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At least 163 records · Page 9Linked to original sources
USGS2003

Photographs of the Sea floor Offshore of New York and New Jersey

This DVD-ROM contains photographs of the sea floor and sediment texture data collected as part of studies carried out by the U.S. Geological Survey (USGS) in the New York Bight (Figure 1a (PDF format)). The studies were designed to map the sea floor (Butman, 1998, URL: http://pubs.usgs.gov/fs/fs133-98/) and to develop an understanding of the transport and long-term fate of sediments and associated contaminants in the region (Mecray and others, 1999, URL: http://pubs.usgs.gov/fs/fs114-99/). The data were collected on four research cruises carried out between 1996 and 2000 (Appendix I). The images and texture data were collected to provide direct observations of the sea floor geology and to aid in the interpretation of backscatter intensity data obtained from sidescan sonar and multibeam surveys of the sea floor. Preliminary descriptions of the sea floor geology in this region may be found in Schwab and others (2000, URL: http://pubs.usgs.gov/of/of00-295/; 2003), Butman and others (1998, URL: http://pubs.usgs.gov/of/of98-616/.), and Butman and others (2002, URL: http://pubs.usgs.gov/of/of00-503/). Schwab and others (2000 URL: http://pubs.usgs.gov/of/of00-295/; 2003) have identified 11 geologic units in New York Bight (Figure 2 (PDF format)). These units identify areas of active sediment transport, extensive anthropogenic influence on the sea floor, and various geologic units. Butman and others (2003) and Harris and others (in press) present the results of a moored array experiment carried out in the Hudson Shelf Valley to investigate the transport of sediments during winter. Summaries of these and other studies may be found at USGS studies in the New York Bight (URL: http://woodshole.er.usgs.gov/project-pages/newyork/). This DVD-ROM contains digital images of bottom still photographs, images digitized from videos, sediment grain-size analysis results, and short QuickTime movies from video transects. The data are presented in tabular form and in an ESRI (Environmental Systems Research Institute, URL: http://www.esri.com) ArcView project where the image and sample locations may be viewed superimposed on maps showing side-scan sonar and/or multibeam backscatter intensity and bottom topography.

Open-File Report↗
USGS2015

U.S. recreational water quality criteria: a vision for the future

This manuscript evaluates the U.S. Recreational Water Quality Criteria (RWQC) of 2012, based upon discussions during a conference held 11–13 March 2013, in Honolulu, Hawaii. The RWQC of 2012 did not meet expectations among the research community because key recommended studies were not completed, new data to assess risks to bathers exposed to non-point sources of fecal indicator bacteria (FIB) were not developed, and the 2012 RWQC did not show marked improvements in strategies for assessing health risks for bathers using all types of recreational waters. The development of the 2012 RWQC was limited in scope because the epidemiologic studies at beach sites were restricted to beaches with point sources of pollution and water samples were monitored for only enterococci. The vision for the future is development of effective RWQC guidelines based on epidemiologic and quantitative microbial risk assessment (QMRA) studies for sewage specific markers, as well as human enteric pathogens so that health risks for bathers at all recreational waters can be determined. The 2012 RWQC introduced a program for states and tribes to develop site-specific water quality criteria, and in theory this approach can be used to address the limitations associated with the measurements of the traditional FIB.

International Journal of Environmental Research an↗
USGS2023

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integrates in-situ measurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697 µ molCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Alaska↗
USGS2020

Subsea permafrost carbon stocks and climate change sensitivity estimated by expert assessment

