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Flash droughts, characterized by rapid onset and development, present significant challenges to agriculture and climate mitigation strategies. Operational drought monitoring systems, based on precipitation, soil moisture deficits, or temperature anomalies, often fall short in timely detection of these events, underscoring the need for customized identification and monitoring indices that account for the rapidity of flash drought onset. Recognizing this need, this paper introduces a global flash drought inventory from 1990 to 2021 derived using the Soil Moisture Volatility Index (SMVI). Our work expands the application of the SMVI methodology, previously focused on the United States, to a global scale, providing a tool for understanding and predicting these rapidly developing phenomena. The dataset encompasses detailed event characteristics, including onset, duration, and severity, across diverse climate zones. By integrating atmospheric variables through their impact on soil moisture, the inventory offers a platform for analyzing the drivers and impacts of flash droughts, and serves as a large, consistent dataset for use in training and evaluating flash drought prediction models. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site‐specific studies, little is known about how SM varies locally (1–100‐m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long‐standing challenge in hydrology. We reveal the striking variability of local‐scale SM across the United States using SMAP‐HydroBlocks — a novel satellite‐based surface SM data set at 30‐m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local‐scale complexity yields a remarkable and unique multi‐scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1‐km resolution, with complete loss expected at the scale of current state‐of‐the‐art SM monitoring and modeling systems (1–25 km resolution). Plain Language Summary: Soil moisture (SM) widely varies in space and time. This variability critically influences freshwater availability, agriculture, ecosystem dynamics, climate and land‐atmosphere interactions, and it can also trigger hazards such as droughts, floods, landslides, and aggravate wildfires. Limited SM observational data constrained our understanding of this variability and its impact on the Earth system. Here, we present the first continental assessment of how SM varies at the local scales using SMAP‐HydroBlocks – the first 30‐m surface SM data set over the United States. This study maps the SM spatial variability, characterizes the landscape drivers, and quantifies how this variability persists across larger spatial scales. Results revealed striking SM spatial variability across the United States, mainly driven by local spatial variations in soil properties and less so by vegetation and topography. However, this SM variability does not persist at coarser spatial scales resulting in extensive information loss. This information loss implicates inaccuracies when predicting non‐linear SM‐dependent hydrological, ecological, and biogeochemical processes using coarse‐scale models and satellite estimates. By mapping the SM spatial variability locally and its scaling behavior, we provide a pathway toward understanding SM‐dependent hydrological, biogeochemical, and ecological processes at local (and so far unresolved) spatial scales. Key Points: 30‐m soil moisture (SM) data shows striking and complex spatial variability driven mainly by climate and local variations in soil propertiesThis variability yields a remarkable and unique multi‐scale behavior at each location that cannot be generalized across the diverse USUp to 80% of SM information is lost at the 1‐km scale, with complete loss at the scale of state‐of‐the‐art SM observation/monitoring systems [ABSTRACT FROM AUTHOR]

Bromus tectorum (cheatgrass) has successfully invaded and established throughout the western United States. Bromus tectorum grows early in the season and this early growth allows B. tectorum to outcompete native species, which has led to dramatic shifts in ecosystem function and plant community composition after B. tectorum invades. If the phenology of native species is unable to track changing climate as effectively as B. tectorum 's phenology then climate change may facilitate further invasion. To better understand how B. tectorum phenology will respond to future climate, we tracked the timing of B. tectorum germination, flowering, and senescence over a decade in three in situ climate manipulation experiments with treatments that increased temperatures (2°C and 4°C above ambient), altered precipitation regimes, or applied a combination of each. Linear mixed-effects models were used to analyze treatment effects on the timing of germination, flowering, senescence, and on the length of the vegetative growing season (time from germination to flowering) in each experiment. Altered precipitation treatments were only applied in early years of the study and neither precipitation treatments nor the treatments' legacies significantly affected B. tectorum phenology. The timing of germination did not significantly vary between any warming treatments and their respective ambient plots. However, plots that were warmed had advances in the timing of B. tectorum flowering and senescence, as well as shorter vegetative growing seasons. The phenological advances caused by warming increased with increasing degrees of experimental warming. The greatest differences between warmed and ambient plots were seen in the length of the vegetative growing season, which was shortened by approximately 12 and 7 days in the +4°C and +2°C warming levels, respectively. The effects of experimental warming were small compared to the effects of interannual climate variation, suggesting that interactive controls and the timing of multiple climatic factors are important in determining B. tectorum phenology. Taken together, these results help elucidate how B. tectorum phenology may respond to future climate, increasing our predictive capacity for estimating when to time B. tectorum control efforts and how to more effectively manage this exotic annual grass. [ABSTRACT FROM AUTHOR]

