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Aridland breeding bird communities of the United States are among the most vulnerable to drought, with many species showing significant population declines associated with decreasing precipitation and increasing temperature. Individual breeding bird species have varied responses to drought which suggests complex responses to changes in water availability. Here, we evaluated the influence of water deficit, an integrative metric of drought stress, on breeding bird communities within 3 distinct aridland habitat types: riparian, pinyon-juniper, and sagebrush shrubland. We used 12 years of breeding bird survey data from 11 national parks and monuments in the Northern Colorado Plateau Inventory and Monitoring Network (NCPN). We used a multivariate community-level approach to test for the effect of annual water deficit on the bird communities in the 3 habitats. We found that bird communities responded to water deficit in all 3 habitat types, and 70% of the 30 species–habitat combinations show significant relationships between density and variation in water deficit. Our analyses revealed that the direction and magnitude of species responses to water deficit were habitat-dependent. The habitat-specific responses we observed suggest that the adaptive capacity of some species depends on the habitat in which they occur, a pattern only elucidated with our habitat-based approach. The direction and magnitude of the relationships between predicted densities and annual water deficit can be used to predict the relative sensitivity of species within habitat climate changes. These results provide the first attempt to determine how the indirect effect of changes in water availability might affect aridland breeding birds in distinct habitat types. Linking breeding bird density to annual water deficit may be valuable for predicting changes in species persistence and distribution in response to climate change.

Researchers often use gridded datasets to develop statistical relationships between climate and natural resources at locations that are distant from weather stations. These gridded datasets may provide inaccurate estimates of temperature at sites with elevations that differ significantly from the elevation assigned to the corresponding grid cell. We assess the accuracy of three gridded climate datasets commonly used in ecological studies (800‐m resolution PRISM, 4‐km GRIDMET, and 1‐km Daymet) compared with a network of 153 temperature dataloggers arranged along elevation transects in Yellowstone National Park (Wyoming, Montana, Idaho). Measured lapse rates for monthly average daytime high temperatures were generally steeper (more cooling per unit of elevation increase) than lapse rates in gridded datasets and steeper than those in similar mountainous regions. Measured lapse rates for monthly average nighttime lows were similar to gridded data lapse rates. Our measured lapse rates are most useful for the adjustment of daytime highs from weather stations or gridded data during warmer months. Temperatures during cooler months sometimes are strongly affected by other factors. Ecologically relevant metrics calculated from temperatures adjusted with constant lapse rates can increase multiplicatively and by varying amounts from year to year. For example, high‐elevation estimates of climatic water deficit (CWD) calculated from gridded data that were not adjusted with our measured lapse rates were 2.5–4 times greater (depending on the year) than calculations from temperatures that were adjusted. Our results emphasize the importance of correcting grid‐based climate estimates for elevation in complex terrain when accurate, site‐specific data are required. When the elevation assigned to grid cells differs significantly from the elevation of points within the cell, the lapse rate obtained for data extracted from grids can be shallower than the true rate. We illustrate the implications of these findings with case studies in Yellowstone.

A robust method for characterizing the biophysical environment of terrestrial vegetation uses the relationship between Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD). These variables are usually estimated from a water balance model rather than measured directly and are often more representative of ecologically-significant changes than temperature or precipitation. We evaluate trends and spatial patterns in AET and CWD in the Continental United States (CONUS) during 1980–2019 using a gridded water balance model. The western US had linear regression slopes indicating increasing CWD and decreasing AET (drying), while the eastern US had generally opposite trends. When limits to plant performance characterized by AET and CWD are exceeded, vegetation assemblages change. Widespread increases in aridity throughout the west portends shifts in the distribution of plants limited by available moisture. A detailed look at Sequoia National Park illustrates the high degree of fine-scale spatial variability that exists across elevation and topographical gradients. Where such topographical and climatic diversity exists, appropriate use of our gridded data will require sub-setting to an appropriate area and analyzing according to categories of interest such as vegetation communities or across obvious physical gradients. Recent studies have successfully applied similar water balance models to fire risk and forest structure in both western and eastern U.S. forests, arid-land spring discharge, amphibian colonization and persistence in wetlands, whitebark pine mortality and establishment, and the distribution of arid-land grass species and landscape scale vegetation condition. Our gridded dataset is available free for public use. Our findings illustrate how a simple water balance model can identify important trends and patterns at site to regional scales. However, at finer scales, environmental heterogeneity is driving a range of responses that may not be simply characterized by a single trend.

