Calibrating hourly rainfall-runoff models with daily forcings for streamflow forecasting applications in meso-scale catchments

Bennett, James; Robertson, David; Ward, Phillip G. D.; Hapuarachchi, H. A. P.; Wang, QJ

doi:10.1016/j.envsoft.2015.11.006

Cited by 47 publications

(38 citation statements)

References 25 publications

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“…Finally, GWGEN only downscales meteorology from monthly to daily values; for models that require an even shorter time step, e.g., 6 hourly, some extension of the model functionality would be required. For certain variables, e.g., temperatures, subdaily downscaling could be easily implemented (Cesaraccio et al, 2001); for other variables, such as precipitation, a large literature on downscaling methods exists (e.g., Bennett et al, 2016), and global datasets of hourly meteorology for model calibration are available (e.g., the Integrated Surface Database; Smith et al, 2011).…”

Section: Discussion and Outlookmentioning

confidence: 99%

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

Sommer

Kaplan

2017

Geosci. Model Dev.

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Abstract. While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions.

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Section: Discussion and Outlookmentioning

confidence: 99%

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

Sommer

Kaplan

2017

Geosci. Model Dev.

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“…However, longterm data are not always available; in many cases, they are generally too short for reliable estimations of extreme quantiles (Seckin et al, 2013b;Nguyen et al, 2014). Rainfall-runoff models, which deal with estimation of runoff from rainfall, are considered to be the most extensively used approach for flood prediction and management (Nayak et al, 2013;Ciabatta et al, 2016;Bennett et al, 2016). Various types of rainfall-runoff models can be found in the literature, varying from empirical models to highly sophisticated physical processes.…”

Section: A Review Of Related Work On Flood Susceptibility Predictionmentioning

confidence: 99%

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Bui

Hoang

2017

Geosci. Model Dev.

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Abstract. In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

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“…Robertson et al (2015) proposed an approach to control hourly rain gauge data against a reference daily rainfall product which had already been quality controlled. The advantage is that it can be effectively and efficiently implemented for operational purpose (Bennett et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…There have been a number of studies on controlling the quality of rainfall observations at hourly, daily, and annual time steps (Steiner et al, 1999;Oudin et al, 2006;Robertson et al, 2015;Bennett et al, 2016), some of which also tested the impact on hydrological modeling (Oudin et al, 2006;Robertson et al, 2015;Bennett et al, 2016). The selection of the quality control approach depends on the types of errors to be controlled.…”

Section: Introductionmentioning

confidence: 99%

Impact of Rain Gauge Quality Control and Interpolation on Streamflow Simulation: An Application to the Warwick Catchment, Australia

Liu

Pauwels

et al. 2018

Front. Earth Sci.

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Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash-Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In terms of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2008-31/12/2011) and four gauges during the validation period (01/01/2012-30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness, and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored under different catchment hydrological systems in future studies.

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Calibrating hourly rainfall-runoff models with daily forcings for streamflow forecasting applications in meso-scale catchments

Cited by 47 publications

References 25 publications

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Impact of Rain Gauge Quality Control and Interpolation on Streamflow Simulation: An Application to the Warwick Catchment, Australia

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