Journal of Hydrology最新文献_第5页

Differences in organic matter sources between suspended particulates and sediments in a lake during the frozen period 冰冻期湖泊中悬浮微粒和沉积物有机质来源的差异

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-02-03 DOI: 10.1016/j.jhydrol.2026.135094

Xiaohui Ren , Ruihong Yu , Yanjie Mi

Elucidating organic matter sources in lakes during the frozen period (FP) is crucial for understanding carbon and nitrogen cycling in global cold-region lakes. However, limited information exists on the sources and differences between suspended particulate organic matter and sediment organic matter in lakes during the FP. This study examined the stable carbon (δ¹³C) and nitrogen (δ¹⁵N) isotopic compositions of organic matter in suspended particulates and sediments from Daihai Lake to investigate the sources and implications of organic matter during the FP (January). Organic carbon in suspended particulates and total nitrogen in suspended particulates ranged from 0.45 to 1.22 mg/L and 0.08 to 0.20 mg/L, respectively, while organic carbon in sediments and total nitrogen in sediments ranged from 3.57 to 14.90 g/kg and 0.44 to 1.68 g/kg, respectively. Based on organic index (OI) and organic nitrogen (ON), suspended particulates (OI: 0.10 mg/L; ON: 0.12 mg/L) were slightly contaminated, whereas sediments (OI: 13.13 g/kg; ON: 1.08 g/kg) were heavily contaminated. The suspended particulate organic matter exhibited mixed source signatures, with exogenous inputs (sewage: 27.9% and soil: 22.2%) and endogenous production (phytoplankton: 25.8%). In contrast, sediment organic matter was predominantly exogenous inputs (soil: 40.3% and sewage: 26.6%). This discrepancy highlights a key process in organic matter transport and transformation under ice-covered conditions: phytoplankton-derived organic matter underwent preferential degradation during sedimentation, whereas terrestrial organic matter was more readily deposited and preserved. Notably, significant nitrogen isotope fractionation during sedimentation indicates that preferential mineralization of organic nitrogen and denitrification played key regulatory roles in the nitrogen cycling. The findings highlight the need to prioritize controlling exogenous organic matter inputs to lakes to ensure sustainable ecosystem.

阐明冻结期湖泊有机质来源对了解全球寒区湖泊碳氮循环具有重要意义。然而，关于FP期间湖泊中悬浮颗粒有机质和沉积物有机质的来源和差异的信息有限。本研究通过对滇东南东南东南东南地区沉积物和悬浮颗粒物中有机质的稳定碳（δ13C）和稳定氮（δ15N）同位素组成的分析，探讨滇东南东南东南东南地区FP（1月）的有机质来源及其意义。悬浮颗粒物中有机碳和总氮含量分别为0.45 ~ 1.22 mg/L和0.08 ~ 0.20 mg/L，沉积物中有机碳和总氮含量分别为3.57 ~ 14.90 g/kg和0.44 ~ 1.68 g/kg。以有机指数（OI）和有机氮（on）为指标，悬浮颗粒（OI: 0.10 mg/L, on: 0.12 mg/L）为轻度污染，沉积物（OI: 13.13 g/kg, on: 1.08 g/kg）为重度污染。悬浮颗粒有机质呈现混合来源特征，外源输入（污水占27.9%，土壤占22.2%）和内源产生（浮游植物占25.8%）。相比之下，沉积物有机质主要是外源输入（土壤：40.3%，污水：26.6%）。这种差异突出了冰覆盖条件下有机物运输和转化的一个关键过程：浮游植物来源的有机物在沉积过程中优先降解，而陆源有机物更容易沉积和保存。值得注意的是，沉积过程中显著的氮同位素分馏表明有机氮的优先矿化和反硝化作用在氮循环中起关键调节作用。研究结果强调，需要优先控制外源有机物质输入到湖泊，以确保可持续的生态系统。

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引用次数: 0

Cross-sectional average velocity predictions for double-layered vegetated open channels incorporating vegetation sheltering and blockage effects 考虑植被遮挡和阻塞效应的双层植被明渠的横断面平均流速预测

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-02-03 DOI: 10.1016/j.jhydrol.2026.135076

