Abstract

The accurate prediction of plant phenology is of significant importance for more sustainable and effective land management. This research develops a framework of phenological modeling to estimate vegetation abundance [indicated by the normalized difference vegetation index (NDVI)] 7 days into the future in the geographically diverse Upper Colorado River basin (UCRB). This framework uses phenological regions (phenoregions) as the basic units of modeling to account for the spatially variant environment–vegetation relationships. The temporal variation of the relationships is accounted for via the identification of phenological phases. The modeling technique of Multivariate Adaptive Regression Splines (MARS) is employed and tested as an approach to construct enhanced predictive phenological models in each phenoregion using a comprehensive set of environmental drivers and factors. MARS has the ability to deal with a large number of independent variables and to approximate complex relationships. The R ² values of the models range from 91.62% to 97.22%. The root-mean-square error values of all models are close to their respective standard errors ranging from 0.016 to 0.035, as indicated by the results of cross and field validations. These demonstrate that the modeling framework ensures the accurate prediction of short-term vegetation abundance in regions with various environmental conditions.

Abstract

Lakes have been suggested as an indicator of climate change; however, long-term, systematic records of lake temperature are limited. Satellite remote sensing is capable of supporting lake temperature mapping with the advantage of large-area and systematic observations. The goal of this research application was to assess spatiotemporal trends in lake skin temperature for all lakes over 8 ha across northern New England for the past three decades. Nearly 10 000 Landsat scenes for July, August, and September from 1984 to 2014 were processed using MODTRAN and MERRA parameterizations to generate atmospherically corrected lake skin temperature records. Results show, on average, lakes warmed at a rate of 0.8°C decade⁻¹, with smaller lakes warming at a faster rate. Complementing regression and space–time analyses showed similar results (R ² = 0.63) for lake temperature trends and found lakes, on average, are warming faster than daily maximum or minimum air temperature. No major hot spots were found as lake temperature changes were heterogeneous on a local scale and evenly distributed across the region. Maximum and minimum daily temperature, lake size, and elevation were found as significant drivers of lake temperature. This effort provides the first regionally focused and comprehensive spatiotemporal assessment of thousands (n = 3955) of lakes concentrated in one geographic region. The approach is scalable and adaptable to any region for assessing lake temperature trends and potential drivers.

Abstract

The rising emissions of reactive nitrogen (Nr) from transport, industrial, and agricultural sectors in India have resulted in its consequent interactions with the removal mechanism of the atmospheric dust. This study, therefore, reports the fluxes of reactive nitrogen along with other inorganic species through dustfall over six sites of Delhi–National Capital Region (NCR) characterized by the changing dynamics of its different land-use pattern. The highest Nr fluxes were observed at site SMA Industrial estate (SMA; NO₃ ⁻ = 16.45 ± 10.17 mg m⁻² day⁻¹, NH₄ ⁺ = 16.33 ± 16.00 mg m⁻² day⁻¹) and lowest at site Chuchchakwas village (CV; NO₃ ⁻ = 1.24 ± 0.16 mg m⁻² day⁻¹, NH₄ ⁺ = 0 mg m⁻² day⁻¹). Sites Mukherjee Nagar (MN), Peeragarhi Chowk (PC), Jawaharlal Nehru University (JNU), and Noida Phase II (N-II), on the other hand, showed 3.59 ± 1.00, 3.39 ± 0.61, 2.98 ± 0.84, and 3.36 ± 0.78 mg m⁻² day⁻¹ of NO₃ ⁻ fluxes and 0.30 ± 0.06, 0.22 ± 0.04, 0.21 ± 0.04, and 0.22 ± 0.05 mg m⁻² day⁻¹ of NH₄ ⁺ fluxes, respectively. The fraction of the total ions in the water soluble extract of the dustfall was also noticed to be the highest at the SMA site (22.2%) and lowest at the CV site (1.5%) with MN, PC, JNU, and N-II showing 3.5%, 3.7%, 2.9%, and 3.9% of their respective contributions. Relative abundances of Ca²⁺ and SO₄ ²⁻ in the dustfall substantiated the stoichiometric reactions involved in Nr scavenging. The role of Ca²⁺ in the spatiotemporal variability of Nr fluxes was established with the help of neutralization ratios and regression plots. Morphological and particle size analysis further confirmed the anthropogenic-induced crustal interferences in the summertime dustfall fluxes of Nr species.

Abstract

The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.

Abstract

The vast forests and natural areas of the Pacific Northwest compose one of the most productive ecosystems in the Northern Hemisphere. The heterogeneous landscape of Oregon poses a particular challenge to ecosystem models. This study presents a framework using a scaling factor Bayesian inversion to improve the modeled atmosphere–biosphere exchange of CO₂. Observations from five CO/CO₂ towers, eddy covariance towers, and airborne campaigns were used to constrain the Community Land Model, version 4.5 (CLM4.5), simulated terrestrial CO₂ exchange at a high spatial and temporal resolution (1/24°; 3 hourly). To balance aggregation errors and the degrees of freedom in the inverse modeling system, the authors applied an unsupervised clustering approach for the spatial structuring of the model domain. Data from flight campaigns were used to quantify the uncertainty introduced by the Lagrangian particle dispersion model that was applied for the inversions. The average annual statewide net ecosystem productivity (NEP) was increased by 32% to 29.7 TgC yr⁻¹ by assimilating the tropospheric mixing ratio data. The associated uncertainty was decreased by 28.4%–29% on average over the entire Oregon model domain with the lowest uncertainties of 11% in western Oregon. The largest differences between posterior and prior CO₂ fluxes were found for the Coast Range ecoregion of Oregon that also exhibits the highest availability of atmospheric observations and associated footprints. In this area, covered by highly productive Douglas fir forest, the differences between the prior and posterior estimate of NEP averaged 3.84 TgC yr⁻¹ during the study period from 2012 through 2014.

