: the Global Precipitation Climatology Centre (GPCC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation, the most recent ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast evaluation focuses on the periods where precipitation deficits are likely to have higher drought impacts, and the results were summarized over different regions in the world. The verification of the forecasts with lead time indicated that generally for all regions the least reduction on skill was found for (i) long lead times using ERAI or GPCC for monitoring and (ii) short lead times using ECMWF or climatological seasonal forecasts. The memory effect of initial conditions was found to be 1 month of lead time for the SPI-3, 4 months for the SPI-6 and 6 (or more) months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value with skills at least equal to and often above that of climatological forecasts. Furthermore, it is very difficult to improve on the use of climatological forecasts for long lead times. Our results also support recent questions of whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

scholarly journals Insights and experiences from assessing the added value of PROSNOW, a prediction system to support snow management in Alpine ski resorts

2021 ◽  
Author(s):  
Judith Köberl ◽  
Hugues François ◽  
Carlo Carmagnola ◽  
Pirmin Ebner ◽  
Daniel Günther ◽  
...  
Keyword(s):  
Real Time ◽  
Winter Season ◽  
Added Value ◽  
Significant Saving ◽  
The Real ◽  
Ski Resort ◽  
Ski Resorts ◽  
Ex Post ◽  
Simulation Based ◽  
Ex Ante

<p>Within the H2020 project PROSNOW (www.prosnow.org), a demonstrator of a forecasting system that aims at increasing the anticipatory power of ski resorts in the field of snow management has been developed and tested. The PROSNOW® demonstrator, which includes a web-based user interface, represents a meteorological prediction and snow management system with the aim to provide improved anticipation capabilities at various time-scales, spanning from a few days to the seasonal scale of several months. The system holds significant potential to increase the resilience of socio-economic stakeholders and support their real-time adaptation. However, it is expected to take some time until users will gain confidence with the service, completely realize its power and its limitations, and learn to use it in the most effective way to exploit its potential. Although the final actual added value of the PROSNOW® prediction and snowmaking system can thus only be assessed several years after its initial implementation, some ex-ante and preliminary ex-post valuations have already been carried out following the real-time testing of the demonstrator in nine Alpine pilot ski resorts in the winter season 2019/20.</p><p>We applied two different approaches to assess the added value of PROSNOW®: (i) a simulation-based approach and (ii) a survey-based approach. The simulation-based approach consisted of the ex-ante valuation of PROSNOW®’s cost saving potential in the field of snowmaking, using meteorological hindcast data and simulations from snowpack models. The approach is based on decision theory and aims at estimating the cost savings achievable by using the PROSNOW® system to support a ski resort’s daily and strategic snowmaking decisions, compared to the information sources and strategies used so far. In the survey-based approach, which included both ex-ante and ex-post elements, pilot ski resorts were asked to (e)valuate the PROSNOW® demonstrator, based on their experiences from the real-time testing in the winter season 2019/20. The survey included questions about the perceived forecasting accuracy, observed positive impacts, the experienced as well as expected usefulness of the PROSNOW® demonstrator for different areas of application within the ski resort, and the ski resort’s willingness to pay (WTP). For the latter, both direct and indirect stated preference methods (e.g. limit conjoint analysis) were applied.</p><p>Both, simulations and survey results revealed that increases in the ability to anticipate weather and snow conditions bear significant saving potentials for some ski resorts. Areas of application for which PROSNOW® is considered particularly useful include snowmaking decisions for the upcoming hours and days, the optimization of water and energy use and avoidance of snow overproduction. Even though some pilot ski resorts experienced problems with the demonstrator, the majority indicated to be willing to pay a non-zero price for the service, ranging from 2,500€ to 12,700€ per season.</p>

scholarly journals The suitability of remotely sensed soil moisture for improving operational flood forecasting

2013 ◽  
Vol 10 (11) ◽  
pp. 13783-13816 ◽  
Author(s):  
N. Wanders ◽  
D. Karssenberg ◽  
A. de Roo ◽  
S. M. de Jong ◽  
M. F. P. Bierkens
Keyword(s):  
Soil Moisture ◽  
Flood Forecasting ◽  
Remotely Sensed ◽  
Added Value ◽  
Validation Dataset ◽  
Lead Times ◽  
Distributed Hydrological Model ◽  
Danube Basin ◽  
Discharge Data ◽  
Soil Moisture Data

Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model for flood predictions with lead times up to 10 days. For this study, satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF, are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

scholarly journals The benefits of pre- and postprocessing streamflow forecasts for an operational flood-forecasting system of 119 Norwegian catchments

2021 ◽  
Author(s):  
Trine J. Hegdahl ◽  
Kolbjørn Engeland ◽  
Ingelin Steinsland ◽  
Andrew Singleton
Keyword(s):  
Lead Time ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Flood Forecasting ◽  
Added Value ◽  
Flood Events ◽  
Ensemble Forecasts ◽  
Processing Scheme ◽  
Temperature And Precipitation ◽  
Streamflow Forecasts

