Journal of Hydrology最新文献_第10页

Spatiotemporal variations of cropland phosphorus runoff loss in China

IF 5.9 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2024-11-26 DOI: 10.1016/j.jhydrol.2024.132419

Zheqi Pan , Yufu Zhang , Longdan Ma , Jia Zhou , Yucang Wang , Kaibin Wu , Qian Zhang , Dingjiang Chen

Quantitative assessment of cropland phosphorus (P) loss via surface runoff is essential for developing effective pollution mitigation strategies. In this study, we compiled 812 datasets from 114 peer-reviewed papers for cropland P loss across China. We then developed machine learning (ML) approaches to estimate temporal and spatial variations in P runoff loss across China from 1990 to 2020. Four prevalent ML models were considered, namely, multiple linear regression (MLR), random forest (RF), classification and regression trees (CART), and boosted regression trees (BRT). Among these four models, RF exhibited the highest predictive accuracy for both uplands (calibration: R² = 0.86, n = 293; validation: R² = 0.61, n = 96) and paddy fields (calibration: R² = 0.88, n = 137; validation: R² = 0.60, n = 44). According to RF, China’s croplands are estimated to have lost an average of 148 ± 27 Gg P yr⁻¹ from 1990 to 2020, with uplands and paddy fields contributing 114 ± 26 Gg P yr⁻¹ and 34 ± 4 Gg P yr⁻¹, respectively. There was a significant increase in upland TP runoff loss over the study period (p < 0.001), whereas paddy field TP loss remained relatively constant. Regions in southern, eastern, and southwestern China, notably in Hainan, Guangxi, and Fujian provinces, were identified as hotspots of cropland TP runoff loss. Improved cropland management scenarios were predicted to reduce TP runoff loss by 1.4–11.8 %, with the best results obtained by minimizing runoff depth. To effectively mitigate TP runoff loss, an integrated management approach involving water, soil, and fertilizer is recommended. This study enhances quantitative understanding of cropland TP runoff loss in China, providing crucial insights for efficient cropland P management, which is key to managing nonpoint source pollution on a national level.

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引用次数: 0

Investigating the influence of nonlinear spatial heterogeneity in urban flooding factors using geographic explainable artificial intelligence 利用地理可解释人工智能研究城市洪水因素中非线性空间异质性的影响

IF 5.9 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2024-11-24 DOI: 10.1016/j.jhydrol.2024.132398

Entong Ke , Juchao Zhao , Yaolong Zhao

Urban pluvial flooding is one of the most significant environmental challenges impacting human society. Understanding the mechanisms through which geographical elements affect flooding is essential for developing effective flood mitigation strategies. However, due to limitations in current research methods, the nonlinear spatial heterogeneity of urban flooding factors remains underexplored. This study aims to design a novel framework based on geographic explainable artificial intelligence (GeoXAI) to investigate the nonlinear spatial heterogeneity of urban flooding factors in a case study of Guangzhou, China. In the attribution analysis of urban flooding susceptibility (UFS), a spatial statistical method and a conventional explainable artificial intelligence method were used for comparative evaluation with the GeoXAI method. The results reveal that: (a) flooding factors exert varying influences across different regions, although they generally increase UFS in the central-southern, western, and southeastern sectors of Guangzhou; (b) kernel normalized difference vegetation index and impervious surface density are dominant factors in urban flooding, with optimal thresholds for effectively mitigating flooding at above 0.25 and below 0.2, respectively; (c) GeoXAI demonstrates superior performance over traditional methods, offering enhanced model accuracy, more reliable interpretability, and better consideration of geospatial variables and spatial effects. These findings provide significant guidance for flood management in Guangzhou and underscore the broader potential of GeoXAI for disaster management in various regions.