Yecong Liu, Mengyang Liu, Wenxin Huai, Yidan Ai, Liu Yang, Zhonghua Yang

Investigating the cross-sectional average velocity in open channel flows with double-layered vegetation is pivotal for evaluating flood discharge capacity in real river engineering. Utilizing genetic programming (GP), a machine learning technique, and building upon the Chezy formula structure, this study innovatively incorporates parameters characterizing vegetation sheltering and blockage effects to develop a cross-sectional average velocity predictive model balancing accuracy and computational efficiency. Analysis of the influence of vegetation-related model parameters on the Chezy coefficient C confirmed the model’s physical soundness. Comparative assessment against existing analytical velocity distribution models demonstrated the superior performance of the proposed GP model across multiple evaluation metrics. Furthermore, the study explores potential limitations in traditional velocity distribution models, highlighting the advantages of the GP approach. Specifically, the GP model establishes a robust mapping between hydraulic and geometric parameters to cross-sectional average velocity without relying on empirical vegetation drag coefficients, while effectively capturing vegetation-induced sheltering and blockage effects. In conclusion, this research provides an effective tool for predicting average velocity in rivers with complex vegetation, offering practical guidance for assessing flood discharge capacity in ecological river engineering.

在实际河流工程中，研究具有双层植被的明渠水流的断面平均流速是评价河道泄洪能力的关键。本研究利用遗传规划（GP）这一机器学习技术，在Chezy公式结构的基础上，创新性地引入表征植被遮挡和阻塞效应的参数，建立了平衡精度和计算效率的横截面平均速度预测模型。分析了植被相关模型参数对Chezy系数C的影响，证实了模型的物理合理性。与现有的分析速度分布模型进行了对比评估，证明了所提出的GP模型在多个评价指标上的优越性能。此外，该研究还探讨了传统速度分布模型的潜在局限性，突出了GP方法的优势。具体而言，GP模型在不依赖于经验植被阻力系数的情况下，建立了水力和几何参数与横截面平均流速之间的鲁棒映射，同时有效地捕获了植被引起的遮挡和阻塞效应。总之，本研究为复杂植被河流平均流速预测提供了有效工具，为生态河流工程的泄洪能力评价提供了实践指导。

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引用次数: 0

Integrated evaluation of snow density reanalysis products in the Northern Hemisphere 北半球雪密度再分析产品的综合评价

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-02-03 DOI: 10.1016/j.jhydrol.2026.135071

Yizhuo Li , Xin Miao , Xinyun Hu , Le Wang , Xueliang Zhang , Pengfeng Xiao , Weidong Guo

Snow density, as a key snow element, not only reflects the physical characteristics of snow cover but also plays a vital role in data assimilation, climate modeling, and hydrological cycle studies. However, systematic evaluations of snow density in reanalysis datasets are still lacking. In this study, we use 4,319 snow stations across major snow-covered regions in the Northern Hemisphere, to assess the applicability of snow density data from five widely used reanalysis datasets (ERA5-Land, GLDAS-Noah, GLDAS-CLSM, GLDAS-VIC, and JRA-3Q) during water year 2001–2023. Our results indicate that ERA5-Land and GLDAS-Noah better capture the spatial patterns and temporal variability of snow density across the study regions. Using these two datasets and in-situ observations, we analyze long-term trends of snow density in Canada, Russia, and the Western U.S. We find that, reanalysis datasets fail to reproduce the observed interannual trends. Reanalysis products tend to underestimate observed interannual changes of snow density in early winter or shift them to late winter months. Moreover, the impact of snow density biases on snow depth biases in reanalysis datasets varies by region and dataset through offsetting or amplifying snow water equivalent biases. This study provides a comprehensive evaluation of snow density accuracy in reanalysis datasets, and reveals the distinct spatiotemporal trends in snow density under global warming. Our results also highlight the divergent contribution of snow density biases to snow depth biases in reanalysis datasets across regions.

