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At least 343 records · Page 19Linked to original sources

USGS2015

A new approach for continuous estimation of baseflow using discrete water quality data: Method description and comparison with baseflow estimates from two existing approaches

Understanding how watershed characteristics and climate influence the baseflow component of stream discharge is a topic of interest to both the scientific and water management communities. Therefore, the development of baseflow estimation methods is a topic of active research. Previous studies have demonstrated that graphical hydrograph separation (GHS) and conductivity mass balance (CMB) methods can be applied to stream discharge data to estimate daily baseflow. While CMB is generally considered to be a more objective approach than GHS, its application across broad spatial scales is limited by a lack of high frequency specific conductance (SC) data. We propose a new method that uses discrete SC data, which are widely available, to estimate baseflow at a daily time step using the CMB method. The proposed approach involves the development of regression models that relate discrete SC concentrations to stream discharge and time. Regression-derived CMB baseflow estimates were more similar to baseflow estimates obtained using a CMB approach with measured high frequency SC data than were the GHS baseflow estimates at twelve snowmelt dominated streams and rivers. There was a near perfect fit between the regression-derived and measured CMB baseflow estimates at sites where the regression models were able to accurately predict daily SC concentrations. We propose that the regression-derived approach could be applied to estimate baseflow at large numbers of sites, thereby enabling future investigations of watershed and climatic characteristics that influence the baseflow component of stream discharge across large spatial scales.

Arizona, Colorado, New Mexico, Utah, Wyoming↗

USGS2006

Effect of tree line advance on carbon storage in NW Alaska

We investigated the size, distribution, and temporal dynamics of ecosystem carbon (C) pools in an area of recent tree line advance, northwest Alaska. Repeat aerial photographs show forest cover increased ???10% in our study area since 1949. We sampled C pools of four principal ecosystem types, tussock tundra, shrub tundra, woodland, and forest, all located on a 600-800 year old river terrace. Significant differences between ecosystem C pools, both above ground and below ground existed. Tundra sites store >22.2 kg C/m2, shrub tundra sites and woodland sites store 9.7 kg C/m2 and 14.3 kg C/m2, respectively, and forest sites store 14.4 kg C/m2. Landscape variation of total ecosystem C was primarily due to organic soil C and was secondarily due to C stored in trees. Soil C/N profiles of shrub tundra sites and woodland sites showed similarities with forest site soils at surface and tundra site soils at depth. We hypothesize that tundra systems transformed to forest systems in this area under a progression of permafrost degradation and enhanced drainage. On the basis of C pool estimates for the different ecosystem types, conversion of tundra sites to forest may have resulted in a net loss of > 7.8 kg C/m2, since aboveground C gains were more than offset by belowground C losses to decomposition in the tundra sites. Tree line advance therefore might not increase C storage in high-latitude ecosystems and thus might not, as previously suggested, act as a negative feedback to warming. Key to this hypothesis and to its projection to future climate response is the fate of soil carbon upon warming and permafrost drainage. Copyright 2006 by the American Geophysical Union.

Journal of Geophysical Research G: Biogeosciences↗

USGS2009

Responses of stream nitrate and dissolved organic carbon loadings to hydrological forcing and climate change in an upland forest of the northeast USA

[1] In coming decades, higher annual temperatures, increased growing season length, and increased dormant season precipitation are expected across the northeastern United States in response to anthropogenic forcing of global climate. We synthesized long-term stream hydrochemical data from the Sleepers River Research Watershed in Vermont, United States, to explore the relationship of catchment wetness to stream nitrate and DOC loadings. We modeled changes in growing season length and precipitation patterns to simulate future climate scenarios and to assess how stream nutrient loadings respond to climate change. Model results for the 2070–2099 time period suggest that stream nutrient loadings during both the dormant and growing seasons will respond to climate change. During a warmer climate, growing season stream fluxes (runoff +20%, nitrate +57%, and DOC +58%) increase as more precipitation (+28%) and quick flow (+39%) occur during a longer growing season (+43 days). During the dormant season, stream water and nutrient loadings decrease. Net annual stream runoff (+8%) and DOC loading (+9%) increases are commensurate with the magnitude of the average increase of net annual precipitation (+7%). Net annual stream water and DOC loadings are primarily affected by increased dormant season precipitation. In contrast, decreased annual loading of stream nitrate (−2%) reflects a larger effect of growing season controls on stream nitrate and the effects of lengthened growing seasons in a warmer climate. Our findings suggest that leaching of nitrate and DOC from catchment soils will be affected by anthropogenic climate forcing, thereby affecting the timing and magnitude of annual stream loadings in the northeastern United States.

