Leveraging Emerging Datasets for Enhanced Hydrologic Modeling in The Era of Open-Science and Data

Código

I-EBHE0077

Autores

Henrique Haas, Latif Kalin

Tema

WG 1.12: Development & application of river basin simulators

Resumo

The increased availability of Earth System science datasets and remote-sensing information presents a great opportunity to improve the accuracy and reliability of hydrologic models. By informing models with biophysical data, we can move beyond the traditional calibration-focused approach considering a specific variable (e.g., streamflow) at a specific point in the landscape (e.g., watershed outlet). This research illustrates how diverse and innovative data sources can be harnessed to better represent physical processes, hydrologic and water quality trends, and the impacts of conservation strategies in hydrologic models. In this study, we integrated the widely used SWAT model with a dynamic land-use update tool and various datasets to investigate the magnitude and drivers of streamflow and water quality changes across a large watershed system in the Southeast United States. We developed a strategy to account for the influences of tree species and land-atmosphere interactions in assessing the hydrological impacts of large-scale forest restoration. Our results demonstrate that accurately representing different tree species and forest structures in the model can lead to significant variations in water yield, nutrient, and sediment load predictions. We also propose an enhanced approach to simulate forest canopy evaporation in SWAT, leveraging remote-sensing information to parameterize the model across diverse scales, climates, soils, and elevation conditions. Furthermore, we critically assess the representation of channel geometric features, such as bankfull channel width, in watershed models like SWAT and HSPF. By incorporating channel width estimates from alternative data sources?such as satellite imagery, LiDAR, global databases, and local knowledge?we achieved improved agreement with field measurements and better predictions of high flows and water quality. This paper aims to highlight the potential of leveraging emerging datasets to refine hydrologic models. This approach not only improves model accuracy but also provides deeper insights into the interactions affecting water quantity and quality, thereby supporting more informed management decisions and effective mitigation of environmental changes.