Time-varying parameter models for catchments with land use change: the importance of model structure

Abstract

Abstract. Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a mediumsized catchment (2880 km 2 ) in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a timevarying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD) that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors) contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.

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

“…However, it is no longer appropriate to use constant parameters, especially for hydrological modeling under the changing environment. A temporal variability of the model parameters exists due to the influence of catchment condition changes including climate condition and catchment characteristics (e.g., land use and land cover) [12][13][14][15][16]. 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.…”

Dynamics of hydrological-model parameters: mechanisms, problems and solutions

“…However, due to the complex correlations among the parameters and imperfect model structures (missing processes or oversimplified parameterizations in the model), the individual parameters may not represent their defined physical characteristics, such as temporal changes in soil, land cover and climate conditions. Hence, the parameter with the highest sensitivity was chosen as the dynamic parameter (Merz et al, 2011;Pfannerstill et al, 2014;Zhang et al, 2015;Deng et al, 2016Deng et al, , 2018Guse et al, 2016;Ouyang et al, 2016;Xiong et al, 2019). In this study, the dynamic parameter K q with the highest identifiability and the other fixed parameters are optimized.…”

“…For a concise model evaluation, the model performance is analyzed with multi-metric frameworks with appropriate performance metrics, including five-segment evaluation (5 FDC; flow duration curve with the root mean square error) (Pfannerstill et al, 2014), the Nash-Sutcliffe efficiency index (NSE) (Nash and Sutcliffe, 1970) and the logarithmic transformation. For the robustness of model evaluation, the transferability of the optimized parameters between the calibration period and the validation period is considered.…”

Supplementary material to "Dynamics of hydrological model parameters: mechanisms, problems, and solution"

“…The HYMOD model (Moore, 1985;Wagener et al, 2001;Vrugt et al, 2002;Yadav et al, 2007;De Vos et al, 2010;Pathiraja et al, 2018) consists of a simple rainfall excess model based on the probability-distributed moisture store which characterizes the catchment storage as a Pareto distribution of buckets of varying depth as the soil moisture accounting component. It routes through three parallel tanks for quick flow and a tank for slow flow and required five adjustable parameters:…”