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Yang Hong
,
Thomas Connor
,
Huan Luo
,
Xiaoxing Bian
,
Zhaogang Duan
,
Zhuo Tang
, and
Jindong Zhang

Abstract

There is increasing conflict between snow leopards and humans in many protected areas, the main driver of which is the overlap in spatial utilization between snow leopards and livestock. Understanding the spatial utilization and microhabitat selection of snow leopards in areas featuring different levels of livestock grazing is important to better understand and resolve this conflict, but such studies are rare. Here, we conducted line transect and plot surveys in low- and high-grazing-disturbance areas (LGDAs and HGDAs) in Wolong National Reserve, southwestern China. We compared snow leopard spatial utilization and microhabitat characteristics between LGDAs and HGDAs. Results showed that snow leopards had aggregated distribution in both LGDAs and HGDAs, but the distribution of snow leopards in HGDAs was more centralized than in LGDAs. Herb cover and height in LGDAs were greater than in HGDAs. We fit a resource selection function (RSF) that showed that snow leopards preferentially selected higher elevation, smaller basal diameter of shrubs, and lower height of herbs in LGDAs. In contrast, there were no significant microhabitat factors in our snow leopard RSF in HGDAs. Our results indicate that high-intensity grazing tends to reduce the habitat types available to and preferential selectivity of habitat by snow leopards. We recommend that livestock grazing should be controlled to restore the diversity of the alpine ecosystems in Wolong Nature Reserve. Our findings also highlight the need for evaluating the impact of livestock grazing on rare animals in alpine environments (e.g., snow leopard) in other areas facing similar issues.

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Jason Naylor
and
Aaron D. Kennedy

Abstract

This study analyzes the frequency of strong, isolated convective cells in the vicinity of Louisville, Kentucky. Data from the Severe Weather Data Inventory are used to compare the frequency of convective activity over Louisville with the observed frequency at nearby rural locations from 2003 to 2019. The results show that Louisville experiences significantly more isolated convective activity than do the rural locations. The difference in convective activity between Louisville and the rural locations is strongest during summer, with peak differences occurring between May and August. Relative to the rural locations, Louisville experiences more isolated convective activity in the afternoon and early evening but less activity after midnight and into the early morning. Isolated convective events over Louisville are most likely during quiescent synoptic conditions, whereas rural events are more likely during active synoptic patterns. To determine whether these differences can be attributed primarily to urban effects, two additional cities are shown for comparison—Nashville, Tennessee, and Cincinnati, Ohio. Both Nashville and Cincinnati experience more isolated convective activity than all five of their nearby rural comparison areas, but the results for both are statistically significant at four of the five rural locations. In addition, the analysis of Cincinnati includes a sixth comparison site that overlaps the urbanized area of Columbus, Ohio. For that location, differences in convective activity are not statistically significant.

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Ansar Khan
,
Samiran Khorat
,
Rupali Khatun
,
Quang-Van Doan
,
U. S. Nair
, and
Dev Niyogi

Abstract

India responded to the severe acute respiratory syndrome (SARS) coronavirus disease 2019 (COVID-19) pandemic through a three-phase nationwide lockdown: 25 March–14 April, 15 April–3 May, and 4–17 May 2020. We utilized this unique opportunity to assess the impact of restrictions on the air quality of Indian cities. We conducted comprehensive statistical assessments for the air quality index (AQI) and criteria pollutant concentrations for 91 cities during the lockdown phases relative to the preceding seven days (prelockdown phase of 18–24 March 2020) and to corresponding values from the same days of the year in 2019. Both comparisons show statistically significant countrywide mean decrease in AQI (33%), PM2.5 (36%), PM10 (40%), NO2 (58%), O3 (5%), SO2 (25%), NH3 (28%), and CO (60%). These reductions represent a background or the lower bound of air quality burden of industrial and transportation sectors. The northern region was most impacted by the first two phases of the lockdown, whereas the southern region was most affected in the last phase. The northeastern region was least affected, followed by the eastern region, which also showed an increase in O3 during the lockdown. Analysis of satellite-retrieved aerosol optical depth (AOD) shows that effects of restrictions on particulate pollution were variable—locally confined in some areas or having a broader impact in other regions. Anomalous behavior over the eastern region suggests a differing role of regional societal response or meteorological conditions. The study results have policy implications because they provide the observational background values for the industrial and transportation sector’s contribution to urban pollution.

