Spatio-Temporal Changes in Vegetation in the Last Two Decades (2001–2020) in the Beijing–Tianjin–Hebei Region

In terrestrial ecosystems, vegetation is sensitive to climate change and human activities. Its spatial-temporal changes also affect the ecological and social environment. In this paper, we considered the Beijing–Tianjin–Hebei region to study the spatio-temporal vegetation patterns. The detailed analysis of a moderate-resolution imaging spectroradiometer (MODIS) data were carried out through the Google Earth Engine (GEE) platform. Our results show a slow and tortuous upward trend in the average leaf area index (LAI) in the study region for the periods 2001–2020. Specifically, Beijing had the highest LAI value, with an average of 1.64 over twenty years, followed by Hebei (1.30) and Tianjin (1.04). Among different vegetation types, forests had the highest normalized difference vegetation index (NDVI) with the range of 0.62–0.78, followed by shrubland (0.58–0.75), grassland (0.34–0.66), and cropland (0.38–0.54) over the years. Spatially, compared to the whole study area, index value in the northwestern part of the Beijing–Tianjin–Hebei region increased greatly in many areas, such as northwest Beijing, Chengde, and Zhangjiakou, indicating a significant ecological optimization. Meanwhile, there was ecological degradation in the middle and southeast regions, from Tangshan southeastward to Handan, crossing Tianjin, Langfang, the east part of Baoding, Shijiazhuang, and the west part of Cangzhou. Air temperature and precipitation were positively and significantly correlated with net primary production (NPP) and precipitation stood out as a key driver. Additionally, an intensification of the urbanization rate will negatively impact the vegetation NPP, with the shrubland and forest being affected most relative to the cropland

Hydrometeorological Responses to Abrupt Land Surface Change Following Hurricane Michael

While many of the destructive environmental conditions associated with tropical cyclones are well recognized, tropical cyclone-induced defoliation, a reduction in green leaves and mature vegetation, has been largely overlooked as a source of environmental stress following tropical cyclone passage. The land surface change associated with defoliation reduces evapotranspiration and shade, thus altering boundary layer moisture and energy fluxes that drive the local water cycle, for many months after tropical cyclone passage. Understanding the potential for any hydrometeorological impacts arising from such abrupt land surface change is important for guiding future post-hurricane preparedness and recovery planning in coastal communities. This thesis investigates spatial and temporal changes in defoliation-related precipitation and cloud activity in the month following Hurricane Michael’s (2018) passage through Florida, as well as the potential modification of flash flood frequency one year following the storm. Two Weather Research and Forecasting (WRF) model, version 3.8, simulations are employed to determine the degree to which defoliation from Michael alters heat fluxes, temperature, relative humidity, cloud fraction, and precipitation during the one-month post-storm study period near the storm’s track. A preliminary analysis of historical flash flood reports is also performed to assess relative changes in flash flood frequency near the defoliated area during the year after landfall. In the month following Michael, modeled 2-m temperature increased by 0.7 C°, with the greatest temperature change occurring at night, and sensible heat flux increased by 8.3 W m-2. Average relative humidity decreased from 73% to 70.1%, and latent heat flux decreased by an average of 13.9 W m-2. The discrepancy between the decrease in latent heat flux and increase in sensible heat flux approximately matches the increased daytime downward ground heat flux. Additionally, the defoliated simulation demonstrated decreased low-cloud fraction while mid-level cloud cover showed an increasing trend, indicating a potential ascension in the cloud base height. Coupled with the reduction in relative humidity, this suggests that with less near-surface moisture, air parcels needed to ascend higher to reach saturation. Precipitation accumulation change is insignificant when averaged over one month, yet evidence of redistribution nearest Michael’s track is found

Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)

