Scale-Dependent Worth of QPF for Real-Time Streamflow Forecasting

2020 ◽  
Author(s):  
Witold Krajewski ◽  
Ganesh Ghimire
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Time Horizon ◽  
Hydrologic Model ◽  
Open Loop ◽  
Streamflow Forecasting ◽  
Trade Off ◽  
Basin Scale ◽  
Medium Range ◽  
Streamflow Forecasts

<p>The authors explore uncertainty associated with the quantitative precipitation forecasts (QPF) and its implication to the predictability of real-time streamflow forecasts. Including rainfall forecasts into real-time streamflow forecasting system extends the forecast lead time. As rainfall is a key driver of rainfall-runoff models both past and future rainfall estimates should be used in streamflow and flood forecasting. Since both QPE and QPF are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. Particularly QPF is notorious for its significant uncertainty with respect to location, timing and magnitude. Operational hydrologic services often limit their use of the QPF to one or two days into the future. The authors study this problem systematically using operational models and QPF. Their focus is on scale-dependence of the trade-off between the QPF time horizon and streamflow accuracy. To address this question, the authors first perform comprehensive independent evaluation of QPF at about 140 basins with wide range of spatial scales (10 - 40000 km2) corresponding to U.S Geological Survey (USGS) streamflow monitoring stations over the state of Iowa in Midwestern United States. High Resolution Rapid Refresh (HRRR) is an hourly short-medium range rainfall forecast of up to 18 hours updated every hour with spatial resolution of about 3 km by 3 km. Six-hourly rainfall forecasts are available for up to seven days ahead. Since basins are hydrologically relevant, the authors perform HRRR skill verification for the years 2016-2019 using conventional verification techniques and mean areal precipitation (basin scale rainfall volume) with respect to multi-radar</p><p>multi-sensor (MRMS) QPE (gauge-corrected) rainfall. The authors show that the QPF errors/uncertainties are scale-dependent. The QPF skills show increase as the basin scale and lead time of the forecast increases at short-medium range. In the second part of the study, both QPE and QPFs are forced separately to the hydrologic model called hillslope-link model (HLM) used at the Iowa Flood Center for real-time streamflow forecasting for Iowa. The objective is to understand the contribution of QPF uncertainty structure on the skill of streamflow forecasts. Since real-time streamflow observations (15 minutes resolution) are available at USGS sites, the authors incorporate them using a simple data assimilation framework. Several scenarios of forecasts, such as open-loop combined with QPF, persistence-based approach (using streamflow observations) combined with QPF, and open-loop combined with QPF for more than 18 hours horizon is explored. The authors report the contribution of QPF errors on hydrologic predictions across scales and suggest a forecasting scenario that shows the most enhanced predictability of streamflows.</p>

Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting

2021 ◽  
Author(s):  
Ganesh R. Ghimire ◽  
Witold F. Krajewski ◽  
Felipe Quintero
Keyword(s):  
Real Time ◽  
Time Scale ◽  
Lead Time ◽  
Time Horizon ◽  
Streamflow Forecasting ◽  
Forecasting Accuracy ◽  
Trade Off ◽  
Forecast Lead Time ◽  
Medium Range ◽  
Forecasting System

AbstractIncorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space-time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10 – 40,000 km2) over the state of Iowa in the Midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale-dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1,000 km2. The space-time scale, or reference time (tr) (ratio of forecast lead time to basin travel time) ~ 1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for tr > 1.

scholarly journals Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 188
Author(s):  
Rodrigo Valdés-Pineda ◽  
Juan B. Valdés ◽  
Sungwook Wi ◽  
Aleix Serrat-Capdevila ◽  
Tirthankar Roy
Keyword(s):  
Real Time ◽  
Optimal Parameter ◽  
Ensemble Forecasting ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Satellite Precipitation ◽  
Daily Streamflow ◽  
Medium Range ◽  
Streamflow Forecasts ◽  
Forecasting System

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

Decomposition of Sources of Errors in Monthly to Seasonal Streamflow Forecasts in a Rainfall–Runoff Regime

2014 ◽  
Vol 15 (6) ◽  
pp. 2470-2483 ◽  
Author(s):  
Tushar Sinha ◽  
A. Sankarasubramanian ◽  
Amirhossein Mazrooei
Keyword(s):  
Real Time ◽  
Lead Time ◽  
General Circulation ◽  
Streamflow Forecasting ◽  
Climate Forecasts ◽  
Monthly Streamflow ◽  
Streamflow Forecasts ◽  
Temporal Disaggregation ◽  
Seasonal Streamflow ◽  
Spatial Downscaling