Research Highlights: The efficacy of planting for restoration is important for ecosystem managers. Planting efforts represent an opportunity for conserving and managing species during a population crisis. Background and Objectives: Federal agencies have been planting whitebark pine (WBP), an important subalpine species that is late to mature and long-lived, for three decades in the Greater Yellowstone Ecosystem (GYE). These efforts have been met with varying success, and they have not been evaluated beyond the first five years post-planting. Ecosystem managers will continue to plant WBP in the GYE for years to come, and this research helps to inform and identify higher quality habitat during a period of changing climate and high GYE WBP mortality rates. Materials and Methods: We use a combination of field sampling and a water balance model to investigate local biophysical gradients as explanatory variables for WBP performance at twenty-nine GYE planting sites. Results: We found that the WBP growth rate was positively correlated with actual evapotranspiration (AET) and was greatest when cumulative growing season AET was above 350 mm. Growth rate was not strongly affected by competition at the levels found in this study. However, site density change over time was negatively affected by mean growing season temperature and when more than five competitors were present within 3.59 m radius. Conclusions: If they make it to maturity, trees that are planted this season will not begin to produce cones until the latter half of this century. We recommend planting efforts that optimize AET for growth rate objectives, minimize water deficit (WD) that cause stress and mortality, and removing competitors if they exceed five within a short distance of seedlings.

Research and management issues related to pine forests, snowpack and refugia are relevant to mountainous ecosystems globally. For this study, we investigated local snowpack longevity as an explanatory variable for whitebark pine performance (survival rate, growth rate and condition). We used Sentinel-2 imagery to monitor local snowpack longevity. This new imagery is spatially and temporally more appropriate than other publicly available satellite imagery, and early results indicate that Sentinel-2 imagery can be successfully used for this purpose. Sites were selected based on a multi-decade management effort by federal agencies to plant whitebark pine in the Greater Yellowstone Ecosystem. Relative to initial planting records, present-day field sampling affords an opportunity to evaluate whitebark pine performance over time. Featured photo from Figure 3 in report.

Despite the importance of hydrologic regimes to the phenology, demography, and abundance of fishes such as salmonids, there have been surprisingly few syntheses that holistically assess regional, species-specific trends in hydrologic regimes within a framework of climate change. Here, we consider hydrologic regimes within the Greater Yellowstone Area in the Rocky Mountains of western North America to evaluate changes in hydrologic metrics anticipated to affect salmonids, a group of fishes with high regional ecological and socioeconomic value. Our analyses assessed trends across different sites and time periods (1930–, 1950–, and 1970–2015) as means to evaluate spatial and temporal shifts. Consistent patterns emerged from our analyses indicating substantial shifts to (1) earlier peak discharge events; (2) reductions of summer minimum streamflows; (3) declines in the duration of river ice; and (4) decreases in total volume of water. We found accelerated trends in hydrologic change for the 1970–2015 period, with an average peak discharge 7.5 days earlier, 27.5% decline in summer minimum streamflows, and a 15.6% decline in the annual total volume of water (1 October–September 30) across sites. We did observe considerable variability in magnitude of change across sites, suggesting different levels of vulnerability to a changing climate. Our analyses provide an iterative means for assessing climate predictions and an important step in identifying the climate resilience of landscapes.

