Xin-Zhong Liang
@umd.edu
Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center
University of Maryland
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Jennifer Kennedy, George C Hurtt, Xin-Zhong Liang, Louise Chini, and Lei Ma
IOP Publishing
Abstract Climate change is impacting global crop productivity, and agricultural land suitability is predicted to significantly shift in the future. Responses to changing conditions and increasing yield variability can range from altered management strategies to outright land use conversions that may have significant environmental and socioeconomic ramifications. However, the extent to which agricultural land use changes in response to variations in climate is unclear at larger scales. Improved understanding of these dynamics is important since land use changes will have consequences not only for food security but also for ecosystem health, biodiversity, carbon storage, and regional and global climate. In this study, we combine land use products derived from the Moderate Resolution Imaging Spectroradiometer with climate reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 to analyze correspondence between changes in cropland and changes in temperature and water availability from 2001 to 2018. While climate trends explained little of the variability in land cover changes, increasing temperature, extreme heat days, potential evaporation, and drought severity were associated with higher levels of cropland loss. These patterns were strongest in regions with more cropland change, and generally reflected underlying climate suitability—they were amplified in hotter and drier regions, and reversed direction in cooler and wetter regions. At national scales, climate response patterns varied significantly, reflecting the importance of socioeconomic, political, and geographic factors, as well as differences in adaptation strategies. This global-scale analysis does not attempt to explain local mechanisms of change but identifies climate-cropland patterns that exist in aggregate and may be hard to perceive at local scales. It is intended to supplement regional studies, providing further context for locally-observed phenomena and highlighting patterns that require further analysis.
Sijal Dangol, Xuesong Zhang, Xin-Zhong Liang, Martha Anderson, Wade Crow, Sangchul Lee, Glenn E. Moglen, and Gregory W. McCarty
MDPI AG
Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling.
Chao Sun and Xin-Zhong Liang
American Meteorological Society
Abstract Most climate models still suffer large warm and dry summer biases in the central United States (CUS). As a solution, we improved cumulus parameterization to represent 1) the lifting effect of small-scale rising motions associated with Great Plains low-level jets and midtropospheric perturbations by defining the cloud base at the level of condensation, 2) the constraint of the cumulus entrainment rate depending on the boundary layer depth, and 3) the temperature-dependent cloud-to-rainwater conversion rate. These improvements acted to (i) trigger mesoscale convective systems in unfavorable environmental conditions to enhance total rainfall amount, (ii) lower cloud base and increase cloud depth to increase low-level clouds and reduce surface shortwave radiation, (iii) suppress penetrative cumuli from shallow boundary layers to remedy the overestimation of precipitation frequency, and (iv) increase water detrainment to form sufficient cirrus clouds and balanced outgoing longwave radiation. Much of these effects were nonlocal and nonlinear, where more frequent but weaker convective rainfall led to stronger (and sometimes more frequent) large-scale precipitation remotely. Together, they produced consistently heavier precipitation and colder temperature with a realistic atmospheric energy balance, essentially eliminating the CUS warm and dry biases through robust physical mechanisms.
Sijal Dangol, Xuesong Zhang, Xin-Zhong Liang, and Fernando Miralles-Wilhelm
MDPI AG
Groundwater use for irrigation has a major influence on agricultural productivity and local water resources. This study evaluated the groundwater irrigation schemes, SWAT auto-irrigation scheduling based on plant water stress (Auto-Irr), and prescribed irrigation based on well pumping rates in MODFLOW (Well-Irr), in the U.S. Northern High Plains (NHP) aquifer using coupled SWAT-MODFLOW model simulations for the period 1982–2008. Auto-Irr generally performed better than Well-Irr in simulating groundwater irrigation volume (reducing the mean bias from 86 to −30%) and groundwater level (reducing the normalized root-mean-square-error from 13.55 to 12.47%) across the NHP, as well as streamflow interannual variations at two stations (increasing NSE from 0.51, 0.51 to 0.55, 0.53). We also examined the effects of groundwater irrigation on the water cycle. Based on simulation results from Auto-Irr, historical irrigation led to significant recharge along the Elkhorn and Platte rivers. On average over the entire NHP, irrigation increased surface runoff, evapotranspiration, soil moisture and groundwater recharge by 21.3%, 4.0%, 2.5% and 1.5%, respectively. Irrigation improved crop water productivity by nearly 27.2% for corn and 23.8% for soybean. Therefore, designing sustainable irrigation practices to enhance crop productivity must consider both regional landscape characteristics and downstream hydrological consequences.
