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Geology topics

S. Kumar

Publications and source records attributed to S. Kumar.

At least 19 recordsLinked to original sources
USGS2026

Net CO2 emissions from dry inland waters persist in the presence of vegetation

Many inland waters are shrinking due to shifts in climate and water diversion for human uses. As they dry out, their exposed sediments emit large amounts of carbon dioxide (CO 2 ) to the atmosphere. However, current global estimates of CO 2 emissions from dry inland waters are derived exclusively from bare sediment dark-chamber measurements that do not account for the colonization of desiccated areas by vegetation. To understand the impact of vegetation on CO 2 emissions from dry sediments, we analyzed 164 dry inland water bodies across five climatic regions and five inland water body types (lakes, ponds, reservoirs, streams and wetlands). On average, within vegetated zones, vegetation occupied 47 ± 35% in measured biomass quadrants. Light-induced decreases in instantaneous CO 2 emissions in vegetated dry sediments were lower (mean ± SD = −3.7 ± 12.9 mmol CO 2 m −2 hr −1 ) than increases during dark conditions (14.7 ± 20.1 mmol CO 2 m −2 hr −1 ). Diel (24-hr) CO 2 emissions from dry, vegetated sediments (mean ± SD = 100 ± 261 mmol CO 2 m −2 d −1 ) were 25% lower than in bare sediments (133 ± 245 mmol CO 2 m −2 d −1 ). These results indicate that vegetation can partially off-set sediment respiration, although the magnitude of this effect is insufficient to switch dry beds from net sources to net sinks of carbon.

Global Biogeochemical Cycles↗
USGS2021

A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya

On 7 Feb 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. Over 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27x10 6 m 3 of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders >20 m in diameter, and scoured the valley walls up to 220 m above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

Chamoli↗
USGS2017

Distributional changes and range predictions of downy brome (Bromus tectorum) in Rocky Mountain National Park

Downy brome (Bromus tectorum L.), an invasive winter annual grass, may be increasing in extent and abundance at high elevations in the western United States. This would pose a great threat to high-elevation plant communities and resources. However, data to track this species in high-elevation environments are limited. To address changes in the distribution and abundance of downy brome and the factors most associated with its occurrence, we used field sampling and statistical methods, and niche modeling. In 2007, we resampled plots from two vegetation surveys in Rocky Mountain National Park for presence and cover of downy brome. One survey was established in 1993 and had been resampled in 1999. The other survey was established in 1996 and had not been resampled until our study. Although not all comparisons between years demonstrated significant changes in downy brome abundance, its mean cover increased nearly fivefold from 1993 (0.7%) to 2007 (3.6%) in one of the two vegetation surveys (P = 0.06). Although the average cover of downy brome within the second survey appeared to be increasing from 1996 to 2007, this slight change from 0.5% to 1.2% was not statistically significant (P = 0.24). Downy brome was present in 50% more plots in 1999 than in 1993 (P = 0.02) in the first survey. In the second survey, downy brome was present in 30% more plots in 2007 than in 1996 (P = 0.08). Maxent, a species-environmental matching model, was generally able to predict occurrences of downy brome, as new locations were in the ranges predicted by earlier generated models. The model found that distance to roads, elevation, and vegetation community influenced the predictions most. The strong response of downy brome to interannual environmental variability makes detecting change challenging, especially with small sample sizes. However, our results suggest that the area in which downy brome occurs is likely increasing in Rocky Mountain National Park through increased frequency and cover. Field surveys along with predictive modeling will be vital in directing efforts to manage this highly invasive species. ?? Weed Science Society of America 2011.