城市冲积洪水是影响人类社会的最重大环境挑战之一。了解地理要素影响洪水的机制对于制定有效的洪水缓解战略至关重要。然而，由于目前研究方法的局限性，城市洪水因素的非线性空间异质性仍未得到充分探索。本研究旨在设计一种基于地理可解释人工智能（GeoXAI）的新型框架，以中国广州为例研究城市内涝因素的非线性空间异质性。在城市内涝易感性（UFS）的归因分析中，使用了空间统计方法和传统的可解释人工智能方法与 GeoXAI 方法进行比较评估。结果表明(a) 洪涝因素对不同区域的影响各不相同，但在广州的中南部、西部和东南部，洪涝因素普遍会增加城市易涝性；(b) 核归一化差异植被指数和不透水表面密度是城市洪涝的主导因素，有效缓解洪涝的最佳阈值分别为 0.25 以上和 0.2 以下；(c) GeoXAI 与传统方法相比表现出更优越的性能，模型精度更高，可解释性更可靠，并能更好地考虑地理空间变量和空间效应。这些研究结果为广州的洪水管理提供了重要指导，并凸显了 GeoXAI 在不同地区灾害管理中的广泛潜力。

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引用次数: 0

Seasonal dynamics of water-use strategies and response to precipitation in different habitats of Nitraria L.

IF 5.9 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2024-11-24 DOI: 10.1016/j.jhydrol.2024.132388

Huli Gu , Guopeng Chen , Heng Ren , Bing Liu , Qiyue Yang , Xiangyan Feng , Mingyan Fan , Hai Zhou

Nitraria L. is a dominant shrub in arid areas and its survival is hampered by low and unpredictable precipitation and uncertain future water conditions. However, little is known about the shrub’s water requirements or its response to precipitation. We determined the water utilization strategies of Nitraria L. species in habitats with different soil textures using the isotopic composition of xylem water and those of potential water sources (groundwater and vadose zone soil water). We used the MixSIAR model to quantify the relative contribution of potential water sources to shrub water in different soil textures. Our results showed that (1) The dynamic characteristics of water use of Nitraria L. species differed in different soil textures. In gravel soils, water in all soil layers was recharged by precipitation, and the shrub water source was controlled by precipitation with significant seasonal changes. In sandy and clay soils, shallow soil water was recharged by precipitation infiltration, but deep soil water was recharged by capillary rise. Nonetheless, the shrub water sources exhibited considerable seasonal fluctuations. During wet seasons, the shrub’s primary water sources were in shallow and mid-soil depths. However, during the dry season, the shrubs relied on groundwater, with more than half of their water originating in deep soil layers. (2) Nitraria L. species generally responded significantly to precipitation events, and those that survived in three soil textures were able to rapidly switch water sources to varying degrees. In particular, in sandy and gravelly soils, the proportion of deep soil water (24.3 %) and groundwater (16.2 %) used by Nitraria L. plants decreased significantly after a large precipitation event (e.g., 18.8 and 11.9 mm), shifting to a predominantly transient use of shallow soil water (48.5 %). Nitraria L. could shift water-absorbing soil layers according to the availability of potential water sources in different soil textures. This flexibility allows them to access the most readily available water more rapidly. Such optimal ecological adaptation can ensure that the plants will have an advantage in future predicted water shortage conditions.

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引用次数: 0

Quantifying hydrologic fluxes in an irrigated region characterized by groundwater return flows

IF 5.9 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2024-11-24 DOI: 10.1016/j.jhydrol.2024.132402

Ryan T. Bailey

In flood irrigation systems in which water is diverted from a river system, the return of recharge water to the river via groundwater discharge can play a key role in sustaining streamflow during irrigation and post-irrigation months. In this study, we use a combination of field data analysis and numerical hydrologic modeling to quantify the spatio-temporal hydrologic fluxes in a flood irrigated canal-field-aquifer-river system. To accomplish this objective, we develop a new irrigation package for MODFLOW that includes all major hydrologic features and fluxes: precipitation; canal diversions; irrigation type (sprinkler, drip, flood); runoff capture by downgradient canals; seepage from irrigation canals; and a soil water balance for each field, soil unit, and natural area that simulates crop ET and recharge. The model is applied to the White River Valley in the Meeker, Colorado (USA) region (180 km²), noted for extensive flood irrigation practices. From results, we conclude that of the water diverted from the White River for irrigation, approximately 75 % returns to the river. The 25 % irrigation efficiency is extremely low but, through extensive groundwater recharge, creates conditions conducive to groundwater return flow to the White River. The aquifer therefore acts as a slow-release reservoir of diverted river water to maintain streamflow and its ecosystem function during post-irrigation months. A holistic, basin-scale approach should be taken when considering conversion from flood irrigation to sprinkler irrigation, as benefits in conserving water at the farm scale likely will result in a decrease in groundwater return flows and therefore late season streamflow.