Whitebark pine, a foundation species at tree line in the Western U.S. and Canada, has declined due to native mountain pine beetle epidemics, wildfire, and white pine blister rust. These declines are concerning for the multitude of ecosystem and human benefits provided by this species. An understanding of the climatic correlates associated with spread is needed to successfully manage impacts from forest pathogens. Since 2000 mountain pine beetles have killed 75% of the mature cone-bearing trees in the Greater Yellowstone Ecosystem, and 40.9% of monitored trees have been infected with white pine blister rust. We identified models of white pine blister rust infection which indicated that an August and September interaction between relative humidity and temperature are better predictors of white pine blister rust infection in whitebark pine than location and site characteristics in the Greater Yellowstone Ecosystem. The climate conditions conducive to white pine blister rust occur throughout the ecosystem, but larger trees in relatively warm and humid conditions were more likely to be infected between 2000 and 2018. We mapped the infection probability over the past two decades to identify coarse-scale patterns of climate conditions associated with white pine blister rust infection in whitebark pine.

Questions Can we improve understanding of vegetation response to water availability on monthly time scales in semi-arid environments using remote sensing methods? What climatic or water balance variables and antecedent windows of time associated with these variables best relate to the condition of vegetation? Can we develop credible near-term forecasts from climate data that can be used to prepare for future climate change effects on vegetation? Location Semi-arid grasslands in Capitol Reef National Park, Utah, USA. Methods We built vegetation response models by relating the normalized difference vegetation index (NDVI) from MODIS imagery in Mar–Nov 2000–2013 to antecedent climate and water balance variables preceding the monthly NDVI observations. We compared how climate and water balance variables explained vegetation greenness and then used a multi-model ensemble of climate and water balance models to forecast monthly NDVI for three holdout years. Results Water balance variables explained vegetation greenness to a greater degree than climate variables for most growing season months. Seasonally important variables included measures of antecedent water input and storage in spring, switching to indicators of drought, input or use in summer, followed by antecedent moisture availability in autumn. In spite of similar climates, there was evidence the grazed grassland showed a response to drying conditions 1 mo sooner than the ungrazed grassland. Lead times were generally short early in the growing season and antecedent window durations increased from 3 mo early in the growing season to 1 yr or more as the growing season progressed. Forecast accuracy for three holdout years using a multi-model ensemble of climate and water balance variables outperformed forecasts made with a naive NDVI climatology. Conclusions We determined the influence of climate and water balance on vegetation at a fine temporal scale, which presents an opportunity to forecast vegetation response with short lead times. This understanding was obtained through high-frequency vegetation monitoring using remote sensing, which reduces the costs and time necessary for field measurements and can lead to more rapid detection of vegetation changes that could help managers take appropriate actions.

Managing climate impacts to natural resources in protected areas can be hampered by lack of monitoring data, poor understanding of natural resource responses to climate, or lack of timely condition assessments that can inform management actions. Here we demonstrate the utility of water balance as a tool for understanding natural resource responses to climate by developing case studies focused on stream flow, vegetation production, and wildfire ignition at Great Sand Dunes National Park and Preserve (GSDNP), U.S.A. The efficacy of water balance to predict these responses stems from the explicit integration of climate with site conditions that modify the effects of climate. This in turn results in estimates of water availability, water use, and water need that are proximal drivers of aquatic and terrestrial natural resource conditions. The water balance model successfully forecasted stream flow (r2 = 0.69, P

In Grand Teton and Yellowstone national parks wetlands offer critical habitat and play a key role in supporting biological diversity. The shallow depths and small size of many palustrine wetlands in these protected areas and elsewhere make them vulnerable to changes in climate compared with larger and deeper aquatic habitats. Here, we use a simple water balance model to generate estimates of biophysical drivers of wetland change. We then examine the relationship between wetland inundation status and four principal drivers (i.e., temperature, precipitation, evapotranspiration, and runoff) spanning varying meteorological conditions over an 8-year time series from Grand Teton and Yellowstone national parks. We found that models containing snowmelt runoff outperformed models with other meteorological drivers and determined that a higher percentage of surveyed wetlands were dry in years characterized by lower runoff. Our work further shows that wetland drying was widespread across both parks, but sub-regional variations were best described at the hydrologic subbasin-level. Documenting the varying responses of wetlands to meteorological drivers is a necessary first step to identifying which subbasins are most sensitive to recent climatic change and contemplating how future change may alter the distribution of wetlands and their dependent taxa.