Reactive gases and aerosols are produced by terrestrial ecosystems, processed within plant canopies, and can then be emitted into the above-canopy atmosphere. Estimates of the above-canopy fluxes are needed for quantitative earth system studies and assessments of past, present and future air quality and climate. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) is described and used to quantify net terrestrial biosphere emission of isoprene into the atmosphere. MEGAN is designed for both global and regional emission modeling and has global coverage with ~1 km² spatial resolution. Field and laboratory investigations of the processes controlling isoprene emission are described and data available for model development and evaluation are summarized. The factors controlling isoprene emissions include biological, physical and chemical driving variables. MEGAN driving variables are derived from models and satellite and ground observations. Tropical broadleaf trees contribute almost half of the estimated global annual isoprene emission due to their relatively high emission factors and because they are often exposed to conditions that are conducive for isoprene emission. The remaining flux is primarily from shrubs which have a widespread distribution. The annual global isoprene emission estimated with MEGAN ranges from about 500 to 750 Tg isoprene (440 to 660 Tg carbon) depending on the driving variables which include temperature, solar radiation, Leaf Area Index, and plant functional type. The global annual isoprene emission estimated using the standard driving variables is ~600 Tg isoprene. Differences in driving variables result in emission estimates that differ by more than a factor of three for specific times and locations. It is difficult to evaluate isoprene emission estimates using the concentration distributions simulated using chemistry and transport models, due to the substantial uncertainties in other model components, but at least some global models produce reasonable results when using isoprene emission distributions similar to MEGAN estimates. In addition, comparison with isoprene emissions estimated from satellite formaldehyde observations indicates reasonable agreement. The sensitivity of isoprene emissions to earth system changes (e.g., climate and land-use) demonstrates the potential for large future changes in emissions. Using temperature distributions simulated by global climate models for year 2100, MEGAN estimates that isoprene emissions increase by more than a factor of two. This is considerably greater than previous estimates and additional observations are needed to evaluate and improve the methods used to predict future isoprene emissions

Attribution of Soil Surface Temperature Sensitivity to Hydro-climatic Drivers

Greenhouse gas emissions caused by human economic activity are altering the global hydrologic cycle and the energy exchanges at the land surface. In large portions of the western US there is evidence of reduced summertime precipitation and increased air temperatures and longwave irradiation. At local scales, these changes can translate into more frequent and intense extreme land surface temperature events during the summer, with potential impacts on wildfire activity, forest health, soil biochemical cycles, and thermal comfort for human populations. However, because increases in radiation and sensible heat (air temperature) inputs to the land surface are confounded with changes in water availability, which alter the way the surface energy balance is reapportioned, it is difficult to disentangle the specific contributions of these factors to the observed dynamics of land surface temperatures. This thesis contributes insight into this problem using a combination of analytical and numerical model applications in a plot and for the city of Missoula, MT. In the first chapter of this thesis we used analytical method on a surface energy balance equation to identify and assess the attribution of surface temperature sensitivities to key hydro-climatic drivers in a plot of soil with and without vegetation canopy cover. The second chapter uses an ecohydrological model to investigate the effect of perturbations in water input regimes (additions to soil moisture) on surface temperatures for different land covers in a semi-arid urban area (Missoula, MT)

APPLICATION OF HIERARCHICAL SPECIES DISTRIBUTION MODELS TO AVIAN SPECIES OF SOUTH DAKOTA AND THE UPPER MISSOURI RIVER BASIN

Recognizing the distributional patterns of species can inform management actions and increase scientific knowledge about species. Habitat Suitability Models (HSMs) are valuable tools in modeling species’ niches and effects of climate change and anthropogenic and natural disturbances on species’ distributions and abundances. In this dissertation, I expanded the application of hierarchical HSMs for a rare bird (Virginia’s warbler) and an economically valuable bird (ring-necked pheasant) in South Dakota. Also, we developed multiscale HSMs for grassland birds in the Upper Missouri River Basin (UMRB) to quantify current habitat associations and predict the influences of climate and landcover change associated with the implementation of bioenergy with carbon capture and storage (BECCS) and other carbon mitigation scenarios. We found that applying an Ensemble of Small Models (ESMs) approach within a hierarchical framework can lead to detailed information about niches of rare species, limiting factors at each habitat selection order, and potential distribution, which could help inform multiscale management. At the broadest habitat selection order, Virginia’s warbler had a narrow climatic niche. The importance of environmental variables changed across finer orders, such that at broader orders many covariates were important whereas at finer orders certain covariates became more important than others. For the model of pheasant abundance, my results showed that our hierarchical Bayesian approach allows for simultaneous selection of variables and scales of effect. I found that pheasant abundance was positively affected by intermediate levels of grassland cover. Scales of effect and spatiotemporal variation influenced predictor variable impacts on pheasant abundance. For the modeling of grassland birds across the UMRB, my results showed that the influence of climate change on abundance, distribution and species richness of grassland species is more pronounced than the influence of landcover changes due to implementing BECCS scenarios. This finding implies that regardless of landcover and land-use changes, climate change may limit or expand abundance and distribution of grassland bird species in the UMRB. Further, we found that grassland birds will be more affected by regional increases in temperature than decreases in precipitation

