scholarly journals Evaluation of the ECMWF System 4 climate forecasts for streamflow forecasting in the Upper Hanjiang River Basin

2018 ◽  
Vol 49 (6) ◽  
pp. 1864-1879 ◽  
Author(s):  
Fuqiang Tian ◽  
Yilu Li ◽  
Tongtiegang Zhao ◽  
Hongchang Hu ◽  
Florian Pappenberger ◽  
...  
Keyword(s):  
River Basin ◽  
Processing Method ◽  
Water Diversion ◽  
Ensemble Prediction ◽  
Distributed Hydrological Model ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Ensemble Prediction System ◽  
Streamflow Forecasts ◽  
Hanjiang River

Abstract This paper assesses the potential of the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 forecasts and investigates the post-processing precipitation to enhance the skill of streamflow forecasts. The investigation is based on hydrological modelling and is conducted through the case study of the Upper Hanjiang River Basin (UHRB). A semi-distributed hydrological model, TsingHua Representative Elementary Watershed (THREW), is implemented to simulate the rainfall–runoff processes, with the help of hydrological ensemble prediction system (HEPS) approach. A post-processing method, quantile mapping method, is applied to bias correct the raw precipitation forecasts. Then we evaluate the performance of raw and post-processed streamflow forecasts for the four hydrological stations along the mainstream of Hanjiang River from 2001 to 2008. The results show that the performance of the streamflow forecasts is greatly enhanced with post-processing precipitation forecasts, especially in pre-dry season (November and December), thus providing useful information for water supply management of the central route of South to North Water Diversion Project (SNWDP). The raw streamflow forecasts tend to overpredict and present similarly to forecast accuracy with the extended streamflow prediction (ESP) approach. Streamflow forecast skill is considerably improved when applying post-processing method to bias correct the ECMWF System 4 precipitation forecasts.

scholarly journals Ensemble Streamflow Forecasting across the U.S. Mid-Atlantic Region with a Distributed Hydrological Model Forced by GEFS Reforecasts

2017 ◽  
Vol 18 (7) ◽  
pp. 1905-1928 ◽  
Author(s):  
Ridwan Siddique ◽  
Alfonso Mejia
Keyword(s):  
Hydrological Model ◽  
Hydrologic Model ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Distributed Hydrological Model ◽  
Streamflow Forecasting ◽  
Atlantic Region ◽  
Meteorological Forcing ◽  
Streamflow Forecasts ◽  
The U.S

Abstract The quality of ensemble streamflow forecasts in the U.S. mid-Atlantic region (MAR) is investigated for short- to medium-range forecast lead times (6–168 h). To this end, a regional hydrological ensemble prediction system (RHEPS) is assembled and implemented. The RHEPS in this case comprises the ensemble meteorological forcing, a distributed hydrological model, and a statistical postprocessor. As the meteorological forcing, precipitation, and near-surface temperature outputs from the National Oceanic and Atmospheric Administration (NOAA)/National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast, version 2 (GEFSRv2), are used. The Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) is used as the distributed hydrological model, and a statistical autoregressive model with an exogenous variable is used as the postprocessor. To verify streamflow forecasts from the RHEPS, eight river basins in the MAR are selected, ranging in drainage area from ~262 to 29 965 km2 and covering some of the major rivers in the MAR. The verification results for the RHEPS show that, at the initial lead times (1–3 days), the hydrological uncertainties have more impact on forecast skill than the meteorological ones. The former become less pronounced, and the meteorological uncertainties dominate, across longer lead times (>3 days). Nonetheless, the ensemble streamflow forecasts remain skillful for lead times of up to 7 days. Additionally, postprocessing increases forecast skills across lead times and spatial scales, particularly for the high-flow conditions. Overall, the proposed RHEPS is able to improve streamflow forecasting in the MAR relative to the deterministic (unperturbed GEFSRv2 member) forecasting case.

