Data-driven analyses of watersheds as coupled human-nature systems

Data Mining;Streamflow Prediction;Fish-IBI;Dominant Controls;Streamflow Trends;Frequent Patterns;Watershed Hydrology;Human Impacts

Schnier, Spencer T

关键词: Data Mining;    Streamflow Prediction;    Fish-IBI;    Dominant Controls;    Streamflow Trends;    Frequent Patterns;    Watershed Hydrology;    Human Impacts;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/92732/SCHNIER-DISSERTATION-2016.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Both climate and human activities alter watershed characteristics. Because climate will continue to change and human population will continue to increase, we can expect that all watersheds will change during the foreseeable future. Interactions between climate change and human activity drive non-stationary trends in the hydrologic cycle, which make watersheds more difficult to manage. In the 21st century, successful management of water resources requires an improved understanding of the emergent properties of coupled human and natural systems and fully integrated land and water management. The primary goal of this research is to better understand the emergent properties of watersheds as coupled human-nature systems, namely streamflow frequency and instream biotic integrity. We also aim to understand how the dominant controls of these emergent properties change through time. This study targets these needs by quantitatively linking natural and anthropogenic drainage area characteristics to flow frequency and measures of ecological health using data mining techniques. The fact that drainage area characteristics for watersheds in the U.S. are known (in the case of ungauged watersheds) or readily predicted (in the case of future conditions) makes them ideal independent variables for predicting watershed response. The basic idea is to define quantitative indices (e.g., flow duration curve, index of biotic integrity) as a function of known drainage area characteristics using data mining algorithms. A novel algorithm called Model Tree Ensembles was developed and tested as a way to predict flow duration curves in ungaged, human-impacted basins. The importance of environmental factors to fish biotic metrics was assessed using conventional measures of variable importance as well as more advanced information theory-based techniques. Understanding the relative influence of natural and man-made drainage area characteristics on streamflow and fisheries is essential for sustainable management of regional water resources (i.e., water management) and watershed protection (i.e., land management). A lack of understanding of the combined effects and relative importance of major components of the hydrologic cycle hinders sustainable management of water supplies. In summary, this study determined the relative importance of natural and anthropogenic drainage area characteristics to emergent properties of coupled human-nature systems, namely streamflow frequency and instream biotic integrity, as well as how those variables change through time. The ability of these models to quantitatively assess a wide range of possible causes of streamflow and biotic community variability is evaluated. The concepts and methods are generalized and can be applied to other watersheds and response variables.