Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Laugesen, Richard; Tuteja, Narendra; Kuczera, George

doi:10.1029/2019wr026979

Cited by 35 publications

(49 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The original MuTHRE model represents innovations using a two-component mixed-Gaussian distribution, which was found to improve the reliability of forecasts at short lead times by allowing for excess kurtosis in the innovations (Li et al 2016;McInerney et al 2020). This innovation model assumes the distribution of innovations does not depend on the flow magnitude (see Figure 3a).…”

Section: Mixed-gaussian Modelmentioning

confidence: 99%

“…Hydrological uncertainty (represented by the MuTHRE/MuTHRE-FD models) is then added to the raw streamflow forecasts, ( raw q in Section 2.1), to produce post-processed streamflow forecasts. See McInerney et al (2020) for a detailed description of this procedure. Forecasts of cumulative flow volumes at lead time  are obtained by aggregating the daily forecasts between 0 1 t + and  .…”

Section: Generation Of Streamflow Forecasts Accounting For Rainfall Forecast Uncertaintymentioning

confidence: 99%

“…We compare forecasts from the MuTHRE and MuTHRE-FD models using a case study with 11 catchments in the Murray Darling Basin (McInerney et al 2020). Daily time series of observed rainfall, PET and streamflow over a 22-year period from 1991-2012 are obtained from the Bureau of Meteorology's Hydrologic Reference Stations (HRS) dataset.…”

Section: Hydrological Data and Modelmentioning

confidence: 99%

“…The analysis is carried out using the Multi-Temporal Hydrological Residual Error (MuTHRE) model (McInerney et al 2020), which was shown to achieve seamless forecasts with good reliability across a range of lead times (1-30 days), stratifications (months and years), and time scales (daily and monthly).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving sub-seasonal streamflow forecasts across flow regimes

2021

MODSIM2021, 24th International Congress on Modelling and Simulation.

View full text Add to dashboard Cite

Sub-seasonal streamflow forecasts are important for a range of water resource management applications, with a distinct practical interest in forecasts of high flows (e.g. for managing flood events) and low flows (e.g. for managing environmental flows). Despite this interest, differences in forecast performance for high and low flow events are not routinely investigated. Our study reveals that while forecasts evaluated over the full flow range can appear reliable, stratification into high/low flow ranges highlights significant under/over-estimation of forecast uncertainty, respectively. This study introduces a flow-dependent (FD) non-parametric component into a post-processing model of hydrological forecasting errors, the Multi-Temporal Hydrological Residual Error (MuTHRE) model, yielding the MuTHRE-FD model. We use a case study with 11 catchments in the Murray Darling Basin, the GR4J rainfall-runoff model and post-processed rainfall forecasts from ACCESS-S, to compare the MuTHRE and MuTHRE-FD models. Through its improved treatment of flow-dependence, the MuTHRE-FD model achieves practically significant improvements over the original MuTHRE model in the reliability of forecasted cumulative volumes for: (i) high flows out to 7 days; (ii) low flows out to 2 days; and (iii) mid flows for majority of lead times. Example cumulative flow time series are provided in Figure 1. The new MUTHRE-FD model provides sub-seasonal forecasts with high quality performance for both high and low flows over a range of lead times. This improvement provides forecast users with increased confidence in using sub-seasonal forecasts across a wide range of applications.Figure 1. Example time series of predictive limits of cumulative volume forecasts out to 28 days for Hughes Creek (catchment ID 405228). Results are shown for forecasts issued on 1 November 2010, which is a high flow period (left side), and 1 December 2009, which is a low flow period (right side).

show abstract

Section: Mixed-gaussian Modelmentioning

confidence: 99%

Section: Generation Of Streamflow Forecasts Accounting For Rainfall Forecast Uncertaintymentioning

confidence: 99%

Section: Hydrological Data and Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving sub-seasonal streamflow forecasts across flow regimes

2021

MODSIM2021, 24th International Congress on Modelling and Simulation.

View full text Add to dashboard Cite

show abstract

“…and 1 E w are estimated by likelihood maximization using the entire set of empirical innovations  E y in the calibration period (McInerney et al, 2020).…”

mentioning

confidence: 99%

Improving the Reliability of Sub‐Seasonal Forecasts of High and Low Flows by Using a Flow‐Dependent Nonparametric Model

McInerney

Thyer

Kavetski

et al. 2021

Water Resources Research

Self Cite

View full text Add to dashboard Cite

Sub-seasonal streamflow forecasts offer valuable information for a range of real-time water resource applications. For example, forecasts of high flows can be used for flood storage reservoir operations, while forecasts of low flows are useful for environmental flow management (Campolo et al., 1999;Delaney et al., 2020). In large water resource systems, with travel times of days to weeks, forecasts of both daily flows and cumulative volumes are required at lead times up to the maximum travel time within the system. Seamless forecasts, that is, forecasts obtained using a single method that maintains high quality across a range of time scales, are attractive in both research and practical applications. This technical note focuses on improving the performance of sub-seasonal streamflow forecasting (1-30 days) across both high and low flows, in the context of a "seamless" forecasting method (McInerney et al., 2020).

show abstract

Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes

Huang

Zhao

2022

WIREs Water

View full text Add to dashboard Cite

Predictive performance is one of the most important issues for practical applications of ensemble hydroclimatic forecasts. While different forecasting studies tend to use different combinations of verification metrics and diagnostic plots, this paper presents a literature survey of popular verification metrics, diagnostic plots and their corresponding forecast attributes. Twenty metrics to quantify predictive performance of ensemble, deterministic and categorical forecasts are detailed; six types of diagnostic plots to visualize the relationship between ensemble forecasts and observations are presented; and the correspondence relationships of verification metrics and diagnostic plots with ten forecast attributes are illustrated. Numerical experiments are devised for raw ensemble precipitation forecasts generated by the Climate Forecast System version 2 (CFSv2) to illustrate the verification metrics, diagnostic plots and forecast attributes. The results suggest that verification metrics that pay attention to some similar attributes can be related to one another. Meanwhile, the metrics are not necessarily in full accordance as they tend to reflect different attributes. Time series plots visualize forecasts over a period of interest and graphically illustrate forecast bias, reliability and skill. Quantile range plots provide an overall diagnosis for the association of ensemble forecasts with observations. Overall, verification metrics can be illustrated by diagnostic plots for individual grid cells and also be screened by spatial plots at regional and global scales. The survey presented in this article can serve as a roadmap for the selection of verification metrics, diagnostic plots and forecast attributes for verifying ensemble hydroclimatic forecasts. This article is categorized under: Science of Water > Hydrological Processes

show abstract

Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting

Cited by 35 publications

References 72 publications

Improving sub-seasonal streamflow forecasts across flow regimes

Improving sub-seasonal streamflow forecasts across flow regimes

Improving the Reliability of Sub‐Seasonal Forecasts of High and Low Flows by Using a Flow‐Dependent Nonparametric Model

Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes

Contact Info

Product

Resources

About