Search

Monthly temperature and precipitation data for 923 United States Geological Survey 8‐digit hydrologic units are used as inputs to a monthly water balance model to compute monthly actual evapotranspiration, soil moisture storage, and runoff across the western United States (U.S.) for the period 1900 through 2020. Time series of these water balance variables are examined to characterize and explain the dry conditions across the western U.S. since the year 2000. Results indicate that although precipitation deficits account for most of the changes in actual evapotranspiration and runoff, increases in temperature primarily explain decreases in soil moisture storage. Specifically, temperature has been particularly impactful on the magnitude of negative departures of soil moisture storage during the spring (April through June) and summer (July through September) seasons. These effects on soil moisture may be particularly detrimental to agriculture in regions already stressed by drought such as the western U.S. [ABSTRACT FROM AUTHOR]

Drought is a recurring natural hazard that has substantial human and environmental impacts. Given continued global warming and associated climate change, there is concern that droughts could become more severe and longer lasting. To better monitor and understand drought development and persistence, it is helpful to understand the development and climatic drivers of past droughts. In this study we use monthly runoff percentiles to identify five major drought events in the conterminous United States (CONUS) from 1901 through 2020. For each drought event we examined spatial patterns of departures of mean monthly precipitation, temperature, soil moisture storage, and runoff for 2,107 hydrologic units (HUs) across the CONUS. Results indicated that precipitation deficits have been the primary driver of past major‐drought events and temperature a secondary driver, even of the most recent drought event (September 1999 through September 2015) when positive temperature anomalies occurred over most of the CONUS. Additionally, negative soil moisture storage departures were more negative than runoff departures during the five drought events we examined, which emphasizes the importance of measuring both runoff and soil moisture to monitor drought conditions. We also examined the use of statistical persistence to develop short‐term (i.e., 1 month) forecasts of runoff drought conditions in the CONUS by developing autoregressive integrated moving average (ARIMA) models for each HU. Results indicated that persistence can be used to predict short‐term changes in the spatial pattern of drought and the areal extent of drought, but that predictions of runoff magnitude for any particular site are often poor. [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]

Plant community response to climate change will be influenced by individual plant responses that emerge from competition for limiting resources that fluctuate through time and vary across space. Projecting these responses requires an approach that integrates environmental conditions and species interactions that result from future climatic variability. Dryland plant communities are being substantially affected by climate change because their structure and function are closely tied to precipitation and temperature, yet impacts vary substantially due to environmental heterogeneity, especially in topographically complex regions. Here, we quantified the effects of climate change on big sagebrush (Artemisia tridentata Nutt.) plant communities that span 76 million ha in the western United States. We used an individual‐based plant simulation model that represents intra‐ and inter‐specific competition for water availability, which is represented by a process‐based soil water balance model. For dominant plant functional types, we quantified changes in biomass and characterized agreement among 52 future climate scenarios. We then used a multivariate matching algorithm to generate fine‐scale interpolated surfaces of functional type biomass for our study area. Results suggest geographically divergent responses of big sagebrush to climate change (changes in biomass of −20% to +27%), declines in perennial C3 grass and perennial forb biomass in most sites, and widespread, consistent, and sometimes large increases in perennial C4 grasses. The largest declines in big sagebrush, perennial C3 grass and perennial forb biomass were simulated in warm, dry sites. In contrast, we simulated no change or increases in functional type biomass in cold, moist sites. There was high agreement among climate scenarios on climate change impacts to functional type biomass, except for big sagebrush. Collectively, these results suggest divergent responses to warming in moisture‐limited versus temperature‐limited sites and potential shifts in the relative importance of some of the dominant functional types that result from competition for limiting resources. [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]