雪密度作为雪的关键要素，不仅反映了积雪的物理特征，而且在数据同化、气候模拟和水文循环研究中起着至关重要的作用。然而，在再分析数据集中对雪密度的系统评价仍然缺乏。在本研究中，我们利用北半球主要积雪地区的4319个雪站，评估了5个广泛使用的再分析数据集（ERA5-Land、GLDAS-Noah、GLDAS-CLSM、GLDAS-VIC和JRA-3Q） 2001-2023水年雪密度数据的适用性。结果表明，ERA5-Land和GLDAS-Noah较好地捕捉了研究区域积雪密度的空间格局和时空变化。利用这两个数据集和现场观测数据，我们分析了加拿大、俄罗斯和美国西部积雪密度的长期趋势。我们发现，再分析数据集不能再现观测到的年际趋势。再分析结果倾向于低估初冬观测到的雪密度年际变化或将其转移到冬末月份。此外，在再分析数据中，雪密度偏差对雪深偏差的影响因区域和数据集而异，主要通过抵消或放大雪水等效偏差。本研究对再分析数据集的积雪密度精度进行了综合评价，揭示了全球变暖背景下积雪密度的明显时空变化趋势。我们的研究结果还强调了各地区再分析数据集中雪密度偏差对雪深偏差的不同贡献。

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引用次数: 0

A novel deep learning coupled model for extracting flood control scheduling rules for reservoir groups 基于深度学习耦合模型的水库群防洪调度规则提取

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-02-03 DOI: 10.1016/j.jhydrol.2026.135083

Ling Kang , Xilong Wu , Liwei Zhou , Yunliang Wen

Traditional rule curves designed for single reservoirs often underperform when multiple, hydrologically linked reservoirs must be coordinated during extreme events. We propose a coupled model—BiTCN-STA-SHAP—that integrates a bidirectional temporal convolutional network (BiTCN), a spatio-temporal attention (STA) module, and Shapley Additive Explanations (SHAP) for interpretability to extract flood-control scheduling rules for a reservoir group (i.e., multiple hydrologically or geographically connected reservoirs operated jointly). Using five reservoirs in the Yangtze River Basin as a case study, the model maps the previous T = 6 days of inflows, interval inflows, outflows, and water levels to the current-day outflow for each reservoir. Development data were partitioned by flood events into training and validation subsets; the 1998 historical flood was held out for independent testing, and the 100-year design flood was used as a stress test. Compared with LSTM and TCN baselines, BiTCN-STA-SHAP achieved strong development performance (reservoir-level NSE > 0.90; group-mean NSE ≥ 0.94) and the best independent-test results (group-mean NSE = 0.97; MAPE = 7.75%). Under the 100-year stress test, it remained superior across most reservoirs, indicating robustness to extreme out-of-sample conditions. SHAP analyses reveal physically consistent patterns: previous-day inflow/outflow dominate, Jinping Level I–Ertan and Xiluodu–Xiangjiaba show strong linkages, and features related to the Three Gorges Reservoir are most influential.

当在极端情况下必须协调多个水文相连的水库时，为单个水库设计的传统规则曲线往往表现不佳。我们提出了一个耦合模型- BiTCN-STA-SHAP，该模型集成了双向时间卷积网络（BiTCN）、时空关注（STA）模块和Shapley加性解释（SHAP）的可解释性，以提取水库组（即多个水文或地理上连接的水库联合运行）的防洪调度规则。该模型以长江流域5个水库为例，将前T = 6天的流入、间隔流入、流出和水位映射到每个水库当前的流出。开发数据按洪水事件划分为训练子集和验证子集；1998年历史洪水作为独立测试，100年设计洪水作为压力测试。与LSTM和TCN基线相比，BiTCN-STA-SHAP具有较强的开发性能（油藏水平NSE >； 0.90，组平均NSE≥0.94）和最佳的独立测试结果（组平均NSE = 0.97, MAPE = 7.75%）。在100年的压力测试中，它在大多数油藏中都保持优越，表明了对极端样本外条件的稳健性。

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引用次数: 0

Subsurface stormflow concentration-discharge relationships reveal DOC and nitrate transport mechanisms across land uses in karst hillslopes 地下雨流浓度-流量关系揭示了喀斯特山坡土地利用中DOC和硝酸盐的运移机制