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引用次数: 0

Adaptive assessment of reservoir scheduling to hydrometeorological comprehensive dry and wet condition evolution in a multi-reservoir region of southeastern China 中国东南部多水库地区水库调度对水文气象综合干湿条件演变的适应性评估

IF 5.9 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2024-11-24 DOI: 10.1016/j.jhydrol.2024.132392

Hao Chen , Bingjiao Xu , He Qiu , Saihua Huang , Ramesh S.V. Teegavarapu , Yue-Ping Xu , Yuxue Guo , Hui Nie , Huawei Xie

The role of reservoirs in water resource management is becoming crucial for flood control and drought mitigation in any basin because of the frequent occurrence of extreme weather events attributed to global climate change and human activities. Therefore, evaluating the relationship between reservoir storage (discharge) and wet (dry) evolution is crucial. This study explores the time-delay effect and spatial heterogeneity of reservoir discharge and storage on dry and wet conditions in several basins of Lin’an District (LAD) in southeastern China. An integrated methodology is developed in this study to assess the relationship by a monthly streamflow simulation model, the meteorological and hydrological comprehensive drought index (CDI) using a Frank Copula function, and an eXtreme Gradient Boosting (XGBoost) model and Shapley Additive exPlanations (SHAP) framework were used to develop a model to forecast dry and wet conditions and to evaluate the key factors affecting their changes. Results from the study indicate that the monthly water balance model can simulate the monthly hydrological processes with relatively high accuracy in the LAD region. The CDI reflects the intensity of wet and dry events more precisely, thoroughly, sensitively, and consistently by combining the benefits of hydrological and meteorological drought indicators. Precipitation, evaporation, streamflow, the Pacific Decadal Oscillation (PDO), and the Indian Ocean Dipole (IOD) were the main contributing factors influencing the above 80% accuracy of the wet and dry forecast models. The average correlation between the outflow of each reservoir in LAD and CDI is 0.47, which is higher than the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI). Moreover, the delay in months of dry (wet) events based on SPI, SRI, and CDI are 0.45 (0.41), 1.07 (0.65), and 0.87 (0.60), respectively. It suggests reservoirs are less capable of adaptive scheduling for drought events than for wet events, and they respond most quickly to SPI defined events. The results can provide scientific and technological support for water safety and security in the study area.

由于全球气候变化和人类活动导致极端天气事件频发，水库在水资源管理中的作用对于任何流域的防洪和抗旱都变得至关重要。因此，评估水库蓄水（排水）与湿（干）演变之间的关系至关重要。本研究探讨了中国东南部临安地区（LAD）几个流域的水库泄洪和蓄水对干湿条件的时间延迟效应和空间异质性。本研究开发了一种综合方法，通过月度流场模拟模型、使用 Frank Copula 函数的气象和水文综合干旱指数 (CDI)、极梯度提升 (XGBoost) 模型和 Shapley Additive exPlanations (SHAP) 框架来评估三者之间的关系，从而建立干湿情预报模型并评估影响干湿情变化的关键因素。研究结果表明，月度水平衡模型能够以相对较高的精度模拟拉丁美洲和加勒比干旱地区的月度水文过程。通过结合水文和气象干旱指标的优势，CDI 能更准确、更全面、更灵敏、更一致地反映干湿事件的强度。降水、蒸发、溪流、太平洋十年涛动（PDO）和印度洋偶极子（IOD）是影响干湿预报模式准确率超过 80% 的主要因素。LAD 各水库出流与 CDI 的平均相关性为 0.47，高于标准化降水指数（SPI）和标准化径流指数（SRI）。此外，基于 SPI、SRI 和 CDI 的干（湿）月延迟分别为 0.45（0.41）、1.07（0.65）和 0.87（0.60）。这表明水库对干旱事件的适应性调度能力低于对湿润事件的适应性调度能力，而且水库对 SPI 界定的事件响应最快。研究结果可为研究区域的水安全保障提供科技支持。

