Climate policy developers and natural resource managers frequently desire high-resolution climate data to prepare for future effects of climate change. But they face a long-standing problem: the vast majority of climate models have been run at coarse resolutions—from hundreds of kilometers in global climate models (GCMs) down to 25–50 kilometers in regional climate models (RCMs).

Abstract (from http://journals.ametsoc.org/doi/abs/10.1175/WCAS-D-15-0042.1): Drought is a natural part of the historical climate variability in the northern Rocky Mountains and high plains region of the United States. However, recent drought impacts and climate change projections have increased the need for a systematized way to document and understand drought in a manner that is meaningful to public land and resource managers. The purpose of this exploratory study was to characterize the ways in which some federal and tribal natural resource managers experienced and dealt with drought on lands managed by the U.S. Department of the Interior (DOI) and tribes in two case site examples (northwest Colorado and southwest South Dakota) that have experienced high drought exposure in the last two decades. The authors employed a social–ecological system framework, whereby key informant interviews and local and regional drought indicator data were used characterize the social and ecological factors that contribute to drought vulnerability and the ways in which drought onset, persistence, severity, and recovery impact management. Results indicated that local differences in the timing, decisions, and specific management targets defined within the local social–ecological natural resource contexts are critical to understanding drought impacts, vulnerabilities, and responses. These findings suggest that manager-defined social–ecological contexts are critically important to understand how drought is experienced across the landscape and the indices that are needed to inform adaptation and response strategies.

Abstract (from http://onlinelibrary.wiley.com/doi/10.1002/15-1061/abstract): Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates? (2) Are there significant regional differences in accuracy among data sets? (3) How accurate are their mean values compared with extremes? (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

EDDI is a drought indicator that uses atmospheric evaporative demand (E0) anomalies across a time-window of interest relative to its climatology to indicate the spatial extent and severity of drought. This page provides access to near-real-time (with a five-day latency, i.e., the most recent information is five days old) EDDI plots with time windows integrating E0 anomalies from 1 to 12 weeks and 1 to 12 months from the most current date. E0 is calculated using the Penman Monteith FAO56 reference evapotranspiration formulation driven by temperature, humidity, wind speed, and incoming solar radiation from the North American Land Data Assimilation System (NLDAS-2) dataset. For a particular time-window, EDDI is estimated by standardizing the E0 anomalies relative to the whole period of the record (1979-present), using a non-parametric method (see Hobbins et al., 2016). For plotting purposes, EDDI values are binned into different percentile categories analogous to the US Drought Monitor plots. However, in case of EDDI plots, both drought and anomalously wet categories are shown. EDDI data are available at a ~12-km resolution across CONUS since January 1, 1980, and are updated daily. EDDI has the potential to offer early warning of agricultural drought, hydrologic drought, and fire-weather risk by providing real-time information on the emergence or persistence of anomalous evaporative demand in a region. A particular strength of EDDI is in capturing the precursor signals of water stress at weekly to monthly timescales, which makes EDDI a strong tool for drought preparedness at those timescales.

The NC CSC project "Wind River Indian Reservation’s (WRIR) Vulnerability to the Impacts of Drought and the Development of Decision Tools to Support Drought Preparedness" supports tribal resource managers working with university and government partners to co-develop science, decision support tools, and a management plan for drought.

Abstract (from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174045): Several studies have projected increases in drought severity, extent and duration in many parts of the world under climate change. We examine sources of uncertainty arising from the methodological choices for the assessment of future drought risk in the continental US (CONUS). One such uncertainty is in the climate models’ expression of evaporative demand (E0), which is not a direct climate model output but has been traditionally estimated using several different formulations. Here we analyze daily output from two CMIP5 GCMs to evaluate how differences in E0 formulation, treatment of meteorological driving data, choice of GCM, and standardization of time series influence the estimation of E0. These methodological choices yield different assessments of spatio-temporal variability in E0 and different trends in 21st century drought risk. First, we estimate E0 using three widely used E0 formulations: Penman-Monteith; Hargreaves-Samani; and Priestley-Taylor. Our analysis, which primarily focuses on the May-September warm-season period, shows that E0 climatology and its spatial pattern differ substantially between these three formulations. Overall, we find higher magnitudes of E0 and its interannual variability using Penman-Monteith, in particular for regions like the Great Plains and southwestern US where E0 is strongly influenced by variations in wind and relative humidity. When examining projected changes in E0 during the 21st century, there are also large differences among the three formulations, particularly the Penman-Monteith relative to the other two formulations. The 21st century E0 trends, particularly in percent change and standardized anomalies of E0, are found to be sensitive to the long-term mean value and the amplitude of interannual variability, i.e. if the magnitude of E0 and its interannual variability are relatively low for a particular E0 formulation, then the normalized or standardized 21st century trend based on that formulation is amplified relative to other formulations. This is the case for the use of Hargreaves-Samani and Priestley-Taylor, where future E0 trends are comparatively much larger than for Penman-Monteith. When comparing Penman-Monteith E0 responses between different choices of input variables related to wind speed, surface roughness, and net radiation, we found differences in E0 trends, although these choices had a much smaller influence on E0 trends than did the E0 formulation choices. These methodological choices and specific climate model selection, also have a large influence on the estimation of trends in standardized drought indices used for drought assessment operationally. We find that standardization tends to amplify divergences between the E0 trends calculated using different E0 formulations, because standardization is sensitive to both the climatology and amplitude of interannual variability of E0. For different methodological choices and GCM output considered in estimating E0, we examine potential sources of uncertainty in 21st century trends in the Standardized Precipitation Evapotranspiration Index (SPEI) and Evaporative Demand Drought Index (EDDI) over selected regions of the CONUS to demonstrate the practical implications of these methodological choices for the quantification of drought risk under climate change.

