Destructive earthquakes result in significant damage to a wide variety of buildings. The resulting damage data is crucial for evaluating the seismic resilience of buildings in the region and investigating urban resilience. Field damage data from 38 destructive earthquakes in Sichuan Province were collected, classified, and statistically analysed according to the criteria of the latest Chinese seismic intensity scale for evaluating building damage levels. Meanwhile, the construction features and seismic damage characteristics of these buildings were also examined. These results facilitated the development of a damage probability matrix (DPM) for various building typologies, such as raw-soil structures (RSSs), stone-wood structures (SWSs), brick-wood structures (BWSs), masonry structures (MSs), and reinforced concrete frame structures (RCFSs). The damage ratio was employed as the parameter for vulnerability assessment, and a comprehensive analysis was performed on the differences in damage levels among all buildings in various intensity zones and time frames. Furthermore, the DPMs were further refined by simulating additional data from high-intensity zones to more accurately represent the seismic resistance of existing buildings in multiple-intensity zones. Vulnerability prediction models were developed using the biphasic Hill model, which elucidates varying damage trends across different construction typologies. Finally, empirical fragility curves were established based on horizontal peak ground acceleration (PGA) as the damage indicator. This study is based on multiple seismic damage samples from various regions, accounting for the influence of earthquake age. The DPMs, representative of the regional characteristics of Sichuan Province, were developed for different building types. Furthermore, multidimensional vulnerability regression models and empirical fragility curves are established based on these DPMs. These models and curves provide a theoretical foundation for seismic disaster scenario simulations and the seismic capacity analysis of buildings within Sichuan Province.

Saltwater intrusion (SWI) exposed the significant risk to rice production in the tropical lowland delta, especially under the contact of climate change. This study have developed the economic loss functions for both direct and indirect losses caused by SWI after investigating several regression models (such as: Ordinary Least Squares (OLS), Fixed Effects Model (FEM), Random Effects Model (REM), and Feasible Generalized Least Squares (FGLS), based on the 85 questionaires colleted in the tropical rice fields located in Ho Chi Minh City (HCMC). Direct damages were estimated based on cultivated area, rice yield, and salinity levels; while indirect damages were included the costs of water pumping, soil improvement, and irrigation infrastructure construction. The results showed that rice yield decreases sharply when salinity exceeds the threshold level of 1.5 parts per thousand, and indirect costs account for 9% of total damages. The new finding of this study is integrating indirect factors (water pumping, soil improvement, and irrigation infrastructure construction) into the economic loss function, enabling the estimation of both direct and indirect damages cause by SWI; which is a critical tool for water related disasters prevention and management, or land use planning, or developing socio-economic strategies to ensure food security for the deltas strongly affected by SWI.

Remote sensing plays an increasingly important role in agriculture, especially in monitoring the quality of agricultural crops. Optical sensing is often limited in Central Europe due to cloud cover; therefore, synthetic aperture radar data is increasingly being used. However, synthetic aperture radar data is limited by more difficult interpretation mainly due to the influence of speckles. For this reason, its use is often limited to larger territorial units and field blocks. The main aim of this study therefore was to verify the possibility of using satellite synthetic aperture radar images to assess the within-field variability of winter wheat. The lowest radar vegetation index values corresponded to the area of the lowest production potential and the greatest damage to the stand. Also for VH and VV polarizations, the highest values corresponded to the area of the lowest stand quality. Qualitative changes in the stand across the zones defined by frost damage and production potential were assessed with the help of the logistic regression model with resampled data for 10, 50, and 100 m pixel size. The best correlation coefficients were achieved at a spatial resolution of 50 m for both options. The F-score still yielded a promising result ranging from 0.588 to 0.634 for frost damage categories. The regression model of the production potential performed slightly better in terms of the F-score, recall, and precision at higher resolutions. It was proved that modern statistical methods could be used to reduce problems associated with speckles of radar images for practical purposes.

