Journal of Hydrology X最新文献

{"title":"Toward an integrated sustainability assessment of Water-Energy-Food nexus indicators","authors":"Adrija Roy ,&nbsp;Hamid Moradkhani","doi":"10.1016/j.hydroa.2025.100205","DOIUrl":"10.1016/j.hydroa.2025.100205","url":null,"abstract":"<div><div>The interdependence of crucial resources and the imperative for ensuring sustainability through integrated management approaches is underscored by the Water-Energy-Food (WEF) Nexus. The current study focuses on Alabama, Arkansas, Louisiana, Mississippi, and Tennessee in the Deep South USA to analyze the trade-offs and synergies in WEF Nexus. We propose an Integrated WEF Sustainability Index (IWSI) to provide a quantitative assessment of sustainability across these states. The IWSI is constructed by integrating standardized indicators across the water, energy, and food sectors, with weights derived from inter-sectoral economic interactions, to capture both trade-offs and synergies in a single composite score to provide an aggregated sustainability assessment. USA has an IWSI value of 1.62. Tennessee has an IWSI value of 2.34, characterized by efficient water utilization, substantial contributions from renewable sources, and robust agricultural productivity. Conversely, Louisiana and Arkansas encounter notable sustainability challenges, respectively, primarily attributable to low energy and water efficiency, reliance on fossil fuels, high emissions, and large water footprints. Arkansas demonstrates a significant water footprint in agriculture, well above the national average, highlighting its heavy reliance on irrigation. There is variation in hydropower conditions across states, with Tennessee leading in renewable energy use. The study underscores regional disparities in sustainability and emphasizes the need for tailored strategies to enhance resource efficiency and renewable energy adoption. A global assessment using datasets from the World Bank and Our World in Data highlights disparities across regions, providing insights into region-specific opportunities and challenges.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"28 ","pages":"Article 100205"},"PeriodicalIF":3.1,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flooding from Hurricane Helene and associated impacts: A historical perspective","authors":"Renato Amorim ,&nbsp;Gabriele Villarini ,&nbsp;Jeffrey Czajkowski ,&nbsp;James Smith","doi":"10.1016/j.hydroa.2025.100204","DOIUrl":"10.1016/j.hydroa.2025.100204","url":null,"abstract":"<div><div>During September 2024, Hurricane Helene devasted large areas of western North Carolina and eastern Tennessee, causing extensive loss of life and widespread damage due to heavy rainfall and extreme flooding. Despite the impacts of this storm, Helene’s heavy rainfall and resulting floods were not entirely unprecedented, as the region experienced several floods linked to tropical cyclones in the past, including multiple storms during the 2004 hurricane season. To make matters worse, this is an area with historically low market penetration by the National Flood Insurance Program, highlighting a strong asymmetry with respect to the coastal areas: while roughly 14% of the buildings in the eastern third of North Carolina were insured against floods, inland areas had less than a tenth of that coverage. Therefore, to improve resiliency and reduce the residual flood losses, it is critical to reconcile perceived versus actual flood risk and expand insurance coverage in hurricane-prone areas.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"27 ","pages":"Article 100204"},"PeriodicalIF":3.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decadal drought prediction via spectral transformation of projected Sea Surface Temperatures","authors":"Ze Jiang,&nbsp;Ashish Sharma","doi":"10.1016/j.hydroa.2025.100203","DOIUrl":"10.1016/j.hydroa.2025.100203","url":null,"abstract":"<div><div>Knowledge of impending drought can help significantly with water planning and management. This study introduces a novel forecasting framework for decadal drought projection which relies on climate model projections of Sea Surface Temperature Anomaly (SSTA) indices over the next decade and a spectral transformation methodology to maximise forecast skill. Decadal SSTA projections from the Decadal Climate Prediction Project (DCPP) undergo spectral transformation using Wavelet System Prediction (WASP). WASP modulates the frequency spectrum of predictor variables to better mimic the response spectrum of drought indices. The transformed SSTA indices are then used in a multiple linear regression (MLR) model to forecast drought indices across multiple time scales. This framework significantly improves drought forecasting skills, especially for lead times exceeding 24 months. While demonstrated for Australia, the MLR-WASP framework is transferable to other regions, offering a reliable tool for long-term water resource management by projecting drought risk over the coming decade. The implications of this research extend beyond hydroclimatology, impacting environmental science and engineering, sustainable planning, and adaptation efforts to climate change.</div></div><div><h3>Plain language summary</h3><div>Projecting drought risk over the next decade is essential for effective long-term water resources management. This study presents a new framework that reliably projects drought conditions up to 10 years ahead by optimizing decadal climate model data. It uses a spectral transformation technique to adjust predictors like Sea Surface Temperature Anomalies to better match drought patterns. These transformed predictors are then integrated into a regression model to forecast drought indices. When applied to Australia, this approach significantly outperformed existing methods, especially for 2-year forecasts. By combining advanced climate predictions with prediction-oriented data transformation, this framework enables reliable drought risk projections a decade out, offering invaluable insights for proactive planning in drought-prone regions worldwide.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"27 ","pages":"Article 100203"},"PeriodicalIF":3.