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NASA NTRSDate not supplied

Establishing an Urban Heat Exposure Severity Index for Infrastructure Prioritization in Tempe, Arizona, Using NASA Earth Observations and LiDAR

Located on the banks of the Salt River in the Sonoran Desert, Tempe, Arizona, features a semi-arid climate with summer daily maximum temperatures regularly exceeding 37.8°C. Tempe is also subject to the southwestern monsoon season from July-September and the humidity exacerbates the high temperatures. Furthermore, the rapid urbanization experienced in Tempe has resulted in an intensification of the urban heat island. The summer of 2020 shattered the previous record of days exceeding 43.4°C, leading to higher energy and water costs, lower comfort, and increased risk of heat stroke for residents. Recognizing the impacts of extreme heat, the City of Tempe partnered with the Healthy Urban Environments initiative and NASA DEVELOP to identify census tracts that experience a higher mean land surface temperature than the city average. The NASA DEVELOP team used remotely sensed land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and albedo data calculated from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) instruments from 2015 to 2020 to create heat hazard and exposure maps. LiDAR point cloud data, provided by the United States Geological Survey through Arizona State University’s Map and Geospatial Hub, were used to derive 3D buildings, building footprints, and tree point data for a shading analysis of walking paths, roads, and buildings at the census tract level. In situ meteorological measurements including air temperature and humidity were used to compare the macro-scale temperature measurements. The team worked with the City of Tempe to develop a methodology to process available data and identify areas of highest concern for urban heat effects within the city. With these insights, Tempe, Arizona can better address these issues with data-driven information to make decisions regarding heat mitigation and adaptation efforts.

John Dialesandro↗
NASA NTRS2017

FOREWORD: Satellite Remote Sensing Beyond 2015

Satellite remote sensing has progressed tremendously since the first Landsat was launched on June 23, 1972. Since the 1970s, satellite remote sensing and associated airborne and in situ measurements have resulted in vital and indispensable observations for understanding our planet through time. These observations have also led to dramatic improvements in numerical simulation models of the coupled atmosphere-land-ocean systems at increasing accuracies and predictive capability. The same observations document the Earth's climate and are driving the consensus that Homo sapiens is changing our climate through greenhouse gas emissions. These accomplishments are the combined work of many scientists from many countries and a dedicated cadre of engineers who build the instruments and satellites that collect Earth observation data from satellites, all working toward the goal of improving our understanding of the Earth. This edition of the Remote Sensing Handbook (Vol. I, II, and III) is a compendium of information for many research areas of our Planet that have contributed to our substantial progress since the 1970s. Remote sensing community is now using multiple sources of satellite and in situ data to advance our studies, what ever they might be. In the following paragraphs, I will illustrate how valuable and pivotal role satellite remote sensing has played in climate system study over last five decades, The Chapters in the Remote Sensing Handbook (Vol. I, II, and III) provides many other specific studies on land, water, and other applications using EO data of last five decades, The Landsat system of Earth-observing satellites has led the way in pioneering sustained observations of our planet. From 1972 to the present, at least one and sometimes two Landsat satellites have been in operation. Starting with the launch of the first NOAA-NASA Polar Orbiting Environmental Satellites NOAA-6 in 1978, improved imaging of land, clouds, and oceans and atmospheric soundings of temperature were accomplished. The NOAA system of polar-orbiting meteorological satellites has continued uninterrupted since that time, providing vital observations for numerical weather prediction. These same satellites are also responsible for the remarkable records of sea surface temperature and land vegetation index from the Advanced Very High Resolution Radiometers (AVHRR) that now span more than 33 years, although no one anticipated these valuable climate records from this instrument before the launch of NOAA-7 in 1981. The success of data from the AVHRR led to the design of the MODIS instruments on NASA's Earth Observing System of satellite platforms that improved substantially upon the AVHRR. The first of the EOS platforms, Terra, was launched in 2000 and the second of these platforms, Aqua, was launched in 2002.

