Journal of Hydrology最新文献

{"title":"Using an ensemble Kalman filter method for a soil nitrogen transport model in the real rice field","authors":"Juxiu Tong,&nbsp;Yang Gu,&nbsp;Kuan Cheng","doi":"10.1016/j.jhydrol.2024.132224","DOIUrl":"10.1016/j.jhydrol.2024.132224","url":null,"abstract":"<div><div>The overuse of nitrogen fertilizer in rice field of China leads to nitrogen loss and serious water pollution, so it is vital to accurately predict soil nitrogen transport in rice field. But the prediction errors of soil nitrogen transport are great due to complex chemical and reactive conditions and uncertain parameters in real rice fields. In this study, a prediction model of soil nitrogen transport in a rice field was established via modifying the HYDRUS-1D source code, and a data assimilation method called the ensemble Kalman filtering (EnKF) was coupled, based on the observed NH₄⁺-N and NO₃^–-N concentrations at different depths in a real rice field. Study results for two different protocols of assimilating observed NH₄⁺-N and NO₃^–-N concentrations simultaneously and separately were compared. It indicated the predictions accuracy of NH₄⁺-N and NO₃^–-N concentrations was improved significantly via the EnKF method, and the former protocol is better than the latter. Moreover, for the latter protocol, observations of NO₃^–-N concentrations were more efficient than NH₄⁺-N to improve the predictions accuracy of NH₄⁺-N and NO₃^–-N concentrations at different depths. Inversed parameters of urea hydrolysis, NH₄⁺-N volatilization, soil adsorption of NH₄⁺-N, nitrification and denitrification increased over time. On the whole, the inversed model parameters were more stable at deep soil than shallow soil, which were different at different depths. With soil depths increase, parameters of the NH₄⁺-N adsorption and NO₃^–-N denitrification increased, while parameters of urea hydrolysis, NH₄⁺-N volatilization and nitrification decreased. This study improved the model predictions accuracy and inversed the model parameters, revealing the mechanism of nitrogen loss in real rice fields, which can provide scientific basis to reduce serious environmental problems caused by the overuse of nitrogen fertilizer.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132224"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530776","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":"Spatially seamless and temporally continuous assessment on compound flood risk in Hong Kong","authors":"Jiewen You ,&nbsp;Shuo Wang ,&nbsp;Boen Zhang","doi":"10.1016/j.jhydrol.2024.132217","DOIUrl":"10.1016/j.jhydrol.2024.132217","url":null,"abstract":"<div><div>Compound flooding results from the simultaneous occurrence of extreme storm surges, sea level rise, and heavy rainfall. These events often lead to impacts significantly more severe than those caused by any individual flood-inducing factor alone. However, the limited and sparse data from tidal gauges hampers precise risk assessment at ungauged sites in coastal cities. Our study addresses this gap by integrating ensemble machine learning with Bayesian inference, offering a comprehensive spatial–temporal analysis of compound flood risk from 1979 to 2022 in Hong Kong. We developed an ensemble machine learning approach within the Bayesian hierarchical modeling framework to achieve spatial–temporal continuity in the estimation of extreme storm surges and mean sea level at sites without tidal gauge stations. Results show a significant yearly increase in maximum storm surge levels by 3 mm and a significant rise in mean sea level of 25 mm per decade in Hong Kong. Our analysis also indicates a significant increase in daily heavy rainfall intensity. Furthermore, in 14.54 % of cases, extreme storm surges coincided with heavy rainfall, while 13.69 % of heavy rainfall events occurred alongside extreme sea level conditions. The copula-based joint analysis reveals significant positive correlations among these extreme events. Our findings further reveal that the return level for a 100-year heavy rainfall event increases dramatically from 126.36 mm in the univariate case to 261.16 mm in the trivariate scenario, underlining the escalated risk associated with compound flooding. Similarly, for storm surge extremes, trivariate analysis reveals elevated risk during compound flood events, with the return level rising from 1.18 m (univariate scenario) to 1.40 m (trivariate scenario) for a 100-year return period. These spatial–temporal maps and comprehensive compound flood risk assessments offer crucial insights for addressing the multi-hazard flood risk in coastal urban areas.