Vermont↗

USGS2012

Eolian features in the Western Desert of Egypt and some applications to Mars

Relations of landform types to wind regimes, bedrock composition, sediment supply, and topography are shown by field studies and satellite photographs of the Western Desert of Egypt. This desert, which lies at the core of the largest hyperarid region on earth, provides analogs of Martian wind-formed features. These include sand dunes, alternating light and dark streaks, knob ‘shadows,’ and yardangs. Surface particles have been segregated by wind into deposits (dunes, sand sheets, and light streaks) that can be differentiated by their grain size distributions, surface shapes, and colors. Throughgoing sand of mostly fine to medium grain size is migrating southward in longitudinal dune belts and barchan chains whose long axes lie parallel to the prevailing northerly winds, but topographic variations such as scarps and depressions strongly influence the zones of deposition and dune morphology. Sand from the longitudinal dunes on the plains is commonly redistributed into barchans in the depressions. These barchans are generally simple crescents that are morphologically similar to many of the dunes seen on Viking orbiter pictures of the north polar sand sea on mars. Light streaks are depositional features consisting of dune belts and elongate sheets of coarse to medium sand and granules. Intervening dark streaks are erosional features consisting of strips of desert-varnished bedrock and lag gravel surfaces exposed between the sand deposits. The shape of both light and dark streaks is controlled by wind flow around topographic highs. Dark zones (shadows) in the lee of mountains, hills, and knobs are erosional products from the topographic highs; they change shape only in response to movement of the adjacent lighter-colored sand deposits. Streamlined yardangs carved in crystalline limestone constitute one of the largest yardang fields on earth. Yardangs occur also in sandstone of the Nubian Series and in lacustrine sediments. The variables that affect the patterns of wind erosion and deposition in the Western Desert are topographic effects on wind velocities and directions, resistance of the bedrock, sand supply, and climatic change with time; vegetation is essentially absent and is not a controlling factor.

Journal of Geophysical Research Solid Earth↗

USGS2015

A dynamic population model to investigate effects of climate and climate-independent factors on the lifecycle of the tick Amblyomma americanum (Acari: Ixodidae)

The lone star tick, Amblyomma americanum , is a disease vector of significance for human and animal health throughout much of the eastern United States. To model the potential effects of climate change on this tick, a better understanding is needed of the relative roles of temperature-dependent and temperature-independent (day-length-dependent behavioral or morphogenetic diapause) processes acting on the tick lifecycle. In this study, we explored the roles of these processes by simulating seasonal activity patterns using models with site-specific temperature and day-length-dependent processes. We first modeled the transitions from engorged larvae to feeding nymphs, engorged nymphs to feeding adults, and engorged adult females to feeding larvae. The simulated seasonal patterns were compared against field observations at three locations in United States. Simulations suggested that 1) during the larva-to-nymph transition, some larvae undergo no diapause while others undergo morphogenetic diapause of engorged larvae; 2) molted adults undergo behavioral diapause during the transition from nymph-to-adult; and 3) there is no diapause during the adult-to-larva transition. A model constructed to simulate the full lifecycle of A. americanum successfully predicted observed tick activity at the three U.S. study locations. Some differences between observed and simulated seasonality patterns were observed, however, identifying the need for research to refine some model parameters. In simulations run using temperature data for Montreal, deterministic die-out of A. americanum populations did not occur, suggesting the possibility that current climate in parts of southern Canada is suitable for survival and reproduction of this tick.

Journal of Medical Entomology↗

USGS2015

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations

Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment. Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression. Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect. Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond. Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

Journal of Animal Ecology↗

USGS2005

Analysis of vegetation distribution in Interior Alaska and sensitivity to climate change using a logistic regression approach