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-02-03 DOI: 10.1016/j.jhydrol.2026.135070

Na Feng , Jun Zhang , Fa Wang , Zhiyong Fu , Hongsong Chen

The dissolved carbon and nitrogen concentrations, which are crucial for aquatic ecosystem and water quality, are strongly influenced by hydrological processes. However, the transport dynamics of dissolved organic carbon (DOC) and nitrate within the extremely complex and hidden underground remain unclear. Here, we conducted manual high-frequency sampling to capture DOC/nitrate yields and their concentration-discharge relationships across subsurface and epikarst flow in karst hillslope with four land-use types (cropland, forage grassland, planted forestland, shrubland). Results showed that DOC and nitrate concentrations were highest during heavy rainfall (25–49.9 mm d⁻¹) in the early rainy season (e.g., April-May), while were lower during large rainstorms (>100 mm d⁻¹). However, export DOC and nitrate yields reached 1.71–15.19 kg km⁻²h⁻¹ and 0.60–8.88 kg km⁻²h⁻¹ in large rainstorm, respectively, which were 1.11–13.09 times and 1.30–6.53 times those in other rainfall events (25–99.9 mm d⁻¹). Epikarst flow exported 10.27 times more DOC and 17.33 times more nitrate yields per hour than subsurface flow due to its greater runoff depth, establishing it as the dominant pathway for nutrient export. Generally, planted forestland had the highest DOC yields (2.78 kg km⁻²h⁻¹) in subsurface flow, while cropland showed peak nitrate exports (1.77 kg km⁻²h⁻¹). In contrast, forage grassland reduced DOC yield by 80.5% relative to planted forestland, and nitrate by 97.4% relative to cropland. Therefore, planting forage grassland is an effective method of reducing carbon and nitrogen export. Mechanistically, DOC export was source-limited with near-synchronous concentration-discharge coupling in subsurface flow, contrasting with delayed release in epikarst flow. Nitrate export exhibited transport-limited with clockwise hysteresis in cropland and shrubland but anticlockwise in planted forestland, and no significant hysteresis in forage grassland. Additionally, land use indirectly influenced DOC and nitrate concentrations by altering soil water content, while precipitation affected DOC and nitrate via discharge and geogenic ion (e.g., Mg²⁺), respectively. These results highlight the primacy of epikarst systems and rainfall patterns in regulating nutrient exports and point to targeting large rainstorms for groundwater-quality protection.

溶解碳和氮浓度对水生生态系统和水质至关重要，受水文过程的强烈影响。然而，在极其复杂和隐蔽的地下，溶解有机碳（DOC）和硝酸盐的运移动力学尚不清楚。本文采用人工高频采样的方法，对4种土地利用类型（农田、牧草草地、人工林、灌丛）的喀斯特山坡地下流和表层流中DOC/硝酸盐的产量及其浓度-排放关系进行了采集。结果表明：4 ~ 5月初强降雨（25 ~ 49.9 mm d - 1）期间DOC和硝酸盐浓度最高，暴雨（100 mm d - 1）期间浓度较低；而在大暴雨条件下，出口DOC和硝酸盐产量分别达到1.71 ~ 15.19 kg km−2h−1和0.60 ~ 8.88 kg km−2h−1，是其他降雨条件下（25 ~ 99.9 mm d−1）的1.11 ~ 13.09倍和1.30 ~ 6.53倍。由于地表径流深度大，地表径流每小时输出的DOC是地表径流的10.27倍，硝酸盐产量是地表径流的17.33倍，是地表径流输出养分的主要途径。总的来说，人工林在地下流中DOC产量最高（2.78 kg km−2h−1），而农田的硝酸盐出口最高（1.77 kg km−2h−1）。相比之下，草料草地的DOC产量比人工林低80.5%，硝态氮产量比农田低97.4%。因此，种植牧草草地是减少碳氮输出的有效方法。从机理上讲，地下水中DOC的输出受源限制，具有近同步的浓度-流量耦合，与表层岩溶流中的延迟释放形成对比。硝态氮在农田和灌丛中呈顺时针迟滞，在人工林中呈逆时针迟滞，在饲草草地中无显著迟滞。此外，土地利用通过改变土壤含水量间接影响DOC和硝酸盐浓度，而降水分别通过排放和地质离子（如Mg2+）影响DOC和硝酸盐浓度。这些结果强调了表层岩溶系统和降雨模式在调节养分输出方面的首要作用，并指出了针对大暴雨进行地下水质量保护。