The continental shelves of the Arctic Ocean and surrounding seas contain large stocks of organic matter (OM) and methane (CH 4 ), representing a potential ecosystem feedback to climate change not included in international climate agreements. We performed a structured expert assessment with 25 permafrost researchers to combine quantitative estimates of the stocks and sensitivity of organic carbon in the subsea permafrost domain (i.e. unglaciated portions of the continental shelves exposed during the last glacial period). Experts estimated that the subsea permafrost domain contains ~560 gigatons carbon (GtC; 170–740, 90% confidence interval) in OM and 45 GtC (10–110) in CH 4 . Current fluxes of CH 4 and carbon dioxide (CO 2 ) to the water column were estimated at 18 (2–34) and 38 (13–110) megatons C yr −1 , respectively. Under Representative Concentration Pathway (RCP) RCP8.5, the subsea permafrost domain could release 43 Gt CO 2 -equivalent (CO 2 e) by 2100 (14–110) and 190 Gt CO 2 e by 2300 (45–590), with ~30% fewer emissions under RCP2.6. The range of uncertainty demonstrates a serious knowledge gap but provides initial estimates of the magnitude and timing of the subsea permafrost climate feedback.

Environmental Research Letters↗
USGS2023

Where the past meets the present: Connecting nitrogen from watersheds to streams through groundwater flowpaths

Groundwater discharge to streams is a nonpoint source of nitrogen (N) that confounds N mitigation efforts and represents a significant portion of the annual N loading to watersheds. However, we lack an understanding of where and how much groundwater N enters streams and watersheds. Nitrogen concentrations at the end of groundwater flowpaths are the culmination of biogeochemical and physical processes from the contributing land area where groundwater recharges, within the aquifer system, and in the near-stream riparian area where groundwater discharges to streams. Our research objectives were to quantify the spatial distribution of N concentrations at groundwater discharges throughout a mixed land-use watershed and to evaluate how relationships among contributing and riparian land cover, modeled aquifer characteristics, and groundwater discharge biogeochemistry explain the spatial variation in groundwater discharge N concentrations. We accomplished this by integrating high-resolution thermal infrared surveys to locate groundwater discharge, biogeochemical sampling of groundwater, and a particle tracking model that links groundwater discharge locations to their contributing area land cover. Groundwater N loading from groundwater discharges within the watershed varied substantially between and within streambank groundwater discharge features. Groundwater nitrate concentrations were spatially heterogeneous ranging from below 0.03–11.45 mg-N/L, varying up to 20-fold within meters. When combined with the particle tracking model results and land cover metrics, we found that groundwater discharge nitrate concentrations were best predicted by a linear mixed-effect model that explained over 60% of the variation in nitrate concentrations, including aquifer chemistry (dissolved oxygen, Cl − , SO 4 2− ), riparian area forested land cover, and modeled physical aquifer characteristics (discharge, Euclidean distance). Our work highlights the significant spatial variability in groundwater discharge nitrate concentrations within mixed land-use watersheds and the need to understand groundwater N processing across the many spatiotemporal scales within groundwater cycling.

Connecticut↗
USGS2018

Uncertainty in United States coastal wetland greenhouse gas inventorying

Coastal wetlands store carbon dioxide (CO 2 ) and emit CO 2 and methane (CH 4 ) making them an important part of greenhouse gas (GHG) inventorying. In the contiguous United States (CONUS), a coastal wetland inventory was recently calculated by combining maps of wetland type and change with soil, biomass, and CH 4 flux data from a literature review. We assess uncertainty in this developing carbon monitoring system to quantify confidence in the inventory process itself and to prioritize future research. We provide a value-added analysis by defining types and scales of uncertainty for assumptions, burial and emissions datasets, and wetland maps, simulating 10 000 iterations of a simplified version of the inventory, and performing a sensitivity analysis. Coastal wetlands were likely a source of net-CO 2 -equivalent (CO 2 e) emissions from 2006–2011. Although stable estuarine wetlands were likely a CO 2 e sink, this effect was counteracted by catastrophic soil losses in the Gulf Coast, and CH 4 emissions from tidal freshwater wetlands. The direction and magnitude of total CONUS CO 2 e flux were most sensitive to uncertainty in emissions and burial data, and assumptions about how to calculate the inventory. Critical data uncertainties included CH 4 emissions for stable freshwater wetlands and carbon burial rates for all coastal wetlands. Critical assumptions included the average depth of soil affected by erosion events, the method used to convert CH 4 fluxes to CO 2 e, and the fraction of carbon lost to the atmosphere following an erosion event. The inventory was relatively insensitive to mapping uncertainties. Future versions could be improved by collecting additional data, especially the depth affected by loss events, and by better mapping salinity and inundation gradients relevant to key GHG fluxes.

