The third semi-distributed conceptual model used in this study was the HBV model. The rainfall-runoff simulation using the HBV model requires spatial (e.g., LULC), hydrological (discharge) and climate data of temperature and evapotranspiration. The climate and hydrological input data for the catchment were prepared in the format of the HBV model. The LULC of the Katar catchment classified using the SWAT model was merged into three LULC types in view of vegetation characteristics similarity because the model accepts only a maximum of three vegetation zones. The HBV model was configured into different parameters and five elevation zones with three vegetation zones of the catchment. In this study, the identification of sensitive parameters for the HBV model was conducted using Monte Carlo's optimization method, setting the objective function that generates the optimum values of the parameters within the predefined value range of the parameter as shown in Table 5. Accordingly, FC, parameters controlling the contribution of rainfall to runoff (BETA), recession (storage) coefficient 1 (K1) and LP (soil moisture value above which ETact reaches ETpot) were the most sensitive parameters.
Sensitivity result of the HBV model
| Parameter . | Unit . | Range . | Optimum . | Rank . |
|---|---|---|---|---|
| FC | mm | 100–550 | 177.77 | 1 |
| BETA (β) | – | 1–6 | 2.23 | 2 |
| LP | mm | 0.3–1 | 0.893 | 3 |
| K1 | day-1 | 0.01–0.2 | 0.0116 | 4 |
| UZL | mm | 0–100 | 43.49 | 5 |
| PERC | mm/day | 0–4 | 3.4 | 6 |
| K0 | day−1 | 0.1–0.5 | 0.353 | 7 |
| K2 | day−1 | 0.001–0.1 | 0.063 | 8 |
| MAXBAS | 1–2.5 | 2.26109 | 9 |
| Parameter . | Unit . | Range . | Optimum . | Rank . |
|---|---|---|---|---|
| FC | mm | 100–550 | 177.77 | 1 |
| BETA (β) | – | 1–6 | 2.23 | 2 |
| LP | mm | 0.3–1 | 0.893 | 3 |
| K1 | day-1 | 0.01–0.2 | 0.0116 | 4 |
| UZL | mm | 0–100 | 43.49 | 5 |
| PERC | mm/day | 0–4 | 3.4 | 6 |
| K0 | day−1 | 0.1–0.5 | 0.353 | 7 |
| K2 | day−1 | 0.001–0.1 | 0.063 | 8 |
| MAXBAS | 1–2.5 | 2.26109 | 9 |