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Exploring the roles of recorded unprecedented rainfall-runoff characteristics and uplifted riverbeds in the catastrophic 2022 Pakistan megaflood

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Advances in Climate Change Research 2025年第2期16卷 409-424页

作者： Bazai, Nazir Ahmed Cui, Peng Hussain, Tahir Zhang, Guo-Tao Wang, Yan Akhter, Muhammad Gulraiz Wang, Hao Alam, Mehtab Key Laboratory of Mountain Surface Process and Hazards/Institute of Mountain Hazards and Environment Chinese Academy of Sciences Chengdu 610041 China Key Laboratory of Land Surface Pattern and Simulation/Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China China–Pakistan Joint Research Center on Earth Sciences CAS-HEC Islamabad 45320 Pakistan Department of Earth Sciences Quaid-i-Azam University Islamabad 45320 Pakistan Department of Civil Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Swabi Topi 23640 Pakistan

Catastrophic floods triggered by extreme monsoonal rainfall have increasingly posed challenges in flood management, particularly in low-income nations like Pakistan. The 2022 megaflood exposed substantial gaps in understanding the interplay between rainfall, sediment dynamics, and flood amplification processes. This study investigates the causes and impacts of the event, focusing on floodwater sources, geomorphological changes, and future risks and mitigation strategies. A multifaceted approach combined with remote sensing, field observations, hydro-meteorological data, and climate models was employed. In July–August 2022, the lower Hindukush, Koh-e-Suleman, and Kirther ranges experienced rainfall of 200–300 mm, up to 726% above historical averages (1991–2021). This high-intensity rainfall triggered high-magnitude discharges from approximately 1250 streams, 58 % of which were primary contributors with discharge at 4000–5000 m³/s for nearly a month, causing 80%–85% of downstream damage and expanding the flooded area to 49,711 km². Some streams recorded 5–6 m flood levels, resulting in sedimentation deposition of 0.8–1 m in residential areas and about 2 m in the streambeds. Deposition downstream and along the Indus River reached 1.5–2 m, intensifying flood risks by reducing channel capacity and increasing water levels during flood events. Streambed uplift and sediment deposition emerged as critical factors amplifying flood magnitude, severity, and inundation, with floodwater levels rising by up to 2.4–3 m in some areas, posing severe risks for future events. These findings highlight the need to revise traditional flood risk models, which often overlook sediment dynamics and underscore the future challenges posed by ongoing sediment deposition and climate change, which are expected to exacerbate flood risks in the coming decades. The study emphasizes the importance of sediment management, river erosion control, and post-event interventions. Priori

关键词： Extreme monsoon circulation Future challenges Pakistan megaflood Riverbed uplifting Unprecedented high-magnitude streamflow

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Projected changes in future wheat yield across China under CMIP6 climate simulations and agricultural management scenarios

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Journal of Cleaner Production 2025年 508卷

作者： Zhang, Ermei Liu, Yujie Tan, Qinghua Chen, Jiahao Pan, Tao Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China

Sustaining future crop yield growth is significant for ensuring global food security under climate change impact. Agricultural management presents a viable strategy to address the increase in crop production. However, the understanding of the potential effects of climate change and key agricultural management (such as irrigation and machinery) on future crop yields under the latest climate scenarios remains elusive. Here, we collected statistics from historical periods (1980–2011) on wheat yields and agricultural management covering 11 representative agro-ecological zones across China. A yield estimation framework based on machine learning models and Bayesian model averaging was developed. We reliably projected changes in wheat yield and assessed the effectiveness of agricultural management (irrigation, fertilizer, and machinery) across China, for two future periods (near-term: 2021–2040;mid-term: 2041–2060) under updated shared socioeconomic pathway-representative concentration pathways (SSP-RCP) scenarios. Main conclusions are as follows: (1) wheat yield is expected to diminish by an average of approximately 1 % across China for SSP5-8.5 in all periods. Especially, the Northwest spring wheat zone (NWS) was identified as a region with high-risk for future wheat production because of a marked yield loss (over 10 % on average) across all climate scenarios during the mid-term period. (2) Simulated wheat yield in China exhibited an average increase of 2 % with 10 % synergistic increases in irrigation, fertilizer, and machinery levels. Individual increase in irrigation level had a greater positive effect on wheat yield compared to machinery and fertilizer under all climate scenarios and periods. (3) Wheat yields in arid regions (such as NWS) would benefit more from the improvements in irrigation level under all climate scenarios for near-term period. These findings provide valuable insights for policymakers to develop innovative solutions in climate change adaptation. ©

关键词： Global warming

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Geological Complexity: a novel index for measuring the relationship between landslide occurrences and geological conditions

