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In 2023 human populations experienced multiple record‐breaking climate events, with widespread impacts on human health and well‐being. These events include extreme heat domes, drought, severe storms, flooding, and wildfires. Due to inherent lags in the climate system, we can expect such extremes to continue for multiple decades after reaching net zero carbon emissions. Unfortunately, despite these significant current and future impacts, funding for research in climate and health has lagged behind that for other geoscience and biomedical research. While some initial efforts from funding agencies are evident, there is still a significant need to increase the resources available for multidisciplinary research in the face of this issue. As a group of experts at this important intersection, we call for a more concerted effort to encourage interdisciplinary and policy‐relevant investigations into the detrimental health effects of continued climate change. Plain Language Summary: Recent climate events highlight the need for additional funding for interdisciplinary research focused on the impact of climate on human health. Key Points: Climate change is increasingly impacting populations across the globeFunding for interdisciplinary research in climate and health has lagged behind biomedical researchWe call for a more proactive effort to encourage interdisciplinary research into the negative effects of climate change [ABSTRACT FROM AUTHOR]

Remotely sensed inundation may help to rapidly identify areas in need of aid during and following floods. Here we evaluate the utility of daily remotely sensed flood inundation measures and estimate their congruence with self‐reported home flooding and health outcomes collected via the Texas Flood Registry (TFR) following Hurricane Harvey. Daily flood inundation for 14 days following the landfall of Hurricane Harvey was acquired from FloodScan. Flood exposure, including number of days flooded and flood depth was assigned to geocoded home addresses of TFR respondents (N = 18,920 from 47 counties). Discordance between remotely‐sensed flooding and self‐reported home flooding was measured. Modified Poisson regression models were implemented to estimate risk ratios (RRs) for adverse health outcomes following flood exposure, controlling for potential individual level confounders. Respondents whose home was in a flooded area based on remotely‐sensed data were more likely to report injury (RR = 1.5, 95% CI: 1.27–1.77), concentration problems (1.36, 95% CI: 1.25–1.49), skin rash (1.31, 95% CI: 1.15–1.48), illness (1.29, 95% CI: 1.17–1.43), headaches (1.09, 95% CI: 1.03–1.16), and runny nose (1.07, 95% CI: 1.03–1.11) compared to respondents whose home was not flooded. Effect sizes were larger when exposure was estimated using respondent‐reported home flooding. Near‐real time remote sensing‐based flood products may help to prioritize areas in need of assistance when on the ground measures are not accessible. Plain Language Summary: Flood maps that are prepared using satellites monitoring the earth surface may help to rapidly identify areas in need of aid during and following flooding events. In this study, we quantified the risk of self‐reported health outcomes that are associated with home flooding measured using either satellite‐derived flood maps or self‐reported exposure to floodwaters during and following Hurricane Harvey. Responses from 18,920 participants in the Texas Flood Registry included home location of the responders, reported adverse health symptoms, reported hospitalization, flooding of home, contact with flood water, and demographic variables. Respondents whose homes were flooded as per the flood maps were more likely to report injury, concentration problems, skin rash, illness, headaches, and runny nose compared to respondents whose homes were not flooded. The estimated strength of association was larger when exposure was estimated using respondent‐reported home flooding. Flood maps prepared from satellite data may help to prioritize areas in need of assistance in real‐time when on the ground measures are not available. Key Points: Remote sensing identified exposure to floods was associated with self‐reported flood related adverse health symptoms, illness, and injurySelf‐reported illness and injury increased with increase in proximity to the flood watersThe association estimated using self‐reported exposure to flood were stronger than those estimated using remote sensing [ABSTRACT FROM AUTHOR]