Developing semi-distributed hydrological models for streamflow forecasting in upper Narmada River basin

2021 ◽  
Author(s):  
Ankit Singh ◽  
Soubhik Mondal ◽  
Sanjeev Kumar Jha
Keyword(s):  
River Basin ◽  
Stream Flow ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Streamflow Forecasting ◽  
Vic Model ◽  
Narmada River ◽  
Medium Range ◽  
Streamflow Forecasts

<p>Short-term streamflow forecast is important for various hydrological applications such as, estimating inflow to reservoirs, sending alarms in case of extreme events like flood and flash floods etc. Flooding events in last few years in the Indian subcontinent emphasized the importance of more accurate streamflow forecasts and the possible benefit of high-resolution Numerical Weather Prediction (NWP) models has been confirmed. In India, National Center for Medium Range Weather Forecasting (NCMRWF) provides rainfall forecasts from its UK Met office Unified Model based deterministic model (NCUM), and ensemble prediction system (NEPS). The comparison of NCMRWF with the forecast from other agencies such as Japan Metrological Agency (JMA)and European Center for Medium Range Forecast (ECMWF) have been addressed in this work. Global NWP models developed by different international agencies applydifferent algorithms, initial and boundaries conditions.The usefulness of several forecasts in streamflow forecasting is still being investigated in India. Recent studies on streamflow forecasting by using different NWP models shows that the performance of streamflow forecasts directly depends on the skill of NWP models. Hydrological model also plays a vital role in stream flow forecasting, because different hydrological model have different structure, parameters and algorithms to simulate the flow.</p><p>            In this study we use the Soil and Water Assessment Tool (SWAT) a Hydrological Response Unit (HRU’s) based hydrological model. HRU is the area that contains similar type of soil, land use and slope properties in a subbasin. For comparison, the streamflow generated from the forecasted rainfall by NWP, we select three different NWP models namely JMA, ECMWF and NCMRWF for streamflow forecasting. Manot watershed part of Narmada River basin in central India is selected as the study area for this study. Streamflow is examined for monsoon (June to September) period of 2018 at multiple lead times i.e. 1 to 5 days. Rain-gauge based gridded Indian Meteorological Department (IMD) rainfall product is used as observed data in SWAT. All rainfall products are at 0.25*0.25-degree spatial resolution. The preliminary comparison between the simulated streamflow and the observation shows that the stream flow patterns produced by various forecast products are in good comparison with high peaks. Our results also indicate that the forecast accuracy of NCMRWF is closely comparable with other forecast products for all lead time. In addition, the setup of Variable Infiltration Capacity (VIC), the hydrological model for Streamflow forecasting is in progress. The VIC model is a grid-based model with variable infiltration soil layers and each of this layer characterizes the soil hydrological responses and heterogeneity in land cover classes. For routing, VIC model divides the whole basin into grides and water balance is calculated at the outlet of each and every grid and the flow simulate according to the flow direction. This model considers both the baseflow and the surface flow. The detailed results of ongoing work will be presented at the conference.</p>

Exploring the relationship between the skill of hydrological ensemble predictions and catchment descriptors

2020 ◽  
Author(s):  
Emixi Valdez ◽  
Francois Anctil ◽  
Maria-Helena Ramos
Keyword(s):  
Ensemble Forecasting ◽  
Ensemble Prediction ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Forecast Performance ◽  
Ensemble Prediction System ◽  
Sources Of Uncertainty ◽  
Skill Scores ◽  
Forecasting System ◽  
The Relationship