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143703904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving prediction of class-imbalanced time series through curation of training data: A case study of frozen ground prediction","authors":"Mousumi Ghosh ,&nbsp;Aatish Anshuman ,&nbsp;Mukesh Kumar","doi":"10.1016/j.hydroa.2025.100201","DOIUrl":"10.1016/j.hydroa.2025.100201","url":null,"abstract":"<div><div>The field of geosciences is replete with problems where the target variable to be predicted is inherently class-imbalanced, meaning the events of interest are rare and infrequent. Examples include predicting landslides, ice jam breakups, preferential flow, and frozen ground. Such imbalance poses substantial challenges for modeling approaches. Using frozen ground prediction as a case study, this research examines how the frequency of event occurrence influences its prediction performance and proposes a data curation strategy to improve predictability. To this end, a data-driven approach utilizing a Long Short-Term Memory neural network is first implemented to predict soil temperature and determine frozen periods. Application of this approach at 25 gaging sites in Michigan reveals model underperformance, particularly at sites where the frozen data fraction (FDF) or the ratio of the frozen period to the total observation period, is low. The. study further demonstrates that under-sampling of more prevalent non-frozen period in training data improves detection of frozen periods. Greater improvements are experienced at sites with lower FDFs. However, performance peaks after a threshold FDF, plateauing or declining thereafter due to increased class imbalance and reduced training data length. The presented training data curation approach can be used for predictions of other class-imbalanced time series.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"27 ","pages":"Article 100201"},"PeriodicalIF":3.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the organizing scales of winter flood hydroclimatology and the associated drivers over the coterminous United States","authors":"Jeongwoo Hwang ,&nbsp;Carl J. Schreck III ,&nbsp;Anantha Aiyyer ,&nbsp;Arumugam Sankarasubramanian","doi":"10.1016/j.hydroa.2025.100200","DOIUrl":"10.1016/j.hydroa.2025.100200","url":null,"abstract":"<div><div>Floods occur everywhere and in every season. Yet, most studies have focused only on annual maximum floods (AMFs), their climatology, and the associated impacts. Given that monthly/seasonal floods also cause significant damage and disruptions to daily life, this study may be the first to explore winter flood hydroclimatology, a predominantly a non-AMF season, and its associated large-scale climate drivers over the Coterminous US (CONUS). Using a mixed-effects model, we find that the influence of various hydroclimate predictors on winter floods is largely consistent within subregions. Antecedent land-surface conditions are crucial for winter floods in inland areas, while the Pacific sea surface temperatures (SSTs) significantly affects coastal watersheds. The Atlantic SSTs impact winter floods in the south and northeast, while atmospheric conditions influence the Midwest and California. Additional analysis reveals that damage from winter floods is more widespread compared to AMFs across the nation, affecting the entire eastern seaboard, Southwest US, and over the Great Lakes region. Thus, a comprehensive understanding of floods across all seasons (non-AMFs) is critical for developing effective mitigation measures, as it provides information on impacts and required compensation for smaller return period floods.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"27 ","pages":"Article 100200"},"PeriodicalIF":3.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of climate variability modes with concurrent droughts and heatwaves in India","authors":"Ruhhee Tabbussum ,&nbsp;Rajarshi Das Bhowmik ,&nbsp;Pradeep Mujumdar","doi":"10.1016/j.hydroa.2024.100196","DOIUrl":"10.1016/j.hydroa.2024.100196","url":null,"abstract":"<div><div>The natural variability in the occurrence of concurrent extremes of droughts and heatwaves is frequently attributed to climate change and anthropogenic causes, disregarding its association with large-scale global teleconnections. This study explores this association by demonstrating how concurrent droughts and heatwaves (CDHW) in India are temporally and spatially connected to multiple global teleconnections (referred to as climate variability modes). Composite and wavelet coherence analyses are implemented for the univariate evaluation of droughts and heatwaves—measured using the standardized precipitation index (SPI) and the standardized heat index (SHI), respectively—in relation to the climate variability modes. Furthermore, an attribution table framework is employed to examine the extremal dependence of concurrent heatwaves and droughts in India on the climate variability modes during 1951–2018. The results exhibit a higher probability of CDHW events when they are preceded by a large-scale global teleconnection. Overall, the insights drawn from this study suggest the possibility of relying on the climate variability modes to issue season-ahead forecasts of CDHW.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"26 ","pages":"Article 100196"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climatology of extreme precipitation spells induced by cloudburst-like events during the Indian Summer Monsoon","authors":"Akash Singh Raghuvanshi ,&nbsp;Ricardo M. Trigo ,&nbsp;Ankit Agarwal","doi":"10.1016/j.hydroa.2024.100197","DOIUrl":"10.1016/j.hydroa.2024.100197","url":null,"abstract":"<div><div>This study enhances existing understanding of extreme precipitation spells induced by cloudburst-like (EPS-CBL) events in India, emphasizing climatology and geographical distribution often overlooked by traditional observations. EPS-CBL is defined as continuous rainfall exceeding 200 mm/day and intermittent extreme rates above 30 mm/hour or the 99.9th percentile threshold, differing from definitions proposed by the IMD and other studies. Our findings reveal significant biases in various precipitation products compared to IMD data. CMORPH consistently outperforms other datasets by capturing more extreme events and showing significant rising trends in regions influenced by orographic effects, such as the Himalayan foothills and the Western Ghats. Although IMERG aligns well with IMD overall, it exhibits variability in extreme events, while IMDAA tends to underestimate these extremes, especially in complex terrains. Analysis of EPS-CBL trends from 2000 to 2022 highlights regional differences across datasets. Both CMORPH and IMERG show an increase in EPS-CBL events in the hilly region, while IMDAA indicates a decline. Understanding EPS-CBL climatology provides valuable insights for modeling studies exploring the underlying mechanisms of these events.