Brazil has been severely affected by the COVID‐19 pandemic. Temperature and humidity have been purported as drivers of SARS‐CoV‐2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil's 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time‐varying reproductive number (Rt) of SARS‐CoV‐2 were examined using generalized additive models fit to data from the entire 15‐month period and several shorter, 3‐month periods. Accumulated local effects and variable importance metrics were calculated to analyze the relationship between input variables and Rt. We found that transmission is strongly influenced by unmeasured sources of between‐state heterogeneity and the near‐recent trajectory of the pandemic. Increased temperature generally was associated with decreased transmission and increased specific humidity with increased transmission. However, the impacts of meteorology, policy, and mobility on Rt varied in direction, magnitude, and significance across our study period. This time variance could explain inconsistencies in the published literature to date. While meteorology weakly modulates SARS‐CoV‐2 transmission, daily or seasonal weather variations alone will not stave off future surges in COVID‐19 cases in Brazil. Investigating how the roles of environmental factors and disease control interventions may vary with time should be a deliberate consideration of future research on the drivers of SARS‐CoV‐2 transmission. Plain Language Summary: Environmental factors such as outdoor temperature and humidity can affect the spread of the flu and other respiratory viruses. For this reason, early studies on the COVID‐19 pandemic hypothesized that temperature, humidity, and other environmental factors might create favorable or less favorable conditions to facilitate the spread of COVID‐19. At times, politicians and the media have disseminated these hypotheses without proper vetting. COVID‐19 has caused major impacts in Brazil, and in this study we use a statistical model that allows us to investigate how environmental factors, governmental policies, and human mobility are related to COVID‐19 transmission in Brazil from June 2020 to August 2021. We found that temperature and humidity were not very important in explaining COVID‐19 transmission. Governmental policies and human mobility played a larger role in explaining transmission, but whether changes in policies or human mobility led to increased versus decreased transmission varied throughout our study period. These changes with time may explain why the conclusions of other studies on what drives the spread of COVID‐19 may appear at odds with each other. Continuing to rely on proven mitigation measures such as mass vaccinations should be the key priority in the fight against COVID‐19 in Brazil. Key Points: Unmeasured sources of between‐state heterogeneity and recent waves of cases are the dominant drivers of SARS‐CoV‐2 transmission in BrazilThe impacts of policy, meteorology, and mobility on transmission vary in direction and magnitude within subperiods of our studyRelying on proven mitigation measures such as mass vaccinations should be the key priority in the continued fight against COVID‐19 [ABSTRACT FROM AUTHOR]

The Upper Blue Nile river basin (uBN) is vital to three countries' water, food, and energy security (Ethiopia, Sudan, and Egypt). It is also a region with substantial interannual precipitation variability and the potential for significant climate change in coming decades. The El Niño–Southern Oscillation (ENSO) affects both variability and trends in uBN precipitation, with El Niño episodes historically connected with below‐average rainfall. In this context, this study seeks to: (a) examine the representation of ENSO–uBN rainfall teleconnections in simulations from the sixth Coupled Model Intercomparison Project (CMIP6), as compared to the previous generation of models (CMIP5); (b) distinguish between teleconnections during the peak of the rainy season (July–August [JA]) and the agriculturally critical end‐of‐season rains (September–October [SO]); (c) investigate evidence for Tropical Easterly Jet (TEJ) mediation of these teleconnections in CMIP6 versus CMIP5 and for both peak rains and end of the rainy season. Using a subset of high performing models, we find that CMIP6 simulations of uBN precipitation show somewhat less bias than CMIP5 for JA total rainfall, and that correlations with ENSO have become more consistent across models, but that differences between CMIP5 and CMIP6 are modest. CMIP6 simulations, like CMIP5, overestimate SO rainfall and suggest a stronger ENSO association in SO than is indicated by observations. Model representation of the TEJ and its association with both ENSO and uBN precipitation show no systematic change between CMIP5 and CMIP6. The mediating influence of the TEJ appears to be more important in JA than SO. [ABSTRACT FROM AUTHOR]

