Recent Publications

Drought assessment has been outpaced by climate change: empirical arguments for a paradigm shift

Hoylman, Zachary and Bocinsky, R. Kyle and Jencso, Kelsey

Nature Communications

“Despite the acceleration of climate change, erroneous assumptions of climate stationarity are still inculcated in the management of water resources in the United States (US). The US system for drought detection, which triggers billions of dollars in emergency resources, adheres to this assumption with preference towards 60-year (or longer) record lengths for drought characterization. Using observed data from 1,934 Global Historical Climate Network (GHCN) sites across the US, we show that conclusions based on long climate records can substantially bias assessment of drought severity. Bias emerges by assuming that conditions from the early and mid 20th century are as likely to occur in today’s climate. Numerical simulations reveal that drought assessment error is relatively low with limited climatology lengths (~30 year) and that error increases with longer record lengths where climate is changing rapidly. We assert that non-stationarity in climate must be accounted for in contemporary assessments to more accurately portray present drought risk.”

Read Article

IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S

Ketchum, David and Jencso, Kelsey and Maneta, Marco and Melton, Forrest and Jones, Matthew and Huntington, Justin

Remote Sensing

“High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986-2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r2 = 0.90), and high agreement when estimates are aggregated to the state level (r2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km2) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California.”

Read Article

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

Brust, Colin and Kimball, John S. and Maneta, Marco P. and Jencso, Kelsey and Reichle, Rolf H.

Frontiers in Big Data: Data Driven Climate Sciences

“Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.”

Read Article

The influence of hydroclimate and management on forest regrowth across the western U.S

Hoylman, Zachary H. and Jencso, Kelsey and Archer, Vince and Efta, James (Andy) and Holden, Zachary A., and Ballantyne, Ashley P. and Johnson, Marie

Environmental Research Letters

“Forests are subject to a range of management practices but it is unclear which produce the most rapid rates of regrowth across heterogeneous moisture gradients produced by regional climate and complex terrain. We analyzed recovery rates of satellite derived net primary productivity (NPP) over 27 years for 26,069 individual silvicultural treatments (stands) across the western U.S. at a 30 m resolution. Rates of NPP recovery and forest regrowth were on average 116% higher in wet landscapes with lower annual climatic water deficits (8.59 ± 5.07 gC m-2 yr-2, median ± inter-quartile range) when compared to dry landscapes (3.97 ± 2.67 gC m-2 yr-2). This extensive spatial analysis indicates that hydroclimate is a dominant driver of forest regrowth and that responses can be highly nonlinear depending upon local climate conditions. Differences in silvicultural treatment also strongly controlled rates of regrowth within hydroclimatic settings; microclimates produced by shelterwood treatments maximized regrowth in dry landscapes whereas regrowth following clearcutting was among the fastest in wet landscapes due to enhanced energy availability. Conversely, commercial thinning regrowth rates were insensitive to hydroclimate and relatively consistent across the western U.S. Planting had a differential effect on forest structure and rates of regrowth across hydroclimate with negative effects in wet environments and positive effects in dry environments. In aggregate, this study provides a novel remote sensing approach for characterizing forest regrowth dynamics across climatic gradients and the common treatment options employed.”

Read Article

Scalar Mismatches and Underlying Factors for Underutilization of Climate Information: Perspectives From Farmers and Ranchers

Smith, Ada P. and Yung, Laurie and Snitker, Adam J. and Bocinsky, R. Kyle and Metcalf, Elizabeth Covelli and Jencso, Kelsey

Frontiers in Climate

“Growing demand for water resources coupled with climate-driven water scarcity and variability present critical challenges to agriculture in the Western US. Despite extensive resources allocated to downscaling climate projections and advances in understanding past, current, and future climatic conditions, climate information is underutilized in decisions made by agricultural producers. Climate information providers need to understand why this information is underutilized and what would better meet the needs of producers. To better understand how agricultural producers perceive and utilize climate information, we conducted five focus groups with farmers and ranchers across Montana. Focus groups revealed that there are fundamental scalar issues (spatial and temporal) that make climate information challenging for producers to use. While climate information is typically produced at regional, national, or global spatial scales and at a seasonal and mid- to end-of-century temporal scales, producers indicated that decision-making takes place at multiple intermediate and small temporal and spatial scales. In addition, producers described other drivers of decision-making that have little to do with climate information itself, but rather aspects of source credibility, past experience, trust in information, and the politics of climate change. Through engaging directly with end-users, climate information providers can better understand the spatial and temporal scales that align with different types of agricultural producers and decisions, as well as the limitations of information provision given the complexity of the decision context. Increased engagement between climate information providers and end-users can also address the important tradeoffs that exist between scale and uncertainty.”

