scholarly journals Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

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

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

scholarly journals Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

2017 ◽  
Vol 21 (10) ◽  
pp. 5273-5291 ◽  
Author(s):  
Harm-Jan F. Benninga ◽  
Martijn J. Booij ◽  
Renata J. Romanowicz ◽  
Tom H. M. Rientjes
Keyword(s):  
Hydrological Model ◽  
Initial Conditions ◽  
Snow Accumulation ◽  
Lead Times ◽  
Model Parameters ◽  
Streamflow Forecasting ◽  
Weather Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Hydrometeorological Conditions

Abstract. The paper presents a methodology that gives insight into the performance of ensemble streamflow-forecasting systems. We have developed an ensemble forecasting system for the Biała Tarnowska, a mountainous river catchment in southern Poland, and analysed the performance for lead times ranging from 1 to 10 days for low, medium and high streamflow and different hydrometeorological conditions. Precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts served as inputs to a deterministic lumped hydrological (HBV) model. Due to a non-homogeneous bias in time, pre- and post-processing of the meteorological and streamflow forecasts are not effective. The best forecast skill, relative to alternative forecasts based on meteorological climatology, is shown for high streamflow and snow accumulation low-streamflow events. Forecasts of medium-streamflow events and low-streamflow events under precipitation deficit conditions show less skill. To improve performance of the forecasting system for high-streamflow events, the meteorological forecasts are most important. Besides, it is recommended that the hydrological model be calibrated specifically on low-streamflow conditions and high-streamflow conditions. Further, it is recommended that the dispersion (reliability) of the ensemble streamflow forecasts is enlarged by including the uncertainties in the hydrological model parameters and the initial conditions, and by enlarging the dispersion of the meteorological input 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.

scholarly journals Assessment of an ensemble seasonal streamflow forecasting system for Australia

2017 ◽  
Author(s):  
James C. Bennett ◽  
Quan J. Wang ◽  
David E. Robertson ◽  
Andrew Schepen ◽  
Ming Li ◽  
...  
Keyword(s):  
Lead Times ◽  
Streamflow Forecasting ◽  
Forecast Horizon ◽  
Australian Continent ◽  
Wide Range ◽  
Streamflow Forecasts ◽  
Ephemeral Rivers ◽  
Forecasting System ◽  
Rainfall Runoff Model ◽  
Seasonal Streamflow

Abstract. Despite an increasing availability of skillful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skillful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skillful forecasts at shorter lead times (

scholarly journals Prediction of snowmelt derived streamflow in a wetland dominated prairie basin

2010 ◽  
Vol 7 (1) ◽  
pp. 1103-1141 ◽  
Author(s):  
X. Fang ◽  
J. W. Pomeroy ◽  
C. J. Westbrook ◽  
X. Guo ◽  
A. G. Minke ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Snow Accumulation ◽  
Model Parameters ◽  
Field Observations ◽  
Runoff Generation ◽  
Stream Channels ◽  
Physically Based ◽  
Set Up ◽  
Spot 5