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Bulletin of Engineering Geology and the Environment 2025年第6期84卷 1-21页

作者： Zhang, Yifan Wu, Chunhao Cui, Peng Yi, Shujian Yuan, Runjie Jiang, Zhenni Li, Yusheng Kong, Weilin Chengdu610041 China University of Chinese Academy of Sciences Beijing100049 China China-Pakistan Joint Research Center on Earth Sciences CAS-HEC Islamabad45320 Pakistan School of Environment and Resource Southwest University of Science and Technology Mianyang621010 China School of Geography Earth and Atmospheric Sciences The University of Melbourne ParkvilleVIC3010 Australia State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu610059 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China

The types, triggers, and spatial distribution of landslides are closely related to the spatial complexity of geological conditions, which are indispensable factors in landslide susceptibility assessment. However, geological conditions often consider only a single index, leading to underutilization of geological information in assessing landslide hazards. In this research, "Geological Complexity" is proposed as a novel index, consisting of tectonic complexity, lithologic complexity, structural complexity and seismicity, representing the spatial heterogeneity of the materials and structures of geological bodies. The contributions of each complexity component to landslides are quantified using a model that combines the weight of evidence and the analytic hierarchy process and then integrated into the Geological Complexity indicator. The performance of this model was further validated with data from the Eastern Himalayan Syntaxis. The results show that the predictive accuracy of Geological Complexity in bedrock-triggered landslide assessment is higher than any individual components and other traditional indicators such as distance to fault and lithology. This suggests that Geological Complexity can more accurately measure the controlling role of geological conditions on the occurrence of landslides than other indices. Selecting multiple geological parameters is more effective than relying on a single parameter. © The Author(s) 2025.

关键词： Faulting

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Understanding the Efficiency and Uncertainty of Water Supply Service Assessment Based on the Budyko Framework: A Case Study of the Yellow River Basin, China

SSRN

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SSRN 2025年

作者： Zhang, Tingjing Shao, Quanqin Huang, Haibo Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China

Freshwater ecosystem services (ESs) analyses are increasingly employed to address water resource management challenges. However, few studies have systematically examined the efficiency and uncertainty of such assessments, limiting their applicability for decision-making. In this study, the InVEST water yield model was applied to assess water supply service in the Yellow River Basin (YRB) from 2000 to 2022. We evaluated the model's sensitivity to climate variables and the parameter ω. Six sets of data/parameter input combinations were constructed to drive the model independently. Spatiotemporal trends were compared with observed data from 33 hydrological stations along the Yellow River mainstem and tributaries to assess the model performance and uncertainties. Finally, the response of water supply to climate change and vegetation dynamics was further discussed. The results showed that precipitation exhibited the highest sensitivity, and errors in precipitation inputs were the primary source of data input uncertainties. Compared to the raster-scale ω-value calculation method, the method combined with the lumped model delivered the most robust simulation results (R2, RMSE, and MAE for mainstream basins: 0.91, 50.08 mm, and 38.85 mm;for tributary basins: 0.89, 6.43 mm, and 4.04 mm, respectively). Climate change, particularly changes in precipitation, emerged as a key factor driving water supply service dynamics. These findings enhance the understanding of efficiency and uncertainty in water-related ESs assessments and offer valuable insights for applications in other regions. © 2025, The Authors. All rights reserved.

关键词： Rivers

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Historical trends and future projections of compound cloudy-rainy events during the global winter wheat harvest phase

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Agricultural and Forest Meteorology 2025年 371卷

作者： Gao, Xuan Dong, Jinwei Zhou, Sha Tao, Fulu Zhao, Dongsheng Zhang, Xi Zhang, Qiang Chen, Ziwei Ge, Quansheng Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical Science Beijing Normal University Beijing 100875 China

Compound cloudy-rainy events (CCREs), characterized by prolonged rainfall and extensive cloud cover, when occurring during the harvest phase, could adversely affect the drying, harvesting, and transportation of mature grains, and even lead to grain mold and sprouting. However, existing studies have focused more on the growth phases, while trends in CCREs during the harvest phase have been largely undocumented. Here we investigated the spatial-temporal variations of CCREs during the harvest phase of winter wheat globally for the historical (1985–2022) and future (2015–2074) periods. Our results reveal a significant decrease in CCREs during the winter wheat harvest phase on a global scale across the two periods. Spatially, reduced cloudy-rainy days cover about 60 % of the winter wheat-producing area. Additionally, the trend patterns of CCREs exhibit considerable spatial heterogeneity. Specifically, pronounced increases are primarily located in eastern North America, western Europe, and eastern Asia, where agricultural mechanization levels are higher. Meanwhile, the asynchronous trends between cloudy days and rainy days lead to spatial variability in the underlying causes of the observed increases in CCREs across these regions. Overall, the results of this study provide novel insights into agricultural climate mitigation and adaptation for global food security. © 2025 Elsevier B.V.