Tucker, Compton J.↗
NASA NTRS2018

Climatespark: an In-Memory Distributed Computing Framework for Big Climate Data Analytics

The unprecedented growth of climate data creates new opportunities for climate studies, and yet big climate data pose a grand challenge to climatologists to efficiently manage and analyze big data. The complexity of climate data content and analytical algorithms increases the difficulty of implementing algorithms on high performance computing systems. This paper proposes an in-memory, distributed computing framework, ClimateSpark, to facilitate complex big data analytics and time-consuming computational tasks. Chunking data structure improves parallel I/O efficiency, while a spatiotemporal index is built for the chunks to avoid unnecessary data reading and preprocessing. An integrated, multi-dimensional, array-based data model (ClimateRDD) and ETL operations are developed to address big climate data variety by integrating the processing components of the climate data lifecycle. ClimateSpark utilizes Spark SQL and Apache Zeppelin to develop a web portal to facilitate the interaction among climatologists, climate data, analytic operations and computing resources (e.g., using SQL query and Scala/Python notebook). Experimental results show that ClimateSpark conducts different spatiotemporal data queries/analytics with high efficiency and data locality. ClimateSpark is easily adaptable to other big multiple- dimensional, array-based datasets in various geoscience domains.

Hu, Fei↗
NASA NTRSDate not supplied

Evaluating Changes in Freshwater Health in the Tonlé Sap Basin using Remotely Sensed Data

The Tonlé Sap lake and river basin provide freshwater resources to about 4.5 million people in central Cambodia. Accelerating dam construction, intensifying agriculture, deforestation, and changing climate patterns threaten the health of the lake and the food security of nearby communities. NASA DEVELOP collaborated with Conservation International to calculate multiple components of the ecosystem vitality indicator of the Freshwater Health Index for Tonlé Sap lake from 2000 – 2020 using remote sensing. Landcover classifications, lake level altimetry, groundwater storage and nutrient flows were derived from multiple satellites including Landsat 5, Landsat 7, Landsat 8, Sentinel-2, the Global Precipitation Measurement (GPM)mission, and the Gravity Recovery and Climate Experiment (GRACE). Additionally, the Soil and Water Assessment Tool (SWAT) was used to analyze nutrient flow and water quality in the basin. A breakdown in the volume and regularity of annual lake levels from 2000-2020 was observed, reflecting increased pressure on water supply and agricultural productivity. A decrease in landcover naturalness was also observed and at least 8% of forested areas in the basin were lost while rice harvest intensity increased throughout the study period. Additionally, an initial water quality analysis was demonstrated using SWAT. Continued collaboration with Conservation International and local representatives will guide the remaining water quality analyses and allow them to make informed decisions regarding freshwater management and resource allocation in the Tonlé Sap lake and river basin.

Adriana Le Compte↗
NASA NTRS2020

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa

Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (i) benefited less from an increase in atmospheric [CO2], (ii) was less affected by higher temperature or decreasing rainfall and (iii) was more affected by increased rainfall because N leaching was more critical. The model inter-comparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation practices across SSA, because the impact of climate change will be modified if farmers intensify maize production with more mineral fertilizer.

Crop simulation model↗
NASA NTRS2017

Changing Seasonality of Panarctic Tundra Vegetation in Relationship to Climatic Variables

Potential climate drivers of Arctic tundra vegetation productivity are investigated to understand recent greening and browning trends documented by maximum normalized difference vegetation index (NDVI) (MaxNDVI) and time-integrated NDVI (TI-NDVI) for 19822015. Over this period, summer sea ice has continued to decline while oceanic heat content has increased. The increases in summer warmth index (SWI) and NDVI have not been uniform over the satellite record. SWI increased from 1982 to the mid-1990s and remained relatively flat from 1998 onwards until a recent upturn. While MaxNDVI displays positive trends from 19822015, TI-NDVI increased from 1982 until 2001 and has declined since. The data for the first and second halves of the record were analyzed and compared spatially for changing trends with a focus on the growing season. Negative trends for MaxNDVI and TI-NDVI were more common during 19992015 compared to 19821998.

sea ice↗
DOE OSTI2022

A simple framework to characterize land aridity based on surface energy partitioning regimes