Complex network (CN) is a graph theory‐based depiction of relation shared by various elements of a complex dynamical system such as the atmosphere. Here we apply the concept of CN to understand the directionality and topological structure of summer extreme precipitation events (SEPEs) over the conterminous United States (CONUS). The SEPEs are calculated based on the 95th percentile daily rainfall at 0.5° × 0.5° spatial resolution for CONUS to investigate the multidimensional characteristics of precipitation extremes. The derived CN coefficients (e.g., betweenness centrality, clustering coefficient, orientation, and network divergence) reveal important structural and dynamical information about the topology of the SEPEs and improve understanding of the dominant meteorological patterns. The initiation and propagation of SEPEs from the source zones to the sink zones are identified. The SEPEs are influenced by topography, dominant wind patterns, and moisture sources in terms of their topological structure and spatial dynamics. Plain Language Summary: Every year, the United States incurs a loss of $3–$4 billion from flooding caused by extreme rainfall events. Under climate change, extreme rainfall events are likely to intensify further in the future as the atmosphere can hold more moisture with warmer temperatures. However, very few studies have explored the inherent topological structure characterizing extreme precipitation. Understanding of this structure and its driving meteorological conditions holds a key to understanding the response of extreme precipitation systems to climate change. In this study, key influencing factors such as topography, dominant wind pattern, and soil moisture controlling the topology of the summer extreme precipitation events in the United States are identified using complex network analysis along with the regions predominantly acting as moisture source or sink. Key Points: Physical control of dominant weather regime, topography, and soil moisture on synchronization of extreme precipitation events is revealed in the United StatesRegions of moisture inflow, moisture source, and moisture convergence associated with summer extreme precipitation events are identifiedSix clusters interacting through moisture exchange emerged in the United States, demonstrating the dynamical synchronization of summer extreme precipitation [ABSTRACT FROM AUTHOR]

Despite the key importance of soil moisture-evapotranspiration (ET) coupling in the climate system, limited availability of soil moisture and ET observations poses a major impediment for investigation of this coupling regarding spatiotemporal characteristics and potential modifications under climate change. To better understand and quantify soil moisture-ET coupling and relevant processes, this study takes advantage of in situ soil moisture observations from the U.S. Climate Reference Network (USCRN) for the time period of 2010-17 and a satellite-derived version of the evapotranspiration stress index (ESI), which represents anomalies in a normalized ratio of actual to reference ET. The analyses reveal strong seasonality and regional characteristics of the ESI-land surface interactions across the United States, with the strongest control of soil moisture on the ESI found in the southern Great Plains during spring, and in the north-central United States, the northern Great Plains, and the Pacific Northwest during summer. In drier climate regions such as the northern Great Plains and north-central United States, soil moisture control on the ESI is confined to surface soil layers, with subsurface soil moisture passively responding to changes in the ESI. The soil moisture-ESI interaction is more uniform between surface and subsurface soils in wetter regions with higher vegetation cover. These results provide a benchmark for simulation of soil moisture-ET coupling and are useful for projection of associated climate processes in the future. [ABSTRACT FROM AUTHOR]