This webinar was recorded as part of the Climate Change Science and Management Webinar Series (hosted in partnership by the USGS National Climate Change and Wildlife Science Center and FWS National Conservation Training Center). Webinar Summary: Accurate information on the atmospheric evaporative demand (i.e., thirst of the atmosphere) and the land-surface evaporative response (i.e., moisture supply on the land to meet the evaporative demand) is extremely important to assessing water stress on the land surface. In this webinar, the presenters will introduce real-time high resolution (1-10km) monitoring products of atmospheric evaporative demand and land-surface evaporative response models that are currently available to users. They will also discuss the physical relationships between these systems, as well as the potential of the monitoring products discussed above to markedly improve scientists and managers understanding of drought processes (i.e., onset, evolution, persistence and dissipation), and develop a more robust drought early warning framework.

Abstract (from http://journals.ametsoc.org/doi/10.1175/WCAS-D-16-0121.1): Much of the academic literature and policy discussions about sustainable development and climate change adaptation focus on poor and developing nations, yet many tribal communities inside the United States include marginalized peoples and developing nations who face structural barriers to effectively adapt to climate change. There is a need to critically examine diverse climate change risks for indigenous peoples in the United States and the many structural barriers that limit their ability to adapt to climate change. This paper uses a sustainable climate adaptation framework to outline the context and the relationships of power and authority, along with different ways of knowing and meaning, to illustrate the underpinnings of some tribes’ barriers to sustainable climate adaptation. The background of those structural barriers for tribes is traced, and then the case of water rights and management at the Wind River Reservation in Wyoming is used to illustrate the interplay of policy, culture, climate, justice, and limits to adaptation. Included is a discussion about how the rulings of the Big Horn general stream adjudication have hindered tribal climate change adaptation by limiting the quantity of tribal reserved water rights, tying those rights to the sole purposes of agriculture, which undermines social and cultural connections to the land and water, and failing to recognizing tribal rights to groundwater. Future climate projections suggest increasing temperatures, and changes in the amount and timing of snowpack, along with receding glaciers, all of which impact water availability downstream. Therefore, building capacity to take control of land and water resources and preparing for climate change and drought at Wind River Reservation is of critical importance.

Pan evaporation is a measure of atmospheric evaporative demand (E0) for which long term and spatially distributed observations are available from the NOAA Cooperative Observer (COOP) Network. However, this data requires extensive quality control and homogenization due to documented and undocumented station moves and other factors including human errors in recording or digitization. Station-based Pan Evaporation measurements (in mm) from 247 stations across the continental United States were compiled and quality controlled for the analysis shown in Dewes et al., 2017. This dataset reports warm season (May-October; for 21 stations the data is only available for May-September) pan evaporation with at least 20 years of data between 1950 and 2001. Both monthly values and long-term monthly averages are made available, including the climatological measure for standard deviation and coefficient of variation. Dewes et al. (2017) used this dataset to evaluate the ability of different E0 formulations – Hargreaves-Samani, Priestly-Taylor, and Penman-Monteith – to reproduce the spatial patterns of observed warm-season E0 and its interannual variability. This data is an extension of the dataset described in Hobbins (2004) and Hobbins et al. (2004) with 21 additional stations north of 41oN latitude. The extension was needed in order to include data in the North Central Climate Science Center region. For these added stations, the procedure described in Hobbins (2004) for quality control was applied, including an adjustment in the mean when documented station moves occurred, and the removal of obvious outliers. The quality control procedure for the extended dataset did not automate tests for undocumented inhomogeneities for these stations. For all stations, a visual inspection of the timeseries was used to add additional breakpoints in the data for homogenization (only two were added in the extended set), and to eliminate two stations from consideration.