skip to main content

Explore Research Products in the NSF-PAR

  • NSF Public Access
  • Search Results
  • Hotspots of Predictability: Identifying Regions of High Precipitation Predictability at Seasonal Timescales From Limited Time Series Observations
Title: Hotspots of Predictability: Identifying Regions of High Precipitation Predictability at Seasonal Timescales From Limited Time Series Observations
Abstract

Precipitation prediction at seasonal timescales is important for planning and management of water resources as well as preparedness for hazards such as floods, droughts and wildfires. Quantifying predictability is quite challenging as a consequence of a large number of potential drivers, varying antecedent conditions, and small sample size of high‐quality observations available at seasonal timescales, that in turn, increases prediction uncertainty and the risk of model overfitting. Here, we introduce a generalized probabilistic framework to account for these issues and assess predictability under uncertainty. We focus on prediction of winter (Nov–Mar) precipitation across the contiguous United States, using sea surface temperature‐derived indices (averaged in Aug–Oct) as predictors. In our analysis we identify “predictability hotspots,” which we define as regions where precipitation is inherently more predictable. Our framework estimates the entire predictive distribution of precipitation using copulas and quantifies prediction uncertainties, while employing principal component analysis for dimensionality reduction and a cross validation technique to avoid overfitting. We also evaluate how predictability changes across different quantiles of the precipitation distribution (dry, normal, wet amounts) using a multi‐category 3 × 3 contingency table. Our results indicate that well‐defined predictability hotspots occur in the Southwest and Southeast. Moreover, extreme dry and wet conditions are shown to be relatively more predictable compared to normal conditions. Our study may help with water resources management in several subregions of the United States and can be used to assess the fidelity of earth system models in successfully representing teleconnections and predictability.

  more » « less
Award ID(s):
1839441 1839336
NSF-PAR ID:
10444967
Author(s) / Creator(s):
 ;  ;  ;  
Publisher / Repository:
DOI PREFIX: 10.1029
Date Published:
Journal Name:
Water Resources Research
Volume:
58
Issue:
5
ISSN:
0043-1397
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ( , Journal of Hydrometeorology)
    Abstract

    Snowpack provides the majority of predictive information for water supply forecasts (WSFs) in snow-dominated basins across the western United States. Drought conditions typically accompany decreased snowpack and lowered runoff efficiency, negatively impacting WSFs. Here, we investigate the relationship between snow water equivalent (SWE) and April–July streamflow volume (AMJJ-V) during drought in small headwater catchments, using observations from 31 USGS streamflow gauges and 54 SNOTEL stations. A linear regression approach is used to evaluate forecast skill under different historical climatologies used for model fitting, as well as with different forecast dates. Experiments are constructed in which extreme hydrological drought years are withheld from model training, that is, years with AMJJ-V below the 15th percentile. Subsets of the remaining years are used for model fitting to understand how the climatology of different training subsets impacts forecasts of extreme drought years. We generally report overprediction in drought years. However, training the forecast model on drier years, that is, below-median years (P15,P57.5], minimizes residuals by an average of 10% in drought year forecasts, relative to a baseline case, with the highest median skill obtained in mid- to late April for colder regions. We report similar findings using a modified National Resources Conservation Service (NRCS) procedure in nine large Upper Colorado River basin (UCRB) basins, highlighting the importance of the snowpack–streamflow relationship in streamflow predictability. We propose an “adaptive sampling” approach of dynamically selecting training years based on antecedent SWE conditions, showing error reductions of up to 20% in historical drought years relative to the period of record. These alternate training protocols provide opportunities for addressing the challenges of future drought risk to water supply planning.

    Significance Statement

    Seasonal water supply forecasts based on the relationship between peak snowpack and water supply exhibit unique errors in drought years due to low snow and streamflow variability, presenting a major challenge for water supply prediction. Here, we assess the reliability of snow-based streamflow predictability in drought years using a fixed forecast date or fixed model training period. We critically evaluate different training protocols that evaluate predictive performance and identify sources of error during historical drought years. We also propose and test an “adaptive sampling” application that dynamically selects training years based on antecedent SWE conditions providing to overcome persistent errors and provide new insights and strategies for snow-guided forecasts.

