Journal of Hydrology最新文献

{"title":"Monitoring water quality parameters in urban rivers using multi-source data and machine learning approach","authors":"Yongchun Liang ,&nbsp;Fangyu Ding ,&nbsp;Lei Liu ,&nbsp;Fang Yin ,&nbsp;Mengmeng Hao ,&nbsp;Tingting Kang ,&nbsp;Chuanpeng Zhao ,&nbsp;Ziteng Wang ,&nbsp;Dong Jiang","doi":"10.1016/j.jhydrol.2024.132394","DOIUrl":"10.1016/j.jhydrol.2024.132394","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132394"},"PeriodicalIF":5.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748729","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}

{"title":"Optical and molecular techniques are complementary to understand the characteristics of dissolved organic matter in the runoff from sloping croplands with various micro-topographies during rainfall","authors":"Guo Chen ,&nbsp;Qing-wei Zhang ,&nbsp;Hao Wang ,&nbsp;Ren Geng ,&nbsp;Jian Wang ,&nbsp;Yuan-bi Yi ,&nbsp;Ming Li ,&nbsp;Ding He","doi":"10.1016/j.jhydrol.2024.132403","DOIUrl":"10.1016/j.jhydrol.2024.132403","url":null,"abstract":"<div><div>Dissolved organic matter (DOM) is a vital component of biogeochemical cycles in soil and aquatic ecosystems. The distribution of surface and sub-surface runoff was affected by surface micro-topographic conditions during rainfall, which results in the differences in DOM content and composition. Whereas, the connections between the optical and molecular characteristics of DOM in the runoff from different micro-topographies caused by tillage managements remains unclear. Therefore, optical spectroscopy and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) were combined to explore the DOM chemistry in the runoff from different runoff plots with various micro-topographies caused by four typical tillage managements (i.e. flat tillage, longitudinal tillage, contour tillage, and artificial digging tillage) in this study. The results observed a significant difference in optical and molecular parameters between surface and sub-surface runoff for the specified runoff plot, but little variations in DOM chemistry between different runoff plots with various micro-topographies for the given runoff type. These differences in the DOM content and composition of runoff were limited by the flow carrying capacity and source supplying capacity of DOM. Significant correlations between optical and molecular parameters in surface and sub-surface runoff were found by Spearman correlation analysis. Furthermore, the bipartite networks further indicated the optical and molecular datasets in sub-surface runoff showed greater consistency and correlation intensity. These correlations and some of the inconsistencies indicated that optical and molecular technologies are complementary to trace DOM chemistry. This research is of significance to further clarify the migration patterns of DOM in global soil and aquatic ecosystems, and reveal the influence of rainfall-runoff processes on the migration of biogenic elements in ecosystems.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132403"},"PeriodicalIF":5.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701276","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}

{"title":"Attributes of karst lakes in sustaining net autotrophy and carbon sink effects","authors":"Yongqiang Han ,&nbsp;Haibo He ,&nbsp;Zaihua Liu ,&nbsp;Chaowei Lai ,&nbsp;Zhen Ma ,&nbsp;Xing Liu ,&nbsp;Dong Li ,&nbsp;Mingyu Shao ,&nbsp;Wenfang Cao ,&nbsp;Hang Li ,&nbsp;Pengyun Hao ,&nbsp;Yuhao Zhao ,&nbsp;Huiming Xu ,&nbsp;Yunfang Li ,&nbsp;Longyun Yin","doi":"10.1016/j.jhydrol.2024.132404","DOIUrl":"10.1016/j.jhydrol.2024.132404","url":null,"abstract":"<div><div>Natural lakes are significant global sources of CO₂ emissions; however, data from lakes in karst regions, which cover 15.2 % of the Earth’s surface, remain insufficiently categorized. This study seeks to elucidate the carbon budget processes and drivers in karst lakes by selecting representative karst and non-karst lakes within the same region. Utilizing high-resolution monitoring, carbon isotope analysis, and mathematical modeling, we investigated the carbon source-sink functions and their controlling factors across different lithologies. Our findings reveal that metabolic processes, quantified using a bookkeeping model, are crucial for driving the diurnal coupling of hydrochemistry and carbon cycling, with karst lakes displaying a pronounced net autotrophic state. Carbon sink fluxes, determined via the boundary layer method, were estimated to be 38 t C km⁻² yr⁻¹ for the karst lake, and 11 t C km⁻² yr⁻¹ for the non-karst lake. This indicates that despite high dissolved inorganic carbon (DIC) concentrations, the metabolic processes in karst waters, facilitated by their high pH and efficient DIC fertilization, lead to a lower CO₂ emission. Furthermore, the low Revelle factor (3.8–4.8) highlights the strong carbonate buffering capacity of karst lakes against CO₂. These findings emphasize the capacity of karst lakes to maintain net autotrophy and function as carbon sinks, together with the need to consider lithological differences in future assessments of regional or global lake carbon budgets.