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引用次数: 0

Comprehensive framework for agricultural water management in data-scarce regions: Integration of hydrological models and remotely sensed crop type data 数据稀缺地区农业用水管理的综合框架：水文模型和遥感作物类型数据的整合

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-02-03 DOI: 10.1016/j.jhydrol.2026.135073

Wahidullah Hussainzada , Han Soo Lee , Ahmad Tamim Samim

Water resources are essential for human activities, with no substitutes. Agriculture consumes a significant portion of water to sustain food production. This study proposes a framework for agricultural water management by integrating hydrological modeling and remotely sensed crop type data in a data-scarce region. It focuses on the Amu River Basin (ARB), the largest watershed in northeastern Afghanistan, accounting for 57% of the region’s surface water. The WRF-Hydro stand-alone model was used to simulate daily discharge for three rivers from 2014 to 2019 and was calibrated and validated with the Global Land Data Assimilation System version 2. Statistical indicators were used to assess the model performance. The overall performance results for three rivers show a correlation coefficient (R) of 0.85–0.42, Nash-Sutcliffe Efficiency (NSE) 0.52 to −8.64, Killing-Gupta Efficiency (KGE) 0.74 to −0.56, and coefficient of determination (R²) of 0.73–0.17. Three machine learning (ML) algorithms, namely, random forest, support vector machine, and gradient boosting, were ensembled via a maximum voting classifier to predict crop type maps for the study period. The model was trained using Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data from the Aqua and Terra satellites. A high-resolution 2020 crop type map from United Nation Food and Agriculture Organization (UNFAO) was used for training. The model predicted the five major crop types from 2014 to 2019 across eight elevation zones. Then irrigation water requirements (IWR) were estimated for major crops via the UNFAO Penman‒Monteith method. The estimated IWRs were combined with the spatially and temporally explicit crop maps derived from the multi-model ensemble to quantify irrigation water demand and assess its balance with available water under different management scenarios. This study addresses water management challenges in data-scarce regions, improving the performance of the WRF-Hydro model in snowmelt-influenced watersheds and enhancing ML model accuracy through ensemble techniques. The findings of the current study could provide a deeper understanding of the challenges and possible solutions in arid and semiarid climates in developing countries.

水资源对人类活动至关重要，无可替代。农业消耗很大一部分水来维持粮食生产。本研究提出了一个整合水文建模和遥感作物类型数据在数据稀缺地区的农业用水管理框架。它的重点是阿姆河流域（ARB），这是阿富汗东北部最大的流域，占该地区地表水的57%。使用WRF-Hydro独立模型模拟了2014 - 2019年三条河流的日流量，并使用全球土地数据同化系统第2版进行了校准和验证。采用统计指标评价模型的性能。3条河流综合绩效结果的相关系数(R)为0.85 ~ 0.42，Nash-Sutcliffe效率（NSE）为0.52 ~ - 8.64，kill - gupta效率（KGE）为0.74 ~ - 0.56，决定系数（R2）为0.73 ~ 0.17。三种机器学习（ML）算法，即随机森林，支持向量机和梯度增强，通过最大投票分类器集成来预测研究期间的作物类型图。该模型使用Aqua和Terra卫星的MODIS归一化植被指数（NDVI）数据进行训练。培训使用了联合国粮食及农业组织（UNFAO）的2020年高分辨率作物类型图。该模型预测了2014年至2019年八个高程区的五种主要作物类型。然后通过联合国粮农组织Penman-Monteith方法估算了主要作物的灌溉需水量。将估算的iwr与多模式集合得出的时空明确作物图相结合，量化灌溉用水需求，并评估不同管理情景下灌溉用水与可用水的平衡。本研究解决了数据稀缺地区的水资源管理挑战，提高了WRF-Hydro模型在融雪影响流域的性能，并通过集成技术提高了ML模型的准确性。目前的研究结果可以提供对发展中国家干旱和半干旱气候的挑战和可能的解决方案的更深层次的理解。