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引用次数: 0

Understanding the temporal variability and predictability of streamflow signatures in the Colorado River Basin

IF 5.9 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2024-11-23 DOI: 10.1016/j.jhydrol.2024.132386

Patricia Puente , Balaji Rajagopalan , Laura E. Condon

It is well established that streamflow regimes evolve over decadal time scales (i.e., low frequency) leading to long term shifts in distributions. Similar low frequency variations have also been documented in streamflow predictability. Here we explore connections between streamflow distribution attributes and predictability regimes in the Upper Colorado River Basin. We employ nonlinear dynamical time series analysis methods on streamflow timeseries covering the period 762 – 2019 for six locations in the basin. First, a wavelet spectral analysis is performed to obtain the quasi-periodic ‘signal’ of the streamflow. The wavelet analysis also provides the temporal variability of the variance of the signal time series. The signal time series is embedded in a

D

-dimensional space with appropriate lag to reconstruct the phase space of the dynamics – i.e. the attractor. Overall predictability is assessed by quantifying the average divergence trajectories in the phase space using Global Lyapunov Exponents and the temporal variability of predictability via the Local Lyapunov Exponents. Results show clear oscillations in streamflow predictability with periods of both high and low predictability occurring throughout the study period at all gauges. Comparing predictability timeseries across the stream gauges we find that general consistency in high and low predictability periods, although they do not perfectly align temporally. In general, higher (lower) predictability periods are characterized by lower (higher) streamflow variance. While there is not a clear relationship between streamflow magnitude and predictability in general, modern high predictability epochs are characterized by a slightly greater likelihood of dry years and lower likelihood of wet years than other epochs. These findings indicate the potential for statistically significant differences in streamflow signatures between high and low predictability periods. Exploring these fundings further with potential connections to large-scale climate can be helpful in exploiting them for skillful short and medium term flow projections.

{"title":"Understanding the temporal variability and predictability of streamflow signatures in the Colorado River Basin","authors":"Patricia Puente , Balaji Rajagopalan , Laura E. Condon","doi":"10.1016/j.jhydrol.2024.132386","DOIUrl":"10.1016/j.jhydrol.2024.132386","url":null,"abstract":"<div><div>It is well established that streamflow regimes evolve over decadal time scales (i.e., low frequency) leading to long term shifts in distributions. Similar low frequency variations have also been documented in streamflow predictability. Here we explore connections between streamflow distribution attributes and predictability regimes in the Upper Colorado River Basin. We employ nonlinear dynamical time series analysis methods on streamflow timeseries covering the period 762 – 2019 for six locations in the basin. First, a wavelet spectral analysis is performed to obtain the quasi-periodic ‘signal’ of the streamflow. The wavelet analysis also provides the temporal variability of the variance of the signal time series. The signal time series is embedded in a <span><math><mi>D</mi></math></span>-dimensional space with appropriate lag to reconstruct the phase space of the dynamics – i.e. the attractor. Overall predictability is assessed by quantifying the average divergence trajectories in the phase space using Global Lyapunov Exponents and the temporal variability of predictability via the Local Lyapunov Exponents. Results show clear oscillations in streamflow predictability with periods of both high and low predictability occurring throughout the study period at all gauges. Comparing predictability timeseries across the stream gauges we find that general consistency in high and low predictability periods, although they do not perfectly align temporally. In general, higher (lower) predictability periods are characterized by lower (higher) streamflow variance. While there is not a clear relationship between streamflow magnitude and predictability in general, modern high predictability epochs are characterized by a slightly greater likelihood of dry years and lower likelihood of wet years than other epochs. These findings indicate the potential for statistically significant differences in streamflow signatures between high and low predictability periods. Exploring these fundings further with potential connections to large-scale climate can be helpful in exploiting them for skillful short and medium term flow projections.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132386"},"PeriodicalIF":5.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Historical memory in remotely sensed soil moisture can enhance flash flood modeling for headwater catchments in Germany 遥感土壤水分的历史记忆可增强德国山洪暴发的源头流域建模能力