Invasive Plant Science and Management↗
USGS2015

Structure of the Koyna-Warna Seismic Zone, Maharashtra, India: A possible model for large induced earthquakes elsewhere

The Koyna-Warna area of India is one of the best worldwide examples of reservoir-induced seismicity, with the distinction of having generated the largest known induced earthquake (M6.3 on 10 December 1967) and persistent moderate-magnitude (>M5) events for nearly 50 years. Yet, the fault structure and tectonic setting that has accommodated the induced seismicity is poorly known, in part because the seismic events occur beneath a thick sequence of basalt layers. On the basis of the alignment of earthquake epicenters over an ~50 year period, lateral variations in focal mechanisms, upper-crustal tomographic velocity images, geophysical data (aeromagnetic, gravity, and magnetotelluric), geomorphic data, and correlation with similar structures elsewhere, we suggest that the Koyna-Warna area lies within a right step between northwest trending, right-lateral faults. The sub-basalt basement may form a local structural depression (pull-apart basin) caused by extension within the step-over zone between the right-lateral faults. Our postulated model accounts for the observed pattern of normal faulting in a region that is dominated by north-south directed compression. The right-lateral faults extend well beyond the immediate Koyna-Warna area, possibly suggesting a more extensive zone of seismic hazards for the central India area. Induced seismic events have been observed many places worldwide, but relatively large-magnitude induced events are less common because critically stressed, preexisting structures are a necessary component. We suggest that releasing bends and fault step-overs like those we postulate for the Koyna-Warna area may serve as an ideal tectonic environment for generating moderate- to large- magnitude induced (reservoir, injection, etc.) earthquakes.

Journal of Geophysical Research↗
USGS2011

Assessing forest vulnerability and the potential distribution of pine beetles under current and future climate scenarios in the Interior West of the US

The aim of our study was to estimate forest vulnerability and potential distribution of three bark beetles (Curculionidae: Scolytinae) under current and projected climate conditions for 2020 and 2050. Our study focused on the mountain pine beetle ( Dendroctonus ponderosae ), western pine beetle ( Dendroctonus brevicomis ), and pine engraver ( Ips pini) . This study was conducted across eight states in the Interior West of the US covering approximately 2.2 million km 2 and encompassing about 95% of the Rocky Mountains in the contiguous US. Our analyses relied on aerial surveys of bark beetle outbreaks that occurred between 1991 and 2008. Occurrence points for each species were generated within polygons created from the aerial surveys. Current and projected climate scenarios were acquired from the WorldClim database and represented by 19 bioclimatic variables. We used Maxent modeling technique fit with occurrence points and current climate data to model potential beetle distributions and forest vulnerability. Three available climate models, each having two emission scenarios, were modeled independently and results averaged to produce two predictions for 2020 and two predictions for 2050 for each analysis. Environmental parameters defined by current climate models were then used to predict conditions under future climate scenarios, and changes in different species’ ranges were calculated. Our results suggested that the potential distribution for bark beetles under current climate conditions is extensive, which coincides with infestation trends observed in the last decade. Our results predicted that suitable habitats for the mountain pine beetle and pine engraver beetle will stabilize or decrease under future climate conditions, while habitat for the western pine beetle will continue to increase over time. The greatest increase in habitat area was for the western pine beetle, where one climate model predicted a 27% increase by 2050. In contrast, the predicted habitat of the mountain pine beetle from another climate model suggested a decrease in habitat areas as great as 46% by 2050. Generally, 2020 and 2050 models that tested the three climate scenarios independently had similar trends, though one climate scenario for the western pine beetle produced contrasting results. Ranges for all three species of bark beetles shifted considerably geographically suggesting that some host species may become more vulnerable to beetle attack in the future, while others may have a reduced risk over time.

Arizona, Colorado, Idaho, Montana, New Mexico, Nev↗
USGS2010

Ensemble habitat mapping of invasive plant species

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.