Representation of irrigation in Earth System Models has advanced over the past decade, yet large uncertainties persist in the effective simulation of irrigation practices, particularly over locations where the on‐ground practices and climate impacts are less reliably known. Here we investigate the utility of assimilating remotely sensed vegetation data for improving irrigation water use and associated fluxes within a land surface model. We show that assimilating optical sensor‐based leaf area index estimates significantly improves the simulation of irrigation water use when compared to the USGS ground reports. For heavily irrigated areas, assimilation improves the evaporative fluxes and gross primary production (GPP) simulations, with the median correlation increasing by 0.1–1.1 and 0.3–0.6, respectively, as compared to the reference datasets. Further, bias improvements in the range of 14–35 mm mo−1 and 10–82 g m−2 mo−1 are obtained in evaporative fluxes and GPP as a result of incorporating vegetation constraints, respectively. These results demonstrate that the use of remotely sensed vegetation data is an effective, observation‐informed, globally applicable approach for simulating irrigation and characterizing its impacts on water and carbon states. Plain Language Summary: Agricultural irrigation accounts for more than 70% of freshwater use over the globe and can impact local and regional water resources, crop productivities, and climate and weather systems. Investigating impact of irrigation heavily relies on models, which vary in terms of modeling structure, input data sources, assumptions, etc. Given these variations, models are often subject to large uncertainties in estimating irrigation water use. The goal of this study is to explore the potential to integrate satellite observations of vegetation conditions with models to improve estimates of irrigation and its impact across the Contiguous United States. We find that integrating satellite observations is helpful in correcting simulation of vegetation growth, leading to a better estimation of irrigation water use and its impact on surface soil moisture, evapotranspiration, and agricultural productivities. These results underscore the effectiveness of using satellite vegetation observations to improve irrigation modeling. Key Points: Moderate Resolution Imaging Spectroradiometer leaf area index (LAI) data are assimilated into the Noah‐MP land surface model to inform simulation of irrigation schedules and volumesIrrigation simulations without LAI assimilation overestimate irrigation amount and increase the BIAS for evapotranspiration (ET) and GPPAssimilating LAI to constrain irrigation shows the overall best performance for surface soil moisture, ET, and GPP [ABSTRACT FROM AUTHOR]

The Middle East is one of the world's most vulnerable areas to climate change, which has exacerbated environmental, agricultural, water conflict, and public health issues in the region. Here we analyze the latest climate model projections of precipitation and temperature for the very high emissions scenario, SSP5‐8.5, to detect potential future changes in this region. A baseline period (1981–2010) is compared with the middle (2040–2069) and end (2070–2099) of the 21st century. The results, representing the worst‐case scenario, identify the Tigris‐Euphrates headwaters as the hotspot of future compounding effects of climate change in the Middle East. Those effects result from the coincidence of elevated temperature, reduced precipitation, and enhanced interannual variability of precipitation. The hotspot overlays the location of the Southeastern Anatolia Project (in Turkish, Güneydoğu Anadolu Projesi [GAP]) irrigation initiative. In this climate context, risks to GAP viability and downstream water security, and associated potential for water‐related conflicts and migration are considerable and demand a reconsideration of the risk‐benefit assessment of GAP. This need has become more urgent after the recent widespread and deadly climate‐related conflicts and wildfires in summer 2021 across the Middle East that further underlined vulnerability of the region to climate extremes. Plain Language Summary: The Middle East, one of the world's most vulnerable regions to climate change, has experienced a growing number of severe extreme events in recent decades. These events not only have imposed lasting socio‐environmental impacts locally but have contributed to transboundary water issues with implications for regional and global security. Climate model projections show that the adverse effects of such events will likely be exacerbated over downstream countries of the Tigris‐Euphrates basin in coming decades. This conclusion is drawn because concurrent extreme hot and dry conditions and enhanced precipitation variability are projected over the Tigris‐Euphrates headwaters, where the Southeastern Anatolia Project (in Turkish, Güneydoğu Anadolu Projesi [GAP]) is located. As a result, the negative impacts of GAP may outnumber its benefits, and this study attempts to warn decision‐makers of the urgent need to integrate these trends to water resource planning and international negotiations. Key Points: The Tigris‐Euphrates headwaters is identified as the hotspot of future compound effects of climate change in the Middle EastThe adverse impacts of the Southeastern Anatolia Project (in Turkish, Güneydoğu Anadolu Projesi [GAP]) may outnumber its benefits in coming decadesGAP may exacerbate environmental, agricultural, water conflict, and public health issues in the region [ABSTRACT FROM AUTHOR]