Aim: To understand drivers of vegetation type distribution and sensitivity to climate change. Location: Interior Alaska. Methods: A logistic regression model was developed that predicts the potential equilibrium distribution of four major vegetation types: tundra, deciduous forest, black spruce forest and white spruce forest based on elevation, aspect, slope, drainage type, fire interval, average growing season temperature and total growing season precipitation. The model was run in three consecutive steps. The hierarchical logistic regression model was used to evaluate how scenarios of changes in temperature, precipitation and fire interval may influence the distribution of the four major vegetation types found in this region. Results: At the first step, tundra was distinguished from forest, which was mostly driven by elevation, precipitation and south to north aspect. At the second step, forest was separated into deciduous and spruce forest, a distinction that was primarily driven by fire interval and elevation. At the third step, the identification of black vs. white spruce was driven mainly by fire interval and elevation. The model was verified for Interior Alaska, the region used to develop the model, where it predicted vegetation distribution among the steps with an accuracy of 60-83%. When the model was independently validated for north-west Canada, it predicted vegetation distribution among the steps with an accuracy of 53-85%. Black spruce remains the dominant vegetation type under all scenarios, potentially expanding most under warming coupled with increasing fire interval. White spruce is clearly limited by moisture once average growing season temperatures exceeded a critical limit (+2 ??C). Deciduous forests expand their range the most when any two of the following scenarios are combined: decreasing fire interval, warming and increasing precipitation. Tundra can be replaced by forest under warming but expands under precipitation increase. Main conclusion: The model analyses agree with current knowledge of the responses of vegetation types to climate change and provide further insight into drivers of vegetation change. ?? 2005 Blackwell Publishing Ltd.

Journal of Biogeography↗

USGS2025

Too hot for comfort: Elevated temperatures influence gene expression and exceed thermal tolerance of bigmouth shiners, Ericymba dorsalis

Environmental and associated ecosystem change may affect the persistence of fish species based on their ability to adapt to changing conditions, including decreasing flows and rising water temperatures. Exceeding the thermal tolerances of stream fish will likely result in a loss of ability to maintain metabolic processes. We evaluated the critical thermal maximum (CTmax) of bigmouth shiner ( Ericymba dorsalis ) and analysed the expression of heat shock protein 70 messenger RNA (mRNA) (HSP70) to quantify a thermal stress response over a gradient of temperatures (25°C–31°C). E. dorsalis HSP70 mRNA expression was upregulated in response to temperatures >25°C, indicating a stress response. This study supports the existence of a thermal stress threshold for E. dorsalis . The frequency at which this threshold is exceeded may increase under forecasted future climate scenarios for Nebraska.

Nebraska↗

USGS2021

Influence of water temperature and biotic interactions on the distribution of westslope cutthroat trout (Oncorhynchus clarkii lewisi) in a population stronghold under climate change

Climate warming is expected to have substantial impacts on native trout across the Rocky Mountains, but there is little understanding of how these changes affect future distributions of co-occurring native fishes within population strongholds. We used mixed-effects logistic regression to investigate the role of abiotic (e.g., temperature) and biotic factors (bull trout presence, Salvelinus confluentus ) on distributions of westslope cutthroat trout ( Oncorhynchus clarkii lewisi ; WCT) in the North Fork Flathead River, USA and Canada. The probability of WCT presence increased with stream temperature and decreased with channel gradient and bull trout presence, yet the effect of bull trout was reduced with increasing pool densities. Combining this model with spatially explicit stream temperature projections, we predict a 29% increase in suitable habitat under high emissions through 2075, with gains at mid-elevation sites predicted to exceed bull trout thermal tolerances and high-elevation sites expected to become more thermally suitable for WCT. Our study illustrates the importance of considering abiotic and biotic drivers to assess species response to climate change, helping to guide local-scale climate adaptation and management.

Alberta, British Columbia, Montana↗

USGS2022

High-resolution remote sensing and multistate occupancy estimation identify drivers of spawning site selection in fall chum salmon (Oncorhynchus keta) across a sub-Arctic riverscape

Groundwater upwellings provide warmer, stable overwinter temperatures for developing salmon embryos, which may be particularly important in cold, braided, gravel-bed sub-Arctic rivers. We used a three-year time series of aerial counts and remote sensing to estimate the distribution of low and high aggregations of spawning fall chum salmon ( Oncorhynchus keta ), classify approximately 0.5 km long river segments by geomorphic channel type, and map thermal variability along a 25.4 km stretch of the Teedriinjik River, Alaska. We used a dynamic multistate occupancy model to estimate detectability, occupancy, and the dynamics of spawning aggregations among river segments. Detectability was higher for large (>150) relative to smaller aggregations. Unoccupied segments were likely to remain so from year to year; low abundance spawning segments were dynamic and rarely remained in that state for multiple years, while ∼20%–35% of high abundance segments remained stable, indicating the presence of high-quality spawning habitat. Spawning habitat use was associated with warmer water temperatures likely caused by groundwater upwellings. We identified spawning habitat characteristics and trends in usage by fall chum salmon, which will inform land management decisions and assist in evaluating impacts of shifting climate conditions and resource management on Arctic salmon populations.