Abstract. The eastern Canadian Prairies are dominated by cropland, pasture, woodland and wetland areas. The region is characterized by many poor and internal drainage systems and large amounts of surface water storage. Consequently, basins here have proven challenging to hydrological model predictions which assume good drainage to stream channels. The Cold Regions Hydrological Modelling platform (CRHM) is an assembly system that can be used to set up physically based, flexible, object oriented models. CRHM was used to create a prairie hydrological model for the externally drained Smith Creek Research Basin (~400 km2), east-central Saskatchewan. Physically based modules were sequentially linked in CRHM to simulate snow processes, frozen soils, variable contributing area and wetland storage and runoff generation. Five "representative basins" (RBs) were used and each was divided into seven hydrological response units (HRUs): fallow, stubble, grassland, river channel, open water, woodland, and wetland as derived from a supervised classification of SPOT 5 imagery. Two types of modelling approaches calibrated and uncalibrated, were set up for 2007/08 and 2008/09 simulation periods. For the calibrated modelling, only the surface depression capacity of upland area was calibrated in the 2007/08 simulation period by comparing simulated and observed hydrographs; while other model parameters and all parameters in the uncalibrated modelling were estimated from field observations of soils and vegetation cover, SPOT 5 imagery, and analysis of drainage network and wetland GIS datasets as well as topographic map based and LiDAR DEMs. All the parameters except for the initial soil properties and antecedent wetland storage were kept the same in the 2008/09 simulation period. The model performance in predicting snowpack, soil moisture and streamflow was evaluated and comparisons were made between the calibrated and uncalibrated modelling for both simulation periods. Calibrated and uncalibrated predictions of snow accumulation were very similar and compared fairly well with the distributed field observations for the 2007/08 period with slightly poorer results for the 2008/09 period. Soil moisture content at a point during the early spring was adequately simulated and very comparable between calibrated and uncalibrated results for both simulation periods. The calibrated modelling had somewhat better performance in simulating spring streamflow in both simulation periods, whereas the uncalibrated modelling was still able to capture the streamflow hydrographs with good accuracy. This suggests that prediction of prairie basins without calibration is possible if sufficient data on meteorology, basin landcover and physiography are available.

scholarly journals Global simulations of the atmosphere at 1.45 km grid-spacing with the Integrated Forecasting System

2020 ◽  
Author(s):  
Peter Düben ◽  
Nils Wedi ◽  
Sami Saarinen ◽  
Christian Zeman
Keyword(s):  
Initial Conditions ◽  
Weather Forecast ◽  
Wave Model ◽  
Shallow Convection ◽  
Grid Spacing ◽  
Time Step ◽  
Weather Forecasts ◽  
Forecasting System ◽  
Diabatic Forcing ◽  
Physical Phenomena

<p>Global simulations with 1.45 km grid-spacing are presented that were performed with the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Simulations are uncoupled (without ocean, sea-ice or wave model), using 62 or 137 vertical levels and the full complexity of weather forecast simulations including recent date initial conditions, real-world topography, and state-of-the-art physical parametrizations and diabatic forcing including shallow convection, turbulent diffusion, radiation and five categories for the water substance (vapour, liquid, ice, rain, snow). Simulations are evaluated with regard to computational efficiency and model fidelity. Scaling results are presented that were performed on the fastest supercomputer in Europe - Piz Daint (Top 500, Nov 2018). Important choices for the model configuration at this unprecedented resolution for the IFS are discussed such as the use of hydrostatic and non-hydrostatic equations or the time resolution of physical phenomena which is defined by the length of the time step. </p><p>Our simulations indicate that the IFS model — based on spectral transforms with a semi-implicit, semi-Lagrangian time-stepping scheme in contrast to more local discretization techniques — can provide a meaningful baseline reference for O(1) km global simulations.</p>

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 What Is the Predictability Limit of Midlatitude Weather?

2019 ◽  
Vol 76 (4) ◽  
pp. 1077-1091 ◽  
Author(s):  
Fuqing Zhang ◽  
Y. Qiang Sun ◽  
Linus Magnusson ◽  
Roberto Buizza ◽  
Shian-Jiann Lin ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Forecast Model ◽  
Small Scale ◽  
Lead Times ◽  
Winter Storms ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Order Of Magnitude

Abstract Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

Implications of Ensemble Quantitative Precipitation Forecast Errors on Distributed Streamflow Forecasting

2010 ◽  
Vol 11 (1) ◽  
pp. 69-86 ◽  
Author(s):  
Giuseppe Mascaro ◽  
Enrique R. Vivoni ◽  
Roberto Deidda
Keyword(s):  
Hydrological Model ◽  
Initial Conditions ◽  
Forecast Accuracy ◽  
Precipitation Forecast ◽  
Forecast Errors ◽  
Distributed Hydrological Model ◽  
Streamflow Forecasting ◽  
Ensemble Forecasts ◽  
Wide Range ◽  
Operational Flow