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Brian E. Potter
and
Daniel McEvoy

Abstract

“Megafires” are of scientific interest and concern for fire management, public safety planning, and smoke-related public health management. There is a need to predict them on time scales from days to decades. Understanding is limited, however, of the role of daily weather in determining their extreme size. This study examines differences in the daily weather during these and other smaller fires, and in the two sets of fires’ responses to daily weather and antecedent atmospheric dryness. Twenty fires of unusual size (over 36 400 ha), were each paired with a nearby large fire (10 100–30 300 ha). Antecedent dryness and daily near-surface weather were compared for each set of fires. Growth response to daily weather was also examined for differences between the two sets of fires. Antecedent dryness measured as the evaporative demand drought index was greater for most of the fires of unusual size than it was for smaller fires. There were small differences in daily weather, with those differences indicating weather less conducive to fire growth for the unusually large fires than the smaller fires. Growth response was similar for the two sets of fires when weather properties were between 40th and 60th percentiles for each fire pair, but the unusually large fires’ growth was observably greater than the smaller fires’ growth for weather properties between the 80th to 100th percentiles. Response differences were greatest for wind speed, and for the Fosberg fire weather index and variants of the hot-dry-windy index, which combine wind speed with atmospheric moisture.

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A. S. Alhumaima
and
S. M. Abdullaev

Abstract

The primary aim of this work is to study the response of the normalized difference vegetation index (NDVI) of landscapes in the lower Tigris basin to current global and regional climate variability presented, respectively, by the global circulation indices and monthly temperatures and precipitation extracted from five observational/reanalysis datasets. The second task is to find the dataset that best reflects the regional vegetation and climate conditions. Comparison of the Köppen–Trewartha bioclimatic landscapes with the positions of botanical districts, land-cover types, and streamflow estimates led to the conclusion that only two datasets correctly describe regional climatic zones. Therefore, searching for the NDVI response to regional climate variability requires the use of normalized analogs of temperatures and precipitations, as well as the Spearman rank correlation. We found that March/April NDVI, as proxies of the maximum biological productivity of the regional landscapes, are strongly correlated with October–March precipitation derived from three datasets and January–March temperatures derived from one dataset. We discovered the significant impact of autumn–winter El Niño–Southern Oscillation and winter Indian Oceanic dipole states on regional weather (e.g., all five recent severe droughts occurred during strong La Niña events). However, the strength of this impact on the vegetation was clearly linked to the zonal landscape type. By selecting pairs of the temperature/precipitation time series that best correlated with NDVI at a given landscape, we have built a synthetic climate dataset. The landscape approach presented in this work can be used to validate the viability of any dataset when assessing the impacts of climate change and variability on weather-dependent components of Earth’s surface.

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Patricia M. Lawston
,
Joseph A. Santanello Jr.
,
Brian Hanson
, and
Kristi Arsensault

Abstract

Irrigation has the potential to modify local weather and regional climate through a repartitioning of water among the surface, soil, and atmosphere with the potential to drastically change the terrestrial energy budget in agricultural areas. This study uses local observations, satellite remote sensing, and numerical modeling to 1) explore whether irrigation has historically impacted summer maximum temperatures in the Columbia Plateau, 2) characterize the current extent of irrigation impacts to soil moisture (SM) and land surface temperature (LST), and 3) better understand the downstream extent of irrigation’s influence on near-surface temperature, humidity, and boundary layer development. Analysis of historical daily maximum temperature (TMAX) observations showed that the three Global Historical Climate Network (GHCN) sites downwind of Columbia Basin Project (CBP) irrigation experienced statistically significant cooling of the mean summer TMAX by 0.8°–1.6°C in the post-CBP (1968–98) as compared to pre-CBP expansion (1908–38) period, opposite the background climate signal. Remote sensing observations of soil moisture and land surface temperatures in more recent years show wetter soil (~18%–25%) and cooler land surface temperatures over the irrigated areas. Simulations using NASA’s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) Model support the historical analysis, confirming that under the most common summer wind flow regime, irrigation cooling can extend as far downwind as the locations of these stations. Taken together, these results suggest that irrigation expansion may have contributed to a reduction in summertime temperatures and heat extremes within and downwind of the CBP area. This supports a regional impact of irrigation across the study area.