Abstract Despite considerable progress in developing real-time climate forecasts, most studies have evaluated the potential in seasonal streamflow forecasting based on ensemble streamflow prediction (ESP) methods, utilizing only climatological forcings while ignoring general circulation model (GCM)-based climate forecasts. The primary limitation in using GCM forecasts is their coarse resolution, which requires spatiotemporal downscaling to implement land surface models. Consequently, multiple sources of errors are introduced in developing real-time streamflow forecasts utilizing GCM forecasts. A set of error decomposition metrics is provided to address the following questions: 1) How are errors in monthly streamflow forecasts attributed to various sources such as temporal disaggregation, spatial downscaling, imprecise initial hydrologic conditions (IHCs), climatological forcings, and imprecise forecasts? and 2) How do these errors propagate with lead time over different seasons? A calibrated Variable Infiltration Capacity model is used over the Apalachicola River at Chattahoochee in the southeastern United States. The model is forced with a combination of daily precipitation forcings (temporally disaggregated observed precipitation, spatially downscaled and temporally disaggregated observed precipitation, ESP, ECHAM4.5 forecasts, and observed) and IHCs [simulated and climatological ensemble reverse ESP (RESP)] but with observed air temperature and wind speed at ⅛° resolution. Then, errors in forecasting monthly streamflow at up to a 3-month lead time are decomposed by comparing the forecasted streamflow to simulated streamflow under observed forcings. Results indicate that the errors due to temporal disaggregation are much higher than the spatial downscaling errors. During winter and early spring, the increasing order of errors at a 1-month lead time is spatial downscaling, model, temporal disaggregation, RESP, large-scale precipitation forecasts, and ESP.

scholarly journals Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas

2018 ◽  
Vol 19 (9) ◽  
pp. 1467-1483 ◽  
Author(s):  
Sunghee Kim ◽  
Hossein Sadeghi ◽  
Reza Ahmad Limon ◽  
Manabendra Saharia ◽  
Dong-Jun Seo ◽  
...  
Keyword(s):  
River Basin ◽  
Time Horizon ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Trinity River ◽  
Daily Streamflow ◽  
Emergency Managers ◽  
Medium Range ◽  
Streamflow Forecasts ◽  
The Impact

Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.

scholarly journals Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region

Forecasting ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 230-247
Author(s):  
Ganesh R. Ghimire ◽  
Sanjib Sharma ◽  
Jeeban Panthi ◽  
Rocky Talchabhadel ◽  
Binod Parajuli ◽  
...  
Keyword(s):  
Real Time ◽  
Water Policy ◽  
Low Complexity ◽  
Critical Issue ◽  
Forecast Skill ◽  
Streamflow Forecasting ◽  
Streamflow Forecast ◽  
Time Flow ◽  
Wide Range ◽  
Streamflow Forecasts

Improving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological prediction, real-time streamflow forecasting over the Himalayas remains a critical issue and challenge, especially with complex basin physiography, shifting weather patterns and sparse and biased in-situ hydrometeorological monitoring data. In this study, we demonstrate the utility of low-complexity data-driven persistence-based approaches for skillful streamflow forecasting in the Himalayan country Nepal. The selected approaches are: (1) simple persistence, (2) streamflow climatology and (3) anomaly persistence. We generated the streamflow forecasts for 65 stream gauge stations across Nepal for short-to-medium range forecast lead times (1 to 12 days). The selected gauge stations were monitored by the Department of Hydrology and Meteorology (DHM) Nepal, and they represent a wide range of basin size, from ~17 to ~54,100 km2. We find that the performance of persistence-based forecasting approaches depends highly upon the lead time, flow threshold, basin size and flow regime. Overall, the persistence-based forecast results demonstrate higher forecast skill in snow-fed rivers over intermittent ones, moderate flows over extreme ones and larger basins over smaller ones. The streamflow forecast skill obtained in this study can serve as a benchmark (reference) for the evaluation of many operational forecasting systems over the Himalayas.

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.

Real-Time Reservoir Operation by Tree-Based Model Predictive Control Including Forecast Uncertainty

2020 ◽  
Author(s):  
Gokcen Uysal ◽  
Rodolfo-Alvarado Montero ◽  
Dirk Schwanenberg ◽  
Aynur Sensoy
Keyword(s):  
Water Supply ◽  
Real Time ◽  
Predictive Control ◽  
Reservoir Operation ◽  
Lead Time ◽  
Flood Control ◽  
Closed Loop ◽  
Multi Stage ◽  
Streamflow Forecasts ◽  
Related Technique