<p>Skillful hydrological forecasts are essential for decision-making in many areas such as preparedness against natural disasters, water resources management, and hydropower operations. Despite the great technological advances, obtaining skillful predictions from a forecasting system, under a range of conditions and geographic locations, remain a difficult task. It is still unclear why some systems perform better than others at different temporal and spatial scales. Much work has been devoted to investigate the quality of forecasts and the relative contributions of meteorological forcing, catchment’s initial conditions, and hydrological model structure in a streamflow forecasting system. These sources of uncertainty are rarely considered fully and simultaneously in operational systems, and there are still gaps in understanding their relationship with the dominant processes and mechanisms that operate in a given river basin. In this study, we use a multi-model hydrological ensemble prediction system (H-EPS) named HOOPLA (HydrOlOgical Prediction Laboratory), which allows to account separately for these three main sources of uncertainty in hydrological ensemble forecasting. Through the use of EnKF data assimilation, of 20 lumped hydrological models, and of the 50-member ECMWF medium-range weather forecasts, we explore the relationship between the skill of ensemble predictions and the many descriptors (e.g. catchment surface, climatology, morphology, flow threshold and hydrological regime) that influence hydrological predictability. We analyze streamflow forecasts at 50 stations spread across Quebec, France and Colombia, over the period from 2011 to 2015 and for lead times up to 9 days. The forecast performance is assessed using common metrics for forecast quality verification, such as CRPS, Brier skill score, and reliability diagrams. Skill scores are computed using a probabilistic climatology benchmark, which was generated with the hydrological models forced by resampled historical meteorological data. Our results contribute to relevant literature on the topic and bring additional insight into the role of each descriptor in the skill of a hydrometeorological ensemble forecasting chain, serving as a possible guide for potential users to identify the circumstances or conditions in which it is more efficient to implement a given system.</p><p> </p>

scholarly journals From research to applications – examples of operational ensemble post-processing in France using machine learning

2020 ◽  
Vol 27 (2) ◽  
pp. 329-347 ◽  
Author(s):  
Maxime Taillardat ◽  
Olivier Mestre
Keyword(s):  
Machine Learning ◽  
Heat Waves ◽  
Computation Time ◽  
Ensemble Prediction ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Practical Applications ◽  
Medium Resolution ◽  
Hourly Rainfall

Abstract. Statistical post-processing of ensemble forecasts, from simple linear regressions to more sophisticated techniques, is now a well-known procedure for correcting biased and poorly dispersed ensemble weather predictions. However, practical applications in national weather services are still in their infancy compared to deterministic post-processing. This paper presents two different applications of ensemble post-processing using machine learning at an industrial scale. The first is a station-based post-processing of surface temperature and subsequent interpolation to a grid in a medium-resolution ensemble system. The second is a gridded post-processing of hourly rainfall amounts in a high-resolution ensemble prediction system. The techniques used rely on quantile regression forests (QRFs) and ensemble copula coupling (ECC), chosen for their robustness and simplicity of training regardless of the variable subject to calibration. Moreover, some variants of classical techniques used, such as QRF and ECC, were developed in order to adjust to operational constraints. A forecast anomaly-based QRF is used for temperature for a better prediction of cold and heat waves. A variant of ECC for hourly rainfall was built, accounting for more realistic longer rainfall accumulations. We show that both forecast quality and forecast value are improved compared to the raw ensemble. Finally, comments about model size and computation time are made.

scholarly journals From research to applications – Examples of operational ensemble post-processing in France using machine learning

2020 ◽  
Author(s):  
Maxime Taillardat ◽  
Olivier Mestre
Keyword(s):  
Machine Learning ◽  
Heat Waves ◽  
Computation Time ◽  
Ensemble Prediction ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Practical Applications ◽  
Medium Resolution ◽  
Hourly Rainfall

Abstract. Statistical post-processing of ensemble forecasts, from simple linear regressions to more sophisticated techniques, is now a well-known procedure in order to correct biased and misdispersed ensemble weather predictions. However, practical applications in National Weather Services is still in its infancy compared to deterministic post-processing. This paper presents two different applications of ensemble post-processing using machine learning at an industrial scale. The first is a station-based post-processing of surface temperature in a medium resolution ensemble system. The second is a gridded post-processing of hourly rainfall amounts in a high resolution ensemble prediction system. The techniques used rely on quantile regression forests (QRF) and ensemble copula coupling (ECC), chosen for their robustness and simplicity of training whatever the variable subject to calibration. Moreover, some variants of classical techniques used such as QRF or ECC have been developed in order to adjust to operational constraints. A forecast anomaly-based QRF is used for temperature for a better prediction of cold and heat waves. A variant of ECC for hourly rainfall is built, accounting for more realistic longer rainfall accumulations. It is shown that forecast quality as well as forecast value is improved compared to the raw ensemble. At last, comments about model size and computation time are made.