Tropical highland environments present substantial challenges for climate projections due to sparse observations, significant local heterogeneity and inconsistent performance of global climate models (GCMs). Moreover, these areas are often densely populated, with agriculture‐based livelihoods sensitive to transient climate extremes not always included in available climate projections. In this context, we present an analysis of observed and projected trends in temperature and precipitation extremes across agroecosystems (AESs) in the northwest Ethiopian Highlands, to provide more relevant information for adaptation. Limited observational networks are supplemented with a satellite‐station hybrid product, and trends are calculated locally and summarized at the adaptation‐relevant unit of the AES. Projections are then presented from GCM realizations with divergent climate projections, and results are interpreted in the context of agricultural climate sensitivities. Trends in temperature extremes (1981–2016) are typically consistent across sites and AES, but with different implications for agricultural activities in the other AES. Trends in temperature extremes from GCM projected data also generally have the same sign as the observed trends. For precipitation extremes, there is greater site‐to‐site variability. Summarized by AES, however, there is a clear tendency towards reduced precipitation, associated with decreases in wet extremes and a tendency towards temporally clustered wet and dry days. Over the retrospective analysis period, neither of the two analysed GCMs captures these trends. Future projections from both GCMs include significant wetting and an increase in precipitation extremes across AES. However, given the lack of agreement between GCMs and observations with respect to trends in recent decades, the reliability of these projections is questionable. The present study is consistent with the "East Africa Paradox" that observations show drying in summer season rainfall while GCMs project wetting. This has an expression in summertime Ethiopian rain that has not received significant attention in previous studies. [ABSTRACT FROM AUTHOR]

Diarrheal disease, still a major cause of childhood illness, is caused by numerous, diverse infectious microorganisms, which are differentially sensitive to environmental conditions. Enteropathogen‐specific impacts of climate remain underexplored. Results from 15 studies that diagnosed enteropathogens in 64,788 stool samples from 20,760 children in 19 countries were combined. Infection status for 10 common enteropathogens—adenovirus, astrovirus, norovirus, rotavirus, sapovirus, Campylobacter, ETEC, Shigella, Cryptosporidium and Giardia—was matched by date with hydrometeorological variables from a global Earth observation dataset—precipitation and runoff volume, humidity, soil moisture, solar radiation, air pressure, temperature, and wind speed. Models were fitted for each pathogen, accounting for lags, nonlinearity, confounders, and threshold effects. Different variables showed complex, non‐linear associations with infection risk varying in magnitude and direction depending on pathogen species. Rotavirus infection decreased markedly following increasing 7‐day average temperatures—a relative risk of 0.76 (95% confidence interval: 0.69–0.85) above 28°C—while ETEC risk increased by almost half, 1.43 (1.36–1.50), in the 20–35°C range. Risk for all pathogens was highest following soil moistures in the upper range. Humidity was associated with increases in bacterial infections and decreases in most viral infections. Several virus species' risk increased following lower‐than‐average rainfall, while rotavirus and ETEC increased with heavier runoff. Temperature, soil moisture, and humidity are particularly influential parameters across all enteropathogens, likely impacting pathogen survival outside the host. Precipitation and runoff have divergent associations with different enteric viruses. These effects may engender shifts in the relative burden of diarrhea‐causing agents as the global climate changes. Plain Language Summary: Diarrheal disease is a big health problem for children. It can be caused by different bugs, which can be caught more easily in certain weather conditions, though not much is understood about this because the climate varies so much from one place to the next. This study combined data from many different countries where diarrhea‐causing bugs were diagnosed in children's stool. Satellites recorded what the weather was like on the day each sample was collected. Rotavirus is easiest to catch in cold weather and when water washes over the ground after rain. Dry weather also makes it and other viruses easy to catch. Bacteria spread best when the air is warm and humid, and the soil moist, though one type of E. coli can also be spread in rainwater. Climate change will make dry places drier, wet places wetter and everywhere warmer. This might lead to more diarrhea caused by bacteria and less by viruses in some places, though places with moist soil might see more of every kind of bug. Key Points: Many pathogens cause diarrhea in children and are affected in different ways by weather conditions like rainfall, temperature, and humidityDiarrhea‐causing bacteria infections increase in warm, humid weather and when soil is moist, though ETEC is sensitive to rainfall extremesHigh humidity decreases prevalence of several viruses, though only rotavirus increases in cold weather and following heavy surface runoff [ABSTRACT FROM AUTHOR]