Yujie Wang, Yang Xiang, Lianchun Song, and Xin-Zhong Liang
American Meteorological Society
Abstract Determining the contribution of urbanization to extreme high-temperature events is essential to the coordinated development of Beijing, Tianjin, and Hebei (BTH). Based on the dynamic data of land-use change in every 5 years, this study uses the coupled WRF–Building Effect Parameterization/Building Energy Model (BEP/BEM) at 1-km grid spacing to quantify the contribution of BTH urbanization to the intensity and frequency of hourly extreme high-temperature events in summer. From 1990 to 2015, extreme events over Beijing and its south increased by ∼1.5°–2°C in intensity and by 50–100 h in frequency, both of which were even higher in central Beijing and Shijiazhuang. The increases of multiyear average urbanization contribution ratios to the intensity and frequency reached 3.3% and 51.6% at the 99% confidence level (p < 0.01) from 1990 to 2015, respectively. The corresponding contributions increased 1.8 and 1.2 times more significantly in the megacities (i.e., Beijing, Tianjin, and Shijiazhuang) than small and medium-sized cities. Therefore, the rapid urbanization has substantially enhanced the extreme high-temperature events in BTH. It is necessary to limit the urbanization growth rate and implement effective adaptation and mitigation strategies to sustain BTH development.
Minghua Zhang, Rong Fu, Filippo Giorgi, Ruby Leung, Xin‐Zhong Liang, Wahid Mellouki, William Randel, Nicole Riemer, Robert Rogers, Lynn Russell,et al.
American Geophysical Union (AGU)
ing scientists who reviewed manuscripts for the journal in 2021. Peer review is a crucial process to ensure the integrity and rigor of science. Your reviews have helped to improve the quality of papers in the journal, stimulated new ideas, and advanced the careers of many young scientists. They contributed to the high quality of JGR-Atmospheres and the standard of science in our discipline. Of the 2510 reviewers listed below for the 1,581 submitted papers to the journal, 535 people reviewed three or more papers. On behalf of the journal, the authors, and the community, we thank all reviewers for your selfless service and dedication to the scientific community. We look forward to your continuing support! Individuals in italics provided three or more reviews for JGR-Atmospheres during the year.
Yongkang Xue, Ismaila Diallo, Aaron A. Boone, Tandong Yao, Yang Zhang, Xubin Zeng, J. David Neelin, William K. M. Lau, Yan Pan, Ye Liu,et al.
American Meteorological Society
Abstract Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.
Peng Ji, Xing Yuan, Xin‐Zhong Liang, Yang Jiao, Yuyu Zhou, and Zan Liu
American Geophysical Union (AGU)
Heather Randell, Chengsheng Jiang, Xin-Zhong Liang, Raghu Murtugudde, and Amir Sapkota
Elsevier BV
Rongsheng Jiang, Lei Sun, Chao Sun, and Xin-Zhong Liang
Springer Science and Business Media LLC
The regional Climate-Weather Research and Forecasting model (CWRF) was used to downscale the NCAR Community Climate System Model V4.0 (CCSM4) projection of China precipitation changes from the present (1974–2005) to future (2019–2050) under the high emission scenario RCP8.5. The CWRF downscaling at 30-km improved CCSM4 in capturing observed key precipitation spatiotemporal characteristics, correcting rainband dislocations, seasonal-mean biases, extreme-rainfall underestimates and rainy-day overestimates. For the future, CWRF generally reduced CCSM4 projected changes in magnitude, producing still significant increases mostly in summer for mean precipitation in the Northeast, North China and Southwest and for extreme precipitation in North China, South China and the Southwest. These regional precipitation increases were direct responses to enhanced ascending motions and moisture transports from adjacent oceans as the east Asian jet shrunk westward and the Hadley circulation widened northward under global warming. The identification of such robust physical mechanisms added confidence in the CWRF downscaled regional precipitation changes. Furthermore, the CWRF downscaling corrections were systematically carried from the present into future, accounting for projection uncertainties up to 40%. Regional biases, however, could not be simply removed from projected changes because their correspondences were strongly nonlinear, highlighting CWRF’s ability to project more reliable changes by reducing model structural uncertainties.