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引用次数: 0

Budyko scatter reveals interactions between wildfire, land cover change, and climate 布迪科散射揭示了野火、土地覆盖变化和气候之间的相互作用

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-02-03 DOI: 10.1016/j.jhydrol.2026.135096

Nicholas K. Corak , Ana P. Barros , Lauren E.L. Lowman

The Southeastern United States (SEUS) experiences millions of acres of burned area from both prescribed burns and wildfires on an annual basis. Forests in the SEUS also serve as important carbon sinks for the global carbon budget. However, this carbon sink is threatened by extreme weather events. Climate predictions indicate that the risk of large wildfire events is likely to increase in the coming decades with expected prolonged periods of drought, which would affect vegetation growth and carbon uptake dynamics. How fire-induced changes to the land surface affect carbon and water fluxes over seasonal to decadal time scales is a current knowledge gap. The present manuscript addresses this gap by evaluating differences in land surface fluxes for a forested region in the SEUS under “fire” and “no-fire” conditions. As an unmanaged nature preserve prone to wildfires during dry periods, the Okefenokee Swamp located in the SEUS provides an ideal case study of fire-vegetation interactions. While small fires occur annually throughout the Okefenokee region, large wildfires occur every three to five years, keeping with the natural fire frequency of the Coastal Plains of the SEUS. In order to investigate how wildfire events affect carbon, water, and energy budgets, we use a land-surface hydrology model to simulate land-atmosphere interactions for the Okefenokee Swamp. Model simulations with forced phenology data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to evaluate vegetation responses to fire, while simulations coupled with a predictive phenology model allow us to investigate vegetation dynamics as if no fire occurred. Results suggest fire-induced land cover change can reduce annual carbon uptake by up to 50% following large fires. The Budyko framework is used to analyze how post-fire vegetation recovery dynamics and structural changes alter water and energy balances in the coupled water-carbon-energy system. Transiency in Budyko phase space reveals that fire events over the past 20 years shifted the Okefenokee region from a wetter forest towards a drier savanna. These results are significant as the Okefenokee Swamp is representative of fire-affected ecosystems throughout the SEUS. Results from this study improve our understanding of how fire-vegetation dynamics impact the contributions of the SEUS to the global carbon budget.

美国东南部（SEUS）每年都有数百万英亩的烧伤面积来自规定的烧伤和野火。SEUS的森林也是全球碳收支的重要碳汇。然而，这种碳汇受到极端天气事件的威胁。气候预测表明，随着干旱期的延长，未来几十年发生大型野火事件的风险可能会增加，这将影响植被生长和碳吸收动态。火灾引起的陆地表面变化如何影响季节到年代际时间尺度上的碳和水通量是目前的一个知识空白。本手稿通过评估SEUS森林地区在“火”和“无火”条件下的地表通量差异来解决这一差距。位于SEUS的奥克弗诺基沼泽是一个干旱时期容易发生野火的无人管理的自然保护区，为研究火与植被的相互作用提供了理想的案例。虽然整个奥克弗诺基地区每年都会发生小型火灾，但每三到五年就会发生大型野火，这与SEUS沿海平原的自然火灾频率保持一致。为了研究野火事件如何影响碳、水和能量收支，我们使用陆地表面水文模型来模拟奥克弗诺基沼泽的陆地-大气相互作用。利用来自中分辨率成像光谱仪（MODIS）的强迫物候数据进行的模型模拟用于评估植被对火灾的响应，而与预测物候模型相结合的模拟使我们能够在没有发生火灾的情况下研究植被动态。结果表明，火灾引起的土地覆盖变化可以在大火发生后减少高达50%的年碳吸收量。利用Budyko框架分析了火灾后植被恢复动态和结构变化如何改变水-碳-能耦合系统中的水-能平衡。Budyko相空间的短暂性揭示了过去20年的火灾事件将奥克弗诺基地区从潮湿的森林转变为干燥的稀树草原。这些结果很重要，因为奥克弗诺基沼泽是整个SEUS受火灾影响的生态系统的代表。本研究的结果提高了我们对火-植被动态如何影响SEUS对全球碳收支的贡献的理解。