关键词： Climate change Compound cloudy-rainy events Harvest phase Winter wheat

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Potential reduction of greenhouse gas emissions through dietary structure changes and guidelines for environmental sustainability

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Environmental Impact Assessment Review 2025年 115卷

作者： Zhang, Fan Du, Yiqiong Deng, Xiangzheng Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China Institute of Loess Plateau Shanxi University Taiyuan China

Changes in residents' dietary patterns significantly influence both dietary consumption and greenhouse gases (GHGs) emissions. Based on IPCC guidelines and input-output analysis, this study assess the spatiotemporal characteristics of GHGs emissions across different age groups, genders, and urban-rural regions. Furthermore, we simulate the potential for GHGs emissions reduction under five food substitution scenarios by 2050. The results indicate that the 15–64 age group emits approximately four times more GHGs emissions than other age groups, while urban areas emit about twice as much as rural areas. Among the scenarios, adopting a Mediterranean dietary structure to fill protein gaps leads to the lowest GHGs emissions, with an estimated reduction of 533.89 Mt. CO₂eq by 2050. These findings suggest that adjusting sustainable dietary structures should be tailored to specific contexts, with a focus on substituting meat consumption with high-protein, low-carbon alternatives. This study provides insights for optimizing dietary structures and formulating policies aimed at reducing GHGs emissions associated with dietary consumption. © 2025 Elsevier Inc.

关键词： Diet consumption Emission reduction potential Emissions inequality Food alternative scenario Urban-rural difference

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Global occurrence, food web transfer, and human health risks of polycyclic aromatic hydrocarbons in biota

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Science of the Total Environment 2025年 958卷 177969页

作者： Xie, Jingqian Lan, Ruo Zhang, Li Yu, Jun Liu, Xinran You, Zhiyang Yang, Fen Lin, Tian College of Oceanography and Ecological Science Shanghai Ocean University Shanghai201306 China Key Laboratory of Tropical Marine Bio-resources and Ecology Guangdong Provincial Key Laboratory of Applied Marine Biology South China Sea Institute of Oceanology Chinese Academy of Sciences Guangzhou510301 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China

Polycyclic aromatic hydrocarbons (PAHs) are widespread organic pollutants that pose significant health risks due to their bioaccumulation in the biota. This study examines the global distribution of PAHs in biota, identifies key factors influencing using boosted regression tree (BRT) models, analyzes their transfer through trophic magnification factors (TMF), and evaluates health risks using the EPA risk assessment model. Research on PAHs has grown from 1978 to 2023, peaking in 2021, with 171 out of 241 studies (71.1 %) focusing on marine ecosystems. The highest PAH concentrations are observed in the Mediterranean Sea, Red Sea, and North American coastal regions, primarily influenced by industrial and human activities, such as factory emissions and ship transport. BRT analysis shows region factors and feeding habitats significantly influence PAH levels. TMF analysis shows that biodilution is the main mechanism for PAH attenuation, with concentrations decreasing as trophic levels increase. Additionally, health risk assessment further illustrate that toxicity equivalent (TEQ) values are highest in Egypt and Turkey. Across all populations in Egypt, the United States, Turkey, Portugal, and China, as well as children in Portugal and Sweden, there are potential risks from aquatic product consumption (10−6 −4), with CRI values positively correlated with liver cancer incidence. While hazard quotients (HQ) © 2024

关键词： Aromatization

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Impact of spatial transfer of farmland on the food-water-GHG nexus in China during 2000–2020

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Resources, Conservation and Recycling 2025年 221卷

作者： Liu, Yunxi Wang, Xue Tan, Minghong Zhang, Fuyao Li, Xiubin Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing 100049 China

Analyzing the impact of spatial transfer of farmland (STF) on the food-water-greenhouse gases (FWG) nexus through a coupled perspective holds significant scientific value for guiding sustainable farmland management policies. This study developed an integrated assessment framework to quantify FWG-nexus dynamics induced by STF, systematically evaluating changes in crop yields, irrigation water consumption, and GHG emissions across China from 2000 to 2020. The coupling coordination degree (CCD) of the FWG system was further examined. The results show that between 2000 and 2020, the area of farmland in China decreased by 2.63 × 10⁴km², and the center of gravity of farmland shifted approximately 56.90 km towards the northwest. Nationwide, STF led to an increase in average crop yields by 58.54 kg/ha;at the same time, the average irrigation water consumption increased by 1.93 mm, and the average GHG emissions also rose by 23.63 kgCO₂-eq/ha. During the study period, the FWG-nexus exhibited a tendency toward decoupling, as indicated by a 0.21 % decrease in the FWG CCD. The increasing consumption for irrigation water and rising GHG emissions have consistently been the primary constraints to the harmonization of the FWG-nexus at the national level, with notable regional variations of these constraints. These insights help deepen the understanding of the impact of STF on the FWG-nexus, providing scientific support for sustainable land use in China and the achievement of the United Nations Sustainable Development Goals (SDGs). © 2025 Elsevier B.V.