Land aridity is often characterized by the aridity index (AI), which does not account for land surface water-energy interactions that are crucially important in determining regional climate. Such interactions can be captured by the evaporative fraction (EF, ratio of evapotranspiration to available energy) regimes. As EF is subject to energy and water limitations in humid and dry areas, respectively, EF regimes may be used to characterize land aridity to account for the influence of complex land characteristics and their impact on water availability. Here, we propose a simple framework to characterize land aridity by statistically ranking the coupling strength between EF and surface energy and water terms. The framework is demonstrated using gridded data and compared with AI over the U.S. and China. Results show that regionalization of aridity zones based on EF regimes and a two-tiered classification scheme may provide information such as surface energy and water variability complementary to the background aridity depicted by AI, with implications for extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Hybrid predictive modeling approach simulated evapotranspiration and ecosystem respiration data

This dataset includes measured data used for developing hybrid-predictive-modeling (HPM) approach and simulated evapotranspiration and ecosystem respiration data across several Fluxnet sites, SNOTEL sites and East River locations (Chen et al., 2020 in review). Fluxnet sites considered in this study include: CA-OAS, CA-OBS, US-NR1, US-TON, US-VAR, US-SRM, US-WHS, US-WKG. Snotel sites Butte (ER-BT), Porphyry Creek (ER-PK) and Schofield Pass (ER-SP) are included as well 16 east river locations with different vegetation types denoted by evergreen forests (EF), deciduous forests (DF), riparian shrublands (RS) and meadow grassland (MS). This data package includes: 1) A summary spread sheet of the sites and acronyms for data variables. 2) Processed Fluxnet datasets that are obtained from Fluxnet.fluxdata.org with HPM estimated evapotranspiration (ET) and ecosystem respiration (RECO). Trained models and metrics for parameters for the Fluxnet sites were added in an update on November 2, 2020; 3) Processed SNOTEL datasets obtained from www.nrcs.usda.gov/snow/ and HPM estimated ET and RECO; 4) Weather data at east river locations obtained from DAYMET (daymet.ornl.gov) as well as HPM estimated ET and RECO. NDVI datasets are obtained from Landsat 5, 7 and 8 on Google Earth Engine platform. Detailed QA/QC and sampling methods of measured data are available at the original data source.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Haiti Agriculture II: Evaluating the Success of Reforestation Practices in Haiti

The Caribbean country of Haiti has an extensive history of deforestation and environmental degradation stemming from French colonization. Over the past 33 years, the Haiti Reforestation Partnership (HRP) and their partners, Comprehensive Development Program (CODEP), have planted approximately 15.52 million trees. However, these efforts lacked scientific guidance to ensure successful forest stand survival. The NASA DEVELOP team partnered with the HRP to create a habitat suitability model (HSM) by using PlanetScope and Sentinel-2 Multispectral Instrument (MSI) imagery. The team also incorporated Landsat 8 and 9 OLI surface temperature, Centre National de L’Information Geo-Spatiale (CNIGS) Airborne Lidar, and ancillary datasets to analyze areas suitable for future reforestation efforts. The habitat model suggested locations with higher forest stand survival based on topography, soil health, climate, and feasibility to access suggested locations. We confirmed the HSM through a cross-analyzation of high enhanced vegetation index (EVI) values. Areas with lower EVI values and higher suitability based on the HSM were suggested for future planting as they lack well-established forest stands but have optimal conditions for growth. Through the creation of the HSM, the team provided the HRP with static maps of high suitability, a 3D printed elevation model, and a guidebook for animators. Additionally, the team provided a structured video highlighting the HRP’s efforts. Effective reforestation and better forest stand survival would help to achieve the goal of securing community food security. This would also serve as a guide to expand planting efforts into other locations and communities.

Kelli Roberts↗
DOE OSTI2023

Interannual variability and trends of summertime PM2.5-based air quality in the Intermountain West

Abstract Summertime air quality is a growing public health concern in the populated region of Northern Utah. Whereas winter air pollution is highly linked with local atmospheric temperature inversions associated with upper atmospheric high-pressure and radiational cooling in valleys, the relationship between climate factors and the frequency of poor air quality during summer is still unknown. Analyzing the last 20 years of data, we demonstrated that summertime unhealthy days (as defined by PM2.5 air quality index level) in Northern Utah highly correlate with the number of dry-hot days, wildfire size, and an upper atmospheric ridge over the Northwestern United States. The persistent atmospheric ridge enhances lightning-caused fire burned areas in northwestern states and then transports the wildfire smoke toward Northern Utah. Similarly, climate model simulations confirm observational findings, such as an increasing trend of the upper atmospheric ridge and summertime dry days in the northwestern states. Such metrics developed in this study could be used to establish longer-term monitoring and seasonal forecasting for air quality and its compounding factors, which is currently limited to forecasting products for only several days.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