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引用次数: 0

Projecting future exposure to compound precipitation and wind extremes using Copula methods with Bayesian model averaging 利用Copula方法和贝叶斯模型平均预测未来复合降水和极端风的暴露

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-02-02 DOI: 10.1016/j.jhydrol.2026.135074

Shuyou Liu , Jun Xia , Qianzuo Zhao

Compound precipitation and wind extremes (CPWEs) are destructive multivariate events whose risks are amplified under climate change. It is urgent but challenging to assess intensity risk and project future exposure due to complex social-meteorological factor interaction and uncertainty of GCM outputs. To deal with these issues, a novel CPWE risk assessment framework was established containing Bayesian Model Averaging (BMA) based GCM outputs ensemble, Copula-based CPWE selection, and intensity assessment. A comprehensive exposure index was built to evaluate future population and economic exposure. Key findings include: (1) The BMA ensemble outperforms individual GCMs, yielding higher accuracy and robustness in simulating precipitation and windspeed; (2) The intensity of future CPWEs is projected to increase, characterized by enhanced variability and spatial heterogeneity. A significantly larger proportion of regions exhibit upward trends under both SSP245 and SSP585 scenarios, particularly under the latter scenario; (3) High exposure risk persists in the North China Plain and southeastern coast, with most regions experiencing increased risk relative to the historical period. Risk peaks around mid-century, indicating a critical period for climate-socioeconomic tipping points. The results provide critical insights into the spatiotemporal patterns of future CPWEs, supporting the development of effective early warning systems and climate resilience strategies.

复合极端降水和极端风事件是一种破坏性的多变量事件，其危险性在气候变化的影响下被放大。由于复杂的社会气象因素相互作用和GCM输出的不确定性，评估强度风险和预测未来暴露是紧迫但具有挑战性的。为了解决这些问题，建立了一种新的CPWE风险评估框架，该框架包含基于贝叶斯模型平均（BMA）的GCM输出集合、基于copula的CPWE选择和强度评估。建立了综合暴露指数来评价未来的人口和经济暴露。主要发现包括：(1)BMA集合在模拟降水和风速方面优于单个gcm，具有更高的精度和鲁棒性；(2)预测未来CPWEs强度将增加，表现为变异性和空间异质性增强。在SSP245和SSP585两种情景下均呈现上升趋势的区域比例显著增加，尤其是在后一种情景下；③华北平原和东南沿海地区暴露风险较高，大部分地区暴露风险较历史时期有所增加。风险在本世纪中叶左右达到峰值，表明这是气候-社会经济临界点的关键时期。这些结果为未来CPWEs的时空格局提供了重要的见解，支持了有效预警系统和气候适应策略的发展。

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引用次数: 0

Understanding the limits of two lumped hydrological models through divergences between daily and sub-daily projections 通过日预估和次日预估之间的差异了解两种集总水文模型的局限性

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-02-02 DOI: 10.1016/j.jhydrol.2026.135081

Virginie Destuynder, Siavash Pouryousefi Markhali, Annie Poulin

The present paper identifies four common reasons for divergences between projections of flow metrics simulated by lumped conceptual hydrological models at a daily time step, and a daily-averaged 3-h time step. These reasons are based on five case studies simulated by two hydrological models in North America in the context of climate change, and are related to: the filtering of subdaily discrete and cyclic processes; the time-step dependent solving of nonlinear equations involving logical conditions; the time-step dependent solving of nonlinear sequential equations; and the timing of the flood events within the daily window. The time-step dependent simulation of the corresponding internal processes requires compensatory mechanisms in other processes in order to achieve an equivalent performance of the hydrological models in calibration at both time steps. These compensatory mechanisms can be identified in the parameter sets of the models and often imply a time-step dependent seasonality of the internal variables. Divergences between hydrological projections at daily and subdaily time steps reveal significant future shifts in the hydrological regime or in the runoff-generating processes, outside the domain of validity of the calibrated parameter sets. Time-step dependent projections underscore the need for detailed insight before using projections for engineering applications. Furthermore, the comparison of hydrological simulations from different time steps is a valuable approach to assess the domain of validity of calibrated parameter sets, and to understand the behavior of conceptual hydrological models under non-stationary climate conditions.