To determine hydrologic changes in a warmer climate, we impose precipitation and potential evaporation (Eo) perturbations on hydrologic response functions constructed from precipitation and satellite soil moisture observations across the United States. Despite nonlinearities in the evaporation (E) and drainage (D) responses and opposing‐sign perturbations, changes in individual fluxes are superposable. Empirical frameworks (Budyko) can misrepresent changes in E/D partitioning by neglecting shifts/trends in hydrologic regime and subseasonal precipitation dynamics. E/D both increase to balance mean precipitation (P¯) increases, and increased Eo reduces soil moisture. E and D are generally more elastic to changes in P¯ than Eo. The results suggest that (1) the impacts of regional hydrologic perturbations may allow for simple superposition/scaling, (2) changes in timing/intensity of precipitation may have substantial impacts on mean moisture states and fluxes, and (3) changes to the distribution of surface moisture states are likely more relevant for E/D partitioning than common aridity indices. Plain Language Summary: We use satellite observations of soil moisture and expected increase in air temperature to determine how evaporation and soil drainage (to groundwater recharge and rivers/streamflow) will change in a warmer climate. The impacts of drier air, more rainfall, and more extreme rainfall (drier dry days and wetter wet days) can largely be considered separately and then added, which will help when predicting a specific location's water balance using scenarios from global climate models. In typical scenarios, soils are likely to dry, evaporation is likely to increase, and—when precipitation increases—drainage to groundwater/streams is likely to increase as well. Evaporation and drainage are relatively more sensitive to changes in precipitation and humidity in the Western United States than the East, and the Corn Belt is particularly susceptible to changes in precipitation intensity. Common methods of determining changes in evaporation and drainage, which neglect changes in soil moisture, may have large errors in global change scenarios. Key Points: Aridity indices (Budyko) fail to capture effects of daily dynamics and precipitation extremes on long‐term hydroclimate changeClimate perturbations to evaporation/drainage are superposable despite nonlinear flux mechanisms and mixed‐sign effectsLong‐term soil drying tied to trends in increased evaporation, decreased drainage to groundwater and streamflow [ABSTRACT FROM AUTHOR]

This study uses correlation analyses to explore relationships between the satellite-derived Evaporative Stress Index (ESI) - which depicts standardized anomalies in an actual to reference evapotranspiration (ET) fraction - and various land and atmospheric variables that impact ET. Correlations between the ESI and forcing variable anomalies calculated over sub-seasonal timescales were computed at weekly and monthly intervals during the growing season. Overall, the results revealed that the ESI is most strongly correlated to anomalies in soil moisture and 2m dew point depression. Correlations between the ESI and precipitation were also large across most of the US; however, they were typically smaller than those associated with soil moisture and vapor pressure deficit. In contrast, correlations were much weaker for air temperature, wind speed, and radiation across most of the US, with the exception of the south-central US where correlations were large for all variables at some point during the growing season. Together, these results indicate that changes in soil moisture and near-surface atmospheric vapor pressure deficit are better predictors of the ESI than precipitation and air temperature anomalies are by themselves. Large regional and seasonal dependencies were also observed for each forcing variable. Each of the regional and seasonal correlation patterns were similar for ESI anomalies computed over 2-, 4-, and 8-week time periods; however, the maximum correlations increased as the ESI anomalies were computed over longer time periods and also shifted toward longer averaging periods for the forcing variables. [ABSTRACT FROM AUTHOR]

Yield stability is fundamental to global food security in the face of climate change, and better strategies are needed for buffering crop yields against increased weather variability. Regional- scale analyses of yield stability can support robust inferences about buffering strategies for widely-grown staple crops, but have not been accomplished. We present a novel analytical approach, synthesizing 2000–2014 data on weather and soil factors to quantify their impact on county-level maize yield stability in four US states that vary widely in these factors (Illinois, Michigan, Minnesota and Pennsylvania). Yield stability is quantified as both ‘downside risk’ (minimum yield potential, MYP) and ‘volatility’ (temporal yield variability). We show that excessive heat and drought decreased mean yields and yield stability, while higher precipitation increased stability. Soil water holding capacity strongly affected yield volatility in all four states, either directly (Minnesota and Pennsylvania) or indirectly, via its effects on MYP (Illinois and Michigan). We infer that factors contributing to soil water holding capacity can help buffer maize yields against variable weather. Given that soil water holding capacity responds (within limits) to agronomic management, our analysis highlights broadly relevant management strategies for buffering crop yields against climate variability, and informs region-specific strategies. [ABSTRACT FROM AUTHOR]

