Modeling stormwater management at the city district level in response to changes in land use and low impact development

Mitigating the impact of increasing impervious surfaces on stormwater runoff by low impact development (LID) is currently being widely promoted at site and local scales. In turn, the series of distributed LID implementations may produce cumulative effects and benefit the stormwater management at larger regional scales. However, the potential of multiple LID implementations to mitigate the broad-scale impacts of urban stormwater is not yet fully understood, particularly among different design strategies to reduce directly connected impervious areas (DCIA). In this study, the hydrological responses of stormwater runoff characteristics to four different land use conversion scenarios at the city scale were explored using GIS-based Stormwater Management Model (SWMM). Model simulation results confirmed the effectiveness of LID controls; however, they also indicated that even with the most beneficial scenarios hydrological performance of developed areas was still not yet up to the pre-development level, especially with pronounced changes from pervious to impervious land

Spatial characterization of long-term hydrological change in the Arkavathy watershed adjacent to Bangalore, India

The complexity and heterogeneity of human water use over large spatial areas and decadal timescales can impede the understanding of hydrological change, particularly in regions with sparse monitoring of the water cycle. In the Arkavathy watershed in southern India, surface water inflows to major reservoirs decreased over a 40-year period during which urbanization, groundwater depletion, modification of the river network, and changes in agricultural practices also occurred. These multiple, interacting drivers combined with limited hydrological monitoring make attribution of the causes of diminishing water resources in the watershed challenging and impede effective policy responses. To mitigate these challenges, we developed a novel, spatially distributed dataset to understand hydrological change by characterizing the residual trends in surface water extent that remain after controlling for precipitation variations and comparing the trends with historical land use maps to assess human drivers of change. Using an automated classification approach with subpixel unmixing, we classified water extent in nearly 1700 man-made lakes, or tanks, in Landsat images from 1973 to 2010. The classification results compared well with a reference dataset of water extent of tanks (R2  =  0.95). We modeled the water extent of 42 clusters of tanks in a multiple regression on simple hydrological covariates (including precipitation) and time. Inter-annual variability in precipitation accounted for 63 % of the predicted variability in water extent. However, precipitation did not exhibit statistically significant trends in any part of the watershed. After controlling for precipitation variability, we found statistically significant temporal trends in water extent, both positive and negative, in 13 of the clusters. Based on a water balance argument, we inferred that these trends likely reflect a non-stationary relationship between precipitation and watershed runoff. Independently of precipitation, water extent increased in a region downstream of Bangalore, likely due to increased urban effluents, and declined in the northern portion of the Arkavathy. Comparison of the drying trends with land use indicated that they were most strongly associated with irrigated agriculture, sourced almost exclusively by groundwater. This suggests that groundwater abstraction was a major driver of hydrological change in this watershed. Disaggregating the watershed-scale hydrological response via remote sensing of surface water bodies over multiple decades yielded a spatially resolved characterization of hydrological change in an otherwise poorly monitored watershed. This approach presents an opportunity to understand hydrological change in heavily managed watersheds where surface water bodies integrate upstream runoff and can be delineated using satellite imagery

Integration of the WUDAPT, WRF, and ENVI-met models to simulate extreme daytime temperature mitigation strategies in San Jose, California

An obstacle to the modeling of strategies to mitigate extreme urban temperatures is frequently the lack of on-site meteorological data. The current study thus reports on a method that used the Weather Research and Forecasting (WRF) model to generate inputs for the ENVI-met model to produce building-scale canyon temperatures within a 300 m square near downtown San Jose. A land use distribution was generated for WRF by a WUDAPT classification, and the days of interest were then the hottest day in California history and a typical summer day. The source of meteorological data for ENVI-met, run with a 1.5 m cubic grid, was either an urbanized version of WRF; its default version; or observations at the closest NWS site. All WRF simulations were run on a 1 km grid, and output at its grid closest to the study area provided ENVI-met with lateral boundary conditions. The mitigation strategy was comprised of three parts, which either increased vegetation, rooftop albedo, or architectural shade elements. Results showed all strategies with only negligible impacts on ENVI-met nighttime 1 m level street canyon temperatures. Increased vegetation, however, was the most effective daytime strategy on both days, as it affected the largest area. The maximum vegetative cooling on the extreme and average days was −3.5 and −3.3 °C, respectively. While increased rooftop albedos produced near negligible impacts, increased architectural shading produced corresponding values of −1.6 and −1.7 °C, respectively

Projected changes in heatwaves over Central and South America using high-resolution regional climate simulations

