Multi-variable optimization of a hydrological model in a data scarce dry river basin incorporating remotely sensed data

Abstract

Hydrological models are mathematical representations that simulate simplified real-world catchment hydrological processes such as rainfall, streamflow, infiltration, etc. Each model has its own set of parameters that control the model simulations (outputs), ensuring a closer match with the real (observed) hydrological variables. This matching process is known as model calibration or optimization, which is conventionally achieved by considering only one hydrological variable (usually streamflow), known as Single Variable Calibration (SVC). The match between the observed and simulated data is quantified by a goodness-of-fit measurement (objective function), which gives a measure of the accuracy and reliability of model outputs.