Alaska↗

USGS2007

Climate variability controls on unsaturated water and chemical movement, High Plains aquifer, USA

Responses in the vadose zone and groundwater to interannual, interdecadal, and multidecadal climate variability have important implications for groundwater resource sustainability, yet they are poorly documented and not well understood in most aquifers of the USA. This investigation systematically examines the role of interannual to multidecadal climate variability on groundwater levels, deep infiltration (3–23 m) events, and downward displacement (>1 m) of chloride and nitrate reservoirs in thick (15–50 m) vadose zones across the regionally extensive High Plains aquifer. Such vadose zone responses are unexpected across much of the aquifer given a priori that unsaturated total-potential profiles indicate upward water movement from the water table toward the root zone, mean annual potential evapotranspiration exceeds mean annual precipitation, and millennia-scale evapoconcentration results in substantial vadose zone chloride and nitrate reservoirs. Using singular spectrum analysis (SSA) to reconstruct precipitation and groundwater level time-series components, variability was identified in all time series as partially coincident with known climate cycles, such as the Pacific Decadal Oscillation (PDO) (10–25 yr) and the El Niño/Southern Oscillation (ENSO) (2–6 yr). Using these lag-correlated hydrologic time series, a new method is demonstrated to estimate climate-varying unsaturated water flux. The results suggest the importance of interannual to interdecadal climate variability on water-flux estimation in thick vadose zones and provide better understanding of the climate-induced transients responsible for the observed deep infiltration and chemical-mobilization events. Based on these results, we discuss implications for climate-related sustainability of the High Plains aquifer.

Vadose Zone Journal↗

USGS2013

Integrating satellite observations and modern climate measurements with the recent sedimentary record: An example from Southeast Alaska

Assessments of climate change over time scales that exceed the last 100 years require robust integration of high-quality instrument records with high-resolution paleoclimate proxy data. In this study, we show that the recent biogenic sediments accumulating in two temperate ice-free fjords in Southeast Alaska preserve evidence of North Pacific Ocean climate variability as recorded by both instrument networks and satellite observations. Multicore samples EW0408-32MC and EW0408-43MC were investigated with 137 Cs and excess 210 Pb geochronometry, three-dimensional computed tomography, high-resolution scanning XRF geochemistry, and organic stable isotope analyses. EW0408-32MC (57.162°N, 135.357°W, 146 m depth) is a moderately bioturbated continuous record that spans AD ∼1930–2004. EW0408-43MC (56.965°N, 135.268°W, 91 m depth) is composed of laminated diatom oozes, a turbidite, and a hypopycnal plume (river flood) deposit. A discontinuous event-based varve chronology indicates 43MC spans AD ∼1940–1981. Decadal-scale fluctuations in sedimentary Br/Cl ratios accurately reflect changes in marine organic matter accumulation that display the same temporal pattern as that of the Pacific Decadal Oscillation. An estimated Sitka summer productivity parameter calibrated using SeaWiFS satellite observations support these relationships. The correlation of North Pacific climate regime states, primary productivity, and sediment geochemistry indicate the accumulation of biogenic sediment in Southeast Alaska temperate fjords can be used as a sensitive recorder of past productivity variability, and by inference, past climate conditions in the high-latitude Gulf of Alaska.

Alaska↗

USGS2026

Linking distribution and return-on-investment models to optimize woody management for prairie grouse in Nebraska