关键词： Coupled coordination degree Food-water-GHG Spatial transfer of farmland

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CMIP6-driven 10 km super-resolution daily climate projections with PET estimates in China

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Scientific Data 2025年第1期12卷 1-11页

作者： Zhang, Fuyao Li, Xiubin Wang, Xue Tan, Minghong Xin, Liangjie Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing 100049 China

Global warming has intensified extreme weather events, posing challenges to regional climate and hydro-ecological systems. To address the low-resolution limitations of current multi-climate variables and potential evapotranspiration (PET), this study develops a super-resolution fusion framework based on deep residual attention mechanisms, establishing China’s 10-km resolution multi-model-multi-scenario high-resolution climate and PET dataset (SRCPCN10). The Residual Channel Attention Network (RCAN) demonstrates exceptional downscaling performance for temperature, radiation, and pressure (R²/KGE > 0.99), while precipitation exhibits significantly lower accuracy (R² = 0.897) due to spatial discontinuity. The findings reveal distinct emission-gradient responses in China’s future climate variables under SSP scenarios, with temperature, radiation, and precipitation increases escalating alongside radiative forcing intensification. The comparison of annual mean differences between original CMIP6 and downscaled data showed excellent agreement, with most climate indices differing by less than 1%. This work overcomes traditional limitations, providing kilometer-scale multivariate data for watershed hydrological modeling, agricultural climate risk assessment, and carbon-neutral pathway optimization, enhancing the precision of regional adaptation strategies. © The Author(s) 2025.

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Mapping paddy rice in northeast China with a knowledge-based algorithm and time series optical, microwave, and thermal imagery

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Frontiers of Earth Science 2025年 1-16页

作者： Zhang, Chenchen Xiao, Xiangming Wang, Xinxin Qin, Yuanwei Doughty, Russell Yang, Xuebin Meng, Cheng Yao, Yuan Dong, Jinwei School of Biological Sciences Center for Earth Observation and Modeling University of Oklahoma Norman 73019 OK United States Key Laboratory for Biodiversity Science and Ecological Engineering National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary (Ministry of Education) Institute of Biodiversity Science and Institute of Eco-Chongming School of Life Sciences Fudan University Shanghai 200438 China College of Geography and Remote Sensing Hohai University Nanjing 211100 China College of Atmospheric and Geographic Sciences University of Oklahoma Norman 73019 OK United States Geography and the Environment Department Syracuse University Syracuse 13244 NY United States Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China

Accurate and timely large-scale paddy rice maps with remote sensing are essential for crop monitoring and management and are used for assessing its impacts on food security, water resource management, and transmission of zoonotic infectious diseases. Optical image-based paddy rice mapping studies employed the unique spectral feature during the flooding/transplanting period of paddy rice. However, the lack of high-quality observations during the flooding/transplanting stage caused by rain and clouds and spectral similarity between paddy rice and natural wetlands often introduce errors in paddy rice identification, especially in paddy rice and wetland coexistent areas. In this study, we used a knowledge-based algorithm and time series observation from optical images (Sentinel-2 and landsat 7/8) and microwave images (Sentinel-1) to address these issues. The final 10-m paddy rice map had user’s accuracy, producer’s accuracy, F1-score, and overall accuracy of 0.91 ± 0.004, 0.74 ± 0.010, 0.82, and 0.98 ± 0.001 (± value is the standard error), respectively. Over half (62.0%) of the paddy rice pixels had a confidence level of 1 (detected by both optical images and microwave images), while 38.0% had a confidence level of 0.5 (detected by either optical images or microwave images). The estimated paddy rice area in northeast China for 2020 was 60.83 ± 0.86 × 10³ km². Provincial and municipal rice areas in our data set agreed well with other existing paddy rice data sets and the Agricultural Statistical Yearbooks. These findings indicate that knowledge-based paddy rice mapping algorithms and a combination of optical and microwave images hold great potential for timely and frequently accurate paddy rice mapping in large-scale complex landscapes. © Higher Education Press 2025.

关键词： confidence map flooding signal knowledge-based algorithm paddy rice rice-wetland coexistence area

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