Whitebark pine (Pinus albicaulis) forests in the western United States have been adversely affected by an exotic pathogen (Cronartium ribicola, causal agent of white pine blister rust), insect outbreaks (Dendroctonus ponderosae, mountain pine beetle), and drought. We monitored individual trees from 2004 to 2013 and characterized stand‐level biophysical conditions through a mountain pine beetle epidemic in the Greater Yellowstone Ecosystem. Specifically, we investigated associations between tree‐level variables (duration and location of white pine blister rust infection, presence of mountain pine beetle, tree size, and potential interactions) with observations of individual whitebark pine tree mortality. Climate summaries indicated that cumulative growing degree days in years 2006–2008 likely contributed to a regionwide outbreak of mountain pine beetle prior to the observed peak in whitebark mortality in 2009. We show that larger whitebark pine trees were preferentially attacked and killed by mountain pine beetle and resulted in a regionwide shift to smaller size class trees. In addition, we found evidence that smaller size class trees with white pine blister rust infection experienced higher mortality than larger trees. This latter finding suggests that in the coming decades white pine blister rust may become the most probable cause of whitebark pine mortality. Our findings offered no evidence of an interactive effect of mountain pine beetle and white pine blister rust infection on whitebark pine mortality in the Greater Yellowstone Ecosystem. Interestingly, the probability of mortality was lower for larger trees attacked by mountain pine beetle in stands with higher evapotranspiration. Because evapotranspiration varies with climate and topoedaphic conditions across the region, we discuss the potential to use this improved understanding of biophysical influences on mortality to identify microrefugia that might contribute to successful whitebark pine conservation efforts. Using tree‐level observations, the National Park Service‐led Greater Yellowstone Interagency Whitebark Pine Long‐term Monitoring Program provided important ecological insight on the size‐dependent effects of white pine blister rust, mountain pine beetle, and water availability on whitebark pine mortality. This ongoing monitoring campaign will continue to offer observations that advance conservation in the Greater Yellowstone Ecosystem.

Groundwater-fed springs are essential habitat for many dryland species. Climate projections forecast an increasingly arid climate for the southwestern United States. Therefore, an understanding of the relationships between climate and spring discharge is increasingly important. Monthly discharge measurements were recorded from 2001 to 2014 at three jointed bedrock springs in and near Arches National Park, Utah, United States. Discharge was compared with the potential evapotranspiration (PET) and precipitation derived from Daymet gridded climate data. Despite the similarities in location, aquifer type, and climate exposure, all three springs showed different responses to local climate. Two springs emerging from the western aquifer had decreases in discharge during differing portions of their record, while the eastern aquifer spring had stable discharge. At the monthly scale, there was a strong inverse relationship between measured discharge and PET at all three springs, likely due to vegetation accessing the water prior to its surface expression. Annual average winter discharge from both western aquifer springs responded to reductions in 10-year cumulative winter precipitation, while discharge from the eastern aquifer spring did not correspond well to precipitation within the period of record. Uncertainty in climate projections for aquifer recharge remains high, but increasing air temperatures will likely lead to increased PET and reduced spring surface flow. Better characterization of climate and spring discharge relationships will help managers protect contributing areas that may be more susceptible to groundwater withdrawal and better understand the available habitat for groundwater-dependent ecosystems and species.

