scholarly journals A Method to Account for QPF Spatial Displacement Errors in Short-Term Ensemble Streamflow Forecasting

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3505
Author(s):  
Bradley Carlberg ◽  
Kristie Franz ◽  
William Gallus
Keyword(s):  
Heavy Precipitation ◽  
Hydrologic Model ◽  
Probability Of Detection ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Spatial Displacement ◽  
North Central ◽  
Distributed Hydrologic Model ◽  
Streamflow Forecasts ◽  
Made In

To account for spatial displacement errors common in quantitative precipitation forecasts (QPFs), a method using systematic shifting of QPF fields was tested to create ensemble streamflow forecasts. While previous studies addressed spatial displacement using neighborhood approaches, shifting of QPF accounts for those errors while maintaining the structure of predicted systems, a feature important in hydrologic forecasts. QPFs from the nine-member High-Resolution Rapid Refresh Ensemble were analyzed for 46 forecasts from 6 cases covering 17 basins within the National Weather Service North Central River Forecast Center forecasting region. Shifts of 55.5 and 111 km were made in the four cardinal and intermediate directions, increasing the ensemble size to 81 members. These members were input into a distributed hydrologic model to create an ensemble streamflow prediction. Overall, the ensemble using the shifted QPFs had an improved frequency of non-exceedance and probability of detection, and thus better predicted flood occurrence. However, false alarm ratio did not improve, likely because shifting multiple QPF ensembles increases the potential to place heavy precipitation in a basin where none actually occurred. A weighting scheme based on a climatology of displacements was tested, improving overall performance slightly compared to the approach using non-weighted members.

scholarly journals Skilful seasonal forecasts of streamflow over Europe?

2017 ◽  
Author(s):  
Louise Arnal ◽  
Hannah L. Cloke ◽  
Elisabeth Stephens ◽  
Fredrik Wetterhall ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Lead Time ◽  
Climate Model ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Streamflow Forecasts ◽  
Meteorological Observations ◽  
Seasonal Streamflow

Abstract. This paper presents a Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts, benchmarked against the Ensemble Streamflow Prediction (ESP) forecasting approach. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only. However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to seven months of lead time, for certain months within a season. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making. Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for most of Europe. Patterns in the EFAS seasonal streamflow hindcasts skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim to improve climate-model based seasonal streamflow forecasting.

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 The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions

2020 ◽  
Vol 24 (7) ◽  
pp. 3851-3870
Author(s):  
Alexander Kaune ◽  
Faysal Chowdhury ◽  
Micha Werner ◽  
James Bennett
Keyword(s):  
Water Allocation ◽  
Irrigation Season ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Additional Time ◽  
Available Water ◽  
Streamflow Forecasts ◽  
Reservoir Inflow ◽  
Seasonal Streamflow ◽  
Allocation Decisions

Abstract. The area to be cropped in irrigation districts needs to be planned according to the allocated water, which in turn is a function of the available water resource. Initially conservative estimates of future (in)flows in rivers and reservoirs may lead to unnecessary reduction of the water allocated. Though water allocations may be revised as the season progresses, inconsistency in allocation is undesirable to farmers as they may then not be able to use that water, leading to an opportunity cost in agricultural production. We assess the benefit of using reservoir inflow estimates derived from seasonal forecast datasets to improve water allocation decisions. A decision model is developed to emulate the feedback loop between simulated reservoir storage and water allocations to irrigated crops and is evaluated using inflow forecasts generated with the Forecast Guided Stochastic Scenarios (FoGSS) model, a 12-month ensemble streamflow forecasting system. Two forcings are used to generate the forecasts: ensemble streamflow prediction – ESP (historical rainfall) – and POAMA (calibrated rainfall forecasts from the POAMA climate prediction system). We evaluate the approach in the Murrumbidgee basin in Australia, comparing water allocations obtained with an expected reservoir inflow from FoGSS against the allocations obtained with the currently used conservative estimate based on climatology as well as against allocations obtained using observed inflows (perfect information). The inconsistency in allocated water is evaluated by determining the total changes in allocated water made every 15 d from the initial allocation at the start of the water year to the end of the irrigation season, including both downward and upward revisions of allocations. Results show that the inconsistency due to upward revisions in allocated water is lower when using the forecast datasets (POAMA and ESP) compared to the conservative inflow estimates (reference), which is beneficial to the planning of cropping areas by farmers. Overconfidence can, however, lead to an increase in undesirable downward revisions. This is more evident for dry years than for wet years. Over the 28 years for which allocation decisions are evaluated, we find that the accuracy of the available water estimates using the forecast ensemble improves progressively during the water year, especially 1.5 months before the start of the cropping season in November. This is significant as it provides farmers with additional time to make key decisions on planting. Our results show that seasonal streamflow forecasts can provide benefit in informing water allocation policies, particularly by earlier establishing final water allocations to farmers in the irrigation season. This allows them to plan better and use water allocated more efficiently.

