Modeling streamflow in headwater catchments: A data-based mechanistic grounded framework

Abstract

Study Region: The Lenguazaque River Basin is a 290 km2 headwater catchment of the Fuquene Lake Watershed in the Colombian Andes. Regional stakeholders are optimizing water allocations for all users, given numerous quantity related issues such as its simultaneous use for conservation, agriculture, and coal mining. Study Focus: Developing hydrological models in headwater catchments is challenging, especially in developing countries where technical resources and data are limited. This study addresses these challenges by proposing strategies to develop reliable yet mathematically simple models, requiring fewer inputs than data-intensive alternatives. Three stages are proposed, focused on 1) assessing hydrological data, 2) preparing datasets for modeling, and 3) developing models in daily and sub-daily resolutions. The last stage is grounded on a data-based mechanistic modeling approach and includes a novel combination of baseflow separation with digital filters and multi-objective optimization principles. Having a hydrophysical meaning, obtained models are suitable for simulating alternative scenarios. New hydrological insights for the region: Resulting daily models achieved an acceptable performance, significantly better than a semi-distributed model. The performance of sub-daily models was sub-optimal, yet it can be improved by enhancing the quality of sub-daily datasets and the efficiency of computational algorithms. Due to their mathematical nature, the proposed strategies are expected to be applicable to other headwater catchments where improving water management is an imperative need.

Keywords

• Headwater hydrology
• Data-based mechanistic modeling
• Rainfall-runoff
• Baseflow estimation
• Transfer functions
• System identification