Read Article

Lessons learned from the 2017 flash drought across the US Northern Great Plains and Canadian Prairies

Hoell, Andrew and Parker, Britt-Anne and Downey, Michael and Umphlett, Natalie and Jencso, Kelsey and Akyuz, F. Adnan and Peck, Dannele and Hadwen, Trevor and Fuchs, Brian and Kluck, Doug and Edwards, Laura and Perlwitz, Judith and Eischeid, Jon and Deheza, Veva and Pulwarty, Roger and Bevington, Kathryn

Bulletin of American Meteorological Society

“The 2017 flash drought arrived without early warning and devastated the U.S. northern Great Plains region comprising Montana, North Dakota, and South Dakota and the adjacent Canadian Prairies. The drought led to agricultural production losses exceeding $2.6 billion in the United States, widespread wildfires, poor air quality, damaged ecosystems, and degraded mental health. These effects motivated a multiagency collaboration among academic, tribal, state, and federal partners to evaluate drought early warning systems, coordination efforts, communication, and management practices with the goal of improving resilience and response to future droughts. This essay provides an overview on the causes, predictability, and historical context of the drought, the impacts of the drought, opportunities for drought early warning, and an inventory of lessons learned. Key lessons learned include the following: 1) building partnerships during nondrought periods helps ensure that proper relationships are in place for a coordinated and effective drought response; 2) drought information providers must improve their understanding of the annual decision cycles of all relevant sectors, including, and beyond, direct impacts in agricultural sectors; and 3) ongoing monitoring of environmental conditions is vital to drought early warning, given that seasonal forecasts lack skill over the northern Great Plains.”

Read Article

A Numerical Investigation of Bedrock Groundwater Recharge and Exfiltration on Soil Mantled Hillslopes

Gardner, W. Payton and Jencso, Kelsey and Hoylman, Zachary and Livesay, Robert and Maneta, Marco

Hydrological Processes

“Here we use Richards Equation models of variably saturated soil and bedrock groundwater flow to investigate first-order patterns of the coupling between soil and bedrock flow systems. We utilize a Monte Carlo sensitivity analysis to identify important hillslope parameters controlling bedrock recharge and then model the transient response of bedrock and soil flow to seasonal precipitation. Our results suggest that hillslopes can be divided into three conceptual zones of groundwater interaction, (a) the zone of lateral unsaturated soil moisture accumulation (upper portion of hillslope), (b) the zone of soil saturation and bedrock recharge (middle of hillslope) and (c) the zone of saturated-soil lateral flow and bedrock groundwater exfiltration (bottom of hillslope). Zones of groundwater interaction expand upslope during periods of precipitation and drain downslope during dry periods. The amount of water partitioned to the bedrock groundwater system a can be predicted by the ratio of bedrock to soil saturated hydraulic conductivity across a variety of hillslope configurations. Our modelled processes are qualitatively consistent with observations of shallow subsurface saturation and groundwater fluctuation on hillslopes studied in our two experimental watersheds and support a conceptual model of tightly coupled shallow and deep subsurface circulation where groundwater recharge and discharge continuously stores and releases water from longer residence time storage.”

Read Article

The topographic signature of ecosystem climate sensitivity in the western United States

Hoylman, Zachary H. and Jencso, Kelsey and Hu, Jia and Holden, Zachary A. and Allred, Brady and Dobrowski, Solomon and Robinson, Nathaniel and Martin, Justin T. and Affleck, David and Seielstad, Carl

Geophysical Research Letters

“It has been suggested that hillslope topography can produce hydrologic refugia, sites where ecosystem productivity is relatively insensitive to climate variation. However, the ecological impacts and spatial distribution of these sites are poorly resolved across gradients in climate. We quantified the response of ecosystem net primary productivity to changes in the annual climatic water balance for 30 years using pixel-specific linear regression (30-m resolution) across the western United States. The standardized slopes of these models represent ecosystem climate sensitivity and provide a means to identify drought-resistant ecosystems. Productive and resistant ecosystems were most frequent in convergent hillslope positions, especially in semiarid climates. Ecosystems in divergent positions were moderately resistant to climate variability, but less productive relative to convergent positions. This topographic effect was significantly dampened in hygric and xeric climates. In aggregate, spatial patterns of ecosystem sensitivity can be implemented for regional planning to maximize conservation in landscapes more resistant to perturbations.”

Read Article

TOPOFIRE: a topograhically resolved wildfire danger and drought monitoring system for the conterminous United States

Holden, Zachary A. and Jolly, Matt and Swanson, Alan and Warren, Dyer A. and Jencso, Kelsey and Maneta, Marco and Burgard, Mitchell and Gibson, Chris and Hoylman, Zachary and Landguth, Erin L.

Bulletin of the American Meteorological Society

“Patterns of energy and available moisture can vary over small (<1 km) distances in mountainous terrain. Information on fuel and soil moisture conditions that resolves this variation could help to inform fire and drought management decisions. Here, we describe the development of TOPOFIRE, a web-based mapping system designed to provide finely resolved information on soil water balance, drought, and wildfire danger information for the contiguous United States. We developed 8-arc-second-resolution (~250 meter) daily historical, near real-time, and 4-day forecast radiation, temperature, humidity, and snow water equivalent data and used these grids to calculate a suite of drought and wildfire danger indices. Large differences in shortwave radiation and surface air temperature with aspect contribute to greater snow accumulation and delays in melt timing on north-facing slopes, delaying fuel conditioning on shaded slopes. These datasets will help advance our understanding of the role of topography in wildland fire spread and ecological effects. Integration with national programs like the Wildland Fire Assessment System, the Wildland Fire Decision Support System, and drought early warning systems could support more proactive management of wildland fires and refine the characterization of drought in mountainous regions of the United States.”

Read Article