Climate Change Implications to Vegetation Production in Alaska

Investigation of long-term meteorological satellite data revealed statistically significant vegetation response to climate drivers of temperature, precipitation and solar radiation with exclusion of fire disturbance in Alaska. Abiotic trends were correlated to satellite remote sensing observations of normalized difference vegetation index to understand biophysical processes that could impact ecosystem carbon storage. Warming resulted in disparate trajectories for vegetation growth due to precipitation and photosynthetically active radiation variation. Interior spruce forest low lands in late summer through winter had precipitation deficit which resulted in extensive fire disturbance and browning of undisturbed vegetation with reduced post-fire recovery while Northern slope moist alpine tundra had increased production due to warmer-wetter conditions during the late 1990s and early 2000s. Coupled investigation of Alaska s vegetation response to warming climate found spatially dynamic abiotic processes with vegetation browning not a result from increased fire disturbance.

Neigh, Christopher S.R.↗
NASA NTRS2013

Estimating the Mean Annual Surface Air Temperature at Armagh Observatory, Northern Ireland, and the Global Land-Ocean Temperature Index for Sunspot Cycle 24, the Current Ongoing Sunspot Cycle

As noted by Gray et al., Sir William Herschel was the first to suggest a possible close connection between the Sun and the Earth’s climate. The Sun, being the source of energy that impacts and drives the Earth’s climate system, displays a variety of changes over both short and long term time scales, the most obvious examples being the somewhat regular waxing and waning of sunspots with time (i.e., the sunspot cycle (SC)), first described by Samuel Heinrich Schwabe, a German apothecary and amateur astronomer who observed the Sun from Dessau, Germany, and the now well established variation of the Sun’s irradiance over the SC. Other factors related to the SC have been linked to changes in climate as well. Some of these other factors include the role of cosmic rays and the solar wind (i.e., the geomagnetic cycle) on climate, as well as the apparent close association between trends in global and northern hemispheric temperature and the length of the SC, although some investigators have described the inferred association between climate and, in particular, SC length as now being weak. More recently, Solheim et al. have reported on the relation between SC length and the average temperature in the same and immediately following SC for a number of meteorological stations in Norway and in the North Atlantic region. They noted that while they found no significant trend (correlation) between SC length and the average temperature when measured for the same cycle, in contrast, they found a significant negative trend when SC length was compared with the following cycle’s average temperature. From this observation, they suggested that average northern hemispheric temperature during the present ongoing SC (SC24) will be lower by about 0.9 °C than was seen in SC23 (spanning 1996–2007, based on yearly averages of sunspot number (SSN), and onset for SC24 occurring in 2008). The purpose of this Technical Publication (TP) is to examine the annual variations of the Armagh surface air temperature (ASAT) and the Global Land-Ocean Temperature Index (GLOTI) in relation to SSN and the SC in order to determine their likely values during SC24. Hence, it may provide insight as to whether solar forcing of global temperature is now lessening as a contributor to global warming, thereby indicating a possible cooling in the near term immediate future that potentially could ameliorate the effect of increased anthropogenic warming.

Wilson, Robert M.↗
DOE OSTI2026

Evaluating ecosystem water use efficiency under drought stress: a case study of the Helan Mountain region, northwest China

