scholarly journals Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK

2020 ◽  
Author(s):  
Andres Peñuela ◽  
Christopher Hutton ◽  
Francesca Pianosi
Keyword(s):  
Decision Maker ◽  
Water Resource ◽  
Water Resource Management ◽  
Skill Score ◽  
Forecast Skill ◽  
Weather Data ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
The Common ◽  
The Uk

Abstract. Improved skill of long-range weather forecasts has motivated an increasing effort towards developing seasonal hydrological forecasting systems across Europe. Among other purposes, such forecasting systems are expected to support better water management decisions. In this paper we evaluate the potential use of a real-time optimisation system (RTOS) informed by seasonal forecasts in a water supply system in the UK. For this purpose, we simulate the performances of the RTOS fed by ECMWF seasonal forecasting systems (SEAS5) over the past ten years, and we compare them to a benchmark operation that mimics the common practices for reservoir operation in the UK. We also attempt to link the improvement of system performances, i.e. the forecast value, to the forecast skill (measured by the mean error and the Continuous Ranked Probability Skill Score) as well as other factors such as bias correction, the decision maker priorities, hydrological conditions and level of uncertainty consideration. We find that some of these factors control the forecast value much more strongly than the forecast skill. For the (realistic) scenario where the decision-maker prioritises water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow predictions (ESP), which are more easily derived from historical weather data, we find that ESP remains a hard-to-beat reference not only in terms of skill but also in terms of value.

scholarly journals Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK

2020 ◽  
Vol 24 (12) ◽  
pp. 6059-6073
Author(s):  
Andres Peñuela ◽  
Christopher Hutton ◽  
Francesca Pianosi
Keyword(s):  
Decision Maker ◽  
Water Resource ◽  
Water Resource Management ◽  
Forecast Skill ◽  
Weather Data ◽  
Ensemble Size ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Hydrological Conditions ◽  
The Uk

Abstract. Improved skill of long-range weather forecasts has motivated an increasing effort towards developing seasonal hydrological forecasting systems across Europe. Among other purposes, such forecasting systems are expected to support better water management decisions. In this paper we evaluate the potential use of a real-time optimization system (RTOS) informed by seasonal forecasts in a water supply system in the UK. For this purpose, we simulate the performances of the RTOS fed by ECMWF seasonal forecasting systems (SEAS5) over the past 10 years, and we compare them to a benchmark operation that mimics the common practices for reservoir operation in the UK. We also attempt to link the improvement of system performances, i.e. the forecast value, to the forecast skill (measured by the mean error and the continuous ranked probability skill score) as well as to the bias correction of the meteorological forcing, the decision maker priorities, the hydrological conditions and the forecast ensemble size. We find that in particular the decision maker priorities and the hydrological conditions exert a strong influence on the forecast skill–value relationship. For the (realistic) scenario where the decision maker prioritizes the water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered by optimizing against an ensemble of 25 equiprobable forecasts. These operational benefits are also observed when the ensemble size is reduced up to a certain limit. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow prediction (ESP), which is more easily derived from historical weather data, we find that ESP remains a hard-to-beat reference, not only in terms of skill but also in terms of value.

scholarly journals Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

2021 ◽  
Author(s):  
Nicola Cortesi ◽  
Verónica Torralba ◽  
Llorenó Lledó ◽  
Andrea Manrique-Suñén ◽  
Nube Gonzalez-Reviriego ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Winter Season ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Sea Level Pressure ◽  
Weather Regimes ◽  
Start Dates ◽  
Weekly Frequency

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.