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"26 ","pages":"Article 100197"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Practical application of time-lapse camera imagery to develop water-level data for three hydrologic monitoring sites in Wisconsin during water year 2020","authors":"Keegan E. Johnson,&nbsp;Paul C. Reneau,&nbsp;Matthew J. Komiskey","doi":"10.1016/j.hydroa.2024.100199","DOIUrl":"10.1016/j.hydroa.2024.100199","url":null,"abstract":"<div><div>Using camera imagery to measure water level (camera-stage) is a well-researched area of study. Previous camera-stage studies have shown promising results when implementing this technology with tight constraints on test conditions. However, there is a need for a more comprehensive evaluation of the extensibility of camera-stage to practical applications. Therefore, the aim of this study was to test a camera-stage method under a wide variety of test conditions to better understand the successes and challenges of using this technology in real-world scenarios. In this study, this approach was tested during Water Year 2020 at three existing U.S. Geological Study (USGS) stream gaging stations in south central Wisconsin that had existing USGS water-level instrumentation. The specific reference objects tested were white pipes and a concrete wall. Since successful application of camera-stage relies on use of suitable images, all captured images in this study were visually inspected to determine suitability for application of camera-stage. Camera-stage measurements were then computed only on images deemed suitable and the results were compared with ground-truth stage values to determine the accuracy. For the purposes of this study, camera-stage values within ±0.10 ft of the actual stage were considered acceptable. One major challenge highlighted was the potential difficulty in obtaining suitable imagery, with the proportion of suitable images varying greatly between the four trials from 38 % to 92 %. The results from applying camera-stage to suitable images were encouraging though, with 79 % to 99 % of evaluated camera-stage values qualifying as acceptable among the four test trials.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"26 ","pages":"Article 100199"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AutoVL: Automated streamflow separation for changing catchments and climate impact analysis","authors":"Vincent Lyne","doi":"10.1016/j.hydroa.2024.100195","DOIUrl":"10.1016/j.hydroa.2024.100195","url":null,"abstract":"<div><div>The separation of streamflow into fastflow and slowflow components has been historically ambiguous, with existing separation methods like the Lyne-Hollick (LH) algorithm facing challenges due to subjective parameter choices. Here, we address this issue by developing the AutoVL algorithm which objectively and automatically partitions streamflow for no parameter input. AutoVL uses iterative statistical models, including a Signal Reconstructor for fastflow and an autoregressive moving-average (ARMA) model for slowflow, to estimate key hydrologic parameters. The algorithm couples the two models to iteratively estimate these parameters and to accurately separate streamflow. When applied to the Harvey River, Dingo Road station data, AutoVL identified significant seasonal and long-term variations in hydrologic parameters, reflecting the possible influence of climate change altering the temporal dynamics of catchment responses. The algorithm highlighted strongly coupled changes in infiltration and decay rates from altered streamflow patterns, offering a clearer understanding of streamflow responses to climate change. This performance suggests that AutoVL provides a more reliable, objective, efficient, and standard method for streamflow separation compared to previous approaches, enabling more accurate and confident hydrological modeling. By providing objective, dynamic insights into catchment behavior, AutoVL offers a promising tool for climate change studies and streamflow analysis.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"26 ","pages":"Article 100195"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions","authors":"Abhinav Gupta ,&nbsp;Sean A. McKenna","doi":"10.1016/j.hydroa.2024.100198","DOIUrl":"10.1016/j.hydroa.2024.100198","url":null,"abstract":"<div><div>This study examines streamflow simulations using deep learning (DL) to understand the information extraction capability of global DL models trained on multiple watersheds. The study separately examined the entire streamflow time series and recession flow predictions. It introduces a global–local (GL) modeling strategy, where the global model outputs are fed as input to a locally trained model, with the hypothesis that the local model can leverage watershed-specific information that the global model may miss. The GL models demonstrate enhanced accuracy in recession flow prediction for 20-30% of the watersheds compared to the global and local models. However, considering the entire hydrograph, the GL models often perform worse than the global model. Further, the DL models were trained on two different objective functions. The performance of the global model in a watershed depended strongly upon the objective function used. These results suggest that the performance of global models is affected