Paper: Characterizing #Drought Behavior in the #ColoradoRiver Basin Using Unsupervised Machine Learning — AGU

Map credit: AGU

Click the link to access the paper on the AGU website (Carl J. Talsma,Katrina E. Bennett,Velimir V. Vesselinov):

Abstract

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.

Key Points

  • Unsupervised machine learning automatically identifies key sub-watersheds with significant changes in their future drought indicators
  • In the Colorado River Basin mountains, distinct differences in future runoff seasonality and intensity changes are established
  • Significant uncertainty in drought behavior is observed among the applied climate models
  • 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.

    Click the link to read “Colorado will lose half its snow by 2080 and look more like Arizona, federal scientists conclude” on The Denver Post website (Bruce Finley). Here’s an excerpt:

    “We see increased aridity moving forward”

    Parts of Colorado, Wyoming and Utah are drying out due to climate-driven changes in stream flows, and these states will shift to become more like the most arid states of the Southwest, federal researchers found in a scientific study published this week.

    The lead author of the study said Colorado will experience a 50% to 60% reduction in snow by 2080.

    “We’re not saying Colorado is going to become a desert. But we see increased aridity moving forward,” said hydrologist Katrina Bennett at the federal government’s Los Alamos National Laboratory in New Mexico.

    The researchers used an artificial intelligence “machine learning” system that allowed them to analyze massive amounts of data collected over 30 years including soil moisture, volumes of water in streams, evapotranspiration rates, temperature and precipitation across the varying landscapes within the Colorado River Basin. Tracking the West’s hydrology on such a scale previously would have taken years.

    One thought on “Paper: Characterizing #Drought Behavior in the #ColoradoRiver Basin Using Unsupervised Machine Learning — AGU

    Leave a Reply