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132217"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530777","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":"iRainSnowHydro v1.0: A distributed integrated rainfall-runoff and snowmelt-runoff simulation model for alpine watersheds","authors":"Yuning Luo ,&nbsp;Ke Zhang ,&nbsp;Yuhao Wang ,&nbsp;Sheng Wang ,&nbsp;Nan Wu ,&nbsp;Shunzhang Li ,&nbsp;Qinuo Zhang ,&nbsp;Xinyu Chen ,&nbsp;Hongjun Bao","doi":"10.1016/j.jhydrol.2024.132220","DOIUrl":"10.1016/j.jhydrol.2024.132220","url":null,"abstract":"<div><div>Snowmelt runoff is an essential runoff component in alpine watersheds. On the Tibetan Plateau, the complex hydrometeorological and underlying surface conditions make a single runoff generation mode (either snowmelt-runoff or rainfall-runoff) cannot accurately simulate the runoff process. In this study, we developed a new method that combines the curve number, topographic index, and fractional snow cover to identify the sub-basin seasonal dominant runoff generation mode within the Jinsha River Basin. By constructing a surface ‘snow reservoir’ to depict snow melting impact on runoff generation, and quantitatively classifying the precipitation composition, an innovative integrated hydrological model named the distributed integrated Rainfall-runoff and Snowmelt-runoff simulation Hydrological model (iRainSnowHydro) is developed. With model, a method for identifying the seasonal varying dominant runoff generation mode is proposed. The results show that most sub-basins experience both snowmelt and rainfall driven runoff generation in spring, with snowmelt occurring earlier in regions of lower latitude and elevation. Besides, iRainSnowHydro performs well in daily runoff simulations at Zhimenda and Shigu stations with Nash coefficients of 0.81 and 0.85 in the calibration period, and 0.72 and 0.81 in the validation period. The correlation coefficient ranges from 0.92 to 0.96. Additionally, calculation through iRainSnowHydro indicates a noteworthy percentage of spring snowmelt water. Notably, the Zhimenda watershed, characterized by higher latitudes and elevations, displays an escalating trend from 56.6 % to 78.9 % of total precipitation for spring snowmelt water between 2014 and 2020, while the Shigu watershed maintains stable within 27 % ± 6 %. The methodologies outlined bear significance for simulating and predicting runoff in alpine watersheds and offers valuable insights into how snow cover responds to climate change on the Tibetan Plateau.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132220"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530780","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":"The impact of changes in water–sediment relationships at river confluences on the evolution of river bars","authors":"Cheng Dong,&nbsp;Minquan Feng,&nbsp;Haixiao Jing,&nbsp;Ruijing Yang","doi":"10.1016/j.jhydrol.2024.132212","DOIUrl":"10.1016/j.jhydrol.2024.132212","url":null,"abstract":"<div><div>The confluence of tributaries and the main stream affects riverbed siltation and alters the upstream water–sediment relationships and flow structure of the main stream by adding additional flow. The relationship between these changes and the evolution of river bars, however, is yet to be fully understood. In this study, the areas of the river bars were extracted from Landsat image and analyzed using soft clustering to identify evolutionary patterns of the bars at the confluence of the Fen River (FR) and Yellow River (YR), and to analyze the hydrodynamic mechanism with hydrodynamic modeling. The results show that the spatial and temporal evolution of river bars in the study area over the past 50 years exhibits an evolutionary pattern, which exhibited evident clustering distributions and significant stage-based characteristics. This pertains to the water–sediment relationships as well as hydrodynamic fluctuations. At the confluence of FR and YR, the intensity of fluvial erosion experiences a mean increment of 9.67 %, while the incoming sediment coefficient witnesses a mean reduction of 5.74 %. The position of the confluence point exhibits a close association with the evolutionary characterized of spatial clustering and temporal phasing of the river bars. The river confluence area showcases a complex turbulence structure, wherein alterations in sediment transport capacity occur due to the influence of secondary flow and the topography of the confluence area. Consequently, this impacts the flushing of river sandbars and brings about modifications in siltation. Overall, this study provides the scientific basis for YR sediment management and channel modification in response to changing runoff and sediment conditions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132212"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539110","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":"Incorporating changes in land surface temperature improves BESS evapotranspiration estimates under water-deficit conditions: A case study for US Midwest and Great Plains grasslands","authors":"Xiaoman Lu ,&nbsp;Kaiyu Guan ,&nbsp;Chongya Jiang ,&nbsp;Lun Gao ,&nbsp;Sheng Wang ,&nbsp;Jiaying Zhang","doi":"10.1016/j.jhydrol.2024.132201","DOIUrl":"10.1016/j.jhydrol.2024.132201","url":null,"abstract":"<div><div>Evapotranspiration (ET) is a critical climate and ecosystem variable that interconnects water, energy, and carbon cycles. Breathing Earth System Simulator (BESS) is one of the state-of-the-art biophysical models capable of producing spatio-temporal continuous ET results. However, we found that since the BESS model does not use an explicit constraint on soil moisture (SM), it has a relatively