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 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 GloFAS – global ensemble streamflow forecasting and flood early warning

2012 ◽  
Vol 9 (11) ◽  
pp. 12293-12332 ◽  
Author(s):  
L. Alfieri ◽  
P. Burek ◽  
E. Dutra ◽  
B. Krzeminski ◽  
D. Muraro ◽  
...  
Keyword(s):  
Early Warning ◽  
Large Scale ◽  
River Basins ◽  
Developed Countries ◽  
Large River ◽  
Streamflow Forecasting ◽  
Forecast Horizon ◽  
Hydrological Simulation ◽  
Streamflow Forecasts ◽  
Awareness System

Abstract. Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of population affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System, which has been set up to provide an overview on upcoming floods in large world river basins. The Global Flood Awareness System is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the Earth.

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 Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods

2018 ◽  
Vol 22 (6) ◽  
pp. 3533-3549 ◽  
Author(s):  
Stephen P. Charles ◽  
Quan J. Wang ◽  
Mobin-ud-Din Ahmad ◽  
Danial Hashmi ◽  
Andrew Schepen ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Hybrid Approach ◽  
Indus Basin ◽  
Climate Index ◽  
Climate Indices ◽  
Streamflow Forecasting ◽  
Upper Indus Basin ◽  
Probabilistic Forecasts ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

Abstract. Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April–September) streamflow forecasts for inflow to Pakistan's two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM–ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM for this location. Cross validation of the streamflow forecasts shows that the BJP approach using two predictors (March flow and an El Niño Southern Oscillation, ENSO, climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April–September), early Kharif (April–June) and late Kharif (July–September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.

scholarly journals Assessment of methods for seasonal streamflow forecasting in the Upper Indus Basin of Pakistan

2017 ◽  
Author(s):  
Stephen P. Charles ◽  
Quan J. Wang ◽  
Mobin-ud-Din Ahmad ◽  
Danial Hashmi ◽  
Andrew Schepen ◽  
...  
Keyword(s):  
Hybrid Approach ◽  
Joint Probability ◽  
Indus Basin ◽  
Climate Index ◽  
Climate Indices ◽  
Streamflow Forecasting ◽  
Upper Indus Basin ◽  
Probabilistic Forecasts ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

Abstract. Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April-September) streamflow forecasts for inflow to Pakistan's two largest Upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESP) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM-ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM for this location. Cross validation of the streamflow forecasts show that the BJP approach using two predictors (March flow and an ENSO climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April–September), early Kharif (April–June) and late Kharif (July–September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.

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>

scholarly journals GloFAS – global ensemble streamflow forecasting and flood early warning

2013 ◽  
Vol 17 (3) ◽  
pp. 1161-1175 ◽  
Author(s):  
L. Alfieri ◽  
P. Burek ◽  
E. Dutra ◽  
B. Krzeminski ◽  
D. Muraro ◽  
...  
Keyword(s):  
Early Warning ◽  
Large Scale ◽  
River Basins ◽  
Developed Countries ◽  
Large River ◽  
Streamflow Forecasting ◽  
Forecast Horizon ◽  
Hydrological Simulation ◽  
Streamflow Forecasts ◽  
Set Up

Abstract. Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of populations affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System (GloFAS), which has been set up to provide an overview on upcoming floods in large world river basins. GloFAS is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the earth.

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