Freshwater wetlands are particularly vulnerable to climate change. Specifically, changes in temperature, precipitation, and evapotranspiration (i.e., climate drivers) are likely to alter flooding regimes of wetlands and affect the vital rates, abundance, and distributions of wetland‐dependent species. Amphibians may be among the most climate‐sensitive wetland‐dependent groups, as many species rely on shallow or intermittently flooded wetland habitats for breeding. Here, we integrated multiple years of high‐resolution gridded climate and amphibian monitoring data from Grand Teton and Yellowstone National Parks to explicitly model how variations in climate drivers and habitat conditions affect the occurrence and breeding dynamics (i.e., annual extinction and colonization rates) of amphibians. Our results showed that models incorporating climate drivers outperformed models of amphibian breeding dynamics that were exclusively habitat based. Moreover, climate‐driven variation in extinction rates, but not colonization rates, disproportionately influenced amphibian occupancy in monitored wetlands. Long‐term monitoring from national parks coupled with high‐resolution climate data sets will be crucial to describing population dynamics and characterizing the sensitivity of amphibians and other wetland‐dependent species to climate change. Further, long‐term monitoring of wetlands in national parks will help reduce uncertainty surrounding wetland resources and strengthen opportunities to make informed, science‐based decisions that have far‐reaching benefits.

Most of the western United States is experiencing the effects of rapid and directional climate change (Garfin et al. 2013). These effects, along with forecasts of profound changes in the future, provide strong motivation for resource managers to learn about and prepare for future changes. Climate adaptation plans are based on an understanding of historic climate variation and their effects on ecosystems and on forecasts of future climate trends. Frameworks for climate adaptation thus universally identify the importance of a summary of historical, current, and projected climates (Glick, Stein, and Edelson 2011; Cross et al. 2013; Stein et al. 2014). Trends in physical climate variables are usually the basis for evaluating the exposure component in vulnerability assessments. Thus, this chapter focuses on step 2 of the Climate-Smart Conservation framework (chap. 2): vulnerability assessment. We present analyses of historical and current observations of temperature, precipitation, and other key climate measurements to provide context and a baseline for interpreting the ecological impacts of projected climate changes.

Sediment and phosphorus (P) transport from the Minnesota River Basin to Lake Pepin on the upper Mississippi River has garnered much attention in recent years. However, there is lack of data on the extent of sediment and P contributions from riverbanks vis-a-vis uplands and ravines. Using two light detection and ranging (lidar) data sets taken in 2005 and 2009, a study was undertaken to quantify sediment and associated P losses from riverbanks in Blue Earth County, Minnesota. Volume change in river valleys as a result of bank erosion amounted to 1.71 million m over 4 yr. Volume change closely followed the trend: the Blue Earth River > the Minnesota River at the county's northern edge > the Le Sueur River > the Maple River > the Watonwan River > the Big Cobb River > Perch Creek > Little Cobb River. Using fine sediment content (silt + clay) and bulk density of 37 bank samples representing three parent materials, we estimate bank erosion contributions of 48 to 79% of the measured total suspended solids at the mouth of the Blue Earth and the Le Sueur rivers. Corresponding soluble P and total P contributions ranged from 0.13 to 0.20% and 40 to 49%, respectively. Although tall banks (>3 m high) accounted for 33% of the total length and 63% of the total area, they accounted for 75% of the volume change in river valleys. We conclude that multitemporal lidar data sets are useful in estimating bank erosion and associated P contributions over large scales, and for riverbanks that are not readily accessible for conventional surveying equipment.

Measurement sensitivity and bias quality control metrics are commonly reported for water-quality parameters measured in the laboratory. Less commonly recognized is that they should also be reported for field-measured parameters. Periodic evaluation helps document data quality and can help serve as early warning if there are problems with methods or techniques that could negatively affect ability to interpret threshold values and trends over time. This study focuses on traditional assessment of bias and introduces a new method for estimating measurement sensitivity of water-quality parameters measured monthly in the field. Alternative measurement sensitivity is a new data quality indicator used to demonstrate how quantifying sensitivity at the measurement level can improve understanding the uncertainty affecting each reported data value. That, in turn, can help interpret the meaning of results from many separate data points measured in the field. In this 30-month study, pH and specific conductance consistently met, and dissolved oxygen did not always meet NPS and USGS quality control standards for bias. Evaluation of dissolved oxygen bias and sensitivity during the study provided impetus to improve calibration techniques that resulted in data that later met quality goals.