From the heated debates over the airborne transmission of the novel coronavirus to the abrupt Earth system changes caused by the sudden lockdowns, the dire circumstances resulting from the coronavirus disease 2019 (COVID‐19) pandemic have brought the field of GeoHealth to the forefront of visibility in science and policy. The pandemic has inadvertently provided an opportunity to study how human response has impacted the Earth system, how the Earth system may impact the pandemic, and the capacity of GeoHealth to inform real‐time policy. The lessons learned throughout our responses to the COVID‐19 pandemic are shaping the future of GeoHealth. Plain Language Summary: From the heated debates over whether the coronavirus disease 2019 (COVID‐19) is primarily spread by airborne droplets to the abrupt changes in human behavior such as less driving and factory emissions that have caused changes to the Earth, the pandemic has highlighted the importance of the scientific field called GeoHealth. GeoHealth is the scientific study connecting humans, health, and the Earth—all of which are affected by the COVID‐19 pandemic. The unique circumstances from the pandemic have provided an opportunity to study how human behavior has impacted the Earth, how aspects of the Earth such as weather and climate may impact the pandemic, and the importance of GeoHealth throughout the pandemic response. Key Points: The COVID‐19 pandemic has brought the field of GeoHealth to the forefront of visibility, especially by informing disease mitigation policiesThe pandemic provides an opportunity to observe how changes in human behavior have impacted the Earth systemThe pandemic is helping GeoHealth identify areas of future growth in data availability, environmental justice, and scientific communication [ABSTRACT FROM AUTHOR]

Climate variability is an important driver of irrigation water use in many regions. Efforts to anticipate climate change impacts on future water availability can benefit from understanding how irrigation water demand has responded to these drivers to date. Here we apply satellite‐derived data, meteorological reanalysis, an advanced land surface model, and available state‐level reports to quantify irrigation demand sensitivities to temperature and precipitation across the Contiguous United States, for the period of 2002–2017. As expected, strong negative correlations are found between precipitation and irrigation withdrawals, both simulated and reported. Temperature sensitivities, however, vary by region and season, as do the interactive effects of temperature and precipitation on irrigation. Climate‐induced irrigation variability is largest in transitional climate zones. These transitional zones are generally separate from the regions where rates of irrigation withdrawals are greatest, such that climate‐induced variability in irrigation demand represents a water resource consideration that is distinct from chronic overpumping. Key Points: Widespread sensitivity of irrigation water use to precipitation is found in both model and available reportsTemperature and interactions of temperature—precipitation sensitivity vary by region and season and might be underestimated by the modelGeographical disconnects exist between aquifers that are stressed by pumping and the basins that exhibit strong climate sensitivities [ABSTRACT FROM AUTHOR]

North African dust aerosol and climate are known to covary, but modeling studies are uncertain as to whether this covariation arises from a positive feedback loop. This study aims to improve our understanding of the strength of the relationship between dust and climate over the Sahel. A fully coupled meteorology‐land‐aerosol model (NU‐WRF) is applied to simulate mineral dust emission and transport over North Africa from prerainy season to postrainy season (March–October) for the years 2002–2016; results are compared to estimates from the MODerate Resolution Imaging Spectrometer (MODIS), the NASA Modern‐Era Reanalysis for Research and Applications v2 (MERRA‐2), the MERRA‐2 Global Modeling Initiative (GMI), and the Aerosol Robotic Network (AERONET). Two model configurations (Dust‐On and Dust‐Off) are used to investigate the regional dust‐atmosphere coupling during the Sahelian rainy season by assessing the influence of land surface conditions and near‐surface meteorology on dust emission, and the impacts of dust on the atmospheric circulation features linked to climate and dust variability. Results show that coupling appears to be weak on interannual timescales, as interannual variability in dust load and dust impacts on atmospheric circulations is modest. NU‐WRF captures the general spatial and temporal variability in dust, though it underestimates the burden during dusty months, as compared to observational and reanalysis products. Model emission and transport processes during the dustiest months are consistent with other data products, and differences appear to be wind driven. Though the impacts of mineral dust on climatological summertime atmospheric circulation patterns are small, these impacts are consistent with weakened monsoon flow and a southward narrowing of the rain belt. Key Points: NASA Unified WRF captures interannual magnitude and seasonal cycle of North African dust but underestimates DOD in dusty monthsIn controlled experiments, coupling between dust and atmospheric dynamics in the Sahel does not appear strong in NU‐WRF interannuallyChanges to monsoon circulation due to dust show slightly weakened monsoon flow, signifying dust aerosol suppression of Sahelian rainfall [ABSTRACT FROM AUTHOR]