Guwei Zhang, Gang Zeng, Xin-Zhong Liang, and Cunrui Huang
IOP Publishing
Abstract A heat danger day is defined as an extreme when the heat stress index (a combined temperature and humidity measure) exceeding 41 °C, warranting public heat alerts. This study assesses future heat risk (i.e. heat danger days times the population at risk) based on the latest Coupled Model Intercomparison Project phase 6 projections. In recent decades (1995–2014) China’s urban agglomerations (Beijing-Tianjin-Hebei, Yangtze River Delta, Middle Yangtze River, Chongqing-Chengdu, and Pearl River Delta (PRD)) experienced no more than three heat danger days per year, but this number is projected to increase to 3–13 days during the population explosion period (2041–2060) under the high-emission shared socioeconomic pathways (SSP3-7.0 and SSP5-8.5). This increase will result in approximately 260 million people in these agglomerations facing more than three heat danger days annually, accounting for 19% of the total population of China, and will double the current level of overall heat risk. During the period 2081–2100, there will be 8–67 heat danger days per year, 60%–90% of the urban agglomerations will exceed the current baseline number, and nearly 310 million people (39% of the total China population) will be exposed to the danger, with the overall heat risk exceeding 18 times the present level. The greatest risk is projected in the PRD region with 67 heat danger days to occur annually under SSP5-8.5. With 65 million people (68% of the total population) experiencing increased heat danger days, the overall heat risk in the region will swell by a factor of 50. Conversely, under the low-emission pathways (SSP1-2.6 and SSP2-4.5), the annual heat danger days will remain similar to the present level or increase slightly. The result indicates the need to develop strategic plans to avoid the increased heat risk of urban agglomerations under high emission-population pathways.
Rui Wang, Fengxue Qiao, Xin-Zhong Liang, Yiting Zhu, Han Zhang, Qi Li, and Yang Ding
Elsevier BV
Vitaly Kholodovsky and Xin-Zhong Liang
Copernicus GmbH
Abstract. Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damages. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio-Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: (1) construction of essential field quantities; (2) dimension reduction; (3) spatial domain mapping; (4) time series clustering; and (5) threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large-scale well-connected spatial patterns to smaller-scale more isolated rainfall clusters, possibly leading to more localized droughts and heat waves, especially during the summer months. The proposed method automates the threshold selection process through a clustering algorithm and can be directly applicable in conjunction with modeling and spatial forecast verification of extremes. Additionally, it allows for the identification of synoptic-scale spatial patterns that can be directly traced to the individual extreme episodes, and it offers users the flexibility to select an extreme threshold that is linked to the desired geometrical properties. The approach can be applied to broad scientific disciplines.
Jiangfeng Wei, Jingwen Zhao, Haishan Chen, and Xin‐Zhong Liang
American Geophysical Union (AGU)
Linze Li, Chengsheng Jiang, Raghu Murtugudde, Xin-Zhong Liang, and Amir Sapkota
MDPI AG
Climate change driven increases in the frequency of extreme heat events (EHE) and extreme precipitation events (EPE) are contributing to both infectious and non-infectious disease burden, particularly in urban city centers. While the share of urban populations continues to grow, a comprehensive assessment of populations impacted by these threats is lacking. Using data from weather stations, climate models, and urban population growth during 1980–2017, here, we show that the concurrent rise in the frequency of EHE, EPE, and urban populations has resulted in over 500% increases in individuals exposed to EHE and EPE in the 150 most populated cities of the world. Since most of the population increases over the next several decades are projected to take place in city centers within low- and middle-income countries, skillful early warnings and community specific response strategies are urgently needed to minimize public health impacts and associated costs to the global economy.
