Multi-period calibration of a semi-distributed hydrological model based on hydroclimatic clustering

Zhang, Hua; Huang, Guohe; Wang, Dunling; Zhang, Xiaodong

doi:10.1016/j.advwatres.2011.06.005

Cited by 56 publications

(35 citation statements)

References 52 publications

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“…In this regard, Merz et al [2011] showed that some hydrologic model parameters may consistently vary with time and such variations can be related to climate fluctuations. Zhang et al [2011] developed a framework for multiperiod calibration of hydrologic models based on hydroclimatic clustering, which identifies and utilizes different calibrated parameter sets for different hydroclimatic conditions. Merz et al [2011], however, argued that explicitly accounting for nonstationary model parameters by predicting them based on climate condition may not be consistent with the ''usual philosophy of dynamic models'' which is ''to use time-dependent boundary conditions (such as rainfall) and nontime-dependent model parameters to make the model directly applicable to predictions using future (or different) boundary conditions''.…”

Section: Motivation and Objectivementioning

confidence: 99%

An efficient framework for hydrologic model calibration on long data periods

Razavi

Tolson

2013

Water Resour. Res.

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.[1] Long periods of hydrologic data records have become available in many watersheds around the globe. Hydrologic model calibration on such long, full-length data periods is typically deemed the most robust approach for calibration but at larger computational costs. Determination of a representative short period as a ''surrogate'' of a long data period that sufficiently embeds its information content is not trivial and is a challenging research question. The representativeness of such a short period is not only a function of data characteristics but also model and calibration error function dependent. Unlike previous studies, this study goes beyond identifying the best surrogate data period to be used in model calibration and proposes an efficient framework that calibrates the hydrologic model to full-length data while running the model only on a short period for the majority of the candidate parameter sets. To this end, a mapping system is developed to approximate the model performance on the full-length data period based on the model performance for the short data period. The basic concepts and the promise of the framework are demonstrated through a computationally expensive hydrologic model case study. Three calibration approaches, namely calibration solely to a surrogate period, calibration to the full period, and calibration through the proposed framework, are evaluated and compared. Results show that within the same computational budget, the proposed framework leads to improved or equal calibration performance compared to the two conventional approaches. Results also indicate that model calibration solely to a short data period may lead to a range of performances from poor to very well depending on the representativeness of the short data period which is typically not known a priori.

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Section: Motivation and Objectivementioning

confidence: 99%

An efficient framework for hydrologic model calibration on long data periods

Razavi

Tolson

2013

Water Resour. Res.

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“…In hydrological modeling, parameters are usually assumed to be stationary; i.e., the calibrated parameters are constants during the calibration period, and have extrapolative ability outside the range of the observations used for parameter estimation (Merz et al, 2011). The estimated parameters usually depend on the calibration period since the calibration period may contain different climatic conditions and hydrological regimes compared to the simulation period (Merz et al, 2011;Zhang et al, 2011;Coron et al, 2012;Seiller et al, 2012;Westra et al, 2014;Patil and Stieglitz, 2015). The model parameters may change as a response to the variations in climatic conditions and catchment properties.…”

Section: Introductionmentioning

confidence: 99%

Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.

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“…The authors found that three groundwater parameters were highly sensitive during quick flow, while one evaporation parameter was most sensitive during base flow, and model performance was also found to vary significantly for the two flow regimes. Zhang et al (2011) calibrated SWAT hydrological parameters for periods separated on the basis of six climatic indexes. Model performance improved when different values were assigned to parameters based on six hydroclimatic periods.…”

Section: Introductionmentioning

confidence: 99%

Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand

Abell

Hamilton

2015

Hydrol. Earth Syst. Sci.

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Abstract. The Soil Water Assessment Tool (SWAT) was configured for the Puarenga Stream catchment (77 km 2 ), Rotorua, New Zealand. The catchment land use is mostly plantation forest, some of which is spray-irrigated with treated wastewater. A Sequential Uncertainty Fitting (SUFI-2) procedure was used to auto-calibrate unknown parameter values in the SWAT model. Model validation was performed using two data sets: (1) monthly instantaneous measurements of suspended sediment (SS), total phosphorus (TP) and total nitrogen (TN) concentrations; and (2) high-frequency (1-2 h) data measured during rainfall events. Monthly instantaneous TP and TN concentrations were generally not reproduced well (24 % bias for TP, 27 % bias for TN, and R 2 < 0.1, NSE < 0 for both TP and TN), in contrast to SS concentrations (< 1 % bias; R 2 and NSE both > 0.75) during model validation. Comparison of simulated daily mean SS, TP and TN concentrations with daily mean dischargeweighted high-frequency measurements during storm events indicated that model predictions during the high rainfall period considerably underestimated concentrations of SS (44 % bias) and TP (70 % bias), while TN concentrations were comparable (< 1 % bias; R 2 and NSE both ∼ 0.5). This comparison highlighted the potential for model error associated with quick flow fluxes in flashy lower-order streams to be underestimated compared with low-frequency (e.g. monthly) measurements derived predominantly from base flow measurements. To address this, we recommend that high-frequency, event-based monitoring data are used to support calibration and validation. Simulated discharge, SS, TP and TN loads were partitioned into two components (base flow and quick flow) based on hydrograph separation. A manual procedure (one-at-a-time sensitivity analysis) was used to quantify parameter sensitivity for the two hydrologically separated regimes. Several SWAT parameters were found to have different sensitivities between base flow and quick flow. Parameters relating to main channel processes were more sensitive for the base flow estimates, while those relating to overland processes were more sensitive for the quick flow estimates. This study has important implications for identifying uncertainties in parameter sensitivity and performance of hydrological models applied to catchments with large fluctuations in stream flow and in cases where models are used to examine scenarios that involve substantial changes to the existing flow regime.

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Multi-period calibration of a semi-distributed hydrological model based on hydroclimatic clustering

Cited by 56 publications

References 52 publications

An efficient framework for hydrologic model calibration on long data periods

An efficient framework for hydrologic model calibration on long data periods

Identification of hydrological model parameter variation using ensemble Kalman filter

Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand

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