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"647 ","pages":"Article 132404"},"PeriodicalIF":5.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700467","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}

{"title":"Characterizing multi-source heavy metal contaminated sites at the Hun River basin: An integrated deep learning and data assimilation approach","authors":"Yanhao Wu ,&nbsp;Mei Li ,&nbsp;Haijian Xie ,&nbsp;Yanghui Shi ,&nbsp;Qun Li ,&nbsp;Shaopo Deng ,&nbsp;Shengtian Zhang","doi":"10.1016/j.jhydrol.2024.132349","DOIUrl":"10.1016/j.jhydrol.2024.132349","url":null,"abstract":"<div><div>In real-world scenarios involving groundwater contamination, the environmental complexity substantially complicates the tasks of tracing pollution sources and characterizing the features of affected sites. To address these challenges, this study presents an integrated framework that combines deep learning (AR-Net-DA) with data assimilation (ES-MDA). This approach effectively traces pollution sources and characterizes site features using sparse data from complex contamination scenarios. The paper introduces a case study involving multisource heavy metal (manganese) pollution in the Hun River basin, Liaoning Province, China. A high-fidelity model for groundwater flow and solute transport was developed. Subsequently, the innovative convolutional neural network model, AR-Net-DA, was employed to replace traditional process-based groundwater models by dynamically optimizing weights in proximity to various pollution sources. This model was then integrated into the ES-MDA inversion framework to concurrently determine pollution source parameters and the spatial distribution of aquifer permeability fields. The results demonstrate that this coupled inversion framework can accurately pinpoint pollution source locations and their release histories using limited observational data, while also mapping the spatial distribution of hydraulic conductivity fields with enhanced computational efficiency. These findings have significant implications for groundwater resource management, pollution risk control, and the remediation of contaminated sites.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132349"},"PeriodicalIF":5.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748726","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}

{"title":"Rain detection for rain-contaminated ground-based microwave radiometer data using physics-informed machine learning method","authors":"Wenyue Wang ,&nbsp;Wenzhi Fan ,&nbsp;Klemens Hocke","doi":"10.1016/j.jhydrol.2024.132365","DOIUrl":"10.1016/j.jhydrol.2024.132365","url":null,"abstract":"<div><div>Because the radiation signal is strongly influenced by emission and scattering from rain, microwave radiometer data suffer from rain contamination. The traditional method of using rain gauges to detect rain for microwave radiometers has limitations. For example, it can only detect rain that reaches the ground and is ineffective for raindrops suspended in the atmosphere that can still contaminate remote sensing data. This article presents a rain detection method for microwave radiometer measurements, based on Gradient Boosted Decision Trees (GBDT). First, the characteristic that the increase in microwave radiometer brightness temperature when raindrops are present in the atmosphere, along with the seasonal dependency of rainfall patterns, is combined with meteorological variables to form feature vectors. Then, the GBDT is employed to classify data into rain-free and rain-contaminated categories. Microwave radiometer (MWR) measurements and simultaneous Micro Rain Radar (MRR) target classification collected from the Swiss Plateau in 2008 are utilized to train the model, which is subsequently tested using two testing schemes: ten-fold cross-validation technique and time series test sets. Compared with the detection accuracy of the integrated liquid water (ILW) threshold method (73.6% and 68.3%) in both testing schemes, our GBDT-based method achieved superior accuracy, recording approximately 100% and 98.4%, respectively. The proposed method exhibits strong generalization capabilities, allowing it to directly detect rain contamination in time series data and effectively overcome the time dependence of rainfall occurrence. In addition, compared with the ILW threshold method, the GBDT-based method considers various rainfall patterns contained in various seasons. Features selected for this method enable its direct application to other tropospheric microwave radiometer systems.