Wenru Shi, Haishan Chen, and Xin-Zhong Liang
Elsevier BV
Rui Wang, Yiting Zhu, Fengxue Qiao, Xin-Zhong Liang, Han Zhang, and Yang Ding
Springer Science and Business Media LLC
In this study, an extreme rainfall event that occurred on 25 May 2018 over Shanghai and its nearby area was simulated using the Weather Research and Forecasting model, with a focus on the effects of planetary boundary layer (PBL) physics using double nesting with large grid ratios (15:1 and 9:1). The sensitivity of the precipitation forecast was examined through three PBL schemes: the Yonsei University Scheme, the Mellor–Yamada–Nakanishi Niino Level 2.5 (MYNN) scheme, and the Mellor–Yamada–Janjic scheme. The PBL effects on boundary layer structures, convective thermodynamic and large-scale forcings were investigated to explain the model differences in extreme rainfall distributions and hourly variations. The results indicated that in single coarser grids (15 km and 9 km), the extreme rainfall amount was largely underestimated with all three PBL schemes. In the inner 1-km grid, the underestimated intensity was improved; however, using the MYNN scheme for the 1-km grid domain with explicitly resolved convection and nested within the 9-km grid using the Kain–Fritsch cumulus scheme, significant advantages over the other PBL schemes are revealed in predicting the extreme rainfall distribution and the time of primary peak rainfall. MYNN, with the weakest vertical mixing, produced the shallowest and most humid inversion layer with the lowest lifting condensation level, but stronger wind fields and upward motions from the top of the boundary layer to upper levels. These factors all facilitate the development of deep convection and moisture transport for intense precipitation, and result in its most realistic prediction of the primary rainfall peak. 本文利用WRF模式对上海地区的一次暖区大暴雨(2018年5月25日)过程采用大比率的双层嵌套网格(15:1或9:1)进行高分辨率(1 km)单向反馈模拟。着重比较YSU、MYJ和MYNN三种不同边界层方案对于高分辨率1 km内网格极端降水预报的影响,并从湍流混合强度、边界层结构、对流热动力条件和大尺度强迫等方面解释了不同边界层方案对极端降水强度和降水日变化模拟差异的可能原因。研究发现在中尺度网格(15km和9km)上,三种边界层方案均严重低估了极端降水主雨带的雨量,且不同边界层方案模拟的极端降水强度差异较小;经中尺度网格降尺度到1km内网格后,上述偏差均得到了改善,并且局地的MYNN边界层方案由于其边界层内热量、动量的湍流垂直混合较弱,能够模拟出更浅且更潮湿的逆温层和更低的抬升凝结高度,进而容易形成不稳定的边界层顶,使得边界层顶到自由大气中高层的风场和垂直上升运动较强,这些因素都有助于深对流的发展和水汽输送的维持,产生较强的降水;与非局地的YSU边界层方案相比,采用局地的MYNN方案能更有效地改善本次降水事件中极端降水主峰值强度和日变化的预报结果。因此,本研究提出基于大比率网格嵌套(15:1或9:1),在母网格采用传统KF对流参数化方案,并且使用局地的MYNN边界层方案,内网格1 km使用EC时,对上海地区极端暴雨的落区、强度以及小时变化具有较好的预报性能。
Qingquan Li, Tao Wang, Fang Wang, Xin‐Zhong Liang, Chongbo Zhao, Lili Dong, Chunyu Zhao, and Bing Xie
Wiley
Lei Sun, Xin‐Zhong Liang, and Meng Xia
American Geophysical Union (AGU)
Chao Sun and Xin-Zhong Liang
Springer Science and Business Media LLC
Regional Climate-Weather Research and Forecasting model (CWRF) simulations driven by the ECMWF-Interim reanalysis (ERI) showed that cumulus parameterization significantly impacts daily 95th percentile precipitation (P95) over the US Gulf States (GS) and Central-Midwest States (CM). This study compared interannual variations across ERI and five CWRF cumulus parameterization members based on CM and GS regional mean composites during P95 events. A structural equation model framework was used to build regressions of these variations among