Determination of seasonal forecast skill in identifying extreme events of temperature, wind speed, and SPI

2021 ◽  
Author(s):  
Massimiliano Palma ◽  
Franco Catalano ◽  
Irene Cionni ◽  
Marcello Petitta
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Climate Variability ◽  
Extreme Events ◽  
Bias Correction ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Energy Sector ◽  
Seasonal Forecasts ◽  
Weather Forecasts

<p>Renewable energy is the fastest-growing source of electricity globally, but climate variability and impacting events affecting the potential productivity of plants are obstacles to its integration and planning. Knowing a few months in advance the productivity of plants and the impact of extreme events on productivity and infrastructure can help operators and policymakers make the energy sector more resilient to climate variability, promoting the deployment of renewable energy while maintaining energy security.</p><p>The energy sector already uses weather forecasts up to 15 days for plant management; beyond this time horizon, climatologies are routinely used. This approach has inherent weaknesses, including the inability to predict extreme events, the prediction of which is extremely useful to decision-makers. Information on seasonal climate variability obtained through climate forecasts can be of considerable benefit in decision-making processes. The Climate Data Store of the Copernicus Climate Change Service (C3S) provides seasonal forecasts and a common period of retrospective simulations (hindcasts) with equal spatial temporal resolution for simulations from 5 European forecast centres (European Centre for Medium-Range Weather Forecasts (ECMWF), Deutscher Wetterdienst (DWD), Meteo France (MF), UK Met Office (UKMO) and Euro-Mediterranean Centre on Climate Change (CMCC)), one US forecasting centre (NCEP) plus the Japan Meteorological Agency (JMA) model.</p><p>In this work, we analyse the skill and the accuracy of a subset of the operational seasonal forecasts provided by Copernicus C3S, focusing on three relevant essential climate variables for the energy sector: temperature (t2m), wind speed (sfcWind, relevant to the wind energy production), and precipitation. The latter has been analysed by taking the Standard Precipitation Index (SPI) into account.</p><p>First, the methodologies for bias correction have been defined. Subsequently, the reliability of the forecasts has been assessed using appropriate reliability indicators based on comparison with ERA5 reanalysis dataset. The hindcasts cover the period 1993-2017. For each of the variables considered, we evaluated the seasonal averages based on monthly means for two seasons: winter (DJF) and summer (JJA). Data have been bias corrected following two methodologies, one based on the application of a variance inflation technique to ensure the correction of the bias and the correspondence of variance between forecast and observation; the other based on the correction of the bias, the overall forecast variance and the ensemble spread as described in Doblas-Reyes et al. (2005).</p><p>Predictive ability has been assessed by calculating binary (Brier Skill Score, BSS hereafter, and Ranked Probability Skill Score, RPSS hereafter) and continuous (Continuous Ranked Probability Skill Score, CRPSS hereafter) scores. Forecast performance has been assessed using ERA 5 reanalysis as pseudo-observations. </p><p>In this work we discuss the results obtained with different bias correction techniques highlighting the outcomes obtained analyzing the BSS for the first and the last terciles and the first and the last percentiles (10th and 90th). This analysis has the goal to identify the regions in which the seasonal forecast can be used to identify potential extreme events.</p>

Assessing the impact of projected climate change on drought vulnerability in Scotland

2014 ◽  
Vol 45 (6) ◽  
pp. 806-816 ◽  
Author(s):  
Richard Gosling
Keyword(s):  
Water Resource ◽  
Water Resource Management ◽  
Water Shortage ◽  
Resource Scarcity ◽  
Drought Vulnerability ◽  
Collaborative Project ◽  
North West ◽  
Ecological Implications ◽  
The Uk ◽  
The Impact

Although Scotland is relatively water resource rich in a UK and European context, water resource scarcity can occur during exceptional dry periods such as those experienced in North West Scotland during July 2012. Precipitation and flow anomaly indices have been recently developed and introduced operationally by the Scottish Environment Protection Agency, in order to assess the severity of dry episodes and use this information within the decision-making process when managing the ecological implications of measures required to ensure continuity of water supply. The latest projections of future climate in the UK (UKCP09) point to warmer, drier summers across much of Scotland and, as such, imply an increased frequency of periods of water shortage. This study makes use of the results from a collaborative project in which projected values of climate variables have been used to derive projected river flows at a number of catchments across the UK. These datasets have been used to evaluate the change in frequency of significant precipitation and flow deficits in Scotland. The findings suggest a marked increase in frequency of summer water resource scarcity across much of Scotland which has implications for water resource management, particularly where current storage is relatively low.