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132365"},"PeriodicalIF":5.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can storage-discharge characteristics of karst matrix system quantified through recession analysis be reliable?","authors":"Runrun Zhang ,&nbsp;Qingyue Bu ,&nbsp;Xi Chen ,&nbsp;Jintao Liu","doi":"10.1016/j.jhydrol.2024.132378","DOIUrl":"10.1016/j.jhydrol.2024.132378","url":null,"abstract":"<div><div>Storage and subsequent release of water in matrix system is a key function of karst catchments that controlling baseflow variation. Hydrograph recession analysis is currently the economic way to assess storage-discharge characteristics broadly at the catchment scale. However, there is large uncertainty in the related quantification due to recession data extraction. This study, by combining recursive digital filters with different automatic extraction methods, slow flow recession segments were extracted for hydrograph recession analysis and the further dynamic matrix storage (<em>DMS</em>) and related recession time (<em>RT</em>) assessment. The diversity and consistence among <em>DMS</em> and <em>RT</em> derived from different recession extraction methods (REMs) were analyzed, using hydrometric data in 20 catchments in the Wujiang river Basin in southwest China. Results indicate that the estimates of <em>DMS</em> and <em>RT</em> remarkedly varied between different REMs, however the order in which they ranked was mostly consistent. Moreover, the relationships between the derived <em>DMS</em> and <em>RT</em> with catchment physical features that potentially control storage and release processes are mostly consistent. Larger <em>DMS</em> are strongly associated with catchments featured as lower soil saturated hydraulic conductivity and smoother hydrographs. Longer <em>RT</em> are mostly related with drier catchments characterized as less variation of elevation and lower soil saturated hydraulic conductivity. This study highlights not only the uncertainty in quantifying storage and accompanied release characteristics, but also the reliability of storage-discharge characteristics quantified through recession analysis in terms of catchment comparison. Considering the diversity among catchments, ensemble of multi-method estimates of <em>DMS</em> and <em>RT</em> can enhance our understanding of the storage-discharge processes in the karst matrix system.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132378"},"PeriodicalIF":5.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720810","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}

{"title":"Enhancing 2D hydrodynamic flood model predictions in data-scarce regions through integration of multiple terrain datasets","authors":"P.D.P.O. Peramuna ,&nbsp;N.G.P.B. Neluwala ,&nbsp;K.K. Wijesundara ,&nbsp;S. DeSilva ,&nbsp;S. Venkatesan ,&nbsp;P.B.R. Dissanayake","doi":"10.1016/j.jhydrol.2024.132343","DOIUrl":"10.1016/j.jhydrol.2024.132343","url":null,"abstract":"<div><div>Topography highly influences hydraulic model predictions. High-resolution Digital Elevation Models (DEM) are currently used in 2D flood modeling studies to create relatively more accurate flood inundation maps. However, the availability of high-resolution datasets, such as Light Detection And Ranging (LiDAR), remains limited due to cost constraints. Thus, low-resolution global datasets are utilized in data-scarce regions. Merging high and low-resolution terrain datasets will be an alternative approach to improve flood models, and comprehensive analysis of such merged DEMs is lacking. Thus, a new DEM (V-DEM) is developed in this study by incorporating available LiDAR, SRTM, local DEM, and river cross-sectional data. 2D unsteady hydrodynamic model predictions are analyzed using the V-DEM, existing low-resolution global datasets, SRTM and MERIT Hydro, and their modified versions. V-DEM was found to create flood flow predictions with a better Nash–Sutcliffe efficiency, significantly outperforming