scholarly journals Seasonal streamflow forecasting by conditioning climatology with precipitation indices

2016 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger ◽  
Charles Perrin
Keyword(s):  
Time Series ◽  
Hydrological Model ◽  
Historical Data ◽  
Standardized Precipitation Index ◽  
Low Flow ◽  
Drought Risk ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

Abstract. Many fields such as drought risk assessment or reservoir management can benefit from long-range streamflow forecasts. The simplest way to make probabilistic streamflow forecasts can be to use historical streamflow time series, if available. Another approach is to use ensemble climate scenarios as input to a hydrological model. Climatology (i.e. time series of climate conditions recorded over a long time period) has long been used in long-range streamflow forecasting. However, in the last decade, the use of general circulation model (GCM) outputs as input to hydrological models has developed. While precipitation climatology and historical streamflows offer reliable ensembles, forecasts based on GCM outputs can offer sharper ensembles, partly due to the initialisation of GCMs and hydrological models on current conditions. This study proposes to condition historical data based on GCM precipitation forecasts to get the most out of both data sources and improve seasonal streamflow forecasting in France. Four conditioning statistics based on ECMWF System 4 forecasts of cumulative precipitation and of the Standardized Precipitation Index (SPI) were used to select traces within historical streamflows and historical precipitations. The four conditioned precipitation scenarios were used as input to the GR6J hydrological model to obtain eight conditioned streamflow forecast scenarios. These streamflow scenarios were compared to three references: an ensemble based on historical streamflows, the widespread Ensemble Streamflow Prediction (ESP) ensemble, and System 4 precipitation forecasts. These ensembles were evaluated based on their sharpness, reliability and overall performance. An overall comparison of forecast ensembles showed that conditioning past observations based on the three-month Standardized Precipitation Index (SPI3) improved the sharpness of ensembles based on historical data, while maintaining a good reliability. An evaluation of forecast ensembles for low-flow forecasting showed that the SPI3-conditioned ensembles provided reliable forecasts of low-flow duration and deficit volume based on the 80th exceedance percentile. Last, drought risk forecasting was illustrated for the 2003 drought.

scholarly journals Seasonal streamflow forecasting by conditioning climatology with precipitation indices

2017 ◽  
Vol 21 (3) ◽  
pp. 1573-1591 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger ◽  
Charles Perrin
Keyword(s):  
Long Range ◽  
Precipitation Forecast ◽  
Low Flow ◽  
Drought Risk ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Precipitation Indices ◽  
Seasonal Streamflow ◽  
Prediction Approach ◽  
The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

scholarly journals Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

2016 ◽  
Author(s):  
Louise Crochemore ◽  
M.-H. Ramos ◽  
Florian Pappenberger
Keyword(s):  
Bias Correction ◽  
Linear Scaling ◽  
Drought Risk ◽  
Seasonal Precipitation ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Streamflow Forecasts ◽  
Distribution Mapping ◽  
Seasonal Streamflow

Abstract. Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias corrected precipitation and streamflow ensemble forecasts in sixteen catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy, and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e., ESP method), is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contribute mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability.

scholarly journals Objective functions used as performance metrics for hydrological models: state-of-the-art and critical analysis

RBRH ◽  
2020 ◽  
Vol 25 ◽  
Author(s):  
Paloma Mara de Lima Ferreira ◽  
Adriano Rolim da Paz ◽  
Juan Martín Bravo
Keyword(s):  
Time Series ◽  
Model Calibration ◽  
Hydrological Model ◽  
Performance Metrics ◽  
Low Flow ◽  
Hydrological Models ◽  
Objective Functions ◽  
Flow Rates ◽  
Daily Streamflow ◽  
Synthetic Time Series

ABSTRACT Hydrological models (HMs) can be applied for different purposes, and a key step is model calibration using objective functions (OF) to quantify the agreement between observed and calculated discharges. Fully understanding the OF is important to properly take advantage of model calibration and interpret the results. This study evaluates 36 OF proposed in the literature, considering two watersheds of different hydrological regimes. Daily simulated streamflow time-series, using a distributed hydrological model (MGB-IPH), and ten daily streamflow synthetic time-series, generated from the observed and calculated streamflows, were used in the analysis of each watershed. These synthetic data were used to evaluate how does each metric evaluate hypothetical cases that present isolated very well known error behaviors. Despite of all NSE-derived (Nash-Sutcliffe efficiency) metrics that use the square of the residuals in their formulation have shown higher sensitivity to errors in high flows, the ones that use daily and monthly averages of flow rates in absolute terms were more stringent than the others to assess HMs performance. Low flow errors were better evaluated by metrics that use the flow logarithm. The constant presence of zero flow rates deteriorate them significantly, with the exception of the metrics TRMSE (Transformed root mean square error) did not demonstrate this problem. An observed limitation of the formulations of some metrics was that the errors of overestimation or underestimation are compensated. Our results reassert that each metric should be interpreted specifically thinking about the aspects it has been proposed for, and simultaneously taking into account a set of metrics would lead to a broader evaluation of HM ability (e.g. multiobjective model evaluation). We recommend that the use of synthetic time series as those proposed in this work could be useful as an auxiliary step towards better understanding the evaluation of a calibrated hydrological model for each study case, taking into account model capabilities and observed hydrologic regime characteristics.