Risk Analysis↗
USGS2009

Character and spatial distribution of OH/H 2 O on the surface of the moon seen by M 3 on chandrayaan-1

The search for water on the surface of the anhydrous Moon had remained an unfulfilled quest for 40 years. However, the Moon Mineralogy Mapper (M 3 ) on Chandrayaan-1 has recently detected absorption features near 2.8 to 3.0 micrometers on the surface of the Moon. For silicate bodies, such features are typically attributed to hydroxyl- and/or water-bearing materials. On the Moon, the feature is seen as a widely distributed absorption that appears strongest at cooler high latitudes and at several fresh feldspathic craters. The general lack of correlation of this feature in sunlit M 3 data with neutron spectrometer hydrogen abundance data suggests that the formation and retention of hydroxyl and water are ongoing surficial processes. Hydroxyl/water production processes may feed polar cold traps and make the lunar regolith a candidate source of volatiles for human exploration.

Science↗
USGS2008

Effects of spatial heterogeneity on butterfly species richness in Rocky Mountain National Park, CO, USA

We investigated butterfly responses to plot-level characteristics (plant species richness, vegetation height, and range in NDVI [normalized difference vegetation index]) and spatial heterogeneity in topography and landscape patterns (composition and configuration) at multiple spatial scales. Stratified random sampling was used to collect data on butterfly species richness from seventy-six 20 ?? 50 m plots. The plant species richness and average vegetation height data were collected from 76 modified-Whittaker plots overlaid on 76 butterfly plots. Spatial heterogeneity around sample plots was quantified by measuring topographic variables and landscape metrics at eight spatial extents (radii of 300, 600 to 2,400 m). The number of butterfly species recorded was strongly positively correlated with plant species richness, proportion of shrubland and mean patch size of shrubland. Patterns in butterfly species richness were negatively correlated with other variables including mean patch size, average vegetation height, elevation, and range in NDVI. The best predictive model selected using Akaike's Information Criterion corrected for small sample size (AICc), explained 62% of the variation in butterfly species richness at the 2,100 m spatial extent. Average vegetation height and mean patch size were among the best predictors of butterfly species richness. The models that included plot-level information and topographic variables explained relatively less variation in butterfly species richness, and were improved significantly after including landscape metrics. Our results suggest that spatial heterogeneity greatly influences patterns in butterfly species richness, and that it should be explicitly considered in conservation and management actions. ?? 2008 Springer Science+Business Media B.V.

Biodiversity and Conservation↗
USGS2008

Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US

The diatom Didymosphenia geminata is a single-celled alga found in lakes, streams, and rivers. Nuisance blooms of D geminata affect the diversity, abundance, and productivity of other aquatic organisms. Because D geminata can be transported by humans on waders and other gear, accurate spatial prediction of habitat suitability is urgently needed for early detection and rapid response, as well as for evaluation of monitoring and control programs. We compared four modeling methods to predict D geminata's habitat distribution; two methods use presence-absence data (logistic regression and classification and regression tree [CART]), and two involve presence data (maximum entropy model [Maxent] and genetic algorithm for rule-set production [GARP]). Using these methods, we evaluated spatially explicit, bioclimatic and environmental variables as predictors of diatom distribution. The Maxent model provided the most accurate predictions, followed by logistic regression, CART, and GARP. The most suitable habitats were predicted to occur in the western US, in relatively cool sites, and at high elevations with a high base-flow index. The results provide insights into the factors that affect the distribution of D geminata and a spatial basis for the prediction of nuisance blooms. ?? The Ecological Society of America.