low-resolution global datasets. In addition, MERIT Hydro showed more than 50% improvement in the Nash–Sutcliffe efficiency over SRTM in flow discharge predictions. There is a 110% improvement in the Nash–Sutcliffe efficiency for hydrologically corrected SRTMs over the original SRTM. When SRTM is merged with LiDAR and hydrologically corrected, the predictions also showed an improvement of 146% over the original SRTM. Moreover, this study highlights that the vertical accuracy of terrain datasets has a more significant effect on the flood model predictions than the horizontal resolution, especially in the high and low-gradient regions of the study area. Overall, this study would benefit flood modelers in developing accurate DEMs, especially in the unavailability of high-resolution data for the entire study area.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"648 ","pages":"Article 132343"},"PeriodicalIF":5.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive multiobjective evolutionary optimization model for dam management support","authors":"Federico Castiglione ,&nbsp;Salvatore Corrente ,&nbsp;Salvatore Greco ,&nbsp;Paola Bianucci ,&nbsp;Alvaro Sordo-Ward ,&nbsp;Luis Garrote ,&nbsp;Enrico Foti ,&nbsp;Rosaria Ester Musumeci","doi":"10.1016/j.jhydrol.2024.132304","DOIUrl":"10.1016/j.jhydrol.2024.132304","url":null,"abstract":"<div><div>Dam management optimization is a complex multiobjective problem, whose current solutions struggle to spread in normal practice. Here, an interactive evolutionary multiobjective optimization method is proposed, which involves stakeholders by using multi-criteria decision analysis to embed a parsimonious elicitation of their preferences and expertise in the optimization process. Specifically: (i) one or more decision-makers are asked to rank a set of representative dam management strategies in order of preference; (ii) the ranking is used to build a preference model; and (iii) the preference model is used in an evolutionary multiobjective optimization algorithm to converge to the part of the Pareto front most preferred by the decision-makers. The inclusion of a user-friendly interaction in the optimization methodology makes the process more understandable for stakeholders and policy-makers, and it allows to address some of the key obstacles to the practical implementation of multiobjective dam management optimization, even when a large number of objectives is considered. The proposed methodology is applied to a case study (Lake Pozzillo, Sicily, Italy) with five objectives, where flood control is in contrast with irrigation water demand and hydropower production. Five simple and easy to implement optimal strategies are obtained by interacting with five decision-makers. The results are compared to the management practice currently used, indicating that the proposed approach can satisfy water demands while greatly improving flood attenuation.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"647 ","pages":"Article 132304"},"PeriodicalIF":5.9,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing augmented deep Machine learning for effective shallow water table management and forecasting","authors":"Mohammad Zeynoddin ,&nbsp;Silvio José Gumiere ,&nbsp;Hossein Bonakdari","doi":"10.1016/j.jhydrol.2024.132371","DOIUrl":"10.1016/j.jhydrol.2024.132371","url":null,"abstract":"<div><div>This study addresses the gap in understanding and forecasting shallow water table depth (WTD), a critical factor in groundwater resource management and agricultural productivity. Despite the importance of accurately forecasting WTD for sustainable water resource management, current methods frequently struggle to capture the complexities and dynamics of WTD fluctuations. In response, this research, which was conducted in Québec, Canada, leverages machine learning techniques—namely, extreme learning machines (ELMs) and long short-term memory (LSTM) networks, augmented by the Holt-Winters (HW) state-space method—to develop a comprehensive analysis and forecasting approach for shallow WTD. The datasets were recorded by 8 sensors with hourly temporal resolutions from June to September, covering the growing season. The objective was to increase forecast accuracy by employing a detailed structural analysis of WTD time series data, selecting appropriate forecast steps, and fine-tuning model inputs through statistical tests and model-agnostic interpretation methods. The performance was evaluated via various metrics, including the correlation coefficient (R), root mean square error (RMSE), mean absolute