Abstract

This study analyzes a convective outbreak over the Red Sea on 25 August 2009 that generated easterly waves over the Sahel, floods in Ouagadougou, and a hurricane in the east Atlantic. The convective outbreak occurred on the equatorward flank of the African easterly jet 18°–22°N and associated meridional heating gradients over the Arabian Peninsula. The Rift Valley mountains induced a vertical orographic undulation and cyclonic perturbation. Two thunderstorm clusters over the southern Red Sea received moist inflow from the Ethiopian highlands and northern Red Sea. This group of three easterly waves intensified downstream over the Sahel. One of the convective triggers was enhancement of the Arabian Ridge by the northern subtropical jet. Statistical analyses indicate that African easterly waves and subsequent tropical storms are more influenced by upstream kinematic shear than thermodynamic energy. The work offers new insights on the formation of easterly waves over the northern Rift Valley.

Abstract

Land-use and land-cover change (LULCC) due to urban expansion alter the surface albedo, heat capacity, and thermal conductivity of the surface. Consequently, the energy balance in urban regions is different from that of natural surfaces. To evaluate the changes in regional climate that could arise because of projected urbanization in the Phoenix–Tucson corridor, Arizona, this study applied the coupled WRF Model–Noah–Urban Canopy Model (UCM; which includes a detailed urban radiation scheme) to this region. Land-cover changes were represented using land-cover data for 2005 and projections to 2050, and historical North American Regional Reanalysis (NARR) data were used to specify the lateral boundary conditions. Results suggest that temperature changes will be well defined, reflecting the urban heat island (UHI) effect within areas experiencing LULCC. Changes in precipitation are less robust but seem to indicate reductions in precipitation over the mountainous regions northeast of Phoenix and decreased evening precipitation over the newly urbanized area.

Abstract

Forest degradation is the long-term and gradual reduction of canopy cover due to forest fire and unsustainable logging. A critical consequence of this process is increased atmospheric carbon emissions. Although this issue is gaining attention, forest degradation in the Brazilian Amazon has not yet been properly addressed. The claim here is that this process is not constant throughout Amazonia and varies according to colonization frontiers. Moreover, the accurate characterization of degradation requires lengthy observation periods to track gradual forest changes. The forest degradation process, the associated timeframe, spatial patterns, trajectories, and extent were characterized in the context of the Amazon frontiers of the 1990s using 28 years (1984–2011) of annual Landsat images. Given the large database and the characteristic of logging and burning, this study used data mining techniques and cell approach classification to analyze the spatial patterns and to construct associated trajectories. Multitemporal analysis indicated that forest degradation in the last two decades has caused as many interannual landscape changes as have clear-cuts. In addition, selective logging, as a major aspect of forest degradation, affected a larger amount of forest land than did forest fire. Although a large proportion of logged forest was deforested in the following years, selective logging did not always precede complete deforestation. Instead, the results indicate that logged forests were abandoned for approximately 4 years before clearance. Throughout the forest degradation process, there were no recurrent forest fires, and loggers did not revisit the forest. Forest degradation mostly occurred as a result of a single selective logging event and was associated with low-intensity forest damage.

Abstract

This paper explores the link between the anomalous warming hole in the southeastern United States and a major land-use/land-cover (LULC) change in the region. Land surface and satellite observations were analyzed to estimate the net radiative forcing due to LULC change. Albedo and latent energy were specifically addressed for the dominant LULC change of agriculture to forests. It was assumed that in the energy-limited environment of the region, the partition of changes in available energy due to albedo will mostly impact the sensible heat. The results show that in the southeastern United States, for the period of 1920 to 1992, the changes in sensible (as a result of albedo) and latent energies are in direct competition with each other. In the spring and early summer months, the croplands are in peak production and the latent energy associated with their evapotranspiration (ET) is comparable to that of the forests so the decrease in radiation due to albedo dominates the signal. However, during the late summer and fall months, most major crops have matured, thus reducing their transpiration rate while forests (particularly evergreens) maintain their foliage and with their deep roots are able to continue to transpire as long as atmospheric conditions are favorable. This later influence of latent energy appears to more than offset the increased radiative forcing from the spring and early summer. Overall, a mean annual net radiative forcing resulting from a LULC change from cropland to forests was estimated to be −1.06 W m⁻² and thus a probable contribution to the “warming hole” over the Southeast during the majority of the twentieth century.

Abstract

Understanding the interactions between large-scale atmospheric and oceanic circulation patterns and changes in land cover and land use (LCLU) due to urbanization is a relevant subject in many coastal climates. Recent studies by Lebassi et al. found that the average maximum air temperatures during the summer in two populated California coastal areas decreased at low-elevation areas open to marine air penetration during the period of 1970–2005. This coastal cooling was attributed to an increase in sea-breeze activity.

The aims of this work are to better understand the coastal flow patterns and sea–land thermal gradient by improving the land-cover classification scheme in the region using updated airborne remote sensing data and to assess the suitability of the updated regional atmospheric modeling system for representing maritime flows in this region. This study uses high-resolution airborne data from the NASA Hyperspectral Infrared Imager (HyspIRI) mission preparatory flight campaign over Southern California and surface ground stations to compare observations against model estimations.

Five new urban land classes were created using broadband albedo derived from the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) sensor and then assimilated into the Weather Research and Forecasting (WRF) Model. The updated model captures the diurnal spatial and temporal sea-breeze patterns in the region. Results show notable improvements of simulated daytime surface temperature and coastal winds using the HyspIRI-derived products in the model against the default land classification, reaffirming the importance of accounting for heterogeneity of urban surface properties.