Context Water use efficiency (WUE) is a fundamental ecological indicator links carbon assimilation and water loss in terrestrial ecosystems. Understanding its responses to drought stress is essential for adaptive ecosystem management, particularly in climate-sensitive mountain landscapes. Objectives This study aimed to investigate drought-driven variations in WUE across major vegetation types in the Helan Mountain region of Northwest China. Specifically, we sought to identify dominant ecological drivers of WUE variability and to disentangle their relative importance and causal pathways. Methods We quantified WUE using the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Drought Severity Index (DSI) data from 2001 to 2020. To examine WUE – drought relationships across contrasting vegetation types, we employed a spatially explicit analytical framework integrating Random Forest (RF) modeling, partial correlation analysis, and structural equation modeling (SEM). Results Regional WUE exhibited relatively stable interannual dynamics, yet pronounced spatial heterogeneity that was strongly modulated by drought conditions. Vegetation properties, particularly Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), emerged as the dominant determinants of WUE, with NDVI alone explaining over 20% of its spatial variance in forest and grassland during non-drought periods. SEM analyses revealed that climate forcing influenced WUE mainly through indirect pathways mediated by soil moisture availability and vegetation structural dynamics, rather than through direct climatic controls. Among all regulating factors, LAI acted as the central control node governing ecosystem carbon–water coupling. In contrast, short-term climatic stress, especially atmospheric demand and drought duration, exerted weak or negative direct effects on WUE. Ecosystem-specific responses were observed, with croplands mainly regulated by soil water availability, whereas forests and grasslands showed more sensitive to atmospheric drought stress. Together, these results reveal a hierarchical control framework where soil–vegetation interactions mediate climate impacts on WUE, driving strong spatial heterogeneity in drought responses across mountain landscapes. Conclusions Our findings highlight the pivotal role of indirect drought effects mediated by vegetation and soil processes in shaping ecosystem WUE. The identified soil–vegetation–climate regulatory hierarchy provides mechanistic insight into landscape–scale drought sensitivity and supports integrated modeling approaches for evaluating ecosystem resilience and sustainable management in arid mountain regions.

China↗
DOE OSTI2023

Meteorological and hydrological parameters for 17 locations of meteorological stations of the East River Watershed

The Data Package includes a set of csv files of input meteorological parameters for locations of 17 meteorological stations within the East River watershed. These meteorological datasets were downloaded from the (1) PRISM database--monthly precipitation, air temperature (minimum, mean, and maximum), vapor pressure deficit (minimum and maximum), and dewpoint temperature, and (2) NCEP/NCAR Reanalysis database – wind database. The datasets were used for calculations of the Potential Evapotranspiration (ETo), Actual Evapotranspiration (ET), Standard Precipitation Index (SPI) , Standard Evapotranspiration-Precipitation Index (SPEI) for the period from 1966 to 2021. The main research questions addressed are: the evaluation of the long-term temporal trends of climatic parameters, hierarchical clustering, and areal mapping/zonation of the East River watershed. Calculations were conducted in the Rstudio environment. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.The input datasets were downloaded from (a) PRISM database (the Northwest Alliance for Computational Science and Engineering at the Oregon State University), and (b) NCEP/NCAR Reanalysis database.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Relationship between circum-Arctic atmospheric wave patterns and large-scale wildfires in boreal summer

Long-term assessment of severe wildfires and associated air pollution and related climate patterns in and around the Arctic is essential for assessing healthy human life status. To examine the relationships, we analyzed the National Aeronautics and Space Administration (NASA) modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Our investigation based on this state-of-the-art atmospheric reanalysis data reveals that 13 out of the 20 months with the highest PM2.5 (corresponding to the highly elevated organic carbon in the particulate organic matter [POM] form) monthly mean mass concentration over the Arctic for 2003–2017 were all in summer (July and August), during which POM of ⩾0.5 μg/cu. m and PM2.5 were positively correlated. This correlation suggests that high PM2.5 in the Arctic is linked to large wildfire contributions and characterized by significant anticyclonic anomalies (i.e. clockwise atmospheric circulation) with anomalous surface warmth and drier conditions over Siberia and subpolar North America, in addition to Europe. A similar climate pattern was also identified through an independent regression analysis for the July and August mean data between the same atmospheric variables and the sign-reversed Scandinavian pattern index. We named this pattern of recent atmospheric circulation anomalies the circum-Arctic wave (CAW) pattern as a manifestation of eastward group-velocity propagation of stationary Rossby waves (i.e. large-scale atmospheric waves). The CAW induces concomitant development of warm anticyclonic anomalies over Europe, Siberia, Alaska, and Canada, as observed in late June 2019. Surprisingly, the extended regression analysis of the 1980–2017 period revealed that the CAW pattern was not prominent before 2003. Understanding the CAW pattern under future climate change and global warming would lead to better prediction of co-occurrences of European heatwaves and large-scale wildfires with air pollution over Siberia, Alaska, and Canada in and around the Arctic in summer.