This research investigates the appropriate scale for watershed averaged and site specific soil moisture retrieval from high resolution radar imagery. The first approach involved filtering backscatter for input to a retrieval model that was compared against field measures of soil moisture. The second approach involved spatially averaging raw and filtered imagery in an image-based statistical technique to determine the best scale for site-specific soil moisture retrieval. Field soil moisture was measured at 1225 m 2 sites in three watersheds commensurate with 7 m resolution Radarsat image acquisition. Analysis of speckle reducing block median filters indicated that 5 × 5 filter level was the optimum for watershed averaged estimates of soil moisture. However, median filtering alone did not provide acceptable accuracy for soil moisture retrieval on a site-specific basis. Therefore, spatial averaging of unfiltered and median filtered power values was used to generate backscatter estimates with known confidence for soil moisture retrieval. This combined approach of filtering and averaging was demonstrated at watersheds located in Arizona (AZ), Oklahoma (OK) and Georgia (GA). The optimum ground resolution for AZ, OK and GA study areas was 162 m, 310 m, and 1131 m respectively obtained with unfiltered imagery. This statistical approach does not rely on ground verification of soil moisture for validation and only requires a satellite image and average roughness parameters of the site. When applied at other locations, the resulting optimum ground resolution will depend on the spatial distribution of land surface features that affect radar backscatter. This work offers insight into the accuracy of soil moisture retrieval, and an operational approach to determine the optimal spatial resolution for the required application accuracy.

The Integral Equation Model (IEM) is the most widely-used, physically based radar backscatter model for sparsely vegetated landscapes. In general, IEM quantifies the magnitude of backscattering as a function of moisture content and surface roughness, which are unknown, and the known radar configurations. Estimating surface roughness or soil moisture by solving the IEM with two unknowns is a classic example of under-determination and is at the core of the problems associated with the use of radar imagery coupled with IEM-like models. This study offers a solution strategy to this problem by the use of multi-angle radar images, and thus provides estimates of roughness and soil moisture without the use of ancillary field data. Results showed that radar images can provide estimates of surface soil moisture at the watershed scale with good accuracy. Results at the field scale were less accurate, likely due to the influence of image speckle. Results also showed that subsurface roughness caused by rock fragments in the study sites caused error in conventional applications of IEM based on field measurements, but was minimized by using the multi-angle approach.

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.

Surface roughness is a key parameter of radar backscatter models designed to retrieve surface soil moisture (θS) information from radar images. This work offers a theory-based approach for estimating a key roughness parameter, termed the roughness correlation length (L c). The L c is the length in centimetres from a point on the ground to a short distance for which the heights of a rough surface are correlated with each other. The approach is based on the relation between L c and h RMS as theorized by the Integral Equation Model (IEM). The h RMS is another roughness parameter, which is the root mean squared height variation of a rough surface. The relation is calibrated for a given site based on the radar backscatter of the site under dry soil conditions. When this relation is supplemented with the site specific measurements of h RMS, it is possible to produce estimates of L c. The approach was validated with several radar images of the Walnut Gulch Experimental Watershed in southeast Arizona, USA. Results showed that the IEM performed well in reproducing satellite-based radar backscatter when this new derivation of L c was used as input. This was a substantial improvement over the use of field measurements of L c. This new approach also has advantages over empirical formulations for the estimation of L c because it does not require field measurements of θS for iterative calibration and it accounts for the very complex relation between L c and h RMS found in heterogeneous landscapes. Finally, this new approach opens up the possibility of determining both roughness parameters without ancillary data based on the radar backscatter difference measured for two different incident angles.