Understanding of the spatial and temporal dynamics of extreme precipitation not only improves prediction skills, but also helps to prioritize hazard mitigation efforts. This study seeks to enhance the understanding of spatiotemporal covariation patterns embedded in precipitation (P) and soil moisture (SM) by using an event-based, complex-network-theoretic approach. Events concurrences are quantified using a nonparametric event synchronization measure, and spatial patterns of hydroclimate variables are analyzed by using several network measures and a community detection algorithm. SM–P coupling is examined using a directional event coincidence analysis measure that takes the order of event occurrences into account. The complex network approach is demonstrated for Texas, US, a region possessing a rich set of hydroclimate features and is frequented by catastrophic flooding. Gridded daily observed P data and simulated SM data are used to create complex networks of P and SM extremes. The uncovered high degree centrality regions and community structures are qualitatively in agreement with the overall existing knowledge of hydroclimate extremes in the study region. Our analyses provide new visual insights on the propagation, connectivity, and synchronicity of P extremes, as well as the SM–P coupling, in this flood-prone region, and can be readily used as a basis for event-driven predictive analytics for other regions. [ABSTRACT FROM AUTHOR]

• We develop an attribution framework for annual maximum discharge across the US. • We model the year-to-year changes in peak discharge using precipitation and temperature. • Our models capture well the inter-annual variability in flood extremes. • Changes in the climate play the major role in driving the changes in flood extremes. • We do not find a dependence of the results on whether the basin is pristine or not. Floods affect many aspects of our lives, and our improved understanding of the processes driving the historical changes in this natural hazard can provide basic information to enhance our preparation and mitigation efforts. Here we analyze 3,885 streamgages across the conterminous United States and attribute the inter-annual variability in annual maximum discharge to precipitation and temperature. This is accomplished by first developing gamma regression models to describe the seasonal maximum discharge in terms of basin-averaged precipitation, temperature, and antecedent wetness (i.e., the basin-averaged precipitation for the season prior to the one of interest, and used as a proxy for antecedent soil moisture conditions). These seasonal models are then mixed through a Monte Carlo approach to obtain the annual maximum discharge distribution. Despite its simplicity, our results show that the developed statistical attribution approach can describe very well the inter-annual variability in annual maximum discharge across the conterminous United States. [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 variability and change can impact rainfall by varying time, location, magnitude, and frequency of precipitation events. Fluctuations in heavy rainfall events can impact flooding and drought events and water management systems. This research addresses temporal and spatial distributions of extreme daily and monthly rainfall in Kansas using daily rainfall data from 23 stations for the period 1890-2013. The Mann-Kendall non-parametric method was used in trend analysis. Results indicate an increasing trend in the annual daily maximum rainfall, and an increase in the annual number of rainfall events above the 90th, 95th, and 99th percentile thresholds at a majority of stations since 1890. The most recent 30-year climate normal period (1981-2010) was selected to assess contemporary change compared to the entire period (1890-2013). Most stations have a steeper positive slope for all extreme rainfall parameters for 1981-2010. Generally, western Kansas receives smaller and fewer extreme storms than eastern Kansas with respect to both magnitude and frequency. Since 1890, June has been the month that receives the greatest amount of rain in each year at a majority of stations (18 of 23). Stations in eastern Kansas tend to experience earlier maximum rainfall on a monthly basis than central and western stations. Earlier annual maximum monthly rains can affect soil moisture and runoff generation. Timely maintenance and improvement in water, soil, and flood management systems are necessary in order to increase preparedness of the society to flash floods and protect them from water management systems failures. [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]

Abstract: Douglas-fir growth in the Pacific Northwest is thought to be water limited. However, discerning the relative influence of air temperature and plant available soil water (W) on growth is difficult because they interact with each other, with other climate factors and with the inherent seasonal timing of cambial activity. Douglas-fir growth response to air temperature and W patterns during the growing season was examined using time series regression analysis of dendrometer data collected at approximately four-week intervals from 1998 through 2009. Five study sites were located in mature forest stands along an elevation gradient from the Pacific coast to the west slope of the Cascade Mountains (∼1200m) in Oregon, USA. Maximum daily air temperature (T) and W were similar in relative importance to tree growth at four of the five sites. W was substantially more important at one site. Growth rate increased with T to an optimum (T opt) and decreased with higher T. At the two drier sites T and W affected growth interactively in that T opt decreased with decreasing W. We conclude that both T and W affect growth and that T consistently limits growth at three of the five sites and at all sites in years with above average summer temperature. Should climate change result in hotter summers in the region as predicted by climate models, we suggest that Douglas-fir will experience progressive temperature limitation. [Copyright &y& Elsevier]