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 Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

2016 ◽  
Vol 20 (9) ◽  
pp. 3601-3618 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger
Keyword(s):  
Bias Correction ◽  
Linear Scaling ◽  
Drought Risk ◽  
Seasonal Precipitation ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Streamflow Forecasts ◽  
Distribution Mapping ◽  
Seasonal Streamflow

Abstract. Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability.

scholarly journals Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and statistical consistency

2017 ◽  
Author(s):  
Diana Lucatero ◽  
Henrik Madsen ◽  
Jens C. Refsgaard ◽  
Jacob Kidmose ◽  
Karsten H. Jensen
Keyword(s):  
Hydrological Model ◽  
Predictive Distribution ◽  
Low Flow ◽  
Model Errors ◽  
Streamflow Prediction ◽  
Initial State ◽  
Lower Accuracy ◽  
Spatially Distributed ◽  
Streamflow Forecasts ◽  
Statistical Consistency

Abstract. In the present study we analyze the effect of bias adjustments in both meteorological and streamflow forecasts on skill and reliability of monthly average streamflow and low flow forecasts. Both raw and pre-processed meteorological seasonal forecast from the European Center for Medium-Range Weather Forecasts (ECMWF) are used as inputs to a spatially distributed, coupled surface – subsurface hydrological model based on the MIKE SHE code in order to generate streamflow predictions up to seven months in advance. In addition to this, we postprocess streamflow predictions using an empirical quantile mapping that adjusts the predictive distribution in order to match the observed one. Bias, skill and statistical consistency are the qualities evaluated throughout the forecast generating strategies and we analyze where the different strategies fall short to improve them. ECMWF System 4-based streamflow forecasts tend to show a lower accuracy level than those generated with an ensemble of historical observations, a method commonly known as Ensemble Streamflow Prediction (ESP). This is particularly true at longer lead times, for the dry season and for streamflow stations that exhibit low hydrological model errors. Biases in the mean are better removed by postprocessing that in turn is reflected in the higher level of statistical consistency. However, in general, the reduction of these biases is not enough to ensure a higher level of accuracy than the ESP forecasts. This is true for both monthly mean and minimum yearly streamflow forecasts. We highlight the importance of including a better estimation of the initial state of the catchment, which will increase the capability of the system to forecast streamflow at longer leads.

scholarly journals Improving statistical forecasts of seasonal streamflows using hydrological model output

2012 ◽  
Vol 9 (7) ◽  
pp. 8701-8736 ◽  
Author(s):  
D. E. Robertson ◽  
P. Pokhrel ◽  
Q. J. Wang
Keyword(s):  
Hydrological Model ◽  
Historical Record ◽  
Base Flow ◽  
Seasonal Forecasting ◽  
Streamflow Forecasting ◽  
Eastern Australia ◽  
True Source ◽  
Hybrid Forecasting ◽  
Forecasting System ◽  
Seasonal Streamflow

Abstract. Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

scholarly journals Correcting Errors in Streamflow Forecast Ensemble Mean and Spread

2008 ◽  
Vol 9 (1) ◽  
pp. 132-148 ◽  
Author(s):  
Andrew W. Wood ◽  
John C. Schaake
Keyword(s):  
River Basin ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Summer Period ◽  
Streamflow Forecast ◽  
Ensemble Mean ◽  
Ensemble Streamflow Prediction ◽  
Deterministic Components ◽  
Seasonal Streamflow

Abstract When hydrological models are used for probabilistic streamflow forecasting in the Ensemble Streamflow Prediction (ESP) framework, the deterministic components of the approach can lead to errors in the estimation of forecast uncertainty, as represented by the spread of the forecast ensemble. One avenue for correcting the resulting forecast reliability errors is to calibrate the streamflow forecast ensemble to match observed error characteristics. This paper outlines and evaluates a method for forecast calibration as applied to seasonal streamflow prediction. The approach uses the correlation of forecast ensemble means with observations to generate a conditional forecast mean and spread that lie between the climatological mean and spread (when the forecast has no skill) and the raw forecast mean with zero spread (when the forecast is perfect). Retrospective forecasts of summer period runoff in the Feather River basin, California, are used to demonstrate that the approach improves upon the performance of traditional ESP forecasts by reducing errors in forecast mean and improving spread estimates, thereby increasing forecast reliability and skill.

Seasonal streamflow forecasting - Which are the drivers controlling the forecast quality?

2020 ◽  
Author(s):  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Thomas Bosshard
Keyword(s):  
A Priori ◽  
Hydrological Regime ◽  
Climatic Conditions ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Streamflow Forecasts ◽  
Key Drivers ◽  
Seasonal Streamflow ◽  
Forecast Quality

<p>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.</p>

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