scholarly journals A national-scale seasonal hydrological forecast system: development and evaluation over Britain

2017 ◽  
Author(s):  
Victoria A. Bell ◽  
Helen N. Davies ◽  
Alison L. Kay ◽  
Anca Brookshaw ◽  
Adam A. Scaife
Keyword(s):  
Lead Time ◽  
Hydrological Model ◽  
Forecast Skill ◽  
Initial Condition ◽  
Seasonal Forecasts ◽  
National Scale ◽  
The North ◽  
Seasonal Flow ◽  
Ensemble Mean ◽  
The Uk

Abstract. Skilful winter seasonal predictions for the North Atlantic circulation and Northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring/Summer seasonal hydrological forecasts, however, Autumn/Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform the most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ~ 70 % of the forecast skill (mostly in areas of high rainfall to the North and West) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, benefitting practitioners interested in predicting hydrological extremes, not only in the UK, but potentially across Europe.

scholarly journals Synthesizing a Regional Territorial Evapotranspiration Dataset for Northern China

2021 ◽  
Vol 13 (6) ◽  
pp. 1076
Author(s):  
Linjiang Wang ◽  
Bingfang Wu ◽  
Abdelrazek Elnashar ◽  
Hongwei Zeng ◽  
Weiwei Zhu ◽  
...  
Keyword(s):  
Energy Balance ◽  
Water Resource ◽  
Water Resource Management ◽  
Northern China ◽  
Skill Score ◽  
Vital Role ◽  
Heihe River ◽  
Observation Data ◽  
Weighted Mean

As a vital role in the processes of the energy balance and hydrological cycles, actual evapotranspiration (ET) is relevant to many agricultural, ecological and water resource management studies. The available global or regional ET products provide ET estimations with various temporal ranges, spatial resolutions and calculation methods (algorithms, inputs and parameterization, etc.), leading to varying degrees of introduced uncertainty. Northern China is the main agriculturally productive region supporting the whole country; thus, understanding the spatial and temporal changes in ET is essential to ensure water resource and food security. We developed a synthesis ET dataset for Northern China at a 1000 m spatial resolution, with a monthly temporal resolution covering a period ranging from 1982 to 2017, using an in-depth assessment of several ET products. Specifically, assessments were performed using in situ measured ET from eddy covariance (EC) observation towers at the site-pixel scale over interannual months under the conditions of different land cover types, climatic zones and elevation levels to select the most optimally performing ET products to be used in the synthesized ET dataset. Eight indicators under 21 conditions were involved in the assessment sheet, while the statistics of the different ET product occurrences and corresponding ratios were analyzed to select the best-performing ET products to build the synthesis ET dataset using the weighted mean method. The weights were determined by the Taylor skill score (TSS), calculated with ET products and EC ET observation data. Based on the assessment results, the Penman–Monteith–Leuning (PML_v2), ETWatch and Operational Simplified Surface Energy Balance (SSEBop) datasets were selected for implementation in the synthesis ET dataset from 2003 to 2017, while Global Land Evaporation Amsterdam Model (GLEAM) v3.3a, complementary relationship (CR) ET, and Numerical Terradynamic Simulation Group (NTSG) datasets were chosen for the synthesis ET dataset from 1982 to 2002. The weighted mean synthesized results from 2003 to 2017 performed well when compared to the in situ measured EC ET values produced under all of the above conditions, while the synthesized results from 1982 to 2002 performed well through the water balance method in Heihe River Basin. These results can provide more stable ET estimations for Northern China, which can contribute to relevant agricultural, ecological and hydrological studies.