Ensemble regional model simulations over the central US with 30-km resolution are analyzed to investigate the physical processes of projected precipitation changes in the mid-twenty-first century under greenhouse gas forcing. An atmospheric moisture balance is constructed, and changes in the diurnal cycle are evaluated. Wetter conditions over the central US in April and May occur most strongly in the afternoon and evening, supported primarily by moisture convergence by transient eddy activity, indicating enhanced daytime convection. In June, increased rainfall over the northern Great Plains is strongest from 0000 to 0600 LT. It is supported by positive changes in stationary meridional moisture convergence related to a strengthening of the GPLLJ accompanied by an intensification of the western extension of the North Atlantic subtropical high. In the Midwest, decreased rainfall is strongest at 1500 LT and 0000 LT. Both a suppression of daytime convection as well as changes in the zonal flow in the GPLLJ exit region are important. Future drying over the northern Great Plains in summer is triggered by weakened daytime convection, and persists throughout August and September when a deficit in soil moisture develops and land-atmosphere feedbacks become increasingly important. [ABSTRACT FROM AUTHOR]

Abstract: Knowledge about the effects of changing environmental factors on stability of spatial structure of soil moisture is important in measurement design and spatial and land-atmosphere modeling, but was rarely considered. We examined the effects of soil temperature and moisture on spatial structure of daily soil moisture at 10-cm and 20-cm depth covering forest and grass sampling points (31 and 11, respectively) over 10months with a maximum between-point distance of 130m. For average seasonal conditions, the peak variance (sill) was 4.8–7.5%2, spatial correlation scale was 0–56m, and temporal scale was 5–90days depending upon season, vegetation type and depth. Changes in soil temperature and moisture moderately affected parameters of the spatial structure, the variance (heterogeneity) and correlation length (continuity), and the effects were higher at 10-cm depth compared to 20-cm depth. Increase in soil temperature by 1°C increased the correlation length by about 0.6m only at the upper depth. Heterogeneity of soil moisture was affected by interaction of variability of soil temperature and moisture so that the peak variance changed by as much as 5%2 and 0%2 under changing soil temperature and by 6%2 and 3%2 under changing soil moisture over almost a year at 10-cm and 20-cm depths, respectively. The spatial structure was relatively stable under changing soil temperature and moisture as the correlation length would not change considerably under small (e.g., inter-annual) in contrast to large (inter-seasonal) variations in soil temperature and intra-annual changes in variance were also not high. [Copyright &y& Elsevier]

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]

We have applied a full hierarchical Baysian (HB) model to simulate streamflow at the Coweeta Basin that drains western North Carolina, USA under a doubled CO2 climate scenario. The full HB model coherently assimilated multiple data sources and accounted for uncertainties from data, parameters and model structures. Full predictive distributions for streamflow from the Bayesian analysis indicate not only increasing drought, with substantial decrease in fall and summer flows, and soil moisture content, but also increase in the frequency of flood events when they were fit with Generalized Extreme Value (GEV) distribution and Generalized Pareto Distribution (GPD) under this doubled CO2 climate scenario compared to the current climate scenario. Full predictive distributions based on the hierarchical Bayesian model, compared to deterministic point estimates, is capable of providing richer information to facilitate development of adaptation strategy to changing climate for a sustainable water resource management. [ABSTRACT FROM AUTHOR]

The article discusses a study entitled "Potential consequences of climate variability and change for the Midwestern United States. In Climate Change Impact on the United States: the Potential Consequences of Climate Variability and Change," by D.R. Easterling and T.R. Karl.