A GIS framework, the Army Remote Moisture System (ARMS), has been developed to link the Land Information System (LIS), a high performance land surface modeling and data assimilation system, with remotely sensed measurements of soil moisture to provide a high resolution estimation of soil moisture in the near surface. ARMS uses available soil (soil texture, porosity, K"s"a"t), land cover (vegetation type, LAI, Fraction of Greenness), and atmospheric data (Albedo) in standardized vector and raster GIS data formats at multiple scales, in addition to climatological forcing data and precipitation. PEST (Parameter EStimation Tool) was integrated into the process to optimize soil porosity and saturated hydraulic conductivity (K"s"a"t), using the remotely sensed measurements, in order to provide a more accurate estimate of the soil moisture. The modeling process is controlled by the user through a graphical interface developed as part of the ArcMap component of ESRI ArcGIS.

Surface roughness is a crucial input for radar backscatter models. Roughness measurements of root mean-squared height (hrms) of the same surface can vary depending on the measuring instrument and how the data are processed. This letter addresses the error in hrms associated with instrument bias and instrument deployment issues such as number and length of measurement transects. It was found that at least 20 transect measurements, 3 m in length, for study sites ranging from 3.5 to 1225 m2 in size were necessary to get a consistent hrms measurement. Also, roughness heights of longer transect lengths were highly dependent on the method of detrending the transects. Finally, soil moisture was predicted by inverting the integral equation model using roughness heights taking into account instrument bias, number of measurements, and the detrending method. For common configurations of the Radarsat sensor and reasonable hrms values, error associated with measurement of hrms generally exceeded plusmn20% of soil-moisture prediction

Knowledge of climatic variability at small spatial extents (< 50 km) is needed to assess vulnerabilities of biological reserves to climate change. We used empirical and modeled weather station data to test if climate change has increased the synchrony of surface air temperatures among 50 sites within the Greater Yellowstone Area (GYA) of the interior western United States. This important biological reserve is the largest protected area in the Lower 48 states and provides critical habitat for some of the world's most iconic wildlife. We focused our analyses on temporal shifts and shape changes in the annual distributions of seasonal minimum and maximum air temperatures among valley-bottom and higher elevation sites from 1948-2012. We documented consistent patterns of warming since 1948 at all 50 sites, with the most pronounced changes occurring during the Winter and Summer when minimum and maximum temperature distributions increased. These shifts indicate more hot temperatures and less cold temperatures would be expected across the GYA. Though the shifting statistical distributions indicate warming, little change in the shape of the temperature distributions across sites since 1948 suggest the GYA has maintained a diverse portfolio of temperatures within a year. Spatial heterogeneity in temperatures is likely maintained by the GYA's physiographic complexity and its large size, which encompasses multiple climate zones that respond differently to synoptic drivers. Having a diverse portfolio of temperatures may help biological reserves spread the extinction risk posed by climate change.

Worldwide, rivers and streams are negatively impacted by sedimentation. However, there are few broad scale techniques for quantifying the sources of sediment, i.e. upland vs. river bank erosion. This research was designed to evaluate the use of airborne LIDAR for characterizing sediment and phosphorus contributions from river bank erosion. The evaluation was done on the main stem of the Blue Earth River in southern Minnesota. Detailed topographic data were collected on an annual basis in April 2001 and 2002 over a 56 km length of the river with a helicopter mounted Topeye laser system. The raw database included X, Y, Z coordinates of laser returns sampled from the river valley with a density of 1–3.3 elevations per m 2 . Uniform 1 m bare earth digital elevation models were constructed by stripping vegetation laser returns and interpolation. The two models were differenced to determine volume change over time, which was then converted to mass wasting by multiplying volume change with bulk density. Mass wasting rates were further converted to sediment load based on percentage of transportable material in the bank strata. The average difference between LIDAR measured elevations and RTK GPS surveyed elevations on 5 highway bridge surfaces was 2.5 and 8.8 cm for the 2001 and 2002 scans, respectively. The elevation errors were quasi-normally distributed with standard deviation of 6.7 and 6.1 cm for 2001 and 2002, respectively. No elevation or planimetric corrections were made to the laser data before calculating mass wasting rates because it was not possible to determine the source of error or if it was uniform within and between scans. The mass wasting estimate from the LIDAR surveys varied from 23% to 56% of the sediment mass transported past the downstream gauging station depending on the range of textural material that was entrained once in the river. These estimates are in the range of values reported in the literature. Total P contribution due to bank erosion from the river reach was estimated to be 201 t/yr.