Climate change causes species range shifts and potentially alters biological invasions. The invasion of European earthworm species across northern North America has severe impacts on native ecosystems. Given the long and cold winters in that region that to date supposedly have slowed earthworm invasion, future warming is hypothesized to accelerate earthworm invasions into yet non-invaded regions. Alternatively, warming-induced reductions in soil water content (SWC) can also decrease earthworm performance. We tested these hypotheses in a field warming experiment at two sites in Minnesota, USA by sampling earthworms in closed and open canopy in three temperature treatments in 2010 and 2012. Structural equation modeling revealed that detrimental warming effects on earthworm densities and biomass could indeed be partly explained by warming-induced reductions in SWC. The direction of warming effects depended on the current average SWC: warming had neutral to positive effects at high SWC, whereas the opposite was true at low SWC. Our results suggest that warming limits the invasion of earthworms in northern North America by causing less favorable soil abiotic conditions, unless warming is accompanied by increased and temporally even distributions of rainfall sufficient to offset greater water losses from higher evapotranspiration. [ABSTRACT FROM AUTHOR]

The atmospheric moisture budget and surface interactions for the southern Great Plains are evaluated for contrasting May-June periods (1998, 2002, 2006, and 2007) as background for the Cloud and Land Surface Interaction Campaign (CLASIC) of (wet) 7-30 June 2007. Budget components [flux divergence (MFD), storage change (dPW), and inflow (IF/ A)] are estimated from North American Regional Reanalysis data. Precipitation ( P) is calculated from NCEP daily gridded data, evapotranspiration ( E) is obtained as moisture budget equation residual, and the recycling ratio ( PE/ P) is estimated using a new equation. Regional averages are presented for months and five daily P categories. Monthly budget results show that E and E − P are strongly positively related to P; E − P generally is positive and balanced by positive MFD that results from its horizontal velocity divergence component (HD, positive) exceeding its horizontal advection component (HA, negative). An exception is 2007 (CLASIC), when E − P and MFD are negative and supported primarily by negative HA. These overall monthly results characterize low P days (≤0.6 mm), including for nonanomalous 2007, but weaken as daily P approaches 4 mm. In contrast, for 4 < P ≤ 8 mm day−1 E − P and MFD are moderately negative and balanced largely by negative HD except in 2007 (negative HA). This overall pattern was accentuated (including for nonanomalous 2007) when daily P > 8 mm. Daily P E/ P ratios are small and of limited range, with P category averages 0.15-0.19. Ratios for 2007 are above average only for daily P ≤ 4 mm. CLASIC wetness principally resulted from distinctive MFD characteristics. Solar radiation, soil moisture, and crop status/yield information document surface interactions. [ABSTRACT FROM AUTHOR]

This study examines the land-atmosphere coupling strength during summer over subregions of the United States based on observations [Climate Prediction Center (CPC)-Variable Infiltration Capacity (VIC)], reanalysis data [North American Regional Reanalysis (NARR) and NCEP Climate Forecast System Reanalysis (CFSR)], and models [Community Atmosphere Model, version 3 (CAM3)-Community Land Model, version 3 (CLM3) and CAM4-CLM4]. The probability density function of conditioned correlation between soil moisture and subsequent precipitation or surface temperature during the years of large precipitation anomalies is used as a measure for the coupling strength. There are three major findings: 1) among the eight subregions (classified by land cover types), the transition zone Great Plains (and, to a lesser extent, the Midwest and Southeast) are identified as hot spots for strong land-atmosphere coupling; 2) soil moisture-precipitation coupling is weaker than soil moisture-surface temperature coupling; and 3) the coupling strength is stronger in observational and reanalysis products than in the models examined, especially in CAM4-CLM4. The conditioned correlation analysis also indicates that the coupling strength in CAM4-CLM4 is weaker than in CAM3-CLM3, which is further supported by Global Land-Atmosphere Coupling Experiments1 (GLACE1)-type experiments and attributed to changes in CAM rather than modifications in CLM. Contrary to suggestions in previous studies, CAM-CLM models do not seem to overestimate the land-atmosphere coupling strength. [ABSTRACT FROM AUTHOR]