IF 5.9 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2024-11-23 DOI: 10.1016/j.jhydrol.2024.132395

Yan Liu , Yong Chang , Ingo Haag , Julia Krumm , Visakh Sivaprasad , Dirk Aigner , Harry Vereecken , Harrie-Jan Hendricks Franssen

The wetness precondition of a catchment affects available soil water storage capacity and infiltration rate, thus influences flash flood generation. Remotely sensed (RS) soil moisture (SM) can provide valuable information on catchment wetness, but typically only represents the top 5 cm of the land surface. However, hydrological models for flash flood simulation need to consider deeper layers to calculate the total soil water storage. Therefore, a key challenge is to link RS SM to total soil water storage and assimilate RS SM into flash flood models to correctly describe initial catchment wetness. In this study, we developed an approach to combine present and antecedent RS SM to infer present soil water storage based on four regression models. The inferred soil water storage from SMAP (soil moisture active passive) SM was assimilated into the operational LARSIM (Large Area Runoff Simulation Model) hydrological model. We tested this new approach with 12 events in the headwater catchments Körsch, Adenauer Bach and Fischbach in Germany. Results show that random forest regression performs the best among the four regression models. The BIC (Bayesian Information Criterion) score suggests that regressions considering antecedent RS SM can well infer soil water storage, resulting in R² of 0.85, 0.94 and 0.93 for the Körsch, Adenauer Bach and Fischbach catchments, respectively. Compared to the open loop (without data assimilation) simulations, our approach enhanced the general performance of event simulations with average KGE increases of 0.09, 0.24 and 0.33 for the Körsch, Adenauer Bach and Fischbach, respectively; and the mean error in the 12 simulated event peaks is reduced 15 %. Moreover, the simulation uncertainty is reduced, too. The transferability of the proposed approach to other RS products is also discussed. Although assimilating RS SM can enhance flash flood modeling, it is primarily affected by the uncertainty in precipitation. In the future, the proposed approach should be tested with more catchments and events to verify its general validity.

集水区的湿润前提条件会影响土壤的蓄水能力和渗透率，从而影响山洪的生成。遥感（RS）土壤湿度（SM）可提供有关集水区湿润度的宝贵信息，但通常只代表地表顶部 5 厘米。然而，用于山洪模拟的水文模型需要考虑更深的层，以计算土壤的总蓄水量。因此，如何将 RS SM 与土壤总蓄水量联系起来，并将 RS SM 同化到山洪模型中，以正确描述初始流域湿润度，是一项关键挑战。在本研究中，我们基于四个回归模型，开发了一种结合当前和前兆 RS SM 来推断当前土壤蓄水量的方法。从 SMAP（土壤水分主动被动）SM 中推断出的土壤蓄水量被同化到运行中的 LARSIM（大面积径流模拟模型）水文模型中。我们用德国 Körsch、Adenauer Bach 和 Fischbach 等上游集水区的 12 个事件对这一新方法进行了测试。结果表明，随机森林回归在四种回归模型中表现最佳。BIC（贝叶斯信息标准）得分表明，考虑了前因 RS SM 的回归能够很好地推断土壤蓄水量，在 Körsch、Adenauer Bach 和 Fischbach 流域的 R2 分别为 0.85、0.94 和 0.93。与开环（无数据同化）模拟相比，我们的方法提高了事件模拟的总体性能，Körsch、Adenauer Bach 和 Fischbach 流域的 KGE 平均值分别增加了 0.09、0.24 和 0.33；12 个模拟事件峰值的平均误差减少了 15%。此外，模拟的不确定性也有所降低。此外，还讨论了所建议方法对其他 RS 产品的可移植性。虽然吸收 RS SM 可以增强山洪模型，但它主要受到降水不确定性的影响。今后，应使用更多的流域和事件对所提出的方法进行测试，以验证其普遍有效性。

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引用次数: 0

Occurance and pollution risk assessment of emerging contaminants in groundwater in the vicinity of a typical municipal landfill in northeastern China 中国东北某典型城市垃圾填埋场附近地下水中新出现污染物的出现及污染风险评估

IF 5.9 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2024-11-23 DOI: 10.1016/j.jhydrol.2024.132408