Heat stress is a significant health concern that can lead to illness, injury, and mortality. The wet bulb globe temperature (WBGT) index is one method for monitoring environmental heat risk. Generally, WBGT is estimated using a heat stress monitor that includes sensors capable of measuring ambient, wet bulb, and black globe temperature, and these measurements are combined to calculate WBGT. However, this method can be expensive, time consuming, and requires careful attention to ensure accurate and repeatable data. Therefore, researchers have attempted to use standard meteorological measurements, using single data sources as an input (e.g., weather stations) to calculate WBGT. Building on these efforts, we apply data from a variety of sources to calculate WBGT, understand the accuracy of our estimated equation, and compare the performance of different sources of input data. To do this, WBGT measurements were collected from Kestrel 5400 Heat Stress Trackers installed in three locations in Alabama. Data were also drawn from local weather stations, North American Land Data Assimilation System (NLDAS), and low cost iButton hygrometers. We applied previously published equations for estimating natural wet bulb temperature, globe temperature, and WBGT to these diverse data sources. Correlation results showed that WBGT estimates derived from all proxy data sources—weather station, weather station/iButton, NLDAS, NLDAS/iButton—were statistically indistinguishable from each other, or from the Kestrel measurements, at two of the three sites. However, at the same two sites, the addition of iButtons significantly reduced root mean square error and bias compared to other methods. Plain Language Summary: Heat stress is a buildup of body heat that can lead to illness, injury, or death. One method for estimating heat stress is an index called wet bulb globe temperature (WBGT). The index is usually measured with a monitor that records three types of temperature measurements and combines them. However, this method can be expensive, time consuming, and requires careful attention. Therefore, researchers have tried to use standard measurements such as wind speed, temperature, humidity, etc., to calculate WBGT. Building on these efforts, we wanted to determine if it was possible to accurately calculate WBGT with a variety of inexpensive data sources in central Alabama. We used previously published equations to estimate WBGT. Results showed that all proxy methods accurately estimated WBGT in two Alabama locations, but that using local measurements did change estimates of the number of potentially dangerous heat episodes relative to estimates that rely on remote sources of weather data. The ability to use easily accessible measurements could be a powerful tool for studies and interventions related to heat stress. Key Points: A variety of proxy methods offered reasonable estimates of wet bulb globe temperature (WBGT) in downtown and suburban Birmingham, ALEstimating WBGT from easily accessible measurements could be a powerful tool for studies and interventions related to heat stress [ABSTRACT FROM AUTHOR]