lower performance under drier conditions. Given that changes in land surface temperature (LST) are closely associated with surface water status and sensible heat energy, we hypothesize that integrating LST changes could explicitly add the soil moisture constraints and thus enhance BESS’s ability to estimate ET. Here we used the morning rise rate of LST (Trate) as a proxy of LST change because of the low noise level in Trate as well as Trate’s close relationship with daily mean sensible heat. To test the hypothesis, this study first assessed whether the performance of BESS ET can be explained by the LST change, targeting grassland sites of the AmeriFlux network in the US Midwest and Great Plains. Specifically, the ET deviation (i.e., the difference between BESS-modeled ET and field-measured ET) and Trate deviation, as well as their relationships, were investigated under different conditions of precipitation, SM, and vapor pressure deficit at the AmeriFlux sites. Results indicated that BESS ET exhibited consistently higher performance under well-watered conditions than water-deficit conditions. Also, the deviations of ET and Trate became more negatively correlated under water-deficit conditions. Leveraging the empirical relationship between ET and Trate deviations, this study developed a new way to calibrate BESS ET based on Trate calculated from LST diurnal observations, particularly under soil or atmospheric water-deficit conditions. After calibrating BESS ET, the statistical indicators between the calibrated ET and the ground measurements showed meaningful improvements relative to those before calibration. Specifically, in the Midwest (Great Plains), R² increased from 0.42 to 0.51 (from 0.45 to 0.46), and RMSE and absolute bias decreased by 12% and 42% (11% and 45%), respectively. This study highlights that the morning rise rate of LST can effectively constrain the ET models that have no SM constraints under water-deficit conditions and also sheds lights on improved ET estimation for crop, biofuel, and pastureland production in dryland and semi-dryland ecosystems.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132201"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578785","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":"Environmental impacts of the billion tree Tsunami project in Khyber Pakhtunkhwa on the dynamics of Agro-Meteorological Droughts","authors":"Khalil Ur Rahman ,&nbsp;Deqiang Mao ,&nbsp;Nuaman Ejaz ,&nbsp;Quoc Bao Pham ,&nbsp;Anwar Hussain ,&nbsp;Meriame Mohajane ,&nbsp;Muhammad Ali ,&nbsp;Songhao Shang","doi":"10.1016/j.jhydrol.2024.132205","DOIUrl":"10.1016/j.jhydrol.2024.132205","url":null,"abstract":"<div><div>Changes in forest cover are closely associated with the variability in meteorological and hydrological variables. Therefore, this study delves into investigating how forest cover changes impact the environment (i.e., hydro-meteorological variables, including precipitation, streamflow, relative humidity (RH), evapotranspiration (ET), and temperature) using Trend Projection (TP) methods during 1980–2019. The study is carried out in the Khyber Pakhtunkhwa (KP) province of Pakistan, which witnessed deforestation between 1980 and 2010 followed by afforestation (through billion tree tsunami project, BTTP) initiated in 2014. A new drought index, named as agro-meteorological drought index (AMDI), is developed in this study using the remotely sensed data to analyze the impact of forest cover on drought severity. The robust least square regression (RLSR) model is used to regress the normalized difference vegetation index (NDVI) with AMDI at various time scales to investigate the impact of forest cover on drought severity. The RLSR and paired <em>t</em>-test are used to quantify the impact of forest cover and BTTP, in particular, on the environment. Land use maps prepared for KP province over a span of the past four decades revealed significant deforestation during 1985–2005, transitioning gradually to afforestation in the past decade. Results indicated a decline in streamflow throughout different seasons with an increase in forest cover, particularly during the period of afforestation (i.e., 2015–2019). The precipitation, RH (maximum/minimum), and ET displayed an increasing trend over time, whereas a decrease trend is observed in Tmax/Tmin and streamflow at most of the stations. The trend analyses depicted a significant change before and after the BTTP. The paired <em>t</em>-test results revealed that BTTP has statistically significant impact on the environmental variables. Furthermore, the time series plots of AMDI at different time scales indicated that drought events were frequent and severe prior to 2003, whereas significant decrease in both the frequency and severity of drought was observed in the last decade (2010–2019). The RLSR results at pixel scales demonstrated the crucial role of forest covers in alleviating both the frequency and severity of drought events. The elasticities revealed that increase in the forest cover resulted in substantial increase/decrease in each hydro-meteorological variable. Overall, the results highlighted a positive and statistically significant impact of forest cover (i.e., BTTP) on both the environment and drought variability.