Environmental Research Letters↗
USGS2025

An analytical approach to explore prospects and limits of nutrition-sensitive fisheries governance under climate change

Researchers and policymakers increasingly recognize the contribution of aquatic food systems, such as fisheries, to food security and nutrition. Yet governing fisheries for nutrition objectives is complicated by the multiple overlapping processes that shape availability and access to nutrients over time, including fishing sustainability, climate change, trade dynamics, and consumer preferences. Anticipating the effect of governance interventions to sustain or enhance nutritional benefits from fisheries entails accounting for these multiple interacting influences. We develop an analytical approach to link available data on aquatic foods production, nutrition, distribution, and potential climate impacts to evaluate the nutrition implications of fishery management and post-harvest allocation interventions. We demonstrate this approach using national and publicly available datasets for five case study countries: Peru, Chile, Indonesia, Sierra Leone, and Malawi. As examples, we evaluate the potential to enhance domestic supply of key nutrients to nutritionally-vulnerable populations by (a) dynamically adjusting fishing effort in response to climate impacts on fish stocks, and (b) retaining aquatic foods currently diverted via trade or foreign fishing. The results indicate substantial differences across countries in terms of anticipated climate change effects, with potential for substantially increased nutrition yield in Chile and Peru under adaptive management, vs more modest yield increases in Indonesia. The impacts of post-harvest allocation policies related to foreign fishing, exports, fishing sector, and subnational trade also vary, with exports weighing heavily on nutrient availability in Sierra Leone. This methodological approach represents a step toward operationalizing calls to manage fisheries as part of national food and nutrient supplies, in light of climate change risks.

Environmental Research: Food Systems↗
USGS2026

Moving toward a more human-oriented analysis of urban heat: Examining differences of heat exposure intensity at busy commuting locations

Examining urban thermal environments has become a critical area of research spanning epidemiology, urban planning, and ecology. While traditional metrics like air temperature ( T air ) and satellite-derived surface temperature dominate urban heat studies, these measures often fail to reflect how people actually experience thermal exposure intensity. More human-oriented metrics, such as mean radiant temperature (MRT), and the wet bulb globe temperature (WBGT), better capture this lived experience, particularly at locations where people are likely to encounter outdoor heat, such as bus stops. Human demographics further complicate heat exposure, as access to cooling resources like trees and greenspaces can vary by neighborhood income. Our study addresses these complications by collecting thermal data across 60 commuting locations in Denver, Colorado in the summer. We evaluate (1) the extent to which more human-oriented metrics capture thermal exposure compared to T air and LST, and (2) how heat exposure varies by neighborhood income levels. We observed that bus stops in low-income neighborhoods had an MRT increase 2.8 °C compared wealthier neighborhoods, and that income-driven differences in MRT and WBGT strongly depended on bus stop aspect. After accounting for solar orientation, differences in MRT increased to as much as 6.3 °C at north-facing stops. Our results suggest tree canopy shade explains some observed heat exposure patterns, with south facing bus stops seeing a MRT and WBGT decrease of 0.42 °C and 0.11 °C from a percent increase in tree canopy. Interestingly, depending on bus stop aspect, nearby buildings can increase MRT and WBGT (facing east), or decrease MRT and WBGT (facing south) If planners aim to address this issue, consideration of bus stops, and land covers configuration may help.

