FOSS4G-Asia 2023 Seoul

Hydrological models play a pivotal role in mathematically simulating the complexities of real-world hydrological processes. However, accurately capturing the intricate physical nature of basin hydrology through parameterized representations remains a formidable challenge, particularly in regions with limited or unavailable gauge sites, a prevalent scenario in developing nations and remote locales. A notable trend has recently emerged where remote sensing inversion products or reanalysis data, such as evapotranspiration, soil water, and leaf area index, are increasingly used as substitutes for ground truth observations in the calibration of hydrological models. But this introduces a considerable risk when directly employing them for hydrological modeling as these products inherently harbor unknown errors.

This study intends to address the above challenges by proposing an adaptive procedural fitting algorithm to support multivariate calibration, employing an iterative stepwise approach. The algorithm simplifies the hydrological model into a directed graph, with each node representing a subprocess in the hydrological model. This allows for quantifying the impact of a parameter on each subprocess by considering the traversal relationship between the subprocess to which the parameter is attached and other subprocesses. Consequently, the iterative calibration process enables the updating of parameters at different levels or learning rates. Furthermore, two categories of methods were introduced to evaluate the outputs of subprocesses relative to their calibrated datasets: ground truth observations and non-observation data with unknown errors. One category employs standard metrics to evaluate the simulated data against observations, while the other utilizes multiple collocation methods to evaluate the simulation with the data with unknown errors. The proposed multivariate calibration framework offers an innovative approach to hydrological modelling, facilitating the effective utilization of increasingly abundant remote sensing products and reanalysis data to enhance the accuracy and reliability of hydrological models.

The presented approach has been realized through the utilization of an open-source GIS solution, integrating tools such as Python, xarray, geopandas, multiprocessing and SWAT to form an efficiently processed parallel program. The demonstration was applied in a case study conducted within the Malian river Basin situated in Gansu Province, China, involving the integration of remotely sensed ET products and gauged streamflow datasets to showcase the applicability and effectiveness of the proposed method.