Mapping evapotranspiration variability over a complex oasis-desert ecosystem based on automated calibration of Landsat 7 ETM+ data in SEBAL

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature T-s and Normalized Difference Vegetation Index NDVI (). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: - desert ecosystem ETa R-2 = 0.94; oasis ecosystem ETa R-2 = 0.98 (P < 0.05). The improvement uncovered the exact land use contributions to ETa variability, with average estimates ranging from 1.24 mm to 6.98 mm . Additionally, instantaneous ET to NDVI (ETins-NDVI) ratio indicated that desert and oasis consumptive water use vary significantly with time of the season. This study indicates the possibility of continuous daily ET monitoring with considerable implications for improving water resources decision support over complex data-scarce drylands

Remote sensing applications to resource problems in South Dakota

Cooperative projects between RSI and numerous South Dakota agencies have provided a means of incorporating remote sensing techniques into operational programs. Eight projects discussed in detail are: (1) detection of high moisture zones near interstate 90; (2) thermal infrared census of Canada geese in South Dakota; (3) dutch elm disease detection in urban environment; (4) a feasibility study for monitoring effective precipitation in South Dakota using TIROS-N; (5) open and abandoned dump sites in Spink county; (6) the influence of soil reflectance on LANDSAT signatures of crops; (7) A model implementation program for Lake Herman watershed; and (8) the Six-Mile Creek investigation follow-on

Simulations of snow distribution and hydrology in a mountain basin

We applied a version of the Regional Hydro‐Ecologic Simulation System (RHESSys) that implements snow redistribution, elevation partitioning, and wind‐driven sublimation to Loch Vale Watershed (LVWS), an alpine‐subalpine Rocky Mountain catchment where snow accumulation and ablation dominate the hydrologic cycle. We compared simulated discharge to measured discharge and the simulated snow distribution to photogrammetrically rectified aerial (remotely sensed) images. Snow redistribution was governed by a topographic similarity index. We subdivided each hillslope into elevation bands that had homogeneous climate extrapolated from observed climate. We created a distributed wind speed field that was used in conjunction with daily measured wind speeds to estimate sublimation. Modeling snow redistribution was critical to estimating the timing and magnitude of discharge. Incorporating elevation partitioning improved estimated timing of discharge but did not improve patterns of snow cover since wind was the dominant controller of areal snow patterns. Simulating wind‐driven sublimation was necessary to predict moisture losses

Energy-Water Balance and Ecosystem Response to Climate Change in Southwest China

It is important to highlight energy-water balance and ecosystem response to climate changes. The change of water-energy balance and ecosystem due to climate change will affect the regional ecological and human living significantly, especially in Southwest China which is an ecologically fragile area. This chapter presents the retrieval methodology of parameters (reconstruction of vegetation index, land cover semi-automatic classification, a time series reconstruction of land surface temperature based on Kalman filter and precipitation interpolation based on thin plate smoothing splines), time-series analysis methodology (land cover change, vegetation succession and drought index) and correlate analysis methodology (correlation coefficient and principal component analysis). Then, based on the above method, remote sensing data were integrated, a time series analysis on a 30-year data was used to illustrate the water-energy balance and ecosystem variability in Southwest China. The result showed that energy-water balance and ecosystem (ecosystem structures, vegetation and droughts) have severe response to climate change