Assimilating terrestrial water storage observations from the Gravity Recovery and Climate Experiment (GRACE) mission into land surface models (LSMs) provides an opportunity to disaggregate and downscale GRACE information to finer scales and improve water component estimates in LSMs. However, the performance of GRACE data assimilation (GRACE‐DA) is limited by the lack of representation of human activities in most LSMs. To simultaneously improve GRACE‐DA and reduce the uncertainties in the modeled anthropogenic processes, we assimilate mascon‐based GRACE terrestrial water storage into the Noah‐Multiparameterization LSM that includes groundwater extraction for irrigation. Simulations with and without GRACE‐DA and with and without groundwater pumping for irrigation are performed to study the isolated and combined effects of groundwater irrigation and GRACE‐DA on water and energy fluxes over the High Plains Aquifer (HPA). The results reveal that the DA‐only simulation may erroneously distribute increments across water storage components and affect the related fluxes through biased feedbacks, while the irrigation‐only simulation may overestimate groundwater decline due to shortcomings in the irrigation and groundwater parameterizations. Assimilating GRACE when irrigation is simulated produces the best overall performance for water storage trends over the northern HPA. For the southern HPA, GRACE assimilation with irrigation performs similarly to irrigation‐only simulation for water storage components and evapotranspiration. GRACE assimilation also improves results in nonirrigated regions and can potentially alleviate the overestimation of groundwater trends in regions with greater irrigation uncertainties. This study highlights the potential to advance hydrological data assimilation in the context of anthropogenic water consumption and land‐atmosphere interactions. Key Points: GRACE TWS data are assimilated into the Noah‐MP land surface model that includes groundwater extraction for irrigationAssimilating GRACE without irrigation produces biased increments, while simulating irrigation without GRACE may exacerbate model errorCombining GRACE‐DA and irrigation shows the overall best performance for water storage components and evapotranspiration [ABSTRACT FROM AUTHOR]

The scarcity of groundwater storage change data at the global scale hinders our ability to monitor groundwater resources effectively. In this study, we assimilate a state‐of‐the‐art terrestrial water storage product derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal of generating groundwater storage time series that are useful for drought monitoring and other applications. Evaluation using in situ data from nearly 4,000 wells shows that GRACE data assimilation improves the simulation of groundwater, with estimation errors reduced by 36% and 10% and correlation improved by 16% and 22% at the regional and point scales, respectively. The biggest improvements are observed in regions with large interannual variability in precipitation, where simulated groundwater responds too strongly to changes in atmospheric forcing. The positive impacts of GRACE data assimilation are further demonstrated using observed low‐flow data. CLSM and GRACE data assimilation performance is also examined across different permeability categories. The evaluation reveals that GRACE data assimilation fails to compensate for the lack of a groundwater withdrawal scheme in CLSM when it comes to simulating realistic groundwater variations in regions with intensive groundwater abstraction. CLSM‐simulated groundwater correlates strongly with 12‐month precipitation anomalies in low‐latitude and midlatitude areas. A groundwater drought indicator based on GRACE data assimilation generally agrees with other regional‐scale drought indicators, with discrepancies mainly in their estimated drought severity. Key Points: A mascon‐based GRACE terrestrial water storage product is assimilated into the Catchment model at the global scaleGRACE data assimilation improved RMSE and correlation of simulated groundwater storage with in situ data at point and regional scalesUncertainty in model estimated water storage is strongly related to interannual variability of precipitation [ABSTRACT FROM AUTHOR]

Ranking the performance of global climate models (GCMs) is a notoriously difficult exercise. Multi-model comparison studies nearly always show that each model has strengths and weaknesses relative to others, and for many purposes the multi-model ensemble mean delivers better estimates than any individual model. Nevertheless, in regions like East Africa, where there is little consensus between models on the magnitude or sign of 21st century precipitation change, the multi-model ensemble mean approach to climate projection provides little value for adaptation planning. Here, we consider several possible frameworks for model evaluation and ranking, and assess the differences in performance of a subset of models participating in the 5th Coupled Model Intercomparison Project (CMIP5) according to each framework. Our test case is precipitation in the Nile River headwaters regions. We find that there is little consistency in the relative performance of models across frameworks based on amount and seasonality of precipitation, interannual precipitation variability, precipitation teleconnections, and continental scale climate patterns. These analyses offer some guidance on which GCMs are most likely to provide meaningful results for specific applications, but they caution that any effort to select 'best performing' GCMs for the Nile River basin must carefully consider the purposes for which GCMs are being selected. [ABSTRACT FROM AUTHOR]

