Sensitivity and Interdependency Analysis of the HBV Conceptual Model Parameters in a Semi-Arid Mountainous Watershed

Abstract

Hydrological models, with different levels of complexity, have become inherent tools in water resource management. Conceptual models with low input data requirements are preferred for streamflow modeling, particularly in poorly gauged watersheds. However, the inadequacy of model structures in the hydrologic regime of a given watershed can lead to uncertain parameter estimation. Therefore, an understanding of the model parameters’ behavior with respect to the dominant hydrologic responses is of high necessity. In this study, we aim to investigate the parameterization of the HBV (Hydrologiska Byråns Vattenbalansavedelning) conceptual model and its influence on the model response in a semi-arid context. To this end, the capability of the model to simulate the daily streamflow was evaluated. Then, sensitivity and interdependency analyses were carried out to identify the most influential model parameters and emphasize how these parameters interact to fit the observed streamflow under contrasted hydroclimatic conditions. The results show that the HBV model can fairly reproduce the observed daily streamflow in the watershed of interest. However, the reliability of the model simulations varies from one year to another. The sensitivity analysis showed that each of the model parameters has a certain degree of influence on model behavior. The temperature correction factor (ETF) showed the lowest effect on the model response, while the sensitivity to the degree-day factor (DDF) highly depends on the availability of snow cover. Overall, the changes in hydroclimatic conditions were found to be mostly responsible for the annual variability of the optimal parameter values. Additionally, these changes seem to actuate the interdependency between the parameters of the soil moisture and the response routines, particularly Field Capacity (FC), the recession coefficient K0, the percolation coefficient (KPERC), and the upper reservoir threshold (UZL). The latter combines either to shrink the storage capacity of the model’s reservoirs under extremely high peak flows or to enlarge them under overestimated water supply, mainly provoked by abundant snow cover.

A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models

“…Even though, β is one of the influential model parameters as stated above, FC and β have been reported to be greatly inter‐related, whereby the uncertainty of one can counterbalance the water supply offset (i.e., soil moisture deviation) provoked by the other (Ouatiki et al., 2020). Hence, the relative deviation of β can be considered acceptable and not largely affecting the model performance (Ouatiki et al., 2020). PWP and L yielded contradicting performance in both the watersheds.…”

“…The high interdependency between FC and β is obvious as both these influence the soil moisture (Ouatiki et al, 2020), while C and β show relatively lesser interdependency as one controls the soil moisture and other evapotranspiration (Tibangayuka et al, 2022). PWP influencing the evapotranspiration module, is reported to not be highly dominant (Ouatiki et al, 2020).…”

Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data

“…A combination of flat and mountain terrains generally characterize the topography of the basin. Elevation ranges between 100 (e.g., in the western and coastal zones) and 3890 m (e.g., in the eastern zone) above sea level [42] ( Figure 1). The OER River sources are located in the mountainous upstream zones, and the river covers a distance of 550 km, overpassing the Tadla irrigated perimeter (TIP), the coastal areas, and the northern zone of the Doukkala irrigated perimeter (DIP), and flows into the Atlantic Ocean at Azemmour city [4].…”

“…The OER River sources are located in the mountainous upstream zones, and the river covers a distance of 550 km, overpassing the Tadla irrigated perimeter (TIP), the coastal areas, and the northern zone of the Doukkala irrigated perimeter (DIP), and flows into the Atlantic Ocean at Azemmour city [4]. The climate is variable from humid in coastal and mountainous zones to semi-arid in the plains, with cold winters and dry summers [42]. Annual rainfall average varies from 230 to 1000 mm in the plains and the Atlas Mountains, respectively [43].…”

A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

“…Apart from process‐based models, empirical or data‐driven statistical models which establish a statistical relationship among the observed or experimental data, irrespective of the theory underlying the process, have also been widely explored by researchers (Alexander et al., 2018; Alizadeh et al., 2017; Curceac et al., 2020; Kisi, 2010; Kurian et al., 2020; Sun et al., 2014). The commonly explored empirical models include the simple regression models (low accuracy in higher lead streamflow forecasts and often underperform; Abba et al., 2017; Okamura et al., 2021; Vogel et al., 1999), multiple linear regression models (Adamowski, 2008; Khazaee Poul et al., 2019; Rezaeianzadeh et al., 2014), support vector regression models (Nguyen & Chen, 2020; J. Wu et al., 2019), the Gaussian process regression models (Kisi, 2010; Patidar et al., 2021; Schoppa et al., 2020; Shen, Ruijsch, et al., 2022; Shen, Wang, et al., 2022; Sun et al., 2014), etc.…”