optimally selected fields to identify the underlying processes affecting P95. We discovered five distinct physical mechanisms, each involving unique interplays among water and energy supplies and surface and cloud forcings, with varying degrees of relative importance (%). In CM summer and CM and GS autumn, water supply (~ 60%), energy supply (~ 20%), and cloud forcing (~ − 20%) jointly determined P95. In GS spring and winter, surface forcing was predominant (84–87%), while energy and water supplies evenly accounted for the remaining impact. In CM spring, surface forcing was also predominant (85%), but was accompanied by energy supply alone. In GS summer, cloud forcing was predominant (− 84%), while water supply had the opposite impact (− 8%) to energy supply (6%). In CM winter, water supply (− 62%) also counteracted energy supply (31%) while cloud forcing played a positive role (7%). The seasonal reversal in the roles of water supply and cloud forcing occurred because the prevailing precipitation system changed from convective to stratiform processes. The choice of cumulus parameterization affected how water and energy supplies acted through surface and cloud forcings, thus determined CWRF’s ability to simulate extreme precipitation.
Lei Sun, Xin‐Zhong Liang, Tiejun Ling, Min Xu, and Xuhui Lee
American Geophysical Union (AGU)
Chao Sun and Xin-Zhong Liang
Springer Science and Business Media LLC
Climate models tend to underestimate rainfall intensity while producing more frequent light events, leading to significant bias in extreme precipitation simulation. To reduce this bias and better understand its underlying causes, we tested an ensemble of 25 physics configurations in the regional Climate-Weather Research and Forecasting model (CWRF). All configurations were driven by the ECMWF-Interim reanalysis and continuously integrated during 1980–2015 over the contiguous United States with 30-km grid spacing. Together they represent CWRF’s ability to simulate characteristics of US extreme precipitation, and their spread depicts the structural uncertainty from alternate physics parameterizations. The US extreme precipitation simulation was most sensitive to cumulus parameterization among all physics configurations. The ensemble cumulus parameterization (ECP) was overall the most skilled at reproducing seasonal mean spatial patterns of daily 95th percentile precipitation (P95). Other cumulus schemes severely underestimated P95, especially over the Gulf States and the Central-Midwest States in convective prevailing seasons. CWRF with ECP outperformed the driving reanalysis, which substantially underestimated P95 despite its daily atmospheric moisture data assimilation. The CWRF improvement over ERI is much larger in warm than cold seasons. Changing alone ECP closure assumptions produced two distinct clusters of convective heating/drying effects: one altered P95 mainly by changing total precipitation intensity and another by changing rainy-day frequency. Microphysics, radiation, boundary layer, and land surface processes also impacted the result, especially under mixed synoptic and convective forcings, and some of their parameterization schemes worked with ECP to further improve P95.