<p>Streamflow forecasts include uncertainties related with initial conditions, model forcings, hydrological model structure and parameters. Ensemble streamflow forecasts can capture forecast uncertainties by having spread forecast members. Integration of these forecast members into real-time operational decision models which deals with different objectives such as flood control, water supply or energy production are still rare. This study aims to use ensemble streamflows as input of the recurrent reservoir operation problem which can incorporate (i) forecast uncertainty, (ii) forecasts with a higher lead-time and (iii) a higher stability. A related technique for decision making is multi-stage stochastic optimization using scenario trees, referred to as Tree-based Model Predictive Control (TB-MPC). This approach reduces the number of ensemble members by its tree generation algorithms using all trajectories and then proper problem formulation is set by Multi-Stage Stochastic Programming. The method is relatively new in reservoir operation, especially closed-loop hindcasting experiments and its assessment is quite rare in the literature. The aim of this study is to set a TB-MPC based real-time reservoir operation with hindcasting experiments. To that end, first hourly deterministic streamflows having one single member are produced using an observed flood hydrograph. Deterministic forecasts are tested with conventional deterministic optimization setup. Secondly, hourly ensemble streamflow forecasts having a lead-time up to 48 hours are produced by a novel approach which explicitly presents dynamic uncertainty evolution. Produced ensemble members are directly provided to input to related technique. Uncertainty becomes much larger when managing small basins and small rivers. Thus, the methodology is applied to the Yuvacik dam reservoir, fed by a catchment area of 258 km<sup>2</sup> and located in Turkey, owing to its challenging flood control and water supply operation due to downstream flow constraints. According to the results, stochastic optimization outperforms conventional counterpart by considering uncertainty in terms of flood metrics without discarding water supply purposes. The closed-loop hindcasting experiment scenarios demonstrate the robustness of the system developed against biased information. In conclusion, ensemble streamflows produced from single member can be employed to TB-MPC for better real-time management of a reservoir control system.</p>

scholarly journals Design and implementation of an operational multimodel multiproduct real-time probabilistic streamflow forecasting platform

2017 ◽  
Vol 19 (6) ◽  
pp. 911-919 ◽  
Author(s):  
Tirthankar Roy ◽  
Aleix Serrat-Capdevila ◽  
Juan Valdes ◽  
Matej Durcik ◽  
Hoshin Gupta
Keyword(s):  
Real Time ◽  
Modular Design ◽  
Model Averaging ◽  
River Basins ◽  
Streamflow Forecasting ◽  
Hydrologic Models ◽  
Design And Implementation ◽  
Probabilistic Forecasts ◽  
Streamflow Forecasts ◽  
Monitoring And Forecasting

Abstract The task of real-time streamflow monitoring and forecasting is particularly challenging for ungauged or sparsely gauged river basins, and largely relies upon satellite-based estimates of precipitation. We present the design and implementation of a state-of-the-art real-time streamflow monitoring and forecasting platform that integrates information provided by cutting-edge satellite precipitation products (SPPs), numerical precipitation forecasts, and multiple hydrologic models, to generate probabilistic streamflow forecasts that have an effective lead time of 9 days. The modular design of the platform enables adding/removing any model/product as may be appropriate. The SPPs are bias-corrected in real-time, and the model-generated streamflow forecasts are further bias-corrected and merged, to produce probabilistic forecasts that are computed via several model averaging techniques. The platform is currently operational in multiple river basins in Africa, and can also be adapted to any new basin by incorporating some basin-specific changes and recalibration of the hydrologic models.

scholarly journals Russian Rivers Streamflow Forecasting Using Hydrograph Extrapolation Method

Hydrology ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Sergei Borsch ◽  
Yuri Simonov ◽  
Andrei Khristoforov ◽  
Natalia Semenova ◽  
Valeria Koliy ◽  
...  
Keyword(s):  
Real Time ◽  
Lead Time ◽  
River Flow ◽  
Water Levels ◽  
Streamflow Forecasting ◽  
Forecast Verification ◽  
First Approximation ◽  
Average Slope ◽  
Linear Formula ◽  
Forecasting System

This paper presents a method of hydrograph extrapolation, intended for simple and efficient streamflow forecasting with up to 10 days lead time. The forecast of discharges or water levels is expressed by a linear formula depending on their values on the date of the forecast release and the five previous days. Such forecast techniques were developed for more than 2700 stream gauging stations across Russia. Forecast verification has shown that this method can be successfully applied to large rivers with a smooth shape of hydrographs, while for small mountain catchments, the accuracy of the method tends to be lower. The method has been implemented into real-time continuous operations in the Hydrometcentre of Russia. In the territory of Russia, 18 regions have been identified with a single dependency of the maximum lead time of good forecasts on the area and average slope of the catchment surface for different catchments of each region; the possibilities of forecasting river streamflow by the method of hydrograph extrapolation are approximately estimated. The proposed method can be considered as a first approximation while solving the problem of forecasting river flow in conditions of a lack of meteorological information or when it is necessary to quickly develop a forecasting system for a large number of catchments.

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.

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