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Xuebin Yang

Abstract

Woody plant cover, the area of the vertical projection of woody plants (trees, shrubs, and bushes), plays an important role in the structure and function of savanna ecosystems and is needed by the savanna modeling community. Recent problems facing savanna ecosystems such as woody plant encroachment and subsequent habitat fragmentation further underscore the relevance of regional-scale and even larger-scale woody plant cover mapping. The mixture of woody plants and herbaceous vegetation in savanna landscapes lends woody plant cover mapping to fractional representation. This study endeavors to develop a simple and reliable approach for fractional woody plant cover mapping in savanna ecosystems. It was tested in the savanna of central Texas, which features a wide woody plant density gradation. A multiple linear regression model was calibrated between orthophoto-based fractional woody plant cover and metrics derived from time series MODIS products of surface reflectance (MOD09A1) and fraction of photosynthetically active radiation (MOD15A2H). By applying this model, woody plant cover was extrapolated to Texas savanna at MODIS scale (500 m). Validation suggests a mean absolute error of 0.098 and an R-squared value of 0.60. This study demonstrates a potential approach for woody plant cover mapping in other savanna ecosystems of the world. It also highlights the utility of time series MODIS products in savanna woody plant cover estimation.

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Peiyun Zhu
,
Susan J. Cheng
,
Zachary Butterfield
,
Gretchen Keppel-Aleks
, and
Allison L. Steiner

Abstract

Clouds can modify terrestrial productivity by reducing total surface radiation and increasing diffuse radiation, which may be more evenly distributed through plant canopies and increase ecosystem carbon uptake (the “diffuse fertilization effect”). Previous work at ecosystem-level observational towers demonstrated that diffuse photosynthetically active radiation (PAR; 400–700 nm) increases with cloud optical thickness (COT) until a COT of approximately 10, defined here as the “low-COT regime.” To identify whether the low-COT regime also influences carbon uptake on broader spatial and longer temporal time scales, we use global, monthly data to investigate the influence of COT on carbon uptake in three land-cover types: shrublands, forests, and croplands. While there are limitations in global gross primary production (GPP) products, global COT data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reveal that during the growing season tropical and subtropical regions more frequently experience a monthly low-COT regime (>20% of the time) than other regions of the globe. Contrary to ecosystem-level studies, comparisons of monthly COT with monthly satellite-derived solar-induced chlorophyll fluorescence and modeled GPP indicate that, although carbon uptake generally increases with COT under the low-COT regime, the correlations between COT and carbon uptake are insignificant (p > 0.05) in shrublands, forests, and croplands at regional scales. When scaled globally, vegetated regions under the low-COT regime account for only 4.9% of global mean annual GPP, suggesting that clouds and their diffuse fertilization effect become less significant drivers of terrestrial carbon uptake at broader spatial and temporal scales.

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Amanda Markert
,
Robert Griffin
,
Kevin Knupp
,
Andrew Molthan
, and
Tim Coleman

Abstract

North Alabama is among the most tornado-prone regions in the United States and is composed of more spatially variable terrain and land cover than the frequently studied North American Great Plains region. Because of the high tornado frequency observed across north Alabama, there is a need to understand how land surface roughness heterogeneity influences tornadogenesis, particularly for weak-intensity tornadoes. This study investigates whether horizontal gradients in land surface roughness exist surrounding locations of tornadogenesis for weak (EF0–EF1) tornadoes. The existence of the horizontal gradients could lead to the generation of positive values of the vertical components of the 3D vorticity vector near the surface that may aid in the tornadogenesis process. In this study, surface roughness was estimated using parameterizations from the Noah land surface model with inputs from MODIS 500-m and Landsat 30-m data. Spatial variations in the parameterized roughness lengths were assessed using GIS-based grid and quadrant pattern analyses to quantify observed variation of land surface features surrounding tornadogenesis locations across spatial scales. This analysis determined that statistically significant horizontal gradients in surface roughness exist surrounding tornadogenesis locations.

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Jessica V. Fayne
,
Aakash Ahamed
,
Justin Roberts-Pierel
,
Amanda C. Rumsey
, and
Dalia Kirschbaum

Abstract

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open-source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat-8 Operational Land Imager sensor, elevation data from the Shuttle Radar Topography Mission, and precipitation data from the Global Precipitation Measurement mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change-detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-Time Increased Precipitation (DRIP) model that helps to identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state of the art for landslide detection. A case study and validation exercise in Nepal were performed for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium-resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool.

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