Frontiers in Ecology and the Environment↗
USGS2008

Modelling invasion for a habitat generalist and a specialist plant species

Predicting suitable habitat and the potential distribution of invasive species is a high priority for resource managers and systems ecologists. Most models are designed to identify habitat characteristics that define the ecological niche of a species with little consideration to individual species' traits. We tested five commonly used modelling methods on two invasive plant species, the habitat generalist Bromus tectorum and habitat specialist Tamarix chinensis, to compare model performances, evaluate predictability, and relate results to distribution traits associated with each species. Most of the tested models performed similarly for each species; however, the generalist species proved to be more difficult to predict than the specialist species. The highest area under the receiver-operating characteristic curve values with independent validation data sets of B. tectorum and T. chinensis was 0.503 and 0.885, respectively. Similarly, a confusion matrix for B. tectorum had the highest overall accuracy of 55%, while the overall accuracy for T. chinensis was 85%. Models for the generalist species had varying performances, poor evaluations, and inconsistent results. This may be a result of a generalist's capability to persist in a wide range of environmental conditions that are not easily defined by the data, independent variables or model design. Models for the specialist species had consistently strong performances, high evaluations, and similar results among different model applications. This is likely a consequence of the specialist's requirement for explicit environmental resources and ecological barriers that are easily defined by predictive models. Although defining new invaders as generalist or specialist species can be challenging, model performances and evaluations may provide valuable information on a species' potential invasiveness.

Diversity and Distributions↗
USGS2007

How many tigers Panthera tigris are there in Huai Kha Khaeng Wildlife Sanctuary, Thailand? An estimate using photographic capture-recapture sampling

We used capture-recapture analyses to estimate the density of a tiger Panthera tigris population in the tropical forests of Huai Kha Khaeng Wildlife Sanctuary, Thailand, from photographic capture histories of 15 distinct individuals. The closure test results (z = 0.39, P = 0.65) provided some evidence in support of the demographic closure assumption. Fit of eight plausible closed models to the data indicated more support for model Mh, which incorporates individual heterogeneity in capture probabilities. This model generated an average capture probability = 0.42 and an abundance estimate of = 19 (9.65) tigers. The sampled area of = 477.2 (58.24) km 2 yielded a density estimate of = 3.98 (0.51) tigers per 100 km 2 . Huai Kha Khaeng Wildlife Sanctuary could therefore hold 113 tigers and the entire Western Forest Complex c. 720 tigers. Although based on field protocols that constrained us to use sub-optimal analyses, this estimated tiger density is comparable to tiger densities in Indian reserves that support moderate prey abundances. However, tiger densities in well-protected Indian reserves with high prey abundances are three times higher. If given adequate protection we believe that the Western Forest Complex of Thailand could potentially harbour >2,000 wild tigers, highlighting its importance for global tiger conservation. The monitoring approaches we recommend here would be useful for managing this tiger population.

Huai Kha Khaeng Wildlife Sanctuary↗
USGS2007

Modeling aboveground biomass of Tamarix ramosissima in the Arkansas River Basin of Southeastern Colorado, USA

Predictive models of aboveground biomass of nonnative Tamarix ramosissima of various sizes were developed using destructive sampling techniques on 50 individuals and four 100-m2 plots. Each sample was measured for average height (m) of stems and canopy area (m2) prior to cutting, drying, and weighing. Five competing regression models (P < 0.05) were developed to estimate aboveground biomass of T. ramosissima using average height and/or canopy area measurements and were evaluated using Akaike's Information Criterion corrected for small sample size (AICc). Our best model (AICc = -148.69, ??AICc = 0) successfully predicted T. ramosissima aboveground biomass (R2 = 0.97) and used average height and canopy area as predictors. Our 2nd-best model, using the same predictors, was also successful in predicting aboveground biomass (R2 = 0.97, AICc = -131.71, ??AICc = 16.98). A 3rd model demonstrated high correlation between only aboveground biomass and canopy area (R2 = 0.95), while 2 additional models found high correlations between aboveground biomass and average height measurements only (R2 = 0.90 and 0.70, respectively). These models illustrate how simple field measurements, such as height and canopy area, can be used in allometric relationships to accurately predict aboveground biomass of T. ramosissima. Although a correction factor may be necessary for predictions at larger scales, the models presented will prove useful for many research and management initiatives.