scholarly journals Post-processing and its effect on relative-extreme event identification

2021 ◽  
Author(s):  
Michael Sharpe ◽  
Joseph Battershill ◽  
Katherine Hurst
Keyword(s):  
Extreme Events ◽  
Deterministic Model ◽  
Extreme Weather Events ◽  
Ensemble Prediction ◽  
Model Output ◽  
Post Processing ◽  
Ensemble Prediction System ◽  
Site Specific ◽  
The Public ◽  
The Uk

<p>The UK Met Office manages its commitment to the public through the Public Weather Service and an important factor in public safety and concern is extreme weather events. Therefore, a new Key Performance Indicator is being introduced, related to the ability with which extreme events are correctly identified. The Threshold Weighted Continuous Ranked Probability Score (twCRPS) is used to make this assessment by determining how well site-specific Met Office ensemble-based probabilistic forecast solutions predict relative-extreme events. The threshold weighted version of the Mean Absolute Error (twMAE) is the deterministic equivalent to the twCRPS. The twMAE is used for the assessment of the deterministic model output that currently appears on the Met Office App and website.</p><p>Gridded numerical ensemble model data is generated by MOGREPS (the Met Office Global and Regional Ensemble Prediction System). A new program of post-processing work has been undertaken in recent years (IMPROVER) to replace the system of post-processing currently employed by the Met Office. IMPROVER applies a series of post-processing steps to generate both probabilistic and deterministic forecasts and site-specific data is generated from these model fields. Verification of the model output is undertaken at each post-processing stage to ensure that every step is having the expected impact on the performance of the model. To date however, these assessments have concentrated on the performance of more typical conditions rather than the ability with which more extreme events are identified.</p><p>This session outlines very recent work to assess the ability with which raw MOGREPS data and data generated by various of the post-processing stages of IMPROVER, predict relative-extreme events at observation sites throughout the UK. The twMAE and twCRPS are used for this assessment, where in both cases, the threshold weighting function is defined in terms of a distribution formed by sampling the numerical value corresponding to a chosen relatively extreme percentile from the observed 30-year climatology of each UK site.</p>

scholarly journals Fuzzy Postprocessing to Advance the Quality of Continental Seasonal Hydrological Forecasts for River Basin Management

2020 ◽  
Vol 21 (10) ◽  
pp. 2375-2389
Author(s):  
Hector Macian-Sorribes ◽  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Manuel Pulido-Velazquez
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Hydrological Model ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Quantile Mapping ◽  
River Basin Scale ◽  
Basin Scale ◽  
Streamflow Forecasts

AbstractStreamflow forecasting services driven by seasonal meteorological forecasts from dynamic prediction systems deliver valuable information for decision-making in the water sector. Moving beyond the traditional river basin boundaries, large-scale hydrological models enable a coordinated, efficient, and harmonized anticipation and management of water-related risks (droughts, floods). However, the use of forecasts from such models at the river basin scale remains a challenge, depending on how the model reproduces the hydrological features of each particular river basin. Consequently, postprocessing of forecasts is a crucial step to ensure usefulness at the river basin scale. In this paper we present a methodology to postprocess seasonal streamflow forecasts from large-scale hydrological models and advance their quality for local applications. It consists of fuzzy logic systems that bias-adjust seasonal forecasts from a large-scale hydrological model by comparing its modeled streamflows with local observations. The methodology is demonstrated using forecasts from the pan-European hydrological model E-HYPE at the Jucar River basin (Spain). Fuzzy postprocessed forecasts are compared to postprocessed forecasts derived from a quantile mapping approach as a benchmark. Fuzzy postprocessing was able to provide skillful streamflow forecasts for the Jucar River basin, keeping most of the skill of raw E-HYPE forecasts and also outperforming quantile-mapping-based forecasts. The proposed methodology offers an efficient one-to-one mapping between large-scale modeled streamflows and basin-scale observations preserving its temporal dependence structure and can adapt its input set to increase the skill of postprocessed forecasts.

Does the application of multiple hydrological models improve seasonal streamflow forecasting skill?

