Data-driven modeling of hydroclimatic trends and soil moisture: Multi-scale data integration and decision support.

摘要

The techniques and information employed for decision-making vary with the spatial and temporal scope of the assessment required. In modern agriculture, the farm owner or manager makes decisions on a day-to-day or even hour-to-hour basis for dozens of fields scattered over as much as a fifty-mile radius from some central location. Following precipitation events, land begins to dry. Land-owners and managers often trace serpentine paths of 150+ miles every morning to inspect the conditions of their various parcels. His or her objective lies in appropriate resource usage -- is a given tract of land dry enough to be workable at this moment or would he or she be better served waiting patiently? Longer-term, these owners and managers decide upon which seeds will grow most effectively and which crops will make their operations profitable. At even longer temporal scales, decisions are made regarding which fields must be acquired and sold and what types of equipment will be necessary in future operations. This work develops and validates algorithms for these shorter-term decisions, along with models of national climate patterns and climate changes to enable longer-term operational planning.;A test site at the University of Illinois South Farms (Urbana, IL, USA) served as the primary location to validate machine learning algorithms, employing public sources of precipitation and potential evapotranspiration to model the wetting/drying process. In expanding such local decision support tools to locations on a national scale, one must recognize the heterogeneity of hydroclimatic and soil characteristics throughout the United States. Machine learning algorithms modeling the wetting/drying process must address this variability, and yet it is wholly impractical to construct a separate algorithm for every conceivable location. For this reason, a national hydrological classification system is presented, allowing clusters of hydroclimatic similarity to emerge naturally from annual regime curve data and facilitate the development of cluster-specific algorithms. Given the desire to enable intelligent decision-making at any location, this classification system is developed in a manner that will allow for classification anywhere in the U.S., even in an ungauged basin. Daily time series data from 428 catchments in the MOPEX database are analyzed to produce an empirical classification tree, partitioning the United States into regions of hydroclimatic similarity. In constructing a classification tree based upon 55 years of data, it is important to recognize the non-stationary nature of climate data. The shifts in climatic regimes will cause certain locations to shift their ultimate position within the classification tree, requiring decision-makers to alter land usage, farming practices, and equipment needs, and algorithms to adjust accordingly. This work adapts the classification model to address the issue of regime shifts over larger temporal scales and suggests how land-usage and farming protocol may vary from hydroclimatic shifts in decades to come.;Finally, the generalizability of the hydroclimatic classification system is tested with a physically-based soil moisture model calibrated at several locations throughout the continental United States. The soil moisture model is calibrated at a given site and then applied with the same parameters at other sites within and outside the same hydroclimatic class. The model's performance deteriorates minimally if the calibration and validation location are within the same hydroclimatic class, but deteriorates significantly if the calibration and validates sites are located in different hydroclimatic classes. These soil moisture estimates at the field scale are then further refined by the introduction of LiDAR elevation data, distinguishing faster-drying peaks and ridges from slower-drying valleys. The inclusion of LiDAR enabled multiple locations within the same field to be predicted accurately despite non-identical topography. This cross-application of parametric calibrations and LiDAR-driven disaggregation facilitates decision-support at locations without proximally-located soil moisture sensors.