relative error (MARE), and Theil’s U accuracy and quality coefficients, across short- to long-term forecasts (1-, 12-, 24-, 48-, and 72-hour ahead). Integration of HW with the ELM and LSTM models markedly improved the forecasting capabilities, particularly for the LSTM model, which achieved high accuracy of R = 0.988 for 1-hour forecasts and low error rates (RMSE = 0.648 cm, MARE = 0.007, UI = 0.005, and UII = 0.010), although accuracy decreased for longer forecast horizons, resulting in the lowest accuracy for 72-hour forecasts, with R = 0.638, RMSE = 4.550 cm, MARE = 0.051, UI = 0.036, and UII = 0.071. Similarly, the ELM model showed promising results in short-term forecasts when coupled with HW (R = 0.988, RMSE = 0.676 cm, MARE = 0.007, UI = 0.005, and UII = 0.010) but experienced a decrease in performance accuracy over more extended forecast periods (R = 0.707, RMSE = 5.559 cm, MARE = 0.053, UI = 0.045, and UII = 0.089). Although the ELM model presented a negligible strong correlation in some forecast steps, the LSTM model offered consistently higher forecast accuracy and quality across all assessed horizons. The study demonstrates the superiority of the LSTM model in consistently providing more accurate forecasts, highlighting the importance of integrating HW to capture complex temporal patterns in hydrological forecasting. This advancement in forecasting WTD has substantial implications for enhancing groundwater resource management and agricultural decision-making, significantly contributing to sustainable water resource utilization and supporting agricultural productivity through informed data-driven practices.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"647 ","pages":"Article 132371"},"PeriodicalIF":5.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696486","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}

{"title":"Seasonal freeze–thaw front dynamics and effects on hydrothermal processes in diverse alpine grasslands on the northeastern Qinghai–Tibet Plateau","authors":"Fenglin Zuo ,&nbsp;Xiaoyan Li ,&nbsp;Yangyang Zhang ,&nbsp;Zhigang Wang ,&nbsp;Xiong Xiao ,&nbsp;Dongsheng Li","doi":"10.1016/j.jhydrol.2024.132301","DOIUrl":"10.1016/j.jhydrol.2024.132301","url":null,"abstract":"<div><div>The dynamics of the freeze–thaw front strongly affect the transfer and exchange of water and energy in seasonally frozen zones. This study aimed to characterize the variations in the seasonal freeze–thaw fronts of diverse alpine grasslands and their effects on hydrothermal processes in the Qinghai Lake Basin (QLB). Field experiments were carried out on alpine meadow and steppe transition (AMS), alpine steppe (ASP) and <em>Achnatherum splendens</em> steppe (AST) ecosystems in the QLB, with 2 years of continuous high-frequency year-round observations. The results revealed that the seasonal freeze–thaw front was characterized by three distinct stages, in which the durations of slow freezing period (SF) and thawing period (T) were much shorter than that of rapid freezing period (RF). The maximum seasonal freezing depth in 2018–2020 were ranged from AMS (322.65 cm), AST (271.98 cm) and ASP (200.00 cm). The liquid soil volumetric water content (SVWC) decreased in all three ecosystems during the RF period, but increased during the SF and T periods. During the RF period, the SVWC of the AMS, ASP and AST decreased the most by −13.28 %, −9.52 % and −15.29 %, respectively. In the SF period, the SVWC of the AMS, ASP and AST increased the most, by 3.97 %, 9.52 % and 6.33 %, respectively, and by 13.37 %, 12.73 % and 12.44 % in the T period. During the freeze–thaw process, the SVWC and soil temperature mainly exhibit logarithmic and quadratic functions. The changes in the freeze–thaw front and SVWC in AMS were largely consistent, while those in ASP and AST experienced time lags. The freeze–thaw depth and net radiation of the RF, SF and T periods were fitted via the quadratic polynomial method. The freeze–thaw depth and soil heat flux were linearly fitted in the RF and SF periods, and the quadratic polynomial method was used in the T period. The effect of net radiation and soil heat flux accumulation on thawing front were more obvious in AMS than in ASP and AST. These findings are important for improving our understanding of water and heat transfer processes during the freeze–thaw period.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"647 ","pages":"Article 132301"},"PeriodicalIF":5.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696328","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}