This study investigated the potential impact of soil moisture perturbations on the statistical spread of an ensemble forecast for three different synoptic events during the summer of 2006. Soil moisture was perturbed from a control simulation to generate a 12 member ensemble with six drier and six moister soils. The impacts on the near-surface atmospheric conditions and on precipitation were analysed. It was found, as previous studies have confirmed, that soil moisture can change the spatial and temporal distribution of precipitation and of the overlying circulation. It was found that regardless of the conditions in synoptic forcing, temperature, relative humidity and horizontal wind field exhibited a spatial correlation coefficient (R) close to one with respect to the control simulation. Vertical velocity, however, showed a marked decrease in R down to 0.4 as the precipitation activity increased. For vertical velocity, however, this quantity grew to near 1.0 consistent with R near zero and standard deviations very close to that of the control. These results suggested a more complex picture in which soil moisture perturbations played a major role in modifying precipitation and the near-surface circulation but did not broaden the statistical spread of trajectories in phase space of all variables. [ABSTRACT FROM AUTHOR]

Abstract: We use a general circulation model (NASA Goddard Institute for Space Studies GCM 3) to investigate the regional climate response to removal of aerosols over the United States. We perform a pair of transient 2010–2050 climate simulations following a scenario of increasing greenhouse gas concentrations, with and without aerosols over the United States and with present-day aerosols elsewhere. We find that removing U.S. aerosol significantly enhances the warming from greenhouse gases in a spatial pattern that strongly correlates with that of the aerosol. Warming is nearly negligible outside the United States, but annual mean surface temperatures increase by 0.4–0.6 K in the eastern United States. Temperatures during summer heat waves in the Northeast rise by as much as 1–2 K due to aerosol removal, driven in part by positive feedbacks involving soil moisture and low cloud cover. Reducing U.S. aerosol sources to achieve air quality objectives could thus have significant unintended regional warming consequences. [ABSTRACT FROM AUTHOR]

Understanding the occurrence and variability of drought events in historic and projected future climate is essential to managing natural resources and setting policy. The Midwest region is a key contributor in corn and soybean production, and the occurrence of droughts may affect both quantity and quality of these crops. Soil moisture observations play an essential role in understanding the severity and persistence of drought. Considering the scarcity of the long-term soil moisture datasets, soil moisture observations in Illinois have been one of the best datasets for studies of soil moisture. In the present study, the authors use the existing observational dataset and then reconstruct long-term historic time series (1916–2007) of soil moisture data using a land surface model to study the effects of historic climate variability and projected future climate change on regional-scale (Illinois and Indiana) drought. The objectives of this study are to (i) estimate changes and trends associated with climate variables in historic climate variability (1916–2007) and in projected future climate change (2009–99) and (ii) identify regional-scale droughts and associated severity, areal extent, and temporal extent under historic and projected future climate using reconstructed soil moisture data and gridded climatology for the period 1916–2007 using the Variable Infiltration Capacity (VIC) model. The authors reconstructed the soil moisture for a long-term (1916–2007) historic time series using the VIC model, which was calibrated for monthly streamflow and soil moisture at eight U.S. Geological Survey (USGS) gauge stations and Illinois Climate Network’s (ICN) soil moisture stations, respectively, and then it was evaluated for soil moisture, persistence of soil moisture, and soil temperature and heat fluxes. After calibration and evaluation, the VIC model was implemented for historic (1916–2007) and projected future climate (2009–99) periods across the study domain. The nonparametric Mann–Kendall test was used to estimate trends using the gridded climatology of precipitation and air temperature variables. Trends were also estimated for annual anomalies of soil moisture variables, snow water equivalent, and total runoff using a long-term time series of the historic period. Results indicate that precipitation, minimum air temperature, total column soil moisture, and runoff have experienced upward trends, whereas maximum air temperature, frozen soil moisture, and snow water equivalent experienced downward trends. Furthermore, the decreasing trends were significant for the frozen soil moisture in the study domain. The results demonstrate that retrospective drought periods and their severity were reconstructed using model-simulated data. Results also indicate that the study region is experiencing reduced extreme and exceptional droughts with lesser areal extent in recent decades. [ABSTRACT FROM AUTHOR]

