Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and 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.

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Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: the effect of preprocessing and post-processing on skill and statistical consistency

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3601-2018 ◽

2018 ◽

Vol 22 (7) ◽

pp. 3601-3617 ◽

Cited By ~ 9

Author(s):

Diana Lucatero ◽

Henrik Madsen ◽

Jens C. Refsgaard ◽

Jacob Kidmose ◽

Karsten H. Jensen

Keyword(s):

Hydrological Model ◽

Mapping Technique ◽

Model Errors ◽

Post Processing ◽

Seasonal Forecasts ◽

Initial State ◽

Lower Accuracy ◽

Monthly Streamflow ◽

Streamflow Forecasts ◽

Statistical Consistency

Abstract. In the present study we analyze the effect of bias adjustments in both meteorological and streamflow forecasts on the skill and statistical consistency of monthly streamflow and yearly minimum daily flow forecasts. Both raw and preprocessed meteorological seasonal forecasts from the European Centre 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. Streamflow predictions are then generated up to 7 months in advance. In addition to this, we post-process streamflow predictions using an empirical quantile mapping technique. 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 post-processing that in turn is reflected in the higher level of statistical consistency. However, in general, the reduction of these biases is not sufficient to ensure a higher level of accuracy than the ESP forecasts. This is true for both monthly mean and minimum yearly streamflow forecasts. We discuss the importance of including a better estimation of the initial state of the catchment, which may increase the capability of the system to forecast streamflow at longer leads.

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Multi-variable SWAT model calibration with remotely sensed evapotranspiration and observed flow

RBRH ◽

10.1590/2318-0331.011716090 ◽

2017 ◽

Vol 22 (0) ◽

Cited By ~ 10

Author(s):

Ana Clara Lazzari Franco ◽

Nadia Bernardi Bonumá

Keyword(s):

Model Calibration ◽

Hydrological Model ◽

Swat Model ◽

Model Performance ◽

Low Flow ◽

Single Variable ◽

Daily Step ◽

Streamflow Prediction ◽

Negro River ◽

Monthly Streamflow

ABSTRACT Although intrinsic, uncertainty for hydrological model estimation is not always reported. The aim of this study is to evaluate the use of satellite-based evapotranspiration on SWAT model calibration, regarding uncertainty and model performance in streamflow simulation. The SWAT model was calibrated in a monthly step and validated in monthly (streamflow and evapotranspiration) and daily steps (streamflow only). The validation and calibration period covers the years from 2006 to 2009 and the study area is the upper Negro river basin, situated in Santa Catarina and Paraná. SWAT-CUP was used to calibrate and validate the model, using SUFI-2 with KGE (Kling-Gupta Efficiency) as objective function. Different calibration strategies were evaluated, considering single-variable and multi-variable calibration, using streamflow and evapotranspiration. Compared to conventional single-variable calibration (streamflow only), multi-variable calibration (streamflow and evapotranspiration, simultaneously) produce better streamflow performance, especially for low flow periods and daily step validation. Despite that, no evidence of reduction of streamflow prediction uncertainty was observed. SWAT model calibration using solely evapotranspiration still requires further studies.

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A past discharges assimilation system for ensemble streamflow forecasts over France – Part 1: Description and validation of the assimilation system

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-2413-2010 ◽

2010 ◽

Vol 7 (2) ◽

pp. 2413-2453 ◽

Cited By ~ 1

Author(s):

G. Thirel ◽

E. Martin ◽

J.-F. Mahfouf ◽

S. Massart ◽

S. Ricci ◽

...

Keyword(s):

Soil Moisture ◽

Hydraulic Conductivity ◽

State Variables ◽

Streamflow Prediction ◽

Initial State ◽

Model Soil ◽

Streamflow Forecasts ◽

Set Up ◽

The Impact ◽

Assimilation System

Abstract. Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using observed streamflows in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-day period on six different configurations, including two different physics schemes of the model (the use of an exponential profile of hydraulic conductivity or not) and, for each one, three different ways of considering the model soil moisture in the BLUE state variables. Respect of the linearity hypothesis of the BLUE was verified by assessing of the impact of iterations of the BLUE. The configuration including the use of the exponential profile of hydraulic conductivity and the combination of the moisture of the two soil layers in the state variable showed a significant improvement of streamflow simulations. It led to a significantly better simulation than the reference one, and the lowest soil moisture corrections. These results were confirmed by the study of the impacts of the past discharge assimilation system on a set of 49 independent stations.

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Seasonal streamflow forecasting by conditioning climatology with precipitation indices

10.5194/hess-2016-285 ◽

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.