Grasslands in Nebraska, USA, face threats from agricultural conversion, urban development, and woody encroachment, all of which negatively affect prairie grouse ( Tympanuchus spp.) populations. To optimize conservation planning, Nebraska wildlife agencies developed probabilistic area-based surveys for greater prairie-chicken ( T. cupido ) and sharp-tailed grouse ( T. phasianellus ) to sample landscapes across a range of environmental conditions. This design improves historical surveys and enables the development of distribution models that quantitatively define habitat associations and support scenario-based conservation planning. Using survey data collected during 2020–2022, we modeled prairie grouse occurrence and abundance as functions of land cover, topography, and climate using Bayesian logistic and zero-inflated negative binomial models with regularized horseshoe priors. We then conducted a maximum potential return-on-investment analysis of woody cover treatments, assuming sustained treatment success, relative to projected impacts of woody encroachment on prairie grouse populations by 2050. Among modeled associations were a positive association with grasslands having low woody cover and a negative association with grasslands having high woody cover. Across the 3-year period, median estimated annual populations were 142,380 for greater prairie-chicken (range of 95% CIs across years = 68,821–277,615) and 64,154 for sharp-tailed grouse (range of 95% CIs across years = 27,550–146,559). Under projected woody encroachment, mean predicted population declines were 10% for greater prairie-chicken (range of 95% CIs = 7–14%) and 6% for sharp-tailed grouse (range of 95% CIs = 5–7%). Areas with high prairie grouse density and low treatment costs in 2021, and high projected woody encroachment and population loss by 2050, offered the greatest return on investment for woody management. Return on investment was greatest in the northwestern Shortgrass Prairie ecoregion (northwestern Nebraska) for sharp-tailed grouse and the eastern Sandhills ecoregion (central Nebraska) for both species. These models underscore the value of evidence-based, quantitative approaches for prioritizing conservation actions on working lands. Scenario-based modeling could be extended to guide other treatments, such as optimizing restoration (e.g., Conservation Reserve Program) or incentivizing grassland persistence in areas with predicted climate resilience.

Nebraska↗

USGS2014

A regional neural network model for predicting mean daily river water temperature

Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate and land use changes, thereby providing information that is valuable to management of river ecosystems and biota such as brook trout.

Journal of Hydrology↗

USGS2003

Ostracode-based reconstruction from 23,300 to about 20,250 cal yr BP of climate, and paleohydrology of a groundwater-fed pond near St. Louis, Missouri

The water chemistry of a groundwater-fed sinkhole-pond near St. Louis, Missouri, and its associated climate during the last glaciation are reconstructed by comparison with autecological data of modern ostracodes from about 5,500 sites in Canada. A 4.8-m succession of fossiliferous sediment yielded ostracode assemblages that collectively are generally found today in ponds in North America including the species Cyclocypris ampla, C. laevis, Cypridopsis vidua, Candona crogmaniana, C. distincta, and C. ohioensis. Fossils of Picea needles and the ostracode Cyclocypris sharpei imply that best analog sites for the succession are in central to south-central Canada. The pond formed 23,300 ?? 400 cal yr BP when a sinkhole became plugged by a clay bed about 1 m thick. By about 20,250 cal yr BP, the pond had desiccated and was buried by loess. The sediment accumulation rate was about 0.18 cm/yr, and each sample interval (6 cm) represents a time slice of ???33 years. Data from this record provides the first fairly high resolution proxy record of the glacial paleoclimate of the mid-latitude of North America. The analog data indicate the water in the hydrologically-open spring-fed pond was less than 1 m deep. The paleoclimatic reconstructions imply gradually drier conditions and uniform, cool temperatures. The shallow water depth indicates that the temperature reconstruction is robust with mean annual temperatures (MATs) that ranged between 0.8 and 3.9??C, and mean July temperatures that ranged from 16.8 and 18.1??C. Other estimated climatic parameters include mean annual precipitation (MAP; 430 to 840 mm/yr), and moisture balance (P-E; -111 to 298 mm/yr). Compared to values measured today at St. Louis, the MAP was about 400 mm less, MAT about 10??C cooler, and P-E, about the same. These values are consistent with other published reconstructions based on modern analog analysis of fossil beetles and pollen, and paleothermometry based on amino acid racemization. The total dissolved solids (TDS) progressively increased from about 87 to 431 mg/L. Changes in TDS reflect either the balance between the relative inputs of karst groundwater and overland flow, or changes in the duration of water-rock interaction associated with the groundwater. The postulated long-term 900 ?? 200 year cyclicity of growing-season moisture and temperature, attributed to El Nin??o-Southern Oscillation cycles, is not expressed in the reconstructed hydrologic or climatic data. This is attributed, in part, to the mediating effect on temperature by monothermic groundwater input to this flow-through system.