While most projections of climate change and its regional impacts focus on overall changes in the state of the climate system, useful information can also be found in the evolution of the climate system from one state to another. Here the authors introduce one method for identifying regions in which significant and systematic long-term nonlinear evolutions may be present, even given quasi-linear anthropogenic forcing. Using climate change projections taken from simulations of NCAR’s Community Climate System Model, version 3 (CCSM3), the authors then employ the technique to isolate systematic nonlinear behavior of soil moisture variations over the United States. While the projections presented here only represent the results from one model system, it is argued that such nonlinear behavior is an important characteristic of future climate change that should be considered when discussing both short-term and long-term impacts of anthropogenic climate forcing. [ABSTRACT FROM AUTHOR]

Climate warming is expected to differentially affect CO2 exchange of the diverse ecosystems in the Arctic. Quantifying responses of CO2 exchange to warming in these ecosystems will require coordinated experimentation using standard temperature manipulations and measurements. Here, we used the International Tundra Experiment (ITEX) standard warming treatment to determine CO2 flux responses to growing-season warming for ecosystems spanning natural temperature and moisture ranges across the Arctic biome. We used the four North American Arctic ITEX sites (Toolik Lake, Atqasuk, and Barrow [USA] and Alexandra Fiord [Canada]) that span 10° of latitude. At each site, we investigated the CO2 responses to warming in both dry and wet or moist ecosystems. Net ecosystem CO2 exchange (NEE), ecosystem respiration (ER), and gross ecosystem photosynthesis (GEP) were assessed using chamber techniques conducted over 24-h periods sampled regularly throughout the summers of two years at all sites. At Toolik Lake, warming increased net CO2 losses in both moist and dry ecosystems. In contrast, at Atqasuk and Barrow, warming increased net CO2 uptake in wet ecosystems but increased losses from dry ecosystems. At Alexandra Fiord, warming improved net carbon uptake in the moist ecosystem in both years, but in the wet and dry ecosystems uptake increased in one year and decreased the other. Warming generally increased ER, with the largest increases in dry ecosystems. In wet ecosystems, high soil moisture limited increases in respiration relative to increases in photosynthesis. Warming generally increased GEP, with the notable exception of the Toolik Lake moist ecosystem, where warming unexpectedly decreased GEP > 25%. Overall, the respiration response determined the effect of warming on ecosystem CO2 balance. Our results provide the first multiple-site comparison of arctic tundra CO2 flux responses to standard warming treatments across a large climate gradient. These results indicate that (1) dry tundra may be initially the most responsive ecosystems to climate warming by virtue of strong increases in ER, (2) moist and wet tundra responses are dampened by higher water tables and soil water contents, and (3) both GEP and ER are responsive to climate warming, but the magnitudes and directions are ecosystem-dependent. [ABSTRACT FROM AUTHOR]

A statistical model based on canonical correlation analysis (CCA) was used to explore climatic associations and predictability of June–August (JJA) maximum and minimum surface air temperatures (Tmax and Tmin) as well as the frequency of Tmax daily extremes (Tmax90) in the central and western United States (west of 90°W). Explanatory variables are monthly and seasonal Pacific Ocean SST (PSST) and the Climate Division Palmer Drought Severity Index (PDSI) during 1950–2001. Although there is a positive correlation between Tmax and Tmin, the two variables exhibit somewhat different patterns and dynamics. Both exhibit their lowest levels of variability in summer, but that of Tmax is greater than Tmin. The predictability of Tmax is mainly associated with local effects related to previous soil moisture conditions at short range (one month to one season), with PSST providing a secondary influence. Predictability of Tmin is more strongly influenced by large-scale (PSST) patterns, with PDSI acting as a short-range predictive influence. For both predictand variables (Tmax and Tmin), the PDSI influence falls off markedly at time leads beyond a few months, but a PSST influence remains for at least two seasons. The maximum predictive skill for JJA Tmin, Tmax, and Tmax90 is from May PSST and PDSI. Importantly, skills evaluated for various seasons and time leads undergo a seasonal cycle that has maximum levels in summer. At the seasonal time frame, summer Tmax prediction skills are greatest in the Midwest, northern and central California, Arizona, and Utah. Similar results were found for Tmax90. In contrast, Tmin skill is spread over most of the western region, except for clusters of low skill in the northern Midwest and southern Montana, Idaho, and northern Arizona. [ABSTRACT FROM AUTHOR]