A Flood Potential Index for Effective Communication of Streamflow Forecasts at Ungauged Communities

2020 ◽  
Vol 21 (4) ◽  
pp. 807-814 ◽  
Author(s):  
Felipe Quintero ◽  
Witold F. Krajewski ◽  
Marcela Rojas
Keyword(s):  
Peak Flow ◽  
Hydrologic Model ◽  
Drainage Network ◽  
Relative Importance ◽  
Proof Of Concept ◽  
Streamflow Forecasting ◽  
Annual Peak ◽  
Potential Index ◽  
Streamflow Forecasts ◽  
Forecasting System

AbstractThis study proposes a flood potential index suitable for use in streamflow forecasting at any location in a drainage network. We obtained the index by comparing the discharge magnitude derived from a hydrologic model and the expected mean annual peak flow at the spatial scale of the basin. We use the term “flood potential” to indicate that uncertainty is associated with this information. The index helps communicate flood potential alerts to communities near rivers where there are no quantitative records of historical floods to provide a reference. This method establishes a reference that we can compare to forecasted hydrographs and that facilitates communication of their relative importance. As a proof of concept, the authors present an assessment of the index as applied to the peak flows that caused severe floods in Iowa in June 2008. The Iowa Flood Center uses the proposed approach operationally as part of its real-time hydrologic forecasting system.

scholarly journals How Essential is Hydrologic Model Calibration to Seasonal Streamflow Forecasting?

2008 ◽  
Vol 9 (6) ◽  
pp. 1350-1363 ◽  
Author(s):  
Xiaogang Shi ◽  
Andrew W. Wood ◽  
Dennis P. Lettenmaier
Keyword(s):  
Bias Correction ◽  
Model Calibration ◽  
Forecast Error ◽  
Central Element ◽  
Hydrologic Model ◽  
Forecast Errors ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Error Statistics ◽  
Hydrologic Model Calibration

Abstract Hydrologic model calibration is usually a central element of streamflow forecasting based on the ensemble streamflow prediction (ESP) method. Evaluation measures of forecast errors such as root-mean-square error (RMSE) are heavily influenced by bias, which in turn is readily reduced by calibration. On the other hand, bias can also be reduced by postprocessing (e.g., “training” bias correction schemes based on retrospective simulation error statistics). This observation invites the question: How much is forecast error reduced by calibration, beyond what can be accomplished by postprocessing to remove bias? The authors address this question through retrospective evaluation of forecast errors at eight streamflow forecast locations distributed across the western United States. Forecast periods of length ranging from 1 to 6 months are investigated, for forecasts initiated from 1 December to 1 June, which span the period when most runoff occurs from snowmelt-dominated western U.S. rivers. ESP forecast errors are evaluated both for uncalibrated forecasts to which a percentile mapping bias correction approach is applied, and for forecasts from an objectively calibrated model without explicit bias correction. Using the coefficient of prediction (Cp), which essentially is a measure of the fraction of variance explained by the forecast, the authors find that the reduction in forecast error as measured by Cp that is achieved by bias correction alone is nearly as great as that resulting from hydrologic model calibration.

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