How model parameters control hydrological drought simulation in three different hydrological models
|Resource type:||Composite Resource|
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|Created:||Aug 28, 2018 at 1:36 p.m.|
|Last updated:||Feb 19, 2019 at 2 p.m. by Lieke Melsen|
|Citation:||See how to cite this resource|
This folder contains the output files from the study described below. The data contain hydrological drought indicators (e.g. median drought duration) for three different models (SAC, VIC, HBV), where these models were run with a sample of parameters for 605 basins in the US. Furthermore, sensitivity analysis was applied to the indicators.
During natural hydrological drought, water flow is mainly controlled by the release of storage water. Therefore, the simulation of hydrological droughts highly depends on how storage is represented in the model structure. In this study, we conducted sensitivity analysis on parameters of three frequently used hydrological models (HBV, SAC, and VIC) for the simulation of drought duration and drought deficit over 605 basins in the contiguous United States. As such, we identified the processes in the different models that drive the simulation of hydrological drought, and related this to four climate indicators (mean yearly temperature, mean winter temperature, seasonality, and aridity). The sensitivity analysis revealed that HBV and SAC often demonstrated comparable parameters as most sensitive, such as snow parameters, shallow layer parameters, and ET parameters in both models. VIC, however, displayed deviant behaviour: in most instances, drought simulation was mainly driven by deep layer parameters. When relating the sensitivity analysis to climate indicators, all three models had snow parameters dominating in cold regions. Moving towards warmer regions, SAC and HBV increasingly relied on mainly ET parameters, while in VIC, deep layer parameters were most sensitive. Also when focussing on aridity, VIC relied on different processes than SAC and HBV. The results of this study show that different models use different processes to simulate hydrological drought. This implies that direct interpretation of dominant processes during drought cannot be based on models only, but always needs support from observations of drought related variables.
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