Observed vegetation feedbacks on temperature and precipitation are assessed across the United States using satellite-based fraction of photosynthetically active radiation (FPAR) and monthly climate data for the period of 1982–2000. This study represents the first attempt to spatially quantify the observed local impact of vegetation on temperature and precipitation over the United States for all months and by season. Lead–lag correlations and feedback parameters are computed to determine the regions where vegetation substantially impacts the atmosphere and to quantify this forcing. Temperature imposes a significant instantaneous forcing on FPAR, while precipitation's impact on FPAR is greatest at one-month lead, particularly across the prairie. An increase in vegetation raises the surface air temperature by absorbing additional radiation and, in some cases, masking the high albedo of snow cover. Vegetation generally exhibits a positive forcing on temperature, strongest in spring and particularly across the northern states. The local impact of FPAR on precipitation appears to be spatially inhomogeneous and relatively weak, potentially due to the atmospheric transport of transpired water. The computed feedback parameters can be used to evaluate vegetation–climate interactions simulated by models with dynamic vegetation. [ABSTRACT FROM AUTHOR]

Provides information on the 1999-2000 U.S. National Assessment on climate change and agriculture. Explanation on soil moisture; Use of a site-specific crop models; Result of the increased frequency of El Niño events. INSET: Agriculture-Climate-Environment Interactions.

Although mountainous landscapes dominate large areas of the Earth, our understanding of how elevation and aspect influence soil respiration in complex mountainous terrain is very limited. Therefore, we measured soil respiration throughout the growing season in 1999 and 2000 at 11 forested sites in Olympic National Park, Washington, USA along elevation-climatic gradients. The study sites ranged from temperate rain forest to alpine forests near tree line. Soil temperature was a significant predictor of soil respiration at all sites, and soil moisture explained additional variability at three sites (R2 from 0.42 to 0.90, P ≤ 0.01). Soil temperatures at the highest-elevation sites were 4.5°C cooler than those at the lowest elevation, but there were no relationships between soil respiration rates at a given temperature and elevation or mean annual temperature that would indicate acclimation of soil respiration to the cooler temperatures at high-elevation sites. Experimental urea additions (1.0 and 2.0 g N m-2 y-1) made at seven of the sites had no consistent effect on soil respiration. Total soil carbon dioxide (CO2) efflux during the growing season (May–September) varied from 0.34 to 0.75 kg C/m2 and was greater at low-elevation site s wit h warmer soil temperatures and longer growing seasons. Elevation and the length of the frost-free season could both be used to predict growing season (r2 = 0.53) and annual (r2 = 0.81) soil CO2 efflux for the 10 sites located in sleep mountainous terrain. Significant correlations also existed with mean annual temperature. These results suggest that warmer soils and a longer snow-free season associated with climatic warming could cause the mountainous ecosystems of the Olympic peninsula to evolve increasing amounts of CO2 from all elevations and aspects. [ABSTRACT FROM AUTHOR]

Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results. [ABSTRACT FROM AUTHOR]

Changing climate across the United States has been observed in the increasing intensity and amount of precipitation. One of the predicted areas for this impact is in the upper Midwest or the Corn Belt, and one concern is that current soil management practices in this region may not adequately protect the soil under these changes resulting in water quality impacts. To address this concern, this study was conducted to survey the current literature on the water quality impacts from current soil management practices and evaluate potential impacts on runoff and drainage from soil management practices under a number of precipitation scenarios. Soil management practices, e.g., crop residue, no-till, incorporation of manure, provide protection under today's climate. However, increasing precipitation amounts, or frequencies, rapidly decrease the effectiveness of these practices with the deleterious effect being even greater on soils with low water holding capacity and limited depth. The water quality impacts may be even more dramatic with the likelihood of increased surface runoff events. Soil management practices need to be developed and evaluated under precipitation patterns that may represent future scenarios so that producers can begin to adopt these practices into their management programs. [ABSTRACT FROM AUTHOR]