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

Ghimire, Ganesh R.; Krajewski, Witold F.; Quintero, Felipe

doi:10.1175/jhm-d-20-0297.1

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

Full Record
Other Related Research

Abstract

Incorporating 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.more »« less

Authors:

^[1]; Krajewski, Witold F. ^[2]; Quintero, Felipe ^[2]

Univ. of Iowa, Iowa City, IA (United States). Iowa Flood Center and IIHR-Hydroscience & Engineering; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Univ. of Iowa, Iowa City, IA (United States). Iowa Flood Center and IIHR-Hydroscience & Engineering

Publication Date:: Mon Jul 19 00:00:00 EDT 2021

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1811378

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Hydrometeorology

Additional Journal Information:: Journal Volume: 22; Journal Issue: 7; Journal ID: ISSN 1525-755X

Publisher:: American Meteorological Society

Country of Publication:: United States

Language:: English

Subject:: 58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES

Citation Formats


                    Ghimire, Ganesh R., Krajewski, Witold F., and Quintero, Felipe. Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting.  United States: N. p., 2021. 
Web.  doi:10.1175/jhm-d-20-0297.1.

Copy to clipboard


                    Ghimire, Ganesh R., Krajewski, Witold F., & Quintero, Felipe. Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting.  United States.  https://doi.org/10.1175/jhm-d-20-0297.1

Copy to clipboard


                    Ghimire, Ganesh R., Krajewski, Witold F., and Quintero, Felipe. Mon .  
"Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting".  United States.  https://doi.org/10.1175/jhm-d-20-0297.1.  https://www.osti.gov/servlets/purl/1811378.

Copy to clipboard


                    
@article{osti_1811378,

  title        = {Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting},

  author       = {Ghimire, Ganesh R. and Krajewski, Witold F. and Quintero, Felipe},

  abstractNote = {Incorporating 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.},

  doi          = {10.1175/jhm-d-20-0297.1},

  journal      = {Journal of Hydrometeorology},

  number       = 7,

  volume       = 22,

  place        = {United States},

  year         = {Mon Jul 19 00:00:00 EDT 2021},

  month        = {Mon Jul 19 00:00:00 EDT 2021}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1175/jhm-d-20-0297.1

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework

Journal Article Gangrade, Sudershan ; Ghimire, Ganesh R. ; Kao, Shih-Chieh ; ... - Journal of Hydrology

Flood prediction systems need hierarchical atmospheric, hydrologic, and hydraulic models to predict rainfall, runoff, streamflow, and floodplain inundation. The accuracy of such systems depends on the error propagation through the modeling chain, sensitivity to input data, and choice of models. In this study, we used multiple precipitation forcings (hindcast and forecast) to drive hydrologic and hydrodynamic models to analyze the impacts of various drivers on the estimates of flood inundation depth and extent. We implement this framework to unravel the August 2021 extreme flooding event that occurred in Central Tennessee, USA. We used two radar-based quantitative precipitation estimates (STAGE4 andmore »« less
https://doi.org/10.1016/j.jhydrol.2023.130157

Full Text Available
Evaluating precipitation, streamflow, and inundation forecasting skills during extreme weather events: A case study for an urban watershed

Journal Article Li, Xudong ; Rankin, Cheryl ; Gangrade, Sudershan ; ... - Journal of Hydrology

Integrated forecasting systems for precipitation, streamflow, and floodplain inundation are of critical importance in mitigating the impacts of destructive floods caused by extreme weather events. However, the skills of streamflow and floodplain inundation forecasts derived from various Quantitative Precipitation Forecasts (QPF) require a greater level of understanding. In this paper, a set of QPF developed by the National Weather Service (NWS) were used to drive a flood modeling system obtained utilizing offline coupling of a physics-based distributed hydrological model, the Distributed Hydrology Soil and Vegetation Model (DHSVM), and a hydrodynamic model, the Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON).more »« less
https://doi.org/10.1016/j.jhydrol.2021.127126

Full Text Available
Real-time streamflow forecasting: AI vs. Hydrologic insights

Journal Article Krajewski, Witold F. ; Ghimire, Ganesh R. ; Demir, Ibrahim ; ... - Journal of Hydrology X

In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflowmore »« less
https://doi.org/10.1016/j.hydroa.2021.100110

Full Text Available
Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations

Journal Article Zhang, Yuhang ; Ye, Aizhong ; Analui, Bita ; ... - Hydrology and Earth System Sciences (Online)

Abstract. Deep learning (DL) and machine learning (ML) are widely used in hydrological modelling, which plays a critical role in improving the accuracy of hydrological predictions. However, the trade-off between model performance and computational cost has always been a challenge for hydrologists when selecting a suitable model, particularly for probabilistic post-processing with large ensemble members. This study aims to systematically compare the quantile regression forest (QRF) model and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) model as hydrological probabilistic post-processors. Specifically, we evaluate their ability in dealing with biased streamflow simulations driven by three satellite precipitation products acrossmore »« less
https://doi.org/10.5194/hess-27-4529-2023
A dynamically-coupled groundwater, land surface and regional climate model to predict seasonal watershed flow and groundwater response, FINAL LDRD REPORT.

Technical Report Maxwell, R ; Kollet, S ; Chow, F ; ...

This final report is organized in four sections. Section 1 is the project summary (below), Section 2 is a submitted manuscript that describes the offline, or spinup simulations in detail, Section 3 is also a submitted manuscript that describes the online, or fully-coupled simulations in detail and Section 3, which is report that describes work done via a subcontract with UC Berkeley. The goal of this project was to develop and apply a coupled regional climate, land-surface, groundwater flow model as a means to further understand important mass and energy couplings between regional climate, the land surface, and groundwater. Themore »« less
https://doi.org/10.2172/902346

Full Text Available

Similar Records