Monthly versus daily water balance models in simulating monthly runoff

Wang, QJ; Pagano, T. S.; Zhou, SL; Hapuarachchi, H. A. P.; Zhang, Lu; Robertson, David

doi:10.1016/j.jhydrol.2011.04.027

Cited by 92 publications

(55 citation statements)

References 20 publications

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“…The WAter PArtition and BAlance (WAPABA) model recently introduced by Wang et al . [] is used in this study. The WAPABA model is a lumped conceptual monthly rainfall‐runoff model using monthly rainfall and potential evapotranspiration as inputs.…”

Section: Methodsmentioning

confidence: 99%

Accounting for seasonal dependence in hydrological model errors and prediction uncertainty

Wang

Bennett

2013

Water Resour. Res.

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[1] Streamflows often vary strongly with season, and this leads to seasonal dependence in hydrological model errors and prediction uncertainty. In this study, we introduce three error models to describe errors from a monthly rainfall-runoff model: a seasonally invariant model, a seasonally variant model, and a hierarchical error model. The seasonally variant model and the hierarchical error model use month-specific parameters to explicitly account for seasonal dependence, while the seasonally invariant model does not. A Bayesian prior is used in the hierarchical error model to account for potential variation and connection among model parameters of different months. The three error models are applied to predicting streamflows for five Australian catchments and are compared by various performance scores and diagnostic plots. The seasonally variant model and the hierarchical model both perform substantially better than the seasonally invariant model. From a cross-validation analysis, the hierarchical error model provides both the most accurate prediction mean and the most reliable prediction uncertainty distribution in most situations. The use of the prior to constrain the model parameters in the hierarchical model produces more robust parameter estimation than the other two models.Citation: Li, M., Q. J. Wang, and J. Bennett (2013), Accounting for seasonal dependence in hydrological model errors and prediction uncertainty, Water Resour. Res., 49, 5913-5929,

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Section: Methodsmentioning

confidence: 99%

Accounting for seasonal dependence in hydrological model errors and prediction uncertainty

Wang

Bennett

2013

Water Resour. Res.

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“…CBaM corrects biases, removes noise, downscales forecasts to catchment areas and ensures ensembles are statistically reliable. The precipitation forecasts are then used to force the monthly water partitioning and balance (Wapaba) hydrological model (Wang et al, 2011). Hydrological prediction uncertainty is handled with a three-stage error model, which reduces bias and errors, propagates uncertainty and ensures streamflow forecast ensembles are reliable (Wang et al, 2012;Li et al, 2013Li et al, , 2015Li et al, , 2016.…”

Section: Actual Forecasts: Forecast Guided Stochastic Scenarios (Fogss)mentioning

confidence: 99%

“…Wang and Robertson, 2011;Olsson et al, 2016;Pagano et al, 2014; see review by Yuan et al, 2015). Seasonal streamflow forecast services are becoming available in countries such as the United States, Australia and Sweden.…”

Section: Introductionmentioning

confidence: 99%

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Turner

Bennett

Robertson

et al. 2017

Hydrol. Earth Syst. Sci.

110

105

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Abstract. Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made -namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

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“…; Wang et al . ). Furthermore, the abcd model provides the highest values of NS , reaching values close to 0.90 so its use is recommended in catchments with similar characteristics to those in this study.…”

Section: Discussionmentioning

confidence: 97%

Contrast and transferability of parameters of lumped water balance models in the Segura River Basin (Spain)

Pellicer-Martínez

Martínez‐Paz

2014

Water & Environment J

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Knowing the water resources in a river basin is a key aspect to appropriate water planning, and they can be estimated by lumped water balance models. The possibility to transfer the calibrated parameters of a contrasted model to non‐gauged ones improves this knowledge with no need for direct measurements. This work studies the transferability of the parameters of the lumped models in five catchments in the southeast of Spain, testing the influence of the snow and the effect of serial autocorrelation in the goodness of fit. The results are also compared with those of the distributed model of reference in Spain (SIMPA). As principal results, the abcd model is the selected one, the snow effect is negligible, the transferability of parameters for this case is not totally possible, and serial autocorrelation must be included for this kind of modelling.

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Monthly versus daily water balance models in simulating monthly runoff

Cited by 92 publications

References 20 publications

Accounting for seasonal dependence in hydrological model errors and prediction uncertainty

Accounting for seasonal dependence in hydrological model errors and prediction uncertainty

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Contrast and transferability of parameters of lumped water balance models in the Segura River Basin (Spain)

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