An ensemble of six 22-year numerical experiments was conducted to quantify the response of soil moisture to multiple climate change scenarios over the American Midwest. Regional Climate Model version 3 (RegCM3) was run using two surface physics schemes: Integrated Biosphere Simulator (IBIS) and Biosphere-Atmosphere Transfer Scheme 1e (BATS1e); and two convective closure assumptions: Fritsch and Chappell and Arakawa and Schubert. Experiments were forced with a surrogate climate change scenario constructed using the National Centers for Environmental Prediction-Department of Energy Reanalysis 2 dataset and the ECHAM5 A1B climate change scenario. RegCM3-IBIS and RegCM3-BATS1e simulate increased two-meter air temperature and downward longwave radiation throughout the year under both climate change scenarios. While differences in shortwave radiation are relatively small; some model configurations and climate change scenarios produce additional precipitation, evapotranspiration, and total runoff during the spring and summer. Soil moisture is unchanged or increased throughout the growing season as enhanced rainfall offsets greater evaporative demand. Negligible drying in root zone soil moisture is found in all climate change experiments conducted, regardless of surface physics scheme, boundary conditions, or convective closure assumption. [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]

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]

Biological soil crusts (biocrusts) are an integral part of the soil system in arid regions worldwide, stabilizing soil surfaces, aiding vascular plant establishment, and are significant sources of ecosystem nitrogen and carbon. Hydration and temperature primarily control ecosystem CO2 flux in these systems. Using constructed mesocosms for incubations under controlled laboratory conditions, we examined the effect of temperature (5–35 °C) and water content (WC, 20–100%) on CO2 exchange in light (cyanobacterially dominated) and dark (cyanobacteria/lichen and moss dominated) biocrusts of the cool Colorado Plateau Desert in Utah and the hot Chihuahuan Desert in New Mexico. In light crusts from both Utah and New Mexico, net photosynthesis was highest at temperatures >30 °C. Net photosynthesis in light crusts from Utah was relatively insensitive to changes in soil moisture. In contrast, light crusts from New Mexico tended to exhibit higher rates of net photosynthesis at higher soil moisture. Dark crusts originating from both sites exhibited the greatest net photosynthesis at intermediate soil water content (40–60%). Declines in net photosynthesis were observed in dark crusts with crusts from Utah showing declines at temperatures >25 °C and those originating from New Mexico showing declines at temperatures >35 °C. Maximum net photosynthesis in all crust types from all locations were strongly influenced by offsets in the optimal temperature and water content for gross photosynthesis compared with dark respiration. Gross photosynthesis tended to be maximized at some intermediate value of temperature and water content and dark respiration tended to increase linearly. The results of this study suggest biocrusts are capable of CO2 exchange under a wide range of conditions. However, significant changes in the magnitude of this exchange should be expected for the temperature and precipitation changes suggested by current climate models. [ABSTRACT FROM AUTHOR]

Abstract: The amount of soil water at the beginning of the growing season has a large impact on crop yields in rainfed agriculture, especially in semi-arid regions and in years with below-average rainfall in more humid climates. Robust algorithms are needed to estimate soil water storage before planting to aid crop management decisions. The main objectives of this paper are to investigate soil water recharge during the non-growing season (October 20 to May 1) in a semi-arid, temperate ecosystem in south-central Nebraska (USA) and to evaluate empirical models to estimate soil water content at the beginning of the summer-crop growing season. A database of soil water content measurements collected over 5 years at nine locations in south-central Nebraska was used to estimate available water-holding limits in the soil profile and to determine the change in available soil water during the non-growing season. Regression analysis was performed to analyze the relationship among soil water recharge, residual soil water (i.e., soil water content at the end of the previous growing season), total precipitation, and available water-holding capacity (AWHC) in the root zone to 1.5m. Precipitation storage efficiency (PSE) was calculated as the quotient of soil water recharge and total non-growing season precipitation. Predictive models to estimate soil water content at the beginning of summer-crop growing season were derived from these analyses. A large portion of the variation in soil water recharge was explained by residual soil water and precipitation. PSE averaged 28% across site-years; low PSE values were associated with high residual soil water and/or low AWHC. Two predictive models (linear and linear-plateau) that used residual soil water, total precipitation, and AWHC as independent variables explained 75–80% of the variation in the measured soil water content at the beginning of the summer-crop growing season. These empirical models represent a new tool to estimate soil water content by planting date of summer crops. Site-management conditions such as residue amount and its architecture, tillage system, soil texture, and terrain slope are not currently accounted for in these models and would likely improve predictive capacity. [Copyright &y& Elsevier]