Ecosystem Services Value (ESV) are the various beneficial functions and products that natural ecosystems provide to humans, and are important indicators for evaluating ecosystem conditions and human well-being. Opencast mining is one of the human activities that severely damage the surface environment, but its long-term impact on ecosystem services lacks systematic assessment. This study takes the Ordos opencast mining area as an example, and calculates the value of ESV from 1990 to 2020 based on the Google Earth Engine platform. Mann-Kendall Tau-b with Sen ' s Method (Sen + mk test) and Joinpoint regression model were used to analyzes its spatiotemporal variation characteristics. Further revealed the impacts of opencast mining on ESV as well as the trend of ESV changes. The results show that: (1) The dynamic ESV levels in the study area fluctuated considerably from 1990 to 2020 with an overall decreasing trend of 89.45%. (2) Among nine types ecosystem services, most of them were significantly different (p < 0.001) between mining areas and control areas, with biodiversity protection (BP), climate regulation (CR), gas regulation (GR), soil formation and retention (SFR), water supply (WS) and waste treatment (WT) showed a significant decrease between 1990 and 2020. (3) In the past 30 years, the ESV of the study area showed an overall improvement trend, where the improved area accounted for 48.45% of the total area of the study area. However, the degraded area also accounted for 21.28, and 17.19% of the area belonged to severe degradation. With 67% of the significantly degraded areas distributed within mining concessions. (4) The trend of ESV changes in the mining impact areas and the control area showed significant differences. The ESV of the control area increased continuously, with an average annual percentage change (AAPC) of 0.7(95%CI:0.50 similar to 0.9, P < 0.001) from 1990 to 2020; while the ESV of the mining impact areas first stabilized and then decreased significantly, with an AAPC of - 0.2(95%CI:- 0.3 similar to - 0.1,P < 0.001) from 1990 to 2020. This study provides scientific support for formulating ecosystem management, restoration plans, and payment for ecosystem service policies, which is conducive to achieving regional sustainable development and improving human well-being.

This study aims to investigate the economic effects of stormwater best management practices (BMPs) on housing sale prices in Washington, D.C., BMPs play a significant role in mitigating multiple threats, such as water pollution, soil erosion, and property damage. While studies on the economic value of BMPs were limited, literature addresses that housing sale prices can be affected by nearby stormwater BMPs. This study addresses the following research questions: Do stormwater BMPs positively impact housing sale prices? How do proximity and number of structural BMPs affect the housing sale prices? We used the hedonic pricing method by applying multiple linear regression models to determine whether a set of independent variables significantly improved the models. Our primary findings indicate that BMPs have positive, negative, or no effects on housing sale prices. The proximity of BMPs inside of parks increased housing sale prices in all buffers. In contrast, the proximity of BMPs outside of parks and impervious roads decreased housing sale prices in all buffers. Percent tree canopy coverage negatively linked to a 50 m buffer and had no relationship with other buffers on housing sale prices. This study implies that BMPs impact housing prices and can be improved by landscape architects, policymakers, and stakeholders.

The Qinghai-Tibet Plateau (QTP), where is underlain by the highest and most extensive mid-altitude permafrost, is undergoing more dramatic climatic warming than its surrounding regions. Mapping the distribution of permafrost is of great importance to assess the impacts of permafrost changes on the regional climate system. In this study, we applied logistic regression model (LRM) andmulti-criteria analysis (MCA) methods to map the decadal permafrost distribution on the QTP and to assess permafrost dynamics from the 1980s to 2000s. The occurrence of permafrost and its impacting factors (i.e., climatic and topographic elements) were constructed from in-situ field investigation-derived permafrost distribution patterns in 4 selected study regions. The validation results indicate that both LRM and MCA could efficiently map the permafrost distribution on the QTP. The areas of permafrost simulated by LRM and MCA are 1.23 x 10(6) km(2) and 1.20 x 10(6) km(2), respectively, between 2008 and 2012. The LRM and MCA modeling results revealed that permafrost area has significantly decreased at a rate of 0.066 x 10(6) km(2) decade(-1) over the past 30 years, and the decrease of permafrost area is accelerating. The sensitivity test results indicated that LRM did well in identifying the spatial distribution of permafrost and seasonally frozen ground, and MCA did well in reflecting permafrost dynamics. More parameters such as vegetation, soil property, and soil moisture are suggested to be integrated into the models to enhance the performance of both models. (C) 2018 Published by Elsevier B.V.

Assessing possible permafrost degradation related to engineering projects, climate change and land use change is of critical importance for protecting the environment and in developing sustainable designs for vital infrastructure in cold regions. A major challenge in modelling the future degradation of permafrost is finding ways to constrain changes in the upper thermal boundary condition over time and space at appropriate scales. Here, we report on an approach designed to predict time series of air, ground surface and shallow ground temperatures at a spatial scale on the order of 102?m2 for engineering design of a railway or highway project. The approach uses a regional-scale atmospheric model to downscale global climate model output, and then stepwise multiple regression to develop an equation that provides a best-fit prediction of site-specific observational data using bilinearly interpolated output from the atmospheric model. This approach bridges the scale difference between atmospheric climate models and permafrost thermal models, and allows for a wider range of factors to be used in predicting the thermal boundary condition. For a research site located in Beiluhe, China, close to the Qinghai-Tibet Railway, a comparison of model predictions with observational data not used in the construction of the model shows that this method can be used with a high degree of accuracy to determine the upper boundary condition for a permafrost thermal model. Once a model is constructed, it can be used to predict future changes in boundary condition parameters under different greenhouse emission scenarios for climate change. Copyright (c) 2012 John Wiley & Sons, Ltd.