Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning

Drought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate change. Here, we use climate simulations from various Earth System Models (ESMs) to force the Variable Infiltration Capacity hydrologic model and project multiple drought indicators for the sub-watersheds within the CRB. We apply an unsupervised machine learning (ML) based on Non-Negative Matrix Factorization using K-means clustering (NMFk) to synthesize the simulated historical, future, and change in drought indicators. The unsupervised ML approach can identify sub-watersheds where key changes to drought indicator behavior occur, including shifts in snowpack, snowmelt timing, precipitation, and evapotranspiration. While changes in future precipitation vary across ESMs, the results indicate that the Upper CRB will experience increasing evaporative demand and surface-water scarcity, with some locations experiencing a shift from a radiation-limited to a water-limited evaporation regime in the summer. Large shifts in peak runoff are observed in snowmelt-dominant sub-watersheds, with complete disappearance of the snowmelt signal for some sub-watersheds. The work demonstrates the utility of the NMFk algorithm to efficiently identify behavioral changes of drought indicators across space and time and to quickly analyze and interpret hydro climate model results. Plain Language Summary Our study applies a pattern recognition computer program to categorize regions with the Colorado River Basin (CRB), based on the modeled future behavior of several indicators important to drought. We use the results from models of climate and water to estimate how drought will change in the future. We then group the behavior of sub-watersheds based on identified similarities in their response to changes we observed. We show that areas of the Upper CRB could experience a large reduction in available water for evapotranspiration (for use by trees, e.g.,), and that future hydrologic conditions may more closely resemble those of the Southwest CRB regions today. We are also able to pinpoint which sub-watersheds should expect large losses in snowpack based on expected changes to spring runoff contribution to streamflow. The work is important in that it highlights a key tool that can be used for rapid assessment of vast amounts of climate and hydrology data in a region that may be critically impacted by future changes in extreme events, such as drought.

Automated summary

This study uses climate simulations to predict drought indicators in the Colorado River Basin (CRB) and applies an unsupervised machine learning approach to analyze the results. The findings suggest that the CRB will face increasing water scarcity and evaporative demand under climate change. Significant changes in peak runoff are observed in snowmelt-dominant sub-watersheds, with some sub-watersheds experiencing the complete disappearance of the snowmelt signal. This research demonstrates the utility of the unsupervised ML approach in analyzing hydro climate model results and understanding the potential impacts of drought.