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132205"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530905","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 transparency in data-driven urban pluvial flood prediction using an explainable CNN model","authors":"Weizhi Gao ,&nbsp;Yaoxing Liao ,&nbsp;Yuhong Chen ,&nbsp;Chengguang Lai ,&nbsp;Sijing He ,&nbsp;Zhaoli Wang","doi":"10.1016/j.jhydrol.2024.132228","DOIUrl":"10.1016/j.jhydrol.2024.132228","url":null,"abstract":"<div><div>Mitigating severe losses caused by pluvial floods in urban areas with dense population and property requires effective flood prediction for emergency measures. Physics-based models face issues with low computational efficiency for real‐time flood prediction. An alternative approach for rapid prediction instead of physics-based models is to predict from a data-driven perspective. However, data-driven approaches for urban flood prediction are often perceived as “black box” and might raise concerns. In this study, we propose an explainable deep learning (DL) approach for rapid urban pluvial flood prediction with enhanced transparency using a convolutional neural network (CNN) and the explainable artificial intelligence (AI) framework Shapley additive explanation (SHAP). We process a systematic stepwise feature selection process and establish a CNN model considering topography, drainage networks and rainfall to predict maximum inundation depths. Then, SHAP is applied to provide trustworthy explanations for the decision making in model results. The results show that: 1) Forward selection can offer insights into selecting effective input variables for improved predictions and promote understanding of DL modelling. The spatial pattern of inundation depths predicted by the proposed CNN model shows good agreement with those predicted by the physics-based model, demonstrated by average correlation coefficient (CC) and mean absolute error (MAE) values of 0.982 and 0.021 m, respectively. 2) The CNN model substantially outperforms the physics-based model in computational speed when using the same hardware, achieving speedups of 210 times on GPU and 51 times on CPU in the case study (575167 grid cells, 14.38 km²). Particularly, it can still achieve good performance on a CPU-only standard laptop without high-performance hardware, with only a modest increase in computational time. 3) The SHAP explainable analysis confirms that the CNN model correctly captures the relationships between input variables and water depth, revealing a reasonable decision-making process, enhancing its transparency. The explainable DL approach incorporating SHAP for rapid urban pluvial flood prediction is promising to build trust among stakeholders and provide a trustworthy reference for prompt measures aiming at saving lives and protecting assets during flood emergencies. Additionally, the proposed DL approach can potentially be further expanded to analyze the causes of urban flooding events and serve as a foundation for exploring the transferability of data-driven urban flood prediction, providing benefits for better urban flood risk management.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132228"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530778","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":"How different soil surface treatments in urban areas affect soil pore structure and associated soil properties and processes","authors":"Bunthorn Thet,&nbsp;Radka Kodešová,&nbsp;Miroslav Fér,&nbsp;Aleš Klement,&nbsp;Antonín Nikodem","doi":"10.1016/j.jhydrol.2024.132233","DOIUrl":"10.1016/j.jhydrol.2024.132233","url":null,"abstract":"<div><div>Soil water and temperature regimes in urban environments are greatly affected by different surface treatments, and these may lead even to changes in soil properties. The goal of this study was to find out how soil properties had changed after 8 years of soil surface modification. Five surface treatment scenarios were considered: bare soil (BS), bark chips (BC), concrete paving (CP), mown grass (MG), and unmown grass (UG). X-ray computed tomography and micromorphological analyses showed that character of pores in soils with grass (MG, UG), which were mainly influenced by organisms living in soils and roots, differed from pore character in soil covers BC or CP, which mostly were impacted by organisms living in soils. Both groups differed from BS, which was predominantly affected by the regular treatment consisting in weed removal and soil loosening. Soil under BC was more compact than for other treatments due to decomposition of the bark chips mulch and migration of mulch components. Organic matter content was greatest but its quality lowest in the BC soil, followed by UG, MG, CP, and BS. The highest aggregate stability assessed using the water-stable aggregates (WSA) index was found for UG, followed by MG, BC, CP, and BS. The greatest water retention ability was observed for BC followed by UG, MG, CP, and BS. The unsaturated hydraulic conductivities for pressure head of –2 cm measured for UG, MG, and BS were much higher than were those for CP and BC. Finally, the greatest net CO₂ efflux was measured for BC and MG, followed by UG, CP, and BS. CO₂ emission correlated negatively with soil physical quality expressed as slope of the soil water retention curve at its inflection point. In general, the best soil conditions were observed for UG. No treatment considerably aggravated soil condition.