2020 ◽  
Author(s):  
Bastian Klein ◽  
Ilias Pechlivanidis ◽  
Louise Arnal ◽  
Louise Crochemore ◽  
Dennis Meissner ◽  
...  
Keyword(s):  
Central Europe ◽  
Bayesian Model Averaging ◽  
Forecast Skill ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Seasonal Streamflow

<p>Many sectors, such as hydropower, agriculture, water supply and waterway transport, need information about the possible evolution of meteorological and hydrological conditions in the next weeks and months to optimize their decision processes on a long term. With increasing availability of meteorological seasonal forecasts, hydrological seasonal forecasting systems have been developed all over the world in the last years. Many of them are running in operational mode. On European scale the European Flood Awareness System EFAS and SMHI are operationally providing seasonal streamflow forecasts. In the context of the EU-Horizon2020 project IMPREX additionally a national scale forecasting system for German waterways operated by BfG was available for the analysis of seasonal forecasts from multiple hydrological models.</p><p>Statistical post processing tools could be used to estimate the predictive uncertainty of the forecasted variable from deterministic / ensemble forecasts of a single / multi-model forecasting system. Raw forecasts shouldn’t be used directly by users without statistical post-processing because of various biases. To assess the added potential benefit of the application of a hydrological multi-model ensemble, the forecasting systems from EFAS, SMHI and BfG were forced by re-forecasts of the ECMWF’s Seasonal Forecast System 4 and the resulting seasonal streamflow forecasts have been verified for 24 gauges across Central Europe. Additionally two statistical forecasting methods - Ensemble Model Output Statistics EMOS and Bayesian Model Averaging BMA - have been applied to post-process the forecasts.</p><p>Overall, seasonal flow forecast skill is limited in Central Europe before and after post-processing with a current predictability of 1-2 months. The results of the multi-model analysis indicate that post-processing of raw forecasts is necessary when observations are used as reference. Post-processing improves forecast skill significantly for all gauges, lead times and seasons. The multi-model combination of all models showed the highest skill compared to the skill of the raw forecasts and the skill of the post-processed results of the individual models, i.e. the application of several hydrological models for the same region improves skill, due to the different model strengths.</p>

scholarly journals Evaluating post-processing approaches for monthly and seasonal streamflow forecasts

2018 ◽  
Vol 22 (12) ◽  
pp. 6257-6278 ◽  
Author(s):  
Fitsum Woldemeskel ◽  
David McInerney ◽  
Julien Lerat ◽  
Mark Thyer ◽  
Dmitri Kavetski ◽  
...  
Keyword(s):  
Skill Score ◽  
Climatic Conditions ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Processing Scheme ◽  
Wide Range ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Seasonal Streamflow ◽  
Sharpness Metrics

Abstract. Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure, and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates post-processing approaches based on three transformations – logarithmic (Log), log-sinh (Log-Sinh), and Box–Cox with λ=0.2 (BC0.2) – and identifies the best-performing scheme for post-processing monthly and seasonal (3-months-ahead) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau's operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing of forecasts substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme substantially outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh schemes. The improvements in forecast reliability and sharpness achieved using the BC0.2 post-processing scheme will help water managers and users of the forecasting service make better-informed decisions in planning and management of water resources. Highlights. Uncorrected and post-processed streamflow forecasts (using three transformations, namely Log, Log-Sinh, and BC0.2) are evaluated over 300 diverse Australian catchments. Post-processing enhances streamflow forecast reliability, increasing the percentage of catchments with reliable predictions from 50 % to over 90 %. The BC0.2 transformation achieves substantially better forecast sharpness than the Log-Sinh and Log transformations, particularly in dry catchments.

scholarly journals A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts

2010 ◽  
Vol 14 (8) ◽  
pp. 1639-1653 ◽  
Author(s):  
G. Thirel ◽  
E. Martin ◽  
J.-F. Mahfouf ◽  
S. Massart ◽  
S. Ricci ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Meteorological Data ◽  
Skill Score ◽  
Ensemble Prediction ◽  
Time Range ◽  
Prediction Errors ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Streamflow Forecasts ◽  
Assimilation System

Abstract. The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.

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