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Accueil du site → Doctorat → États-Unis → 2020 → Development of novel data applications for improving precipitation-runoff modeling in headwater catchments

University of California Berkeley (2020)

Development of novel data applications for improving precipitation-runoff modeling in headwater catchments

Maurer, Tessa

Titre : Development of novel data applications for improving precipitation-runoff modeling in headwater catchments

Auteur : Maurer, Tessa

Université de soutenance : University of California Berkenley

Grade : Doctor Philosophy (PhD) Engineering–Civil & Environmental Engineering 2020

Résumé
Hydrologic modeling is heavily used in water resources for advancing scientific process understanding and supporting operational decision making, but the capabilities of these models are challenged by climate change, land-use changes, and population growth. Novel ground-based and remotely sensed data sources can provide high-quality and reliable information that can enhance hydrologic models to meet current demands. Here, I examine how these data sources can be leveraged to facilitate and improve use of three major model types : data-driven, physically based, and conceptual, which each fall in a different range of complexity (the extent to which a model represents or simplifies physical processes) and scale (the number of simulation or data points). I focus on the state of California, a region that faces significant water management challenges due to its arid Mediterranean climate, highly seasonal precipitation, and high variability in annual precipitation totals. Chapter II investigates snowpack response to rain-on-snow events by leveraging expanded ground-based wireless-sensor networks to drive a machine-learning algorithm. Supported by the high temporal and spatial resolution of these data, I identified a nonlinear and temporally variable relationship between Leaf Area Index and snow-depth change during rain-on-snow events. Chapter III demonstrates how remotely sensed spatial maps can be used to identify the optimal spatial distribution of input data in a phys ically based hydrologic model through use of a Gaussian-Mixture Model. This novel method addresses issues of overparameterization and improves the efficiency of model design for physically based models while still being grounded in physical principles. Finally, Chapter IV applies new spatially distributed data to a conceptual hydrologic model to quantify the impact of predictable versus unpredictable shifts in the precipitation-runoff relationship during droughts. Collectively, this work demonstrates that 1) data that are gathered strategically by location and water-balance component is most helpful in improving or developing new models ; 2) remotely sensed data are most effective when calibrated or coupled with ground-based sensors ; 3) scale continues to be a challenge both on the modeling and data sides, whether scaling up physically based models or scaling down conceptual models ; and 4) model improvement relies not only on data collection but on making the data usable and easily accessible, particularly for operational applications.

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