Non-stationary hydrological model parameters: a framework based on SOM-B

Wallner, Markus; Haberlandt, Uwe

doi:10.1002/hyp.10430

Cited by 21 publications

(11 citation statements)

References 34 publications

(37 reference statements)

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“…Although the fact that model parameter can vary in time is known in literature [e.g., Merz et al, 2011;Wallner and Haberlandt, 2015], one usually expect to obtain similar values for the different calibration periods (time stability) and narrow parameter ranges (identifiability), as each parameter is assumed to represent given local hydrological characteristics and be independent of calibration procedures and calibration time periods. Figure 5 shows the normalized parameter range of those parameters sets that resulted in the ''best'' model performance for the given objective functions during automatic calibration.…”

Section: Parameter Uncertaintymentioning

confidence: 99%

Effects of input discretization, model complexity, and calibration strategy on model performance in a data‐scarce glacierized catchment in Central Asia

Tarasova

Knoche

Dietrich³

et al. 2016

Water Resources Research

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Glacierized high‐mountainous catchments are often the water towers for downstream region, and modeling these remote areas are often the only available tool for the assessment of water resources availability. Nevertheless, data scarcity affects different aspects of hydrological modeling in such mountainous glacierized basins. On the example of poorly gauged glacierized catchment in Central Asia, we examined the effects of input discretization, model complexity, and calibration strategy on model performance. The study was conducted with the GSM‐Socont model driven with climatic input from the corrected High Asia Reanalysis data set of two different discretizations. We analyze the effects of the use of long‐term glacier volume loss, snow cover images, and interior runoff as an additional calibration data. In glacierized catchments with winter accumulation type, where the transformation of precipitation into runoff is mainly controlled by snow and glacier melt processes, the spatial discretization of precipitation tends to have less impact on simulated runoff than a correct prediction of the integral precipitation volume. Increasing model complexity by using spatially distributed input or semidistributed parameters values does not increase model performance in the Gunt catchment, as the more complex model tends to be more sensitive to errors in the input data set. In our case, better model performance and quantification of the flow components can be achieved by additional calibration data, rather than by using a more distributed model parameters. However, a semidistributed model better predicts the spatial patterns of snow accumulation and provides more plausible runoff predictions at the interior sites.

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Section: Parameter Uncertaintymentioning

confidence: 99%

Effects of input discretization, model complexity, and calibration strategy on model performance in a data‐scarce glacierized catchment in Central Asia

Tarasova

Knoche

Dietrich³

et al. 2016

Water Resources Research

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“…For example, Wang and Tang [17] found that rainfall and vegetation were the dominant controlling factors on the parameter of a Budyko equation, which is derived for mean annual water balance and is independent of temporal scale. Wallner and Haberlandt [18] found that the storage coefficient of the HBV-IWW model, which is a modified version of the HBV model and was developed by the Swedish Meteorological and Hydrological Institute (SMHI) [19], has a close relationship with potential evaporation. Zhang et al [20] quantified the impact of vegetation change on the landscape parameter of a Budyko equation, and found that the parameter was sensitive to changes in catchment vegetation conditions.…”

Section: Introductionmentioning

confidence: 99%

Runoff Predicting and Variation Analysis in Upper Ganjiang Basin under Projected Climate Changes

Deng

Wang

2019

Sustainability

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Catchment runoff is significantly affected by climate condition changes. Predicting the runoff and analyzing its variations under future climates play a vital role in water security, water resource management, and the sustainable development of the catchment. In traditional hydrological modeling, fixed model parameters are usually used to transfer the global climate models (GCMs) to runoff, while the hydrologic model parameters may be time-varying. It is more appropriate to use the time-variant parameter for runoff modeling. This is achieved by incorporating the time-variant parameter approach into a two-parameter water balance model (TWBM) through the construction of time-variant parameter functions based on the identified catchment climate indicators. Using the Ganjiang Basin with an outlet of the Dongbei Hydrological Station as the study area, we developed time-variant parameter scenarios of the TWBM model and selected the best-performed parameter functions to predict future runoff and analyze its variations under the climate model projection of the BCC-CSM1.1(m). To synthetically assess the model performance improvements using the time-variant parameter approach, an index ∆ was developed by combining the Nash-Sutcliffe efficiency, the volume error, the Box-Cox transformed root-mean-square error, and the Kling-Gupta efficiency with equivalent weight. The results show that the TWBM model with time-variant C (evapotranspiration parameter) and SC (water storage capacity of catchment), where growing and non-growing seasons are considered for C, outperformed the model with constant parameters with a ∆ value of approximately 5% and 10% for the calibration and validation periods, respectively. The mean annual values of runoff predictions under the four representative concentration pathways (RCPs) exhibited a decreasing trend over the future three decades (2021-2050) when compared to the runoff simulations in the baseline period , where the values were about −9.9%, −19.5%, −16.6%, and −11.4% for the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The decreasing trend of future precipitation exerts impacts on runoff decline. Generally, the mean monthly changes of runoff predictions showed a decreasing trend from January to August for almost all of the RCPs, while an increasing trend existed from September to November, along with fluctuations among different RCPs. This study can provide beneficial references to comprehensively understand the impacts of climate change on runoff prediction and thus improve the regional strategy for future water resource management.

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“…Another approach to improve the predictive performance of hydrological models under changing environments is to allow model parameters to evolve over time [23][24][25][26][27][28][29][30][31][32][33]. For example, Wallner and Haberlandt [25] linked the time-varying model parameters of a modified version of the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with the climate indices. The results showed a significant improvement in model performance for streamflow simulation when using the time-varying parameters, especially for low flows.…”

Section: Introductionmentioning

confidence: 99%

Improving Parameter Transferability of GR4J Model under Changing Environments Considering Nonstationarity

Zeng

Xiong

Liu

et al. 2019

Water

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Hydrological nonstationarity has brought great challenges to the reliable application of conceptual hydrological models with time-invariant parameters. To cope with this, approaches have been proposed to consider time-varying model parameters, which can evolve in accordance with climate and watershed conditions. However, the temporal transferability of the time-varying parameter was rarely investigated. This paper aims to investigate the predictive ability and robustness of a hydrological model with time-varying parameter under changing environments. The conceptual hydrological model GR4J (Génie Rural à 4 paramètres Journalier) with only four parameters was chosen and the sensitive parameters were treated as functions of several external covariates that represent the variation of climate and watershed conditions. The investigation was carried out in Weihe Basin and Tuojiang Basin of Western China in the period from 1981 to 2010. Several sub-periods with different climate and watershed conditions were set up to test the temporal parameter transferability of the original GR4J model and the GR4J model with time-varying parameters. The results showed that the performance of streamflow simulation was improved when applying the time-varying parameters. Furthermore, in a series of split-sample tests, the GR4J model with time-varying parameters outperformed the original GR4J model by improving the model robustness. Further studies focus on more diversified model structures and watersheds conditions are necessary to verify the superiority of applying time-varying parameters.

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Non-stationary hydrological model parameters: a framework based on SOM-B

Cited by 21 publications

References 34 publications

Effects of input discretization, model complexity, and calibration strategy on model performance in a data‐scarce glacierized catchment in Central Asia

Effects of input discretization, model complexity, and calibration strategy on model performance in a data‐scarce glacierized catchment in Central Asia

Runoff Predicting and Variation Analysis in Upper Ganjiang Basin under Projected Climate Changes

Improving Parameter Transferability of GR4J Model under Changing Environments Considering Nonstationarity

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