Potential changes in summertime hydroclimatology over the northeastern (NE) region of the USA induced by increases in greenhouse gas (GHG) concentrations are investigated using a state-of-the-art regional climate modeling system. Results for a higher emissions scenario illustrate changes that may occur if dependence on fossil fuels continues over the coming century. Summertime precipitation is projected to decrease across much of the central NE, but increase over the southernmost and northernmost portions of the domain. Evaporation is expected to increase across the entire domain. The balance between these two results in a decrease in soil moisture content across most of the domain (by approximately 10 mm) and an increase in the summertime soil-moisture depletion rate (by approximately 10 mm/month). At the same time, an increase in both atmospheric near-surface specific and saturation specific humidity is projected, resulting in an increase in relative humidity across the southern portion of the domain, with slight decreases over the northern portion. Combined with an average increase in summer temperatures of 3.5°C, the projected increase in relative humidity results in a marked increase in the average daily maximum heat index for the region on the order of 3.9°C, as well as a 350–400% increase in the number of days with heat index values exceeding 32.2°C (90°F)—the level of “extreme caution”. Taken together, these high-resolution, dynamically-generated projections confirm the potential for significant summertime climate change impacts on the NE over the coming century as suggested by previous studies. [ABSTRACT FROM AUTHOR]

Abstract: The Great Plains of the central United States contain numerous presently stabilized dune fields that were active during the late Holocene. The most recent period of reactivation occurred about 700–1000 years ago during the Medieval Climatic Anomaly, a period of frequent, severe drought recognized throughout the North American West. Dune orientations and internal bedding preserve information about paleowinds from this time period. We compared paleowind information with modern winds and potential sand transport analyzed from meteorological data. While both reconstructed paleowinds and modern winds include a strong northwesterly component over the northern to central portions of the Great Plains, a significant difference exists to the south. During the late Holocene, southwesterly winds had a strong influence on dune forms over the central and southern Great Plains, while modern potential sand transport is dominated by southerly to southeasterly winds that bring moisture to the region from the Gulf of Mexico. Hypothetical dune types forming under modern winds would be different and have different crestal orientations than those preserved from the late Holocene. In order for such a shift in southerly winds to take place it is likely that: 1) the typical position of the Bermuda High in summer was shifted either to the east or to the south resulting in weaker low-level winds from the south and southeast and greater extent of southwesterly winds on the Great Plains, and/or 2) drought, likely enhanced by evapotranspiration and soil moisture feedbacks, brought the normally higher-level southwesterly flow down to the land surface. Given our understanding of megadroughts over the Great Plains as well as recent climate predictions for increased warmth and drought over this area, future reactivation of the dunes seems likely. [Copyright &y& Elsevier]

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]

Climate models predict, and empirical evidence confirms, that more extreme precipitation regimes are occurring in tandem with warmer atmospheric temperatures. These more extreme rainfall patterns are characterized by increased event size separated by longer within season drought periods and represent novel climatic conditions whose consequences for different ecosystem types are largely unknown. Here, we present results from an experiment in which more extreme rainfall patterns were imposed in three native grassland sites in the Central Plains Region of North America, USA. Along this 600 km precipitation–productivity gradient, there was strong sensitivity of temperate grasslands to more extreme growing season rainfall regimes, with responses of aboveground net primary productivity (ANPP) contingent on mean soil water levels for different grassland types. At the mesic end of the gradient (tallgrass prairie), longer dry intervals between events led to extended periods of below-average soil water content, increased plant water stress and reduced ANPP by 18%. The opposite response occurred at the dry end (semiarid steppe), where a shift to fewer, but larger, events increased periods of above-average soil water content, reduced seasonal plant water stress and resulted in a 30% increase in ANPP. At an intermediate mixed grass prairie site with high plant species richness, ANPP was most sensitive to more extreme rainfall regimes (70% increase). These results highlight the inherent complexity in predicting how terrestrial ecosystems will respond to forecast novel climate conditions as well as the difficulties in extending inferences from single site experiments across biomes. Even with no change in annual precipitation amount, ANPP responses in a relatively uniform physiographic region differed in both magnitude and direction in response to within season changes in rainfall event size/frequency. [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]