     
    more » « less
  2. ; ; ; ; ( , AGU Advances)
    Abstract

    Effective drought management must be informed by an understanding of whether and how current drought monitoring and assessment practices represent underlying nonstationary climate conditions, either naturally occurring or forced by climate change. Here we investigate the emerging climatology and associated trends in drought classes defined by the United States Drought Monitor (USDM), a weekly product that, since 2000, has been used to inform drought management in the United States. The USDM classifies drought intensity based in part on threshold percentiles in key hydroclimate quantities. Here we assess how those USDM‐defined drought threshold percentiles have changed over the last 23 years, examining precipitation, runoff, soil moisture (SM), terrestrial water storage (TWS), vapor pressure deficit (VPD), and near‐surface air temperature. We also assess underlying trends in the frequency of drought classifications across the U.S. Our analysis suggests that the frequency of drought class occurrence is exceeding the threshold percentiles defined by the USDM in a number of regions in the United States, particularly in the American West, where the last 23 years have emerged as a prolonged dry period. These trends are also reflected in percentile‐based thresholds in precipitation, runoff, SM, TWS, VPD, and temperature. Our results emphasize that while the USDM appears to be accurately reflecting observed nonstationarity in the physical climate, such trends raise critical questions about whether and how drought diagnosis, classification, and monitoring should address long‐term intervals of wet and dry periods or trends.

     
    more » « less
  3. ; ( , Geophysical Research Letters)
    Abstract

    Predictable internal climate variability on decadal timescales (2–10 years) is associated with large‐scale oceanic processes, however these predictable signals may be masked by the noisy climate system. One approach to overcoming this problem is investigating state‐dependent predictability—how differences in prediction skill depend on the initial state of the system. We present a machine learning approach to identify state‐dependent predictability on decadal timescales in the Community Earth System Model version 2 pre‐industrial control simulation by incorporating uncertainty estimates into a regression neural network. We leverage the network's prediction of uncertainty to examine state dependent predictability in sea surface temperatures by focusing on predictions with the lowest uncertainty outputs. In particular, we study two regions of the global ocean—the North Atlantic and North Pacific—and find that skillful initial states identified by the neural network correspond to particular phases of Atlantic multi‐decadal variability and the interdecadal Pacific oscillation.

     
    more » « less
  4. ; ( , Water Resources Research)
    Abstract

    Understanding the dominant drivers of hydrological change is essential for water resources management. Watersheds in the United States are experiencing different types of changes (e.g., wet gets wetter and dry gets drier); however, few studies have analyzed what drivers are responsible for these changes, and how the dominant drivers vary over time and as a function of the climate/water regime and land cover. This study uses a time‐varying Budyko framework to quantify the relative importance of precipitation, potential evapotranspiration, and other factors (e.g., climate seasonality, agricultural drainage, and urbanization) in 889 watersheds in the contiguous United States from 1950 to 2009. Results show that watersheds that are getting wetter are primarily due to increases in precipitation. However, watersheds in dry climates that are getting drier are primarily due to other factors, while watersheds in wet climates that are getting drier are primarily due to precipitation. The drivers causing statistically significant streamflow trends vary depending on dominant land‐use types. Temporally, the increasing effects of other factors are more pronounced after the 1980s in the Midwest. The dominant drivers of streamflow in the United States are time‐varying instead of constant. This is consistent with non‐stationary patterns of streamflow. The time‐varying drivers provide information on the processes that are increasingly important and require the most attention in water resources management.

     
    more » « less
  5. ; ; ; ( , Journal of Climate)
    null (Ed.)
    Abstract Droughts that span the states of Washington, Oregon, and California are rare but devastating due to their large spatial coverage and potential loss of redundancies in water, agricultural, and fire-fighting resources. Such pan-coastal droughts [which we define using boreal summer volumetric soil moisture along the U.S. Pacific coast (32°–50°N, 115°–127°W)] require a more precise understanding of the roles played by the Pacific Ocean and internal atmospheric variability. We employ 16-member ensembles of the Community Atmosphere Model version 5 and Community Climate Model version 3 forced with observed sea surface temperatures (SSTs) from 1856 to 2012 to separate and quantify the influences of the tropical Pacific and internal atmospheric variability on pan-coastal droughts; all other boundary conditions are kept at climatological levels to explicitly isolate for the impacts of SST changes. Internal atmospheric variability is the dominant driver of pan-coastal droughts, accounting for 84% of their severity, and can reliably generate pan-coastal droughts even when ocean conditions do not favor drought. Cold phases of the Pacific Ocean play a secondary role and contribute, on average, only 16% to pan-coastal drought severity. Spatiotemporal analyses of precipitation and soil moisture along the U.S. Pacific coast corroborate these findings and identify an antiphased wet–dry dipole pattern induced by the Pacific to play a more secondary role. Our model framework expands on previous observational analyses that point to the spatially uniform forcing of internal atmospheric variability as the more dominant mode of hydroclimate variability along the U.S. Pacific coast. The secondary nature of oceanic forcing suggests limited predictability of pan-continental droughts. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email