Western North American Naturalist↗
USGS2007

Forest legacies, climate change, altered disturbance regimes, invasive species and water

The factors that must be considered in seeking to predict changes in water availability has been examined. These factors are the following: forest legacies including logging, mining, agriculture, grazing, elimination of large carnivores, human-caused wildfire, and pollution; climate change and stream flow; altered disturbances such as frequency intensity and pattern of wildfires and insect outbreaks as well as flood control; lastly, invasive species like forest pests and pathogens. An integrated approach quantifying the current and past condition trends can be combined with spatial and temporal modeling to develop future change in forest structures and water supply. The key is a combination of geographic information system technologies with climate and land use scenarios, while preventing and minimizing the effects of harmful invasive species.

Unasylva↗
USGS2006

Assessing tiger population dynamics using photographic capture-recapture sampling

Although wide-ranging, elusive, large carnivore species, such as the tiger, are of scientific and conservation interest, rigorous inferences about their population dynamics are scarce because of methodological problems of sampling populations at the required spatial and temporal scales. We report the application of a rigorous, noninvasive method for assessing tiger population dynamics to test model-based predictions about population viability. We obtained photographic capture histories for 74 individual tigers during a nine-year study involving 5725 trap-nights of effort. These data were modeled under a likelihood-based, ?robust design? capture?recapture analytic framework. We explicitly modeled and estimated ecological parameters such as time-specific abundance, density, survival, recruitment, temporary emigration, and transience, using models that incorporated effects of factors such as individual heterogeneity, trap-response, and time on probabilities of photo-capturing tigers. The model estimated a random temporary emigration parameter of =K' =Y' 0.10 ? 0.069 (values are estimated mean ? SE). When scaled to an annual basis, tiger survival rates were estimated at S = 0.77 ? 0.051, and the estimated probability that a newly caught animal was a transient was = 0.18 ? 0.11. During the period when the sampled area was of constant size, the estimated population size Nt varied from 17 ? 1.7 to 31 ? 2.1 tigers, with a geometric mean rate of annual population change estimated as = 1.03 ? 0.020, representing a 3% annual increase. The estimated recruitment of new animals, Bt, varied from 0 ? 3.0 to 14 ? 2.9 tigers. Population density estimates, D, ranged from 7.33 ? 0.8 tigers/100 km2 to 21.73 ? 1.7 tigers/100 km2 during the study. Thus, despite substantial annual losses and temporal variation in recruitment, the tiger density remained at relatively high levels in Nagarahole. Our results are consistent with the hypothesis that protected wild tiger populations can remain healthy despite heavy mortalities because of their inherently high reproductive potential. The ability to model the entire photographic capture history data set and incorporate reduced-parameter models led to estimates of mean annual population change that were sufficiently precise to be useful. This efficient, noninvasive sampling approach can be used to rigorously investigate the population dynamics of tigers and other elusive, rare, wide-ranging animal species in which individuals can be identified from photographs or other means.

Ecology↗
USGS2004

Tigers and their prey: Predicting carnivore densities from prey abundance

The goal of ecology is to understand interactions that determine the distribution and abundance of organisms. In principle, ecologists should be able to identify a small number of limiting resources for a species of interest, estimate densities of these resources at different locations across the landscape, and then use these estimates to predict the density of the focal species at these locations. In practice, however, development of functional relationships between abundances of species and their resources has proven extremely difficult, and examples of such predictive ability are very rare. Ecological studies of prey requirements of tigers Panthera tigris led us to develop a simple mechanistic model for predicting tiger density as a function of prey density. We tested our model using data from a landscape-scale long-term (1995-2003) field study that estimated tiger and prey densities in 11 ecologically diverse sites across India. We used field techniques and analytical methods that specifically addressed sampling and detectability, two issues that frequently present problems in macroecological studies of animal populations. Estimated densities of ungulate prey ranged between 5.3 and 63.8 animals per km 2 . Estimated tiger densities (3.2-16.8 tigers per 100 km 2 ) were reasonably consistent with model predictions. The results provide evidence of a functional relationship between abundances of large carnivores and their prey under a wide range of ecological conditions. In addition to generating important insights into carnivore ecology and conservation, the study provides a potentially useful model for the rigorous conduct of macroecological science.

Proceedings of the National Academy of Sciences↗