Zhihao Zhang , Nan Zhang , Meichao Zhao , Yiwu Zhang , Weifei Yang , Bo Liu

Emerging contaminants (ECs) present a significant risk to both the ecological environment and human health. However, there is currently limited knowledge regarding the presence of ECs in leachate and the surrounding groundwater environment of landfills. The heterogeneity of aquifers introduces additional uncertainty into the transport of ECs, thereby impacting the accuracy of pollution risk prediction. In this study, the types and concentrations of ECs in the leachate and surrounding groundwater were firstly investigated in a typical landfill in northeastern China. Results show that 6 different ECs were detected in groundwater monitoring wells around the landfill, with concentrations ranging from 1.25-1471 ng/L, higher than most investigated landfills in China. Leachate contained 18 different ECs with concentrations ranging from 0.25-30414 ng/L. Based on the statistical characteristics of lithology reflected by borehole data, random lithology fields were generated and transformed into heterogeneity parameter fields using Markov chain analysis to facilitate the risk assessment of ECs. Following a simulation period of 100 years, it was observed that due to the low permeability of the aquifer, pollutants only spread up to 700 m northward. While pollution plumes may disperse towards residential areas, the probability of exposure to EC in these regions is minimal. Conversely, areas with high pollution risk are predominantly located on the eastern and northern sides of the landfill. This study contributes to a deeper understanding of the impact of landfills on surrounding groundwater environments, and our proposed pollution risk assessment model can serve as a valuable reference for controlling and treating ECs.

新出现的污染物（ECs）对生态环境和人类健康都构成重大风险。然而，目前人们对垃圾填埋场渗滤液和周围地下水环境中存在的新污染物了解有限。含水层的异质性为氨基甲酸乙酯的迁移带来了额外的不确定性，从而影响了污染风险预测的准确性。本研究首先在中国东北地区的一个典型垃圾填埋场调查了渗滤液和周围地下水中导电率物质的类型和浓度。结果表明，在该垃圾填埋场周围的地下水监测井中检测到 6 种不同的氨基甲酸乙酯，浓度范围为 1.25-1471 纳克/升，高于国内大多数已调查的垃圾填埋场。渗滤液中含有 18 种不同的氨基甲酸乙酯，浓度范围为 0.25-30414 纳克/升。根据钻孔数据反映的岩性统计特征，生成随机岩性场，并利用马尔科夫链分析将其转化为异质性参数场，以方便对ECs进行风险评估。模拟期为 100 年，结果表明，由于含水层渗透性低，污染物只能向北扩散 700 米。雖然污染羽流可能會向住宅區擴散，但這些地區受氨基甲酸乙酯影響的機會甚微。相反，污染风险较高的区域主要位于堆填区的东侧和北侧。這項研究有助我們更深入了解堆填區對周圍地下水環境的影響，而我們建議的污染風險評估模型可作為控制和處理氨基甲酸乙酯的重要參考。

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引用次数: 0

Vegetation as a driver of shifts in rainfall-runoff relationship: Synthesising hydrological evidence with remote sensing 植被是降雨-径流关系变化的驱动因素：利用遥感综合水文证据

IF 5.9 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2024-11-23 DOI: 10.1016/j.jhydrol.2024.132389

Hansini Gardiya Weligamage , Keirnan Fowler , Dongryeol Ryu , Margarita Saft , Tim Peterson , Murray C Peel

Drought-induced hydrological shifts and subsequent non-recovery have been reported globally, including in Australia. These phenomena involve changes in the rainfall-runoff relationship, so a year of given rainfall gives less streamflow than before. Some authors have indicated that vegetation dynamics played a key role in hydrological shifts during Australia’s Millennium Drought (MD, 1997–2009), but such interactions are complex and are yet to be fully examined. This study investigates vegetation responses before, during, and after the MD for the same set of catchments in southeast Australia where hydrological shifts and non-recovery have been reported. The characterisation of vegetation behaviour relies on remotely sensed vegetation indices (VIs), namely Normalised Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Enhanced Vegetation Index (EVI), and Vegetation Optical Depth (VOD). Despite the severe multi-year drought, in most locations, the results indicate increased or maintained VIs over the entire period spanning pre-drought to post-drought. However, the link with hydrological shifts is nuanced and depends on how data are analysed. Contrary to expectations, an initial analysis (focussing on raw VI values) indicated that VI shifts were not correlated with hydrological shifts. It was only when the data were reanalysed to better account for the meteorological conditions that the expected correlations emerged. Overall, the results suggest that vegetation was able to maintain indices such as greenness and, by extension, actual evapotranspiration, leaving less rainfall for streamflow. More broadly, this approach provides greater insights into how vegetation affects hydrological behaviour through matched catchments during this and other multi-year droughts.