Abstract. The novelty of this study is to evaluate the univariate and the combined effects of including both precipitation and temperature forecasts in the preprocessing together with the postprocessing of streamflow for forecasting of floods as well as all streamflow values for a large sample of catchments. A hydrometeorological forecasting chain in an operational flood forecasting setting with 119 Norwegian catchments was used. This study evaluates the added value of pre- and postprocessing methods for ensemble forecasts in a hydrometeorological forecasting chain in an operational flood forecasting setting with 119 Norwegian catchments. Two years of ECMWF ensemble forecasts of temperature (T) and precipitation (P) with a lead-time up to 9 days were used to force the operational hydrological HBV model to establish streamflow forecasts. Two approaches to preprocess the temperature and precipitation forecasts were tested. 1) An existing approach applied to the gridded forecasts using quantile mapping for temperature and a Bernoulli-gamma distribution for precipitation. 2) Bayesian model averaging (BMA) applied to catchment average values of temperature and precipitation. BMA was also used for postprocessing catchment streamflow forecasts. Ensemble forecasts of streamflow were generated for a total of fourteen schemes based on combinations of raw, preprocessed, and postprocessed forecasts in the hydrometeorological forecasting chain. The aim of this study is to assess which pre- and postprocessing approaches should be used to improve streamflow and flood forecasts and look for regional or seasonal patterns in preferred approaches. The forecasts were evaluated for two datasets: i) all streamflows and ii) flood events with streamflow above mean annual flood. Evaluations were based on reliability, continuous ranked probability score (CRPS) and -skill score (CRPSS). For the flood dataset, the critical success index (CSI) was used. Evaluations based on all streamflow data showed that postprocessing improved the forecasts only up to a lead-time of two to three days, whereas preprocessing T and P using BMA improved the forecasts for 50 %–90 % of the catchments beyond three days lead-time. However, for flood events, the added value of pre- and postprocessing is smaller. Preprocessing of P and T gave better CRPS for marginally more catchments compared to the other schemes. Based on CSI, we found that many of the forecast schemes perform equally well. Further, we found large differences in the ability to issue warnings between spring and autumn floods. There was almost no ability to predict autumn floods beyond 3 days, whereas the spring floods had predictability up to 9 days for many events and catchments. The results indicate that the ensemble forecasts have problems in predicting correct autumn precipitation, and the uncertainty is larger for heavy autumn precipitation compared to spring events when temperature driven snow melt is important. To summarize we find that the flood forecasts benefit from most pre-and postprocessing schemes, although the best processing approaches depend on region, catchment, and season, and that the processing scheme should be tailored to each catchment, lead time, season and the purpose of the forecasting.

scholarly journals Temperature and precipitation seasonal forecasts over the Mediterranean region: added value compared to simple forecasting methods

2021 ◽  
Author(s):  
Filippo Calì Quaglia ◽  
Silvia Terzago ◽  
Jost von Hardenberg
Keyword(s):  
Lead Time ◽  
Mediterranean Region ◽  
Added Value ◽  
Lead Times ◽  
Model Ensemble ◽  
Seasonal Forecasts ◽  
Anomaly Correlation ◽  
Temperature And Precipitation ◽  
Precipitation Anomalies ◽  
The Mediterranean

AbstractThis study considers a set of state-of-the-art seasonal forecasting systems (ECMWF, MF, UKMO, CMCC, DWD and the corresponding multi-model ensemble) and quantifies their added value (if any) in predicting seasonal and monthly temperature and precipitation anomalies over the Mediterranean region compared to a simple forecasting method based on the ERA5 climatology (CTRL) or the persistence of the ERA5 anomaly (PERS). This analysis considers two starting dates, May 1st and November 1st and the forecasts at lead times up to 6 months for each year in the period 1993–2014. Both deterministic and probabilistic metrics are employed to derive comprehensive information on the forecast quality in terms of association, reliability/resolution, discrimination, accuracy and sharpness. We find that temperature anomalies are better reproduced than precipitation anomalies with varying spatial patterns across different forecast systems. The Multi-Model Ensemble (MME) shows the best agreement in terms of anomaly correlation with ERA5 precipitation, while PERS provides the best results in terms of anomaly correlation with ERA5 temperature. Individual forecast systems and MME outperform CTRL in terms of accuracy of tercile-based forecasts up to lead time 5 months and in terms of discrimination up to lead time 2 months. All seasonal forecast systems also outperform elementary forecasts based on persistence in terms of accuracy and sharpness.

scholarly journals Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

2016 ◽  
Author(s):  
Harm-Jan F. Benninga ◽  
Martijn J. Booij ◽  
Renata J. Romanowicz ◽  
Tom H. M. Rientjes
Keyword(s):  
Hydrological Model ◽  
Initial Conditions ◽  
Snow Accumulation ◽  
Lead Times ◽  
Model Parameters ◽  
Streamflow Forecasting ◽  
Weather Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Precipitation And Temperature

Abstract. The paper presents a methodology to give insight in the performance of ensemble streamflow forecasting systems. We developed an ensemble forecasting system for the Biała Tarnowska, a mountainous river catchment in southern Poland, and analysed the performance for lead times from 1 day to 10 days for low, medium and high streamflow and related runoff generating processes. Precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts serve as input to a deterministic lumped hydrological (HBV) model. Due to inconsistent bias, the best streamflow forecasts were obtained without pre- and post-processing of the meteorological and streamflow forecasts. Best forecast skill, relative to alternative forecasts based on historical measurements of precipitation and temperature, is shown for high streamflow and for snow accumulation low streamflow events. Forecasts of medium streamflow events and low streamflow events generated by precipitation deficit show less skill. To improve the performance of the forecasting system for high streamflow events, in particular the meteorological forecasts require improvement. For low streamflow forecasts, the hydrological model should be improved. The study recommends improving the reliability of the ensemble streamflow forecasts by including the uncertainties in hydrological model parameters and initial conditions, and by improving the dispersion of the meteorological input forecasts.

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