Hao He, Xin-Zhong Liang, Chao Sun, Zhining Tao, and Daniel Q. Tong
Copernicus GmbH
Abstract. We investigated the ozone pollution trend and its sensitivity to key precursors from 1990 to 2015 in the United States using long-term EPA Air Quality System (AQS) observations and mesoscale simulations. The modeling system, a coupled regional climate–air quality model (CWRF-CMAQ; Climate-Weather Research Forecast and the Community Multiscale Air Quality), captured well the summer surface ozone pollution during the past decades, having a mean slope of linear regression with AQS observations of ∼0.75. While the AQS network has limited spatial coverage and measures only a few key chemical species, CWRF-CMAQ provides comprehensive simulations to enable a more rigorous study of the change in ozone pollution and chemical sensitivity. Analysis of seasonal variations and diurnal cycle of ozone observations showed that peak ozone concentrations in the summer afternoon decreased ubiquitously across the United States, up to 0.5 ppbv yr−1 in major non-attainment areas such as Los Angeles, while concentrations at certain hours such as the early morning and late afternoon increased slightly. Consistent with the AQS observations, CMAQ simulated a similar decreasing trend of peak ozone concentrations in the afternoon, up to 0.4 ppbv yr−1, and increasing ozone trends in the early morning and late afternoon. A monotonically decreasing trend (up to 0.5 ppbv yr−1) in the odd oxygen (Ox=O3+NO2) concentrations are simulated by CMAQ at all daytime hours. This result suggests that the increased ozone in the early morning and late afternoon was likely caused by reduced NO–O3 titration, driven by continuous anthropogenic NOx emission reductions in the past decades. Furthermore, the CMAQ simulations revealed a shift in chemical regimes of ozone photochemical production. From 1990 to 2015, surface ozone production in some metropolitan areas, such as Baltimore, has transited from a VOC-sensitive environment (>50 % probability) to a NOx-sensitive regime. Our results demonstrated that the long-term CWRF-CMAQ simulations can provide detailed information of the ozone chemistry evolution under a changing climate and may partially explain the US ozone pollution responses to regional and national regulations.
Zhining Tao, Hao He, Chao Sun, Daniel Tong, and Xin-Zhong Liang
MDPI AG
A regional modeling system that integrates the state-of-the-art emissions processing (SMOKE), climate (CWRF), and air quality (CMAQ) models has been combined with satellite measurements of fire activities to assess the impact of fire emissions on the contiguous United States (CONUS) air quality during 1997–2016. The system realistically reproduced the spatiotemporal distributions of the observed meteorology and surface air quality, with a slight overestimate of surface ozone (O3) by ~4% and underestimate of surface PM2.5 by ~10%. The system simulation showed that the fire impacts on primary pollutants such as CO were generally confined to the fire source areas but its effects on secondary pollutants like O3 spread more broadly. The fire contribution to air quality varied greatly during 1997-2016 and occasionally accounted for more than 100 ppbv of monthly mean surface CO and over 20 µg m−3 of monthly mean PM2.5 in the Northwest U.S. and Northern California, two regions susceptible to frequent fires. Fire emissions also had implications on air quality compliance. From 1997 to 2016, fire emissions increased surface 8-hour O3 standard exceedances by 10% and 24-hour PM2.5 exceedances by 33% over CONUS.
Wenmin Liao, Jiabing Wu, Lianping Yang, Tarik Benmarhnia, Xin-Zhong Liang, Raghu Murtugudde, Amir Sapkota, Wenjun Ma, Shuang Zhong, and Cunrui Huang
IOP Publishing
Abstract Though a number of studies have shown positive relationships between flooding events and infectious diarrhea, there is a paucity of rigorous evidence regarding the net effect of flooding on diarrhea incidence, controlling for existing pre-trends and meteorological confounders. The study treats the 2016 catastrophic flood event in Anhui Province, China as a natural experiment using a difference-in-differences design with propensity score matching to exclude background variations of diarrhea occurrence and meteorological effects, thus isolating the net effect of flooding on diarrhea. A triple-differences analysis was further deployed to identify the potential effect modifiers, including gender, age, occupation and community health resources. By analyzing 359 580 cases of diarrhea that occurred before, during and after the flooding, we show that the 2016 flood event significantly increased the risk of dysentery (RR: 1.29, 95%CI: 1.15–1.46) in during-flood period, and also increased the risk of all-cause diarrhea (RR: 1.21, 95%CI: 1.17–1.26), typhoidal diarrhea, dysentery, and other infectious diarrhea in post-flood period. Children, males and non-farmers were particularly vulnerable to flooding impacts and the density of health professionals was found to be protective against diarrheal risk in both during-flood (RR: 0.81, 95% CI: 0.72–0.92) and post-flood (RR: 0.83, 95% CI: 0.77–0.88) periods. This study employs quasi-experimental design and provides a better understanding on both acute and sustained effects of flooding on diarrhea, which is important for accurate health impact assessments and developing targeted intervention strategies.