Seeds of cabbage, lettuce, millet, radish, turnip, and wheat were treated with solutions containing Be, Ni, Tl, or V, and subsequent effects on seed germination and radicle elongation were measured after three days. Treatment with low concentrations of Be, Ni, or V stimulated root elongation in most species. Higher concentrations of these elements and all treatment with Tl caused reductions in root elongation. In general, turnip and lettuce were the most sensitive of the plants studied to the metals tested, while wheat and millet were the least sensitive.

[1] Four approaches for deriving estimates of near-surface soil moisture from radar imagery in a semiarid, sparsely vegetated rangeland were evaluated against in situ measurements of soil moisture. The approaches were based on empirical, physical, semiempirical, and image difference techniques. The empirical approach involved simple linear regression of radar backscatter on soil moisture, while the integral equation method (IEM) model was used in both the physical and semiempirical approaches. The image difference or delta index approach is a new technique presented here for the first time. In all cases, spatial averaging to the watershed scale improved agreement with observed soil moisture. In the empirical approach, variation in radar backscatter explained 85% of the variation in observed soil moisture at the watershed scale. For the physical and best semiempirical adjustment to the physical model, the root-mean-square errors (RMSE) between modeled and observed soil moisture were 0.13 and 0.04, respectively. Practical limitations to obtaining surface roughness measurements limit IEM utility for large areas. The purely image-based delta index has significant operational advantage in soil moisture estimates for broad areas. Additionally, satellite observations of backscatter used in the delta index indicated an approximate 1:1 relationship with soil moisture that explained 91% of the variability, with RMSE = 0.03. Results showed that the delta index is scaled to the range in observed soil moisture and may provide a purely image based model. It should be tested in other watersheds to determine if it implicitly accounts for surface roughness, topography, and vegetation. These are parameters that are difficult to measure over large areas, and may influence the delta index.

The integral equation method (IEM) model and a newly defined delta index were used to estimate near surface soil moisture from C-band radar satellite imagery in a semiarid rangeland in southern Arizona, USA. Model results were validated against soil moisture measurements made in the field at the time of satellite overpass. The IEM model performed poorly in this environment possibly due to abundant near-surface rock fragments which were not considered in the model. The delta index performed better than the IEM model and was shown to work with both ERS and Radarsat imagery. Additionally the index was simple to implement and implicitly accounted for both rock fragments and surface roughness

A study was conducted to determine the potential of using Advanced Very High Resolution Radiometer (AVHRR) imagery to monitor short-term changes in rangeland forage conditions on a regional scale. Forage biomass and nitrogen concentration were estimated at 6 study sites throughout a typical grazing season (April to October). Study sites were located in northern and southern Montana in areas classified as foothills grassland and shortgrass prairie. Normalized Difference Vegetation Index (NDVI) values from AVHRR imagery (1 km pixels) were used to predict live biomass, dead standing biomass, total biomass, nitrogen (N) concentration and standing N. Values of the NDVI were correlated (r > 0.4, P 0.05) for all 6 study sites, which indicates that NDVI could be used to predict forage abundance at multiple locations and at variable dates. Using simple linear regression, NDVI accounted for 63% of the variation in live and total biomass, 18% of the variation in dead biomass, 66% of the variation in standing N, but < 1% of the variation in N concentration. The NDVI obtained from AVHRR imagery was a good predictor of forage abundance as measured by live, dead and total biomass as well as standing N, but it was not related to forage quality as measured by N or crude protein concentration. On a regional basis, land managers could use AVHRR-NDVI values to identify areas with high or low levels of forage abundance that may result from factors such as drought, variable precipitation patterns, or uneven grazing. DOI:10.2458/azu_jrm_v55i4_thoma