scholarly journals Water Resource Management: Moving from Single Risk-Based Management to Resilience to Multiple Stressors

2021 ◽  
Vol 13 (15) ◽  
pp. 8609
Author(s):  
Sarah Bunney ◽  
Elizabeth Lawson ◽  
Sarah Cotterill ◽  
David Butler
Keyword(s):  
Resource Management ◽  
Water Resource ◽  
Water Demand ◽  
Water Resource Management ◽  
Large Scale ◽  
Multiple Stressors ◽  
Meteorological Data ◽  
Average Rainfall ◽  
The Uk ◽  
The Impact

Water resource management in the UK is multifaceted, with a complexity of issues arising from acute and chronic stressors. Below average rainfall in spring 2020 coincided with large-scale changes to domestic water consumption patterns, arising from the first UK-wide COVID-19 lockdown, resulting in increased pressure on nationwide resources. A sector wide survey, semi-structured interviews with sector executives, meteorological data, water resource management plans and market information were used to evaluate the impact of acute and chronic threats on water demand in the UK, and how resilience to both can be increased. The COVID-19 pandemic was a particularly acute threat: water demand increased across the country, it was unpredictable and hard to forecast, and compounding this, below average rainfall resulted in some areas having to tanker in water to ‘top up’ the network. This occurred in regions of the UK that are ‘water stressed’ as well as those that are not. We therefore propose a need to look beyond ‘design droughts’ and ‘dry weather average demand’ to characterise the management and resilience of future water resources. As a sector, we can learn from this acute threat and administer a more integrated approach, combining action on the social value of water, the implementation of water trading and the development of nationwide multi-sectoral resilience plans to better respond to short and long-term disruptors.

scholarly journals A national-scale seasonal hydrological forecast system: development and evaluation over Britain

2017 ◽  
Vol 21 (9) ◽  
pp. 4681-4691 ◽  
Author(s):  
Victoria A. Bell ◽  
Helen N. Davies ◽  
Alison L. Kay ◽  
Anca Brookshaw ◽  
Adam A. Scaife
Keyword(s):  
Lead Time ◽  
Hydrological Model ◽  
Forecast Skill ◽  
Initial Condition ◽  
Seasonal Forecasts ◽  
National Scale ◽  
The North ◽  
Seasonal Flow ◽  
Ensemble Mean ◽  
The Uk

Abstract. Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for  ∼ 70 % of the forecast skill (mostly in areas of high rainfall to the north and west) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, potentially benefitting practitioners interested in predicting hydrological extremes, not only in the UK but also across Europe.

Communicating Properties of Changes in Lagged Weather Forecasts

2021 ◽  
Keyword(s):  
Forecast Skill ◽  
Climate Projections ◽  
Seasonal Forecasts ◽  
Numerical Examples ◽  
Weather Forecasts ◽  
Potential Applications

Abstract Weather forecasts, seasonal forecasts, and climate projections can help their users make good decisions. It has recently been shown that when the decisions include the question of whether to act now or wait for the next forecast, even better decisions can be made if information describing potential forecast changes is also available. In this article, we discuss another set of situations in which forecast change information can be useful, which arise when forecast users need to decide which of a series of lagged forecasts to use. Motivated by these potential applications of forecast change information, we then discuss a number of ways in which forecast change information can be presented, using ECMWF reforecasts and corresponding observations as illustration. We first show metrics that illustrate changes in forecast values, such as average sizes of changes, probabilities of changes of different sizes, and percentiles of the distribution of changes, and then show metrics that illustrate changes in forecast skill, such as increase in average skill and probabilities that later forecasts will be more accurate. We give four illustrative numerical examples in which these metrics determine which of a series of lagged forecasts to use. In conclusion, we suggest that providers of weather forecasts, seasonal forecasts, and climate projections might consider presenting forecast change information, in order to help forecast users make better decisions.

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