wildfire↗
DOE OSTI2026

Public water supply infrastructure extensification and diversification in surface waters is insufficient to meet future demands in Texas

The data were developed to evaluate the capacity of existing and potential new surface water supply infrastructure to meet projected public water demands across districts in Texas under multiple future socioeconomic and climate scenarios. The database integrates hydrologic, water quality, infrastructure, energy, cost, demographic, and demand-projection information for candidate surface water supply locations. Candidate sites include stream reaches, waterbodies, reservoir surplus locations, and potential new reservoir sites. Water availability is characterized using historical and projected flow conditions, while site suitability is evaluated using five indicators: Water Availability Index (WAI), Water Quality Index (WQI), Energy Requirement Index (ERI), Water Treatment Cost (WTC), and Water Infrastructure Cost (WIC). The datasets include statewide candidate-site information, district-level demand projections under Shared Socioeconomic Pathways (SSPs), runoff-based allocation constraints, climate-stress metrics, and optimization outputs evaluating alternative infrastructure planning strategies. Optimization results compare Business-as-Usual (BAU) and All Surface Water (AllSW) demand-management approaches under both scaled and fixed cost-cap strategies. Associated validation datasets provide district-level feasibility assessments, infrastructure selection outcomes, cost-cap utilization, demand satisfaction metrics, and constraint diagnostics. Additional datasets quantify projected changes in storage and flow conditions as well as water availability stress for both existing and newly selected intake locations under the SSP5 scenario for mid-century and late-century climate conditions. Together, these datasets support assessment of the extent to which surface-water infrastructure expansion and diversification strategies can satisfy future public water demands while accounting for hydrologic, economic, and planning constraints across Texas. Dataset(s) Description Dataset_preoptimization.xlsx Comprehensive pre-optimization dataset containing candidate water-supply sites and associated hydrologic, water-quality, infrastructure, climate, demographic, runoff, and demand-projection variables used as inputs to the optimization analyses. Includes variable descriptions and the full statewide candidate-site database. District_level_site_selection.zip - Compressed archive containing all SSP-specific district-level optimization result files MESIO_ssp1_results.xlsx District-level site selection results for SSP1 (MESIO). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. MESID_ssp2_results.xlsx District-level site selection results for SSP2 (MESID). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. LCMRD_ssp3_results.xlsx District-level site selection results for SSP3 (LCMRD). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. IRDev-Low_ssp4l_results.xlsx District-level site selection results for SSP4-Low (IRDev-Low). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. IRDev-High_ssp4h_results.xlsx District-level site selection results for SSP4-High (IRDev-High). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. RSIM_ssp5_results.xlsx District-level site selection results for SSP5 (RSIM). Includes variable descriptions, BAU and AllSW site-selection results under scaled and fixed cost strategies, and district-level validation diagnostics. tx_hydrological_stress.xlsx Hydrological stress dataset for existing and newly selected intake locations. Includes projected mid-century and late-century changes, gain/loss classifications, planning strategy information, and accompanying variable descriptions. Also includes water-stress metrics derived from historical and projected low-flow conditions.

Okoye, Perpetua I. (ORCID:0000000215545033)↗
DOE OSTI2022

Earth System Model Overestimation of Cropland Temperatures Scales With Agricultural Intensity

Intensive crop growth can modify regional climate by partitioning energy to latent heating through transpiration, cooling growing season temperatures. Recent work shows that cooling associated with agriculture can dampen anthropogenic warming over breadbasket regions. However, it is unknown whether climate models reproduce crop influences on regional climate, and thus the future risk of extreme climate events over global breadbasket regions. Here, we show that models overestimate growing season temperatures and underestimate evapotranspiration (ET) over global croplands, and that these differences increase with cropped area. We trace this warm and dry difference through each model's representation of the surface energy budget, showing that model differences in transpiration, leaf area index, and the ratio of transpiration to total ET drive the overall effect. While the implications of these model deficiencies for future projections are uncertain, they point to the importance of improving representations of crop-climate processes to better assess breadbasket vulnerability to climate change.

54 ENVIRONMENTAL SCIENCES↗