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132233"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530906","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":"Added value of merging techniques in precipitation estimates relative to gauge-interpolation algorithms of varying complexity","authors":"Yingyi Hu ,&nbsp;Ling Zhang","doi":"10.1016/j.jhydrol.2024.132214","DOIUrl":"10.1016/j.jhydrol.2024.132214","url":null,"abstract":"<div><div>Data-fusion techniques leverage the strengths of multisource precipitation data and can significantly enhance the accuracy of precipitation estimates. However, the extent to which these techniques improve precipitation estimates (i.e., added value) compared to interpolation algorithms and the factors driving this improvement remain unclear. To address these gaps, this study compared the performance of two merging techniques, i.e., double machine learning (DML) and geographically weighted regression (GWR), with multiple interpolation algorithms in estimating precipitation across China. The interpolation algorithms vary in complexity and include typical methods (IDW and Kriging), semi-physical methods (GIDS, DAYMET, and MicroMet), and climatologically aided interpolation (CAI). We quantified the added value of the merging techniques over these interpolation algorithms and investigated the driving factors using a data-driven approach. Results indicate that the merging techniques outperform all the interpolation algorithms, regardless of their complexity. The merging techniques provide greater added value in gauge-scarce regions (e.g., Northeast China) than in gauge-rich regions (e.g., Northwest China). The magnitude of the added value from merging techniques is significantly influenced by the choice of interpolation algorithms due to their varying performance. Additionally, our data-driven model reveals that factors such as the amount of precipitation, number of wet days, performance of precipitation products, and gauge density are key drivers that negatively affect the added value of merging techniques. This study highlights the importance of integrating multisource data to improve precipitation estimates, especially in regions with sparse gauges, rather than relying solely on gauge-only interpolation.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132214"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530907","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":"Relationship between root characteristics and saturated hydraulic conductivity in a grassed clayey soil","authors":"Ling-Xin Cui ,&nbsp;Qing Cheng ,&nbsp;Pui San So ,&nbsp;Chao-Sheng Tang ,&nbsp;Ben-Gang Tian ,&nbsp;Cong-Ying Li","doi":"10.1016/j.jhydrol.2024.132231","DOIUrl":"10.1016/j.jhydrol.2024.132231","url":null,"abstract":"<div><div>Soil saturated hydraulic conductivity plays a crucial role in the fields of hydrology, geotechnical and geological engineering. This study investigated the relationship between root characteristics and soil saturated hydraulic conductivity in a grassed soil. The saturated hydraulic conductivity of soil specimens with varying soil dry densities and planting densities were experimentally determined. Root parameters of each specimen were measured, and the relationship between root parameters and soil saturated hydraulic conductivity was determined through the stepwise multiple regression analysis. Experimental results show that both soil dry density and planting density significantly influence root growth and the saturated hydraulic conductivity of the grassed soil. Root length ratio (&gt;4 cm), root length, and root weight density exhibit a positive correlation with planting density, while root length density and root length ratio (&gt;4 cm) are negatively correlated with dry density. Higher dry density leads to lower soil saturated hydraulic conductivity and the permeable effects become more pronounced with increased planting density. When planting density is higher, roots create more preferential flow channels in the soil, resulting in increased soil saturated hydraulic conductivity. Root length density exerts the most significant effect on soil saturated hydraulic conductivity (|r| = 0.82, p＜0.01), followed by root length ratio (&gt;4 cm) and root weight density. This study provides insights into the relationship between root parameters and soil saturated hydraulic conductivity, and identifies key root parameters that govern the saturated hydraulic conductivity of soil. These findings hold significant implications for enhancing the understanding of slope protection using vegetation.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132231"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530408","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}