Colorado↗
USGS2021

Pervasive changes in stream intermittency across the United States

Non-perennial streams are widespread, critical to ecosystems and society, and the subject of ongoing policy debate. Prior large-scale research on stream intermittency has been based on long-term averages, generally using annually aggregated data to characterize a highly variable process. As a result, it is not well understood if, how, or why the hydrology of non-perennial streams is changing. Here, we investigate trends and drivers of three intermittency signatures that describe the duration, timing, and dry-down period of stream intermittency across the continental United States (CONUS). Half of gages exhibited a significant trend through time in at least one of the three intermittency signatures, and changes in no-flow duration were most pervasive (41% of gages). Changes in intermittency were substantial for many streams, and 7% of gages exhibited changes in annual no-flow duration exceeding 100 days during the study period. Distinct regional patterns of change were evident, with widespread drying in southern CONUS and wetting in northern CONUS. These patterns are correlated with changes in aridity, though drivers of spatiotemporal variability were diverse across the three intermittency signatures. While the no-flow timing and duration were strongly related to climate, dry-down period was most strongly related to watershed land use and physiography. Our results indicate that non-perennial conditions are increasing in prevalence over much of CONUS and binary classifications of 'perennial' and 'non-perennial' are not an accurate reflection of this change. Water management and policy should reflect the changing nature and diverse drivers of changing intermittency both today and in the future.

Environmental Research Letters↗
USGS2019

Reducing wet ammonium deposition in Rocky Mountain National Park: The development and evaluation of a pilot early warning system for agricultural operations in eastern Colorado

Agricultural emissions are the primary source of ammonia (NH3) deposition in Rocky Mountain National Park (RMNP), a Class I area, that is granted special air quality protections under the Clean Air Act. Between 2014 and 2016, the pilot phase of the Colorado agricultural nitrogen early warning system (CANEWS) was developed for agricultural producers to voluntarily and temporarily minimize emissions of NH3 during periods of upslope winds. The CANEWS was created using trajectory analyses driven by outputs from an ensemble of numerical weather forecasts together with the climatological expertise of human forecasters. Here, we discuss the methods for the CANEWS and offer preliminary analyses of 33 months of the CANEWS based on atmospheric deposition data from two sites in RMNP as well as responses from agricultural producers after warnings were issued. Results showed that the CANEWS accurately predicted 6 of 9 high N deposition weeks at a lower-elevation observation site, but only 4 of 11 high N deposition weeks at a higher-elevation site. Sixty agricultural producers from 39 of Colorado’s agricultural operations volunteered for the CANEWS, and a two-way line of communication between agricultural producers and scientists was formed. For each warning issued, an average of 23 producers responded to a post-warning survey. Over 75% of responding CANEWS participants altered their practices after an alert. While the current effort was insufficient to reduce atmospheric deposition, we were encouraged by the collaborative spirit between agricultural, scientific, and resource management communities. Solving a broad and complex social-ecological problem requires both a technological approach, such as the CANEWS, and collaboration and trust from all participants, including agricultural producers, land managers, university researchers, and environmental agencies.

Colorado↗
USGS2008

Kolmogorov-Smirnov test for spatially correlated data

The Kolmogorov-Smirnov test is a convenient method for investigating whether two underlying univariate probability distributions can be regarded as undistinguishable from each other or whether an underlying probability distribution differs from a hypothesized distribution. Application of the test requires that the sample be unbiased and the outcomes be independent and identically distributed, conditions that are violated in several degrees by spatially continuous attributes, such as topographical elevation. A generalized form of the bootstrap method is used here for the purpose of modeling the distribution of the statistic D of the Kolmogorov-Smirnov test. The innovation is in the resampling, which in the traditional formulation of bootstrap is done by drawing from the empirical sample with replacement presuming independence. The generalization consists of preparing resamplings with the same spatial correlation as the empirical sample. This is accomplished by reading the value of unconditional stochastic realizations at the sampling locations, realizations that are generated by simulated annealing. The new approach was tested by two empirical samples taken from an exhaustive sample closely following a lognormal distribution. One sample was a regular, unbiased sample while the other one was a clustered, preferential sample that had to be preprocessed. Our results show that the p-value for the spatially correlated case is always larger that the p-value of the statistic in the absence of spatial correlation, which is in agreement with the fact that the information content of an uncorrelated sample is larger than the one for a spatially correlated sample of the same size. ?? Springer-Verlag 2008.