The Gravity Recovery and Climate Experiment (GRACE) twin satellites observe time variations in Earth's gravity field which yield valuable information about changes in terrestrial water storage (TWS). GRACE is characterized by low spatial (>150,000 km2) and temporal (>10 days) resolution but has the unique ability to sense water stored at all levels (including groundwater) systematically and continuously. The GRACE Data Assimilation System (DAS), based on the Catchment Land Surface Model (CLSM), enhances the value of the GRACE water storage data by enabling spatial and temporal downscaling and vertical decomposition into moisture components (i.e., groundwater, soil moisture, and snow), which individually are more useful for scientific applications. In this study, GRACE DAS was applied to North America, and GRACE-based drought indicators were developed as part of a larger effort to investigate the possibility of more comprehensive and objective identification of drought conditions by integrating spatially, temporally, and vertically disaggregated GRACE data into the U.S. and North American Drought Monitors. Previously, the drought monitors lacked objective information on deep soil moisture and groundwater conditions, which are useful indicators of drought. Extensive data sets of groundwater storage from U.S. Geological Survey monitoring wells and soil moisture from the Soil Climate Analysis Network were used to assess improvements in the hydrological modeling skill resulting from the assimilation of GRACE TWS data. The results point toward modest, but statistically significant, improvements in the hydrological modeling skill across major parts of the United States, highlighting the potential value of a GRACE-assimilated water storage field for improving drought detection. [ABSTRACT FROM AUTHOR]

Advanced land surface models (LSMs) offer detailed estimates of distributed hydrological fluxes and storages. These estimates are extremely valuable for studies of climate and water resources, but they are difficult to verify as field measurements of soil moisture, evapotranspiration, and surface and subsurface runoff are sparse in most regions. In contrast, river discharge is a hydrologic flux that is recorded regularly and with good accuracy for many of the world's major rivers. These measurements of discharge spatially integrate all upstream hydrological processes. As such, they can be used to evaluate distributed LSMs, but only if the simulated runoff is properly routed through the river basins. In this study, a rapid, computationally efficient source-to-sink (STS) routing scheme is presented that generates estimates of river discharge at gauge locations based on gridded runoff output. We applied the scheme as a postprocessor to archived output of the Global Land Data Assimilation System (GLDAS). GLDAS integrates satellite and ground-based data within multiple offline LSMs to produce fields of land surface states and fluxes. The application of the STS routing scheme allows for evaluation of GLDAS products in regions that lack distributed in situ hydrological measurements. We found that the four LSMs included in GLDAS yield very different estimates of river discharge and that there are distinct geographic patterns in the accuracy of each model as evaluated against gauged discharge. The choice of atmospheric forcing data set also had a significant influence on the accuracy of simulated discharge. [ABSTRACT FROM AUTHOR]

A combination of satellite imagery, meteorological station data, and the NCEP/NCAR reanalysis has been used to explore the spatial and temporal evolution of the 2003 heat wave in France, with focus on understanding the impacts and feedbacks at the land surface. Vegetation was severely affected across the study area, especially in a swath across central France that corresponds to the Western European Broadleaf (WEB) Forests ecological zone. The remotely sensed surface temperature anomaly was also greatest in this zone, peaking at +15.4 °C in August. On a finer spatial scale, both the vegetation and surface temperature anomalies were greater for crops and pastures than for forested lands. The heat wave was also associated with an anomalous surface forcing of air temperature. Relative to other years in record, satellite-derived estimates of surface-sensible heat flux indicate an enhancement of 48-61% (24.0-30.5 W m−2) in WEB during the August heat wave maximum. Longwave radiative heating of the planetary boundary layer (PBL) was enhanced by 10.5 W m−2 in WEB for the same period. The magnitude and spatial structure of this local heating is consistent with models of the late twenty-first century climate in France, which predict a transitional climate zone that will become increasingly affected by summertime drought. Models of future climate also suggest that a soil-moisture feedback on the surface energy balance might exacerbate summertime drought, and these proposed feedback mechanisms were tested using satellite-derived heat budgets. Copyright © 2006 Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

Demonstrates models for the representation of uncertainty and spatially unbiased evaluation of a geographical information system (GIS)-based landslide model. Natural variability of soil properties accounted for in a set of model simulations based on parameter distributions; Landslide model uncertainty and scale; Mechanical and hydrologic parameters used in the slope stability model.