Journal of Paleolimnology↗

USGS2008

Surface albedo observations at Gusev Crater and Meridiani Planum, Mars

During the Mars Exploration Rover mission, the Pancam instrument has periodically acquired large-scale panoramic images with its broadband (739??338 nm) filter in order to estimate the Lambert bolometric albedo of the surface along each rover's traverse. In this work we present the full suite of such estimated albedo values measured to date by the Spirit and Opportunity rovers along their traverses in Gusev Crater and Meridiani Planum, respectively. We include estimated bolometric albedo values of individual surface features (e.g., outcrops, dusty plains, aeolian bed forms, wheel tracks, light-toned soils, and crater walls) as well as overall surface averages of the 43 total panoramic albedo data sets acquired to date. We also present comparisons to estimated Lambert albedo values taken from the Mars Global Surveyor Mars Orbiter Camera (MOC) along the rovers' traverses, and to the large-scale bolometric albedos of the sites from the Viking Orbiter Infrared Thermal Mapper (IRTM) and Mars Global Surveyor/Thermal Emission Spectrometer (TES). The ranges of Pancam-derived albedos at Gusev Crater (0.14 to 0.25) and in Meridiani Planum. (0.10 to 0.18) are in good agreement with IRTM, TES, and MOC orbital measurements. These data sets will be a useful tool and benchmark for future investigations of albodo variations with time, including measurements from orbital instruments like the Context Camera and High Resolution Imaging Science Experiment on Mars Reconnaissance Orbiter. Long-term, accurate albedo measurements could also be important for future efforts in climate modeling as well as for studies of active surface processes. Copyright 2008 by the American Geophysical Union.

Journal of Geophysical Research E: Planets↗

USGS2018

Environmental controls, emergent scaling, and predictions of greenhouse gas (GHG) fluxes in coastal salt marshes

Coastal salt marshes play an important role in mitigating global warming by removing atmospheric carbon at a high rate. We investigated the environmental controls and emergent scaling of major greenhouse gas (GHG) fluxes such as carbon dioxide (CO 2 ) and methane (CH 4 ) in coastal salt marshes by conducting data analytics and empirical modeling. The underlying hypothesis is that the salt marsh GHG fluxes follow emergent scaling relationships with their environmental drivers, leading to parsimonious predictive models. CO 2 and CH 4 fluxes, photosynthetically active radiation (PAR), air and soil temperatures, well water level, soil moisture, and porewater pH and salinity were measured during May–October 2013 from four marshes in Waquoit Bay and adjacent estuaries, MA, USA. The salt marshes exhibited high CO 2 uptake and low CH 4 emission, which did not significantly vary with the nitrogen loading gradient (5–126 kg · ha −1 · year −1 ) among the salt marshes. Soil temperature was the strongest driver of both fluxes, representing 2 and 4–5 times higher influence than PAR and salinity, respectively. Well water level, soil moisture, and pH did not have a predictive control on the GHG fluxes, although both fluxes were significantly higher during high tides than low tides. The results were leveraged to develop emergent power law‐based parsimonious scaling models to accurately predict the salt marsh GHG fluxes from PAR, soil temperature, and salinity (Nash‐Sutcliffe Efficiency = 0.80–0.91). The scaling models are available as a user‐friendly Excel spreadsheet named Coastal Wetland GHG Model to explore scenarios of GHG fluxes in tidal marshes under a changing climate and environment.

Massachusetts↗

USGS2021

Postwildfire soil‐hydraulic recovery and the persistence of debris flow hazards

Deadly and destructive debris flows often follow wildfire, but understanding of changes in the hazard potential with time since fire is poor. We develop a simulation‐based framework to quantify changes in the hydrologic triggering conditions for debris flows as postwildfire infiltration properties evolve through time. Our approach produces time‐varying rainfall intensity‐duration thresholds for runoff‐ and infiltration‐generated debris flows with physics‐based hydrologic simulations that are parameterized with widely available hydroclimatic, vegetation reflectance, and soil texture data. When we apply our thresholding protocol to a test case in the San Gabriel Mountains (California, USA), the results are consistent with existing regional empirical thresholds and rainstorms that caused runoff‐ and infiltration‐generated debris flows soon after and three years following a wildfire, respectively. We find that the hydrologic triggering mechanisms for the two observed debris flow types are coupled with the effects of fire on the soil saturated hydraulic conductivity. Specifically, the rainfall intensity needed to generate debris flows via runoff increases with time following wildfire while the rainfall duration needed to produce debris flows via subsurface pore‐water pressures decreases. We also find that variations in soil moisture, rainfall climatology, median grain size, and root reinforcement could impact the median annual probability of postwildfire debris flows. We conclude that a simulation‐based method for calculating rainfall thresholds is a tractable approach to improve situational awareness of debris flow hazard in the years following wildfire. Further development of our framework will be important to quantify postwildfire hazard levels in variable climates, vegetation types, and fire regimes.

Journal of Geophysical Research: Earth Surface↗