1. Previous studies have shown that plant stress and plant vigour impact the preference and performance of many insect species. Global climate-change scenarios suggest that some regions such as continental interiors may become increasingly subject to severe drought. In combination, these two issues suggest that drought-driven plant stress may impact insect communities on a landscape scale. While there have been many population studies relating plant stress to the life history of individual herbivore species, far less is known about how plant stress affects entire communities. 2. To study the effect of plant stress on arthropod communities, arthropods were sampled from the canopies of pinyon pines ( Pinus edulis) growing at sites with a history of chronically high environmental stress (e.g. lower water potentials, soil moisture, and reduced growth rates), and those growing under more favourable conditions. Sampling in these environments yielded >59 000 arthropods, representing 287 species from 14 orders and 80 families, and revealed three major community patterns. 3. First, chronic stress significantly altered community composition. Second, trees growing under high stress supported about 1/10th the number of arthropods, and roughly half the species as trees growing under more favourable conditions. Third, of the 33 abundant herbivore species that exhibited a significantly skewed distribution towards either high- or low-stress trees, 73% were skewed with higher numbers on low-stress trees. 4. The pattern of potentially reduced arthropod diversity and abundance on stressed pines observed in this study may further compound the loss of species resulting from the recent, landscape-scale drought-induced mortality of pines in the southwestern USA. [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]

Theδ13 Cvalues of atmospheric carbon dioxide (CO2) can be used to partition global patterns of CO2 source/sink relationships among terrestrial and oceanic ecosystems using the inversion technique. This approach is very sensitive to estimates of photosynthetic13 Cdiscrimination by terrestrial vegetation (ΔA), and depends onδ13 Cvalues of respired CO2 fluxes (δ13 CR). Here we show that by combining two independent data streams– the stable isotope ratios of atmospheric CO2 and eddy-covariance CO2 flux measurements– canopy scale estimates ofΔA can be successfully derived in terrestrial ecosystems. We also present the first weekly dataset of seasonal variations inδ13 CR from dominant forest ecosystems in the United States between 2001 and 2003. Our observations indicate considerable summer-time variation in the weekly value ofδ13 CR within coniferous forests (4.0‰ and 5.4‰ at Wind River Canopy Crane Research Facility and Howland Forest, respectively, between May and September). The monthly mean values ofδ13 CR showed a smaller range (2–3‰), which appeared to significantly correlate with soil water availability. Values ofδ13 CR were less variable during the growing season at the deciduous forest (Harvard Forest). We suggest that the negative correlation betweenδ13 CR and soil moisture content observed in the two coniferous forests should represent a general ecosystem response to the changes in the distribution of water resources because of climate change. Shifts inδ13 CR andΔA could be of sufficient magnitude globally to impact partitioning calculations of CO2 sinks between oceanic and terrestrial compartments. [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]

To analyze the differences in the status and processes of nitrogen saturation in Japan and northeastern United States, we examined the hydrobiogeochemistry of nitrogen of forested watersheds in these regions. Two distinct differences were found between watersheds in Japan compared with those in US. 1) In Japanese watersheds, marked decreases of NO3- concentration in surface waters during the summer growing season were not found and NO3- concentrations sometimes increased especially in the summer at nitrogen saturated sites. This contrast with watersheds in US where decreases in NO3- concentration during the summer are commonly observed except those watersheds in advanced stages of nitrogen saturation. These differences in NO3- concentration relationships can be attributed to climatic differences, with Japan having high precipitation and high discharge during the summer, while in many regions of North America lowest discharges are found in the summer. The climatic regime in Japan leads to high rates of mineralization and the rapid transport of NO3- to streams in summer. 2) Japanese watersheds, even those with high NO3- concentrations in surface waters, show little evidence of acidification. This is in contrast to sites in US where increased NO3- concentrations, especially during episodic events, result in surface water acidification. [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]