Stochastic Environmental Research and Risk Assessm↗
USGS2011

Building on crossvalidation for increasing the quality of geostatistical modeling

The random function is a mathematical model commonly used in the assessment of uncertainty associated with a spatially correlated attribute that has been partially sampled. There are multiple algorithms for modeling such random functions, all sharing the requirement of specifying various parameters that have critical influence on the results. The importance of finding ways to compare the methods and setting parameters to obtain results that better model uncertainty has increased as these algorithms have grown in number and complexity. Crossvalidation has been used in spatial statistics, mostly in kriging, for the analysis of mean square errors. An appeal of this approach is its ability to work with the same empirical sample available for running the algorithms. This paper goes beyond checking estimates by formulating a function sensitive to conditional bias. Under ideal conditions, such function turns into a straight line, which can be used as a reference for preparing measures of performance. Applied to kriging, deviations from the ideal line provide sensitivity to the semivariogram lacking in crossvalidation of kriging errors and are more sensitive to conditional bias than analyses of errors. In terms of stochastic simulation, in addition to finding better parameters, the deviations allow comparison of the realizations resulting from the applications of different methods. Examples show improvements of about 30% in the deviations and approximately 10% in the square root of mean square errors between reasonable starting modelling and the solutions according to the new criteria.

Stochastic Environmental Research and Risk Assessm↗
USGS2011

Spatial interpolation schemes of daily precipitation for hydrologic modeling

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs.

Colorado, Georgia↗
USGS2013

Aggregation of carbon dioxide sequestration storage assessment units

The U.S. Geological Survey is currently conducting a national assessment of carbon dioxide (CO 2 ) storage resources, mandated by the Energy Independence and Security Act of 2007. Pre-emission capture and storage of CO 2 in subsurface saline formations is one potential method to reduce greenhouse gas emissions and the negative impact of global climate change. Like many large-scale resource assessments, the area under investigation is split into smaller, more manageable storage assessment units (SAUs), which must be aggregated with correctly propagated uncertainty to the basin, regional, and national scales. The aggregation methodology requires two types of data: marginal probability distributions of storage resource for each SAU, and a correlation matrix obtained by expert elicitation describing interdependencies between pairs of SAUs. Dependencies arise because geologic analogs, assessment methods, and assessors often overlap. The correlation matrix is used to induce rank correlation, using a Cholesky decomposition, among the empirical marginal distributions representing individually assessed SAUs. This manuscript presents a probabilistic aggregation method tailored to the correlations and dependencies inherent to a CO 2 storage assessment. Aggregation results must be presented at the basin, regional, and national scales. A single stage approach, in which one large correlation matrix is defined and subsets are used for different scales, is compared to a multiple stage approach, in which new correlation matrices are created to aggregate intermediate results. Although the single-stage approach requires determination of significantly more correlation coefficients, it captures geologic dependencies among similar units in different basins and it is less sensitive to fluctuations in low correlation coefficients than the multiple stage approach. Thus, subsets of one single-stage correlation matrix are used to aggregate to basin, regional, and national scales.

Stochastic Environmental Research and Risk Assessm↗
USGS2016

Resampling of spatially correlated data with preferential sampling for the estimation of frequency distributions and semivariograms

Spatial data are commonly minimal and may have been collected in the process of confirming the profitability of a mining venture or investigating a contaminated site. In such situations, it is common to have measurements preferentially taken in the most critical areas (sweet spots, allegedly contaminated areas), thus conditionally biasing the sample. While preferential sampling makes good practical sense, its direct use leads to distorted sample moments and percentiles. Spatial clusters are a problem that has been identified in the past and solved with approaches ranging from ad hoc solutions to highly elaborate mathematical formulations, covering mostly the effect of clustering on the cumulative frequency distribution. The method proposed here is a form of resample, free of special assumptions, does not use weights to ponder the measurements, does not find solutions by successive approximation and provides variability in the results. The new method is illustrated with a synthetic dataset with an exponential semivariogram and purposely generated to follow a lognormal distribution. The lognormal distribution is both difficult to work with and typical of many attributes of practical interest. Testing of the new solution shows that sample subsets derived from resampled datasets can closely approximate the true probability distribution and the semivariogram, clearly outperforming the original preferentially sampled data.

