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Abstract
The relationship between rainfall characteristics and urbanization over the eastern United States was examined by analyzing four datasets: daily rainfall in 4593 surface stations over the last 50 years (1958–2008), a high-resolution gridded rainfall product, reanalysis wind data, and a proxy for urban land use (gridded human population data). Results indicate that summer monthly rainfall amounts show an increasing trend in urbanized regions. The frequency of heavy rainfall events has a potential positive bias toward urbanized regions. Most notably, consistent with case studies for individual cities, the climatology of rainfall amounts downwind of urban–rural boundaries shows a significant increasing trend. Analysis of heavy (90th percentile) and extreme (99.5th percentile) rainfall events indicated decreasing trends of heavy rainfall events and a possible increasing trend for extreme rainfall event frequency over urban areas. Results indicate that the urbanization impact was more pronounced in the northeastern and midwestern United States with an increase in rainfall amounts. In contrast, the southeastern United States showed a slight decrease in rainfall amounts and heavy rainfall event frequencies. Results suggest that the urbanization signature is becoming detectable in rainfall climatology as an anthropogenic influence affecting regional precipitation; however, extracting this signature is not straightforward and requires eliminating other dynamical confounding feedbacks.
Abstract
The relationship between rainfall characteristics and urbanization over the eastern United States was examined by analyzing four datasets: daily rainfall in 4593 surface stations over the last 50 years (1958–2008), a high-resolution gridded rainfall product, reanalysis wind data, and a proxy for urban land use (gridded human population data). Results indicate that summer monthly rainfall amounts show an increasing trend in urbanized regions. The frequency of heavy rainfall events has a potential positive bias toward urbanized regions. Most notably, consistent with case studies for individual cities, the climatology of rainfall amounts downwind of urban–rural boundaries shows a significant increasing trend. Analysis of heavy (90th percentile) and extreme (99.5th percentile) rainfall events indicated decreasing trends of heavy rainfall events and a possible increasing trend for extreme rainfall event frequency over urban areas. Results indicate that the urbanization impact was more pronounced in the northeastern and midwestern United States with an increase in rainfall amounts. In contrast, the southeastern United States showed a slight decrease in rainfall amounts and heavy rainfall event frequencies. Results suggest that the urbanization signature is becoming detectable in rainfall climatology as an anthropogenic influence affecting regional precipitation; however, extracting this signature is not straightforward and requires eliminating other dynamical confounding feedbacks.
Abstract
A new, high-resolution (4 km), gridded land surface dataset produced with the Land Information System (LIS) is introduced, and the first set of synthesis of key hydroclimatic variables is reported. The dataset is produced over a 33-yr time period (1980–2012) for the U.S. Midwest with the intent to aid the agricultural community in understanding hydroclimatic impacts on crop production and decision-making in operational practices. While approximately 20 hydroclimatic variables are available through the LIS dataset, the focus here is on soil water content, soil temperature, and evapotranspiration. To assess the performance of the model, the LIS dataset is compared with in situ hydrometeorological observations across the study domain and with coarse-resolution reanalysis products [NARR, MERRA, and NLDAS-2 (phase 2 of the North American Land Data Assimilation System)]. In agricultural regions such as the U.S. Midwest, finescale hydroclimatic mapping that links the regional scale to the field scale is necessary. The new dataset provides this link as an intermediate-scale product that links point observations and coarse gridded datasets. In general, the LIS dataset compares well with in situ observations and coarser gridded products in terms of both temporal and spatial patterns, but cases of strong disagreement exist particularly in areas with sandy soils. The dataset is made available to the broader research community as an effort to fill the gap in spatial hydroclimatic data availability.
Abstract
A new, high-resolution (4 km), gridded land surface dataset produced with the Land Information System (LIS) is introduced, and the first set of synthesis of key hydroclimatic variables is reported. The dataset is produced over a 33-yr time period (1980–2012) for the U.S. Midwest with the intent to aid the agricultural community in understanding hydroclimatic impacts on crop production and decision-making in operational practices. While approximately 20 hydroclimatic variables are available through the LIS dataset, the focus here is on soil water content, soil temperature, and evapotranspiration. To assess the performance of the model, the LIS dataset is compared with in situ hydrometeorological observations across the study domain and with coarse-resolution reanalysis products [NARR, MERRA, and NLDAS-2 (phase 2 of the North American Land Data Assimilation System)]. In agricultural regions such as the U.S. Midwest, finescale hydroclimatic mapping that links the regional scale to the field scale is necessary. The new dataset provides this link as an intermediate-scale product that links point observations and coarse gridded datasets. In general, the LIS dataset compares well with in situ observations and coarser gridded products in terms of both temporal and spatial patterns, but cases of strong disagreement exist particularly in areas with sandy soils. The dataset is made available to the broader research community as an effort to fill the gap in spatial hydroclimatic data availability.
Abstract
Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMM’s successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities, such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.
Abstract
Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMM’s successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities, such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.
Abstract
Half of Earth’s land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affect a myriad of land surface processes and the adaptation behaviors. This study reviews the status and major knowledge gaps in the interactions of land and atmospheric changes and present 11 grand challenge areas for the scientific research and adaptation community in the coming decade. These land-cover and land-use change (LCLUC)-related areas include 1) impacts on weather and climate, 2) carbon and other biogeochemical cycles, 3) biospheric emissions, 4) the water cycle, 5) agriculture, 6) urbanization, 7) acclimation of biogeochemical processes to climate change, 8) plant migration, 9) land-use projections, 10) model and data uncertainties, and, finally, 11) adaptation strategies. Numerous studies have demonstrated the effects of LCLUC on local to global climate and weather systems, but these putative effects vary greatly in magnitude and even sign across space, time, and scale and thus remain highly uncertain. At the same time, many challenges exist toward improved understanding of the consequences of atmospheric and climate change on land process dynamics and services. Future effort must improve the understanding of the scale-dependent, multifaceted perturbations and feedbacks between land and climate changes in both reality and models. To this end, one critical cross-disciplinary need is to systematically quantify and better understand measurement and model uncertainties. Finally, LCLUC mitigation and adaptation assessments must be strengthened to identify implementation barriers, evaluate and prioritize opportunities, and examine how decision-making processes work in specific contexts.
Abstract
Half of Earth’s land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affect a myriad of land surface processes and the adaptation behaviors. This study reviews the status and major knowledge gaps in the interactions of land and atmospheric changes and present 11 grand challenge areas for the scientific research and adaptation community in the coming decade. These land-cover and land-use change (LCLUC)-related areas include 1) impacts on weather and climate, 2) carbon and other biogeochemical cycles, 3) biospheric emissions, 4) the water cycle, 5) agriculture, 6) urbanization, 7) acclimation of biogeochemical processes to climate change, 8) plant migration, 9) land-use projections, 10) model and data uncertainties, and, finally, 11) adaptation strategies. Numerous studies have demonstrated the effects of LCLUC on local to global climate and weather systems, but these putative effects vary greatly in magnitude and even sign across space, time, and scale and thus remain highly uncertain. At the same time, many challenges exist toward improved understanding of the consequences of atmospheric and climate change on land process dynamics and services. Future effort must improve the understanding of the scale-dependent, multifaceted perturbations and feedbacks between land and climate changes in both reality and models. To this end, one critical cross-disciplinary need is to systematically quantify and better understand measurement and model uncertainties. Finally, LCLUC mitigation and adaptation assessments must be strengthened to identify implementation barriers, evaluate and prioritize opportunities, and examine how decision-making processes work in specific contexts.
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
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
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 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 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
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
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.