据报道，干旱引起的水文变化和随后的不恢复现象遍及全球，包括澳大利亚。这些现象涉及降雨-径流关系的变化，因此在降雨量一定的年份，溪流的流量会比以前少。一些学者指出，在澳大利亚千年干旱（MD，1997-2009 年）期间，植被动态在水文变化中发挥了关键作用，但这种相互作用非常复杂，有待全面研究。本研究调查了澳大利亚东南部同一组集水区在千年干旱之前、期间和之后的植被反应，这些集水区曾有过水文变化和未恢复的报道。植被行为的特征依赖于遥感植被指数（VIs），即归一化植被指数（NDVI）、光合有效辐射分量（FPAR）、增强植被指数（EVI）和植被光学深度（VOD）。尽管发生了严重的多年干旱，但大多数地点的结果表明，从干旱前到干旱后的整个期间，植被指数都有所上升或保持不变。然而，与水文变化之间的联系是微妙的，取决于如何分析数据。与预期相反，初步分析（侧重于原始 VI 值）表明，VI 变化与水文变化无关。只有在重新分析数据以更好地考虑气象条件时，才会出现预期的相关性。总体而言，研究结果表明，植被能够保持绿度等指数，进而保持实际蒸散量，从而减少了用于溪流的降雨量。从更广泛的意义上讲，这种方法能让人们更深入地了解在这次干旱和其他多年干旱期间，植被是如何通过匹配的集水区影响水文行为的。

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引用次数: 0

Monitoring water quality parameters in urban rivers using multi-source data and machine learning approach

IF 5.9 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology

Pub Date : 2024-11-23 DOI: 10.1016/j.jhydrol.2024.132394

Yongchun Liang , Fangyu Ding , Lei Liu , Fang Yin , Mengmeng Hao , Tingting Kang , Chuanpeng Zhao , Ziteng Wang , Dong Jiang

The systematic surveillance of nutrients and organic pollution in urban rivers is crucial for enhancing ecological integrity and promoting societal and economic sustainability. Currently, the primary methods of water quality monitoring involve on-site sampling and laboratory analysis, which are constrained by various factors such as terrain and climate. Remote sensing water quality monitoring, which enables large-scale, periodic, and comprehensive coverage, serves as an important supplement to these traditional methods. However, most current research on water quality monitoring predominantly relies on remote sensing technology, often overlooking the application of other multi-source data. In this study, we examined rivers in the Weihe River Basin by integrating field samples, Sentinel-2 multispectral imagery, meteorological elements, and land use types to construct machine learning (ML) models for predicting four water quality parameters (WQPs): ammonia nitrogen (NH₃-N), total phosphorus (TP), chemical oxygen demand (COD), and dissolved oxygen (DO). The results showed that land use types significantly influenced the accuracy of predictions for NH₃-N, TP, COD, and DO. Among the models evaluated, the Extra Tree Regression (ETR), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting Regression (GBR) demonstrated the highest accuracy and transferability for monitoring WQPs in rivers. For instance, the models achieved the following coefficients of determination (R²) in 5-fold cross-validation: for NH₃-N, R² was 0.65 in both the testing and validation datasets; for TP, R² was 0.71 and 0.68; for COD, R² was 0.50 and 0.47; and for DO, R² was 0.68 and 0.64, respectively. Therefore, our findings underscore the feasibility of using multi-source data and ML methods to quantify water pollutants in urban rivers, providing essential technical support for monitoring the spatiotemporal dynamics of river water quality across extensive geographical areas.

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引用次数: 0