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A past discharges assimilation system for ensemble streamflow forecasts over France – Part 1: Description and validation of the assimilation system

Hydrology and Earth System Sciences ◽

10.5194/hess-14-1623-2010 ◽

2010 ◽

Vol 14 (8) ◽

pp. 1623-1637 ◽

Cited By ~ 52

Author(s):

G. Thirel ◽

E. Martin ◽

J.-F. Mahfouf ◽

S. Massart ◽

S. Ricci ◽

...

Keyword(s):

Soil Moisture ◽

Hydraulic Conductivity ◽

State Variables ◽

Streamflow Prediction ◽

Initial State ◽

Model Soil ◽

Streamflow Forecasts ◽

Set Up ◽

The Impact ◽

Assimilation System

Abstract. Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using streamflow observations in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-day period on six different configurations, including two different physics schemes of the model (the use of an exponential profile of hydraulic conductivity or not) and, for each one, three different ways of considering the model soil moisture in the BLUE state variables. Respect of the linearity hypothesis of the BLUE was verified by assessing of the impact of iterations of the BLUE. The configuration including the use of the exponential profile of hydraulic conductivity and the combination of the moisture of the two soil layers in the state variable showed a significant improvement of streamflow simulations. It led to a significantly better simulation than the reference one, and the lowest soil moisture corrections. These results were confirmed by the study of the impacts of the past discharge assimilation system on a set of 49 independent stations.

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Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Hydrology and Earth System Sciences ◽

10.5194/hess-21-1573-2017 ◽

2017 ◽

Vol 21 (3) ◽

pp. 1573-1591 ◽

Cited By ~ 19

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.

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Spatio-temporal Analysis of Hydrological Drought at Catchment Scale Using a Spatially-distributed Hydrological Model

Procedia Engineering ◽

10.1016/j.proeng.2016.07.577 ◽

2016 ◽

Vol 154 ◽

pp. 738-744 ◽

Cited By ~ 5

Author(s):

Vitali Diaz Mercado ◽

Gerald Corzo Perez ◽

Dimitri Solomatine ◽

Henny A.J. van Lanen

Keyword(s):

Hydrological Model ◽

Temporal Analysis ◽

Hydrological Drought ◽

Distributed Hydrological Model ◽

Catchment Scale ◽

Spatially Distributed ◽

Spatio Temporal

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Optimized planning and operation of interconnected multi-purpose reservoir systems using integrated modeling for climate change adaptation

10.5194/egusphere-egu21-8884 ◽

2021 ◽

Author(s):

Patrick Nistahl ◽

Tim Müller ◽

Gerhard Riedel ◽

Hannes Müller-Thomy ◽

Günter Meon

Keyword(s):

Climate Change ◽

Reservoir Operation ◽

Climate Models ◽

Hydrological Model ◽

Regional Climate ◽

Regional Climate Models ◽

Flood Protection ◽

Performance Criteria ◽

Low Flow ◽

Operation Model

Climate change impact studies performed for Northern Germany indicate a growing demand for water storage capacity to account for flood protection, low flow augmentation, drinking and agricultural water supply. At the same time, larger storage volumes for hydropower plants can be used to cope with the demands of changing energy supply from fossil to renewable energies. To tackle these challenges for the next decades, a novel reservoir system planning instrument is developed, which consists of combined numerical models and evaluation components. It allows to model simultaneously the current interconnected infrastructure of reservoirs as well as additional planning variants (structural and operational) as preparation for climate change. This planning instrument consists of a hydrological model and a detailed reservoir operation model.As hydrological model, the conceptual, semi-distributed version of PANTA RHEI is applied. &#160;Bias-corrected regional climate models (based on the RCP 8.5 scenario) are used as meteorological input. The hydrological model is coupled with a detailed reservoir operation model that replicates the complex rules of various interconnected reservoirs based on an hourly time step including pumped storage plants, which may have a subsurface reservoir as a lower basin. Downstream of the reservoirs, the hydrological model is used for routing the reservoir outflows and simulating natural side inflows. In areas of particular interest for flood protection, the hydrological routing is substituted with 2D hydraulic models to calculate the flood risk in terms of expected annual flood damage based on resulting inundation areas.For the performance analysis, the simulation runs for all integrated modeling variants are evaluated for a reference period (1971-2000) and for future periods (2041-2070). Performance criteria involve flood protection, drinking water supply, low flow augmentation and energy production. These performance criteria will be used as stake holder information as well as a base for further optimization and ranking of the planning variants.The combination of the hydrological model and the reservoir operation model shows a good performance of the existing complex hydraulic infrastructure using observed meteorological forcing as input. The usage of regional climate models as input shows a wide dispersion of several performance criteria, confirming the expected need for an innovative optimization scheme and the communication of the underlying uncertainties.

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