本文确定了由集总概念水文模型在每日时间步长和每日平均3小时时间步长模拟的流量度量预测之间存在分歧的四个常见原因。这些原因是基于气候变化背景下北美两个水文模型模拟的五个案例研究得出的，它们与以下因素有关：亚日离散和循环过程的过滤；涉及逻辑条件的非线性方程的随时间步长的求解非线性序列方程的随时间步长解以及每天发生洪水的时间。相应内部过程的时间步长依赖模拟需要其他过程中的补偿机制，以便在两个时间步长的校准中实现水文模型的等效性能。这些补偿机制可以在模型的参数集中识别出来，通常意味着内部变量的时间步相关季节性。日和次日时间步的水文预测之间的差异揭示了在校准参数集的有效性范围之外，水文状态或径流产生过程的重大未来变化。依赖于时间步长的投影强调了在工程应用中使用投影之前需要详细的洞察力。此外，比较不同时间步长的水文模拟是评估校准参数集的有效性域和了解非平稳气候条件下概念水文模型行为的有价值的方法。

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引用次数: 0

Extreme precipitation in eastern China: a centennial-scale analysis across multiple river basins based on return period thresholds 中国东部极端降水：基于回归期阈值的多流域百年尺度分析

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2026-02-02 DOI: 10.1016/j.jhydrol.2026.135058

Yichen Yang , Hanwei Yang , Yue Ma , Yong Zhao , Yuanbiao She , Chikun Luo

Under global warming, China has experienced frequent extreme precipitation events, with growing spatial disparities among various river basins. Traditional methods for identifying extreme precipitation based on percentile thresholds (PTs; e.g., R95p or R99p) have proven insufficient sensitivity in responding to changes in precipitation extremes. This study introduces “return period thresholds” (RPT) from hydrology for extreme precipitation definition and verifies their superior sensitivity via comparative analysis in six major eastern Chinese river basins. Defining heavy and extreme heavy precipitation by 0.5a and 2a return periods, respectively, RPT better reflects actual changes in extreme precipitation than PT. For instance, an upward trend in extreme precipitation has been observed throughout the Songhua River Basin, the central and southern Huai River Basin and the middle reaches of the Yangtze River Basin, consistent with the meridional tripolar pattern (8–10a interdecadal oscillations) of the first mode of Empirical Orthogonal Function (EOF). The second mode (EOF2) displays a southward-shifted dipole structure with dominant 5–6a cycles, historically correlated with flood occurrences. Projections to 2100 indicate an upward trajectory in thresholds across all basins, with two salient characteristics: larger absolute increases in southern compared to northern basins, and faster growth rates for extreme heavy precipitation relative to heavy precipitation. These findings underscore the need for enhanced monitoring of both the frequency and intensity of extremes, particularly within southern basins.

在全球变暖背景下，中国极端降水事件频发，且各流域间的空间差异越来越大。基于百分位阈值（PTs，如R95p或R99p）识别极端降水的传统方法已被证明在响应极端降水变化方面灵敏度不足。本研究从水文学中引入“回归期阈值”（RPT）来定义极端降水，并通过对中国东部6个主要流域的对比分析，验证了其优越的敏感性。RPT分别以0.5a和2a回期来定义强降水和极端强降水，比PT更能反映极端降水的实际变化。例如，整个松花江流域、淮河中南部和长江中游地区的极端降水都呈上升趋势；与经验正交函数（EOF）第一模态的经向三极型（8-10a年代际振荡）一致。第二模态（EOF2）表现为南移偶极子结构，以5-6a旋回为主，历史上与洪水发生相关。到2100年的预估表明，所有盆地的阈值都呈上升趋势，并具有两个显著特征：南部盆地的绝对增幅大于北部盆地，极端强降水的增长率相对于强降水更快。这些发现强调需要加强对极端事件频率和强度的监测，特别是在南部盆地内。

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引用次数: 0