UC Berkeley

Use-inspired hydrology seeks to address specific water resources management and policy questions by improving fundamental understanding of hydrologic systems. This dissertation consists of two use-inspired hydrologic projects in landscapes experiencing rapid anthropogenic change. The first project examines associations between deforestation and rainfall patterns in the Brazilian Amazon-Cerrado (forest-savanna) transition zone. The second project benchmarks several hydrologic models used for streamflow prediction in a California watershed.

Deforestation in the Brazilian Amazon-Cerrado transition zone can lead to changing rainfall patterns. Simulations suggest that deforestation affects rainfall in a seasonally varying, scale- and direction-dependent manner. Yet, few observational studies have attempted to verify these model findings. Though previous observational studies confirmed scale-dependence, these studies typically focused on several agriculturally relevant rainfall metrics rather than a suite of rainfall metrics that broadly characterize hydroclimatic dynamics. They also did not examine all seasons and relied on the assumption that deforestation’s effects on rainfall were isotropic. In Chapter 1, we related deforestation to seven rainfall metrics characterizing observed rainfall intensity, occurrence, and spatial clustering in a seasonal, multi-scale, and multi-directional statistical analysis. We presented three main findings. First, we found that in wet and transitional seasons, deforestation corresponded to more spatially clustered rainfall, with higher intensity, lower frequency, and less total seasonal rainfall. In contrast, in the dry season, lower intensity rainfall occurred more frequently in association with deforestation. Second, our results indicated little scale-dependence in the strength of the deforestation-rainfall relationship across the examined spatial scales. Third, most deforestation-rainfall associations did not depend on the cardinal direction of forest cover relative to the rainfall sites, though there were exceptions to this during the dry and transition seasons. Our findings from Chapter 1 provide a more holistic description of hydroclimatic dynamics in a region undergoing rapid deforestation, with implications for water resources management.

Then, in Chapter 2, we focus on the association between deforestation and rainfall spatial connectivity in the Brazilian Amazon-Cerrado transition zone. Rainfall spatial connectivity describes the statistical similarity of daily rainfall at different geographic locations. It may be indicative of deforestation-caused changes in the spatial extent of rainfall events and, thus, changes in the spatial scale of dominant rainfall generation processes. However, the relationship between deforestation and rainfall spatial connectivity has not yet been fully examined. Here, we quantified rainfall spatial connectivity as a network. The network was summarized using network statistics, such as the number, length, clustering, and sorting of rainfall connections. We then examined seasonal associations between deforestation and the rainfall network statistics. Overall, we found sparse, weak associations between deforestation and the rainfall network statistics. However, we found some evidence for increased rainfall patchiness with deforestation during the wet-to-dry transition season. We also observed a shift towards longer connection distances during the wet season. Additionally, when rainfall connections were more sorted by forest cover, we observed fewer, less direct connections during the transition seasons. Our findings from Chapter 2 may be the result of changes in moisture recycling, the initiation of mesoscale circulations, or intra-seasonal variability in the large-scale drivers of rainfall.

Finally, in Chapter 3, we shift our attention to streamflow prediction in California, a landscape experiencing increased drought risk and precipitation volatility due to climate change. Under these conditions, accurate streamflow predictions are essential for water resources management, yet challenging to produce. Though recent studies have examined the integration of machine learning tools with process-based hydrologic models to improve streamflow predictions, the possibility of replacing parameter calibration or complex process depictions with machine learning has not yet been investigated. By combining machine learning with a simpler or uncalibrated process-based model, hydrologic model post-processing may improve streamflow predictability and minimize time spent on model construction, without sacrificing process understanding. In this study, we used model benchmarking as a diagnostic tool to examine the added value of hydrologic model calibration, complexity, and post-processing within two watersheds in northern California’s Mediterranean climate. The watersheds were managed for both municipal water supplies and aquatic habitat. Consequently, the models were benchmarked using a suite of error metrics that capture a wide variety of streamflow characteristics at annual, monthly, and daily time scales. Though model performance varied substantially by watershed and error metric, we found that (i) parameter calibration reduced error for 71% of the error metrics; (ii) more complex, calibrated process-based models had lower error than less complex, calibrated process-based models for 48% of the error metrics; and (iii) post-processing could replace parameter calibration and model complexity for 43% and 69% of eligible error metrics, respectively. Furthermore, the calibrated hybrid models (calibrated process-based models that were post-processed using the machine learning algorithm) frequently outperformed both the machine learning algorithm (for 83% of error metrics) and the calibrated process-based models (for 72% of error metrics). Our results demonstrate that post-processing is a promising method for replacing parameter calibration and model complexity. However, additional studies using a larger sample of watersheds are needed to uncover more generalizable guidelines regarding its use.

Throughout this dissertation, we investigate use-inspired hydrologic questions in landscapes undergoing rapid anthropogenic change. In Chapters 1 and 2, we used statistical models to examine the association between observations of deforestation and rainfall patterns in the Amazon-Cerrado transition zone. Then, in Chapter 3, we benchmarked a group of process-based, data-driven, and hybrid streamflow prediction models in a California watershed. In all three chapters, our synthesis of hydrologic models and observations has the potential to improve the hydrologic predictions used for water resources management and policy in landscapes facing increasing risks from deforestation, precipitation volatility, and drought.