Stochastic Environmental Research and Risk Assessm↗
USGS2017

Advancements in hydrochemistry mapping: methods and application to groundwater arsenic and iron concentrations in Varanasi, Uttar Pradesh, India

The area east of Varanasi is one of numerous places along the watershed of the Ganges River with groundwater concentrations of arsenic surpassing the maximum value of 10 parts per billion (ppb) recommended by the World Health Organization in drinking water. Here we apply geostatistics and compositional data analysis for the mapping of arsenic and iron to help in understanding the conditions leading to the occurrence of elevated level of arsenic in groundwater. The methodology allows for displaying concentrations of arsenic and iron as maps consistent with the limited information from 95 water wells across an area of approximately 210 km 2 ; visualization of the uncertainty associated with the sampling; and summary of the findings in the form of probability maps. For thousands of years, Varanasi has been on the erosional side in a meander of the river that is free of arsenic values above 10 ppb. Maps reveal two anomalies of high arsenic concentrations on the depositional side of the valley, which has started seeing urban development. The methodology using geostatistics combined with compositional data analysis is completely general, so this study could be used as a prototype for hydrochemistry mapping in other areas.

Uttar Pradesh↗
USGS2019

Self-organizing maps for compositional data: coal combustion products of a Wyoming power plant

A self-organizing map (SOM) is a non-linear projection of a D-dimensional data set, where the distance among observations is approximately preserved on to a lower dimensional space. The SOM arranges multivariate data based on their similarity to each other by allowing pattern recognition leading to easier interpretation of higher dimensional data. The SOM algorithm allows for selection of different map topologies, distances and parameters, which determine how the data will be organized on the map. In the particular case of compositional data (such as elemental, mineralogical, or maceral abundance), the sample space is governed by Aitchison geometry and extra steps are required prior to their SOM analysis. Following the principle of working on log-ratio coordinates, the simplicial operations and the Aitchison distance, which are appropriate elements for the SOM, are presented. With this structure developed, a SOM using Aitchison geometry is applied to properly interpret elemental data from combustion products (bottom ash, fly ash, and economizer fly ash) in a Wyoming coal-fired power plant. Results from this effort provide knowledge about the differences between the ash composition in the coal combustion process.

Stochastic Environmental Research and Risk Assessm↗
USGS2025

Characterizing projected future droughts for south Florida (2056–2095)

Balance anomalies, defined as the deviation of monthly precipitation minus reference evapotranspiration from their long-term monthly historical means (1950–2005), were computed for regions in south Florida and temporally averaged over 6- and 12-month timescales to identify meteorological drought events during a historical (1950–2005) and future (2056–2095) period of interest for 40 CMIP5 general circulation models (GCM) and scenario combinations downscaled by the Multivariate Adaptive Constructed Analogs method. Under the assumption of stomatal resistance ( r s ) remaining constant at the historical standard value (70 s/m), 81% of models project declines in monthly balance anomalies in the future compared to historical, with multimodel ensemble mean declines of 3.7 in/year under RCP4.5 and 8.7 in/year under RCP8.5. Drought events were identified from the downscaled model projections, their characteristics (duration and intensity) extracted, and their historical joint distributions validated against those derived from historical observational datasets. The future joint distributions of drought characteristics were compared across models using hierarchical clustering. A climate model summary plot and table were developed based on these methods to guide climate model selection for hydrologic modeling in support of water-supply planning at the South Florida Water Management District (SFWMD). The model summary plot for the entire SFWMD shows that 35% of GCM/training-dataset combinations have historical joint distributions of drought characteristics that are significantly different at the 10% level from those derived from observational gridded data, whereas 39% of GCM/scenario/training-dataset combinations have future joint distributions that are significantly different from historical. A sensitivity analysis was performed assuming r s increasing with increasing CO 2 .

Florida↗