Advancing river flood forecasting with a collaborative integrated modeling method.

Abstract

River flood forecasting and assessment are crucial for reducing flood risks, as they offer early alerts and allow for proactive actions to safeguard individuals from possible flood-related damage. Effective modeling in this field often multiple interconnected aspects of the hydrologic cycle, such as precipitation, infiltration, runoff, and evaporation, requiring collaboration among hydrology experts. Such collaboration enables experts to handle and manage their specialized processes more effectively, thereby enhancing the efficiency of the development of integrated flood forecasting models. Tight integration and loose integration are two common strategies for integrating different hydrologic cycle process models in river flood forecasting. However, most integration strategies rely on centralized models, necessitating experts to configure models and data on local computers. Currently, there is a deficiency in the capacity for effective collaboration in the integrated modeling of river flood forecasts. This issue arises from multiple obstacles: the complexity of understanding heterogeneous data and hydrologic cycle process models; the difficulty of integrating models with diverse runtime environments; and the challenge of synchronizing forecasting model changes among experts in real time. Therefore, we propose a web-based collaborative integrated modeling method, designed to support both tightly and loosely integrated modes, to enhance collaborative river flood forecasting and assessment. This method includes three core modules: (1) data and model description for providing a structured description of the execution logic of forecasting models and the internal structure of forecast data for expert understanding; (2) model access and integration for access and integration of data and multi-source heterogeneous models of hydrologic cycle processes; and (3) modeling scenario configuration for collaborative development of forecasting models and the execution of simulation tasks. Finally, we illustrate the application of the proposed method by utilizing the GEFS v12 (Global Ensemble Forecast System) rainfall ensemble forecasting dataset with the CREST (Coupled Routing and Excess STorage) hydrologic model. The results show enhanced efficiency in the collaborative development of river flood forecasts by hydrology experts, particularly in model accessibility, data processing, simulation, and evaluation, thereby potentially aiding decision-making.