scholarly journals Real-Time Streamflow Forecasting: AI vs. Hydrologic Insights

2021 ◽  
pp. 100110
Author(s):  
Witold F. Krajewski ◽  
Ganesh R. Ghimire ◽  
Ibrahim Demir ◽  
Ricardo Mantilla
Keyword(s):  
Real Time ◽  
Streamflow Forecasting

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 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 Streamflow Forecasting Without Models

2019 ◽  
Author(s):  
Witold F. Krajewski ◽  
Ganesh Ghimire ◽  
Felipe Quintero
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Strong Dependence ◽  
Area Ratio ◽  
Skill Assessment ◽  
River Networks ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Temporal Persistence ◽  
Basin Area

The authors explore simple concepts of persistence in streamflow forecasting based on the real-time streamflow observations from the years 2002 to 2018 at 140 U.S. Geological Survey (USGS) streamflow gauges in Iowa. The spatial scale of the basins ranges from about 7 km2 to 37,000 km2. Motivated by the need for evaluating the skill of real-time streamflow forecasting systems, the authors perform quantitative skill assessment of different persistence schemes across spatial scales and lead-times. They show that skill in temporal persistence forecasting has a strong dependence on basin size, and a weaker, but non-negligible, dependence on geometric properties of the river networks in the basins. Building on results from this temporal persistence, they extend the streamflow persistence forecasting to space through flow-connected river networks. The approach simply assumes that streamflow at a station in space will persist to another station which is flow-connected; these are referred to as pure spatial persistence forecasts (PSPF). The authors show that skill of PSPF of streamflow is strongly dependent on the monitored vs. predicted basin area-ratio and lead-times, and weakly related to the downstream flow distance between stations. River network topology shows some effect on the hydrograph timing and timing of the peaks, depending on the stream gauge configuration. The study shows that the skill depicted in terms of Kling-Gupta efficiency (KGE) > 0.5 can be achieved for basin area ratio > 0.6 and lead-time up to three days. The authors discuss the implications of their findings for assessment and improvements of rainfall-runoff models, data assimilation schemes, and stream gauging network design.

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 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.

scholarly journals Real-time updating of HEC-RAS model for streamflow forecasting using an optimization algorithm

RBRH ◽  
2016 ◽  
Vol 21 (4) ◽  
pp. 855-870 ◽  
Author(s):  
Vinícius Alencar Siqueira ◽  
Mino Viana Sorribas ◽  
Juan Martin Bravo ◽  
Walter Collischonn ◽  
Auder Machado Vieira Lisboa ◽  
...  
Keyword(s):  
Real Time ◽  
Optimization Algorithm ◽  
Expert Knowledge ◽  
Model Performance ◽  
Small Population ◽  
Model Parameters ◽  
Streamflow Forecasting ◽  
Negative Effects ◽  
Shuffled Complex Evolution ◽  
Updating Procedure

ABSTRACT Real-time updating of channel flow routing models is essential for error reduction in hydrological forecasting. Recent updating techniques found in scientific literature, although very promising, are complex and often applied in models that demand much time and expert knowledge for their development, posing challenges for using in an operational context. Since powerful and well-known computational tools are currently available, which provide easy-to-use and less time-consuming platforms for preparation of hydrodynamic models, it becomes interesting to develop updating techniques adaptable to such tools, taking full advantage of previously calibrated models as well as the experience of the users. In this work, we present a real-time updating procedure for streamflow forecasting in HEC-RAS model, using the Shuffled Complex Evolution - University of Arizona (SCE-UA) optimization algorithm. The procedure consists in a simultaneous correction of boundary conditions and model parameters through: (i) generation of a lateral inflow, based on Soil Conservation Service (SCS) dimensionless unit hydrograph and; (ii) estimation of Manning roughness in the river channel. The algorithm works in an optimization window in order to minimize an objective function, given by the weighted sum of squared errors between simulated and observed flows where differences in later intervals (start of forecast) are more penalized. As a case study, the procedure was applied in a river reach between Salto Caxias dam and Hotel Cataratas stream gauge, located in the Lower Iguazu Basin. Results showed that, with a small population of candidate solutions in the optimization algorithm, it is possible to efficiently improve the model performance for streamflow forecasting and reduce negative effects caused by lag errors in simulation. An advantage of the developed procedure is the reduction of both excessive handling of external files and manual adjustments of HEC-RAS model, which is important when operational decisions must be taken in relatively short times.

scholarly journals Data assimilation using the ensemble Kalman filter in a distributed hydrological model on the Tocantins River, Brasil

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Karena Quiroz Jiménez ◽  
Walter Collischonn ◽  
Rodrigo Cauduro Dias de Paiva
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Real Time ◽  
Ensemble Kalman Filter ◽  
Information Transfer ◽  
Hydrological Model ◽  
Distributed Hydrological Model ◽  
Streamflow Forecasting ◽  
Ungauged Sites ◽  
Tocantins River

ABSTRACT In this work, the data assimilation method namely ensemble Kalman filter (EnKF) is applied to the Tocantins River basin. This method assimilates streamflow results by using a distributed hydrological model. The performance of the EnKF is also compared with an empirical assimilation method for hourly time intervals, in which two applications based on information transfer from gauged to ungauged sites and real time streamflow forecasting are assessed. In the first application, both assimilation methods are able to transfer streamflow to ungauged sites, obtaining better results when more than one station located upstream or downstream of the basin are gauged. In the second application, integration of a real time forecast model with EnKF is able to absorb errors at the beginning of the forecast. Therefore, a greater efficiency in the Nash-Sutcliffe index for the first 144 hours in advance in relation to its counterpart without assimilation is obtained. Finally, a comparison between both data assimilation methods shows a greater advantage for the EnKF in long lead times.

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 Streamflow Forecasting without Models

2020 ◽  
Vol 21 (8) ◽  
pp. 1689-1704
Author(s):  
Witold F. Krajewski ◽  
Ganesh R. Ghimire ◽  
Felipe Quintero
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Strong Dependence ◽  
Area Ratio ◽  
Skill Assessment ◽  
River Networks ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Temporal Persistence ◽  
Basin Area

ABSTRACTThe authors explore persistence in streamflow forecasting based on the real-time streamflow observations. They use 15-min streamflow observations from the years 2002 to 2018 at 140 U.S. Geological Survey (USGS) streamflow gauges monitoring the streams and rivers throughout Iowa. The spatial scale of the basins ranges from about 7 to 37 000 km2. Motivated by the need for evaluating the skill of real-time streamflow forecasting systems, the authors perform quantitative skill assessment of persistence schemes across spatial scales and lead times. They show that skill in temporal persistence forecasting has a strong dependence on basin size, and a weaker dependence on geometric properties of the river networks. Building on results from this temporal persistence, they extend the streamflow persistence forecasting to space through flow-connected river networks. The approach simply assumes that streamflow at a station in space will persist to another station which is flow connected; these are referred to as pure spatial persistence forecasts (PSPF). The authors show that skill of PSPF of streamflow is strongly dependent on the monitored versus predicted basin area ratio and lead times, and weakly related to the downstream flow distance between stations. River network topology shows some effect on the hydrograph timing and timing of the peaks, depending on the stream gauge configuration. The study shows that the skill depicted in terms of Kling–Gupta efficiency (KGE) > 0.5 can be achieved for basin area ratio > 0.6 and lead time up to 3 days. The authors discuss the implications of their findings for assessment and improvements of rainfall–runoff models, data assimilation schemes, and stream gauging network design.

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.

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