Abstract Evaluating the propagation of errors associated with ensemble quantitative precipitation forecasts (QPFs) into the ensemble streamflow response is important to reduce uncertainty in operational flow forecasting. In this paper, a multifractal rainfall downscaling model is coupled with a fully distributed hydrological model to create, under controlled conditions, an extensive set of synthetic hydrometeorological events, assumed as observations. Subsequently, for each event, flood hindcasts are simulated by the hydrological model using three ensembles of QPFs—one reliable and the other two affected by different kinds of precipitation forecast errors—generated by the downscaling model. Two verification tools based on the verification rank histogram and the continuous ranked probability score are then used to evaluate the characteristics of the correspondent three sets of ensemble streamflow forecasts. Analyses indicate that the best forecast accuracy of the ensemble streamflows is obtained when the reliable ensemble QPFs are used. In addition, results underline (i) the importance of hindcasting to create an adequate set of data that span a wide range of hydrometeorological conditions and (ii) the sensitivity of the ensemble streamflow verification to the effects of basin initial conditions and the properties of the ensemble precipitation distributions. This study provides a contribution to the field of operational flow forecasting by highlighting a series of requirements and challenges that should be considered when hydrologic ensemble forecasts are evaluated.

scholarly journals A Bayesian approach to probabilistic streamflow forecasts

2012 ◽  
Vol 15 (2) ◽  
pp. 381-391 ◽  
Author(s):  
Hui Wang ◽  
Brian Reich ◽  
Yeo Howe Lim
Keyword(s):  
Climate Models ◽  
Principal Component ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Streamflow Forecasting ◽  
Promising Alternative ◽  
Probabilistic Information ◽  
Monthly Streamflow ◽  
Streamflow Forecasts ◽  
Streamflow Data

One-month-ahead streamflow forecasting is important for water utilities to manage water resources such as irrigation water usage and hydropower generation. While deterministic streamflow forecasts have been utilized extensively in research and practice, ensemble streamflow forecasts and probabilistic information are gaining more attention. This study aims to examine a multivariate linear Bayesian regression approach to provide probabilistic streamflow forecasts by incorporating gridded precipitation forecasts from climate models and lagged monthly streamflow data. Principal component analysis is applied to reduce the size of the regression model. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the posterior distribution of model parameters. The proposed approach is tested on gauge data acquired during 1961–2000 in North Carolina. Results reveal that the proposed method is a promising alternative forecasting technique and that it performs well for probabilistic streamflow forecasts.

scholarly journals Application of long-range weather forecasts to agricultural decision problems in Europe

2010 ◽  
Vol 149 (1) ◽  
pp. 15-22 ◽  
Author(s):  
P. CALANCA ◽  
D. BOLIUS ◽  
A. P. WEIGEL ◽  
M. A. LINIGER
Keyword(s):  
Long Range ◽  
Extreme Weather Events ◽  
Lead Times ◽  
Widespread Application ◽  
Weather Forecasts ◽  
Weather Events ◽  
Forecasting System ◽  
Scheduling Irrigation ◽  
Range Forecasts

SUMMARYAgriculture can benefit substantially from long-range weather forecasts, for the month or the season, which can help to optimize farming operations and deal more effectively with the adverse impacts of climate variability, including extreme weather events. In the context of climate change, long-range weather forecasts also represent key elements for the development of adaptation strategies. In spite of an undeniable potential, long-range forecasts issued for instance by the European Centre for Medium-Range Weather Forecasts (ECMWF) have yet to find widespread application in European agriculture. To address partially the question of why this is the case, the performance of the ECMWF monthly ensemble forecasting system was examined. It was noted that predictability is currently limited to about 3 weeks for temperature and 2 weeks for precipitation and solar radiation. This may appear deceptive at first sight, but it was noticed that precipitation forecasts over a month are, overall, at least as valuable as information obtained from observed climatology. Encouraged by this finding, the possibility of using monthly forecasts to predict soil water availability was tested. In an operational context, this could serve as a basis for scheduling irrigation. Positive skills were found for lead times of up to 1 month. It was concluded that more systematic investigations of the possibilities offered by long-range forecasts should be undertaken in the future. However, this will require additional efforts to increase the quality of the forecasts, design appropriate application tools and promote the dissemination of the outcome within the agriculture community.

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