Heatwaves (HWs) pose a severe threat to human and ecological systems. Here we assess the projected changes in heatwaves over Latin America using bias corrected high-resolution regional climate simulations under two Representative Concentration Pathway scenarios (RCPs). Heatwaves are projected to be more frequent, long-lasting, and intense in the mid-century under both RCP2.6 and RCP8.5 scenarios, with severe increases under the RCP8.5 scenario. Even under the low emissions scenario of RCP2.6, the frequency of heatwaves doubles over most of the region. A three- to tenfold rise in population exposure to heatwave days is projected over Central and South America, with climate change playing a dominant role in driving these changes. Results show that following the low emission pathway would reduce 57% and 50% of heatwave exposure for Central and South American regions respectively, highlighting the need to control anthropogenic emissions and implement sustainable practices

The equigenic effect of greenness on the association between education with life expectancy and mortality in 28 large Latin American cities.

BACKGROUND: Recent studies highlight the equigenic potential of greenspaces by showing narrower socioeconomic health inequalities in greener areas. However, results to date have been inconsistent and derived from high-income countries. We examined whether urban greenness modifies the associations between area-level education, as a proxy for socioeconomic status, and life expectancy and cause-specific mortality in Latin American cities. METHODS: We included 28 large cities, >137 million inhabitants, in nine Latin American countries, comprising 671 sub-city units, for 2012-2016. Socioeconomic status was assessed through a composite index of sub-city level education, and greenness was calculated using the normalized difference vegetation index. We fitted multilevel models with sub-city units nested in cities, with life expectancy or log(mortality) as the outcome. FINDINGS: We observed a social gradient, with higher levels of education associated with higher life expectancy and lower cause-specific mortality. There was weak evidence supporting the equigenesis hypothesis as greenness differentially modified the association between education and mortality outcomes. We observed an equigenic effect, with doubling magnitudes in the violence-related mortality reduction by education in areas with low greenness compared to medium-high greenness areas among men (16% [95% CI 12%-20%] vs 8% [95% CI 4%-11%] per 1 SD increase in area-level education). However, in contradiction to the equigenesis hypothesis, the magnitude in cardiovascular diseases (CVD) mortality reduction by education was stronger in areas with medium-high greenness compared to areas with low greenness (6% [95% CI 4%-7%] vs 1% [95% CI -1%-3%] and 5% [95% CI 3%-7%] vs 1% [95% CI -1%-3%] per 1 SD increase in area-level education, in women and men, respectively). Similarly, each 1-SD increase in greenness widened the educational inequality in life expectancy by 0.15 years and 0.20 years, in women and men, respectively. The equigenic effect was not observed in violence-related mortality among women and in mortality due to communicable diseases, maternal, neonatal and nutritional conditions (CMNN). INTERPRETATION: Our results confirm socioeconomic health inequalities in Latin American cities and show that the equigenic properties of greenspace vary by health outcome. Although mixed, our findings suggest that future greening policies should account for local social and economic conditions to ensure that greenspaces provide health benefits for all, and do not further exacerbate existing health inequalities in the region. FUNDING: Wellcome Trust (Grant, 205177/Z/16/Z)

Energy saving potential of fragmented green spaces due to their temperature regulating ecosystem services in the summer

Urban green spaces help to moderate the urban heat island (UHI) effects, and can provide important temperature regulating ecosystem services and opportunities for savings in cooling energy. However, because explicit market values for these benefits are still lacking, they are rarely incorporated into urban planning actions. Green spaces can generate a three-dimensional (3D) cool island that may reduce the cooling energy requirements within and around urban areas, but such 3D cooling effect has not been considered in previous studies quantifying energy savings from green spaces. This study presents a new and simple approach to quantify potential energy savings due to the temperature regulating ecosystem services of small-scale fragmented green spaces using the 3D simulation of the summer-day outdoor thermal environment in Nanjing, China. Field survey data and the microclimate model ENVI-met were applied to examine the outdoor 3D thermal environmental patterns at Gulou Campus of Nanjing University under two different scenarios: “with” and “without” green spaces. Modeling results were applied to quantify potential cooling energy savings based on the effect of green spaces on the outdoor urban environment and to calculate the cumulative temperature reduction due to green spaces using a regression model. The results show that, in the horizontal direction, the simulated distribution of wind speed and mean air temperature at 1.5 m height were closely related to the spatial distribution of the underlying surface types. Removal of green spaces increased mean air temperature by 0.5 °C (33.1 °C vs. 33.6 °C). In the vertical direction, removal of green spaces had little effect on the near-surface wind field; however, above the surface, the turbulence perpendicular to the main wind direction significantly increased. Quantification of the cooling benefits of green spaces in relation to the mean height of buildings on Gulou Campus yielded 5.2 W/m2 cooling energy, saving totally 1.3 × 104 kW h during a single daytime hot summer period. This case study corroborates the importance of green space for cooling and informs city planners and decision-makers on how microclimate is impacted by the loss of green spaces. These findings will facilitate preservation, planning, and design of green spaces to increase urban environmental benefits and to improve the microclimate of urban areas at neighborhood, city, and regional scales

Object-Based Image Analysis in Wetland Research: A Review

The applications of object-based image analysis (OBIA) in remote sensing studies of wetlands have been growing over recent decades, addressing tasks from detection and delineation of wetland bodies to comprehensive analyses of within-wetland cover types and their change. Compared to pixel-based approaches, OBIA offers several important benefits to wetland analyses related to smoothing of the local noise, incorporating meaningful non-spectral features for class separation and accounting for landscape hierarchy of wetland ecosystem organization and structure. However, there has been little discussion on whether unique challenges of wetland environments can be uniformly addressed by OBIA across different types of data, spatial scales and research objectives, and to what extent technical and conceptual aspects of this framework may themselves present challenges in a complex wetland setting. This review presents a synthesis of 73 studies that applied OBIA to different types of remote sensing data, spatial scale and research objectives. It summarizes the progress and scope of OBIA uses in wetlands, key benefits of this approach, factors related to accuracy and uncertainty in its applications and the main research needs and directions to expand the OBIA capacity in the future wetland studies. Growing demands for higher-accuracy wetland characterization at both regional and local scales together with advances in very high resolution remote sensing and novel tasks in wetland restoration monitoring will likely continue active exploration of the OBIA potential in these diverse and complex environments

Landscape beauty: A wicked problem in sustainable ecosystem management?

Recent discourses on sustainable ecosystem management have increasingly emphasized the importance of bundling relationships and interactions among multiple ecosystem services supported by similar natural and anthropogenic mechanisms within the total environment. Yet, the aesthetic benefits of ecosystems, playing critical role in management of both wild and anthropogenic landscapes, have been under-represented in these discussions. This disregard contributes to the disconnection between environmental science and practice and limits our understanding of ecological and societal implications of management decisions that either generate aesthetic benefits or impact them while targeting other ecosystem services. This discussion reviews several "wicked problems" that arise due to such limited understanding, focusing on three recognized challenges in present-day ecosystem management: replacement of natural ecosystem functions, spatial decoupling of service beneficiaries from its environmental consequences and increasing inequalities in access to services. Strategies towards solutions to such wicked challenges are also discussed, capitalizing on the potential of innovative landscape design, cross-disciplinary research and collaboration, and emerging economic and policy instruments

The study of seasonal composition and dynamics of wetland ecosystems and wintering bird habitat at Poyang Lake, PR China using object-based image analysis and field observations

Wetlands are among the most productive ecosystems in the world which support critical ecological services and high biological diversity yet are vulnerable to climate change and human activities. Despite their tremendous economic and ecological value, substantial uncertainty still exists about wetland ecosystem function, habitats and response to natural and anthropogenic stressors worldwide. This uncertainty is further aggravated by constrained field access and surface heterogeneity which limit the accuracy of wetland analyses from remote sensing images. In this thesis, I investigated the capabilities of satellite remote sensing with medium spatial resolution and object-based image analysis (OBIA) methods to elucidate seasonal composition and dynamics of wetland ecosystems and indicators of habitat for wintering waterbirds in a large conservation hotspot of Poyang Lake, PR China.I first examined changes in major wetland cover types during the low water period when Poyang Lake provides habitat to large numbers of migratory birds from the East Asian pathway. I used OBIA to map and analyze the transitions among water, vegetation, mudflat and sand classes from four 32-m Beijing-1 microsatellite images between late fall 2007 and early spring 2008. This analysis revealed that, while transitions among wetland classes were strongly associated with precipitation and flood-driven hydrological variation, the overall dynamics were a more complex interplay of vegetation phenology, disturbance and post-flood exposure. Remote sensing signals of environmental processes were more effectively captured by changes in fuzzy memberships to each class per location than by changes in spatial extents of the best-matching classes alone. The highest uncertainty in the image analysis corresponded to transitional wetland states at the end of the major flood recession in November and to heterogeneous mudflat areas at the land-water interface during the whole study period. Results suggest seasonally exposed mudflat features as important targets for future research due to heterogeneity and uncertainty of their composition, variable spatial distribution and sensitivity to hydrological dynamics.I further explored the potential of OBIA to overcome the limitations of the traditional pixel-based image classification methods in characterizing Poyang Lake plant functional types (PFTs) from the medium-resolution Landsat satellite data. I assessed the sensitivity in PFT classification accuracy to image object scale, machine-learning classification method and hierarchical level of vegetation classes determined from ecological functional traits of the locally dominant plant species. Both the overall and class-specific accuracy values were higher at coarser object scales compared to near-pixel levels, regardless of the machine-learning algorithm, with the overall accuracy exceeding 85-90%. However, more narrowly defined PFT classes differed in their highest-accuracy object scale values due to their unique patch structure, ecology of the dominant species and disturbance agents. To improve classification agreement between different levels of vegetation type hierarchy and reduce the uncertainty, future analyses should integrate spectral and geometric properties of vegetation patches with species' functional ecological traits.In periodically flooded wetlands such as Poyang Lake, rapid short-term surface dynamics and frequent inundation may constrain detection of directional long-term effects of climate change, succession or alien species invasions. To address this challenge, I proposed to classify Poyang Lake wetlands into "dynamic cover types" (DCTs) representing short-term ecological regimes shaped by phenology, disturbance and inundation, instead of static classes. I defined and mapped Poyang Lake DCTs for one flood cycle (late summer 2007-late spring 2008) from combined time series of medium-resolution multi-spectral and radar imagery. I further assessed sensitivity of DCTs to hydrological and climatic variation by comparing results with a hypothetical change scenario of a warmer wetter spring simulated by substituting spring 2008 input images with 2007 ones. This analysis identified the major steps in seasonal wetland change driven by flooding and vegetation phenology and spatial differences in change schedules across the heterogeneous study area. Comparison of DCTs from the actual flood season with the hypothetical scenario revealed both directional class shifts away from expanding permanent water and more complex location-specific redistributions of vegetation types and mudflats. These outcomes imply that changes in flooding may have non-uniform effects on different ecosystems and habitats and call for a thorough investigation of the future change scenarios for this landscape. The possibility to disentangle short-term ecological "regimes" from longer-term landscape changes via DCT framework suggests a promising research strategy for landscape ecosystem modeling, conservation and ecosystem management. Following the assessments of Poyang Lake dynamics in the low water season, I further examined which landscape characteristics of the permanent sub-lakes and their 500-m neighborhoods extracted from 30-m Landsat satellite imagery could explain non-uniform spatial distribution of waterbird diversity and abundance in the ground bird survey of December 2006. I hypothesized that the indicators of habitat size, spectral greenness, spectral and geometric patch heterogeneity would be positively associated with bird diversity and abundance, while the proportions of cover types approximating human disturbance would be negatively related to response variables. In the best-fit regression models selected using the Akaike Information Criterion, on average higher bird diversity and abundance were associated with larger sub-lake size, higher spectral greenness of emergent grassland and lower spectral greenness of mudflat as well as lower proportion of flooded/aquatic vegetation. At the same time, predictive performance of the best-fit models was penalized by large amounts of unexplained variation and inconsistencies among bird survey and remote sensing data from another year. Significant spatial autocorrelation in linear regression models raised concerns about missing predictor variables and the utility of sub-lakes as spatial units for diversity analysis, but it also suggested new hypotheses on spatial ecological interactions in bird community variables and habitat characteristics among sub-lakes. Research challenges identified in this study suggest that future monitoring programs should take more rigorous steps to standardize the protocols of bird surveys and improve spatial and temporal frequency of both bird and habitat observations. Rapid short-term surface variation and problematic field access will likely continue to limit remote sensing-based analyses of Poyang Lake wetlands and their habitats by traditional, static-class approaches. Using "dynamic" classes representing characteristic wetland transitions and disturbance regimes may provide more ecologically informative targets for management, conservation and modeling of ecosystem change. Object-based image analysis is a potentially powerful and promising approach to enhance classification accuracy of remote sensing data and ecologically informative interpretations of complex, heterogeneous wetland surfaces such as the study area. However, this methodology should be developed further to allow for more automated optimization of landscape object properties to capture vegetation patch structure and quantitatively assess propagation of the uncertainty among different spatial scales of the analysis. Finally, future studies should explore new ways of overcoming the limitations of problematic field access and frequent cloudiness obstructing the view of remote sensors by more rigorous utilization of in situ wireless sensors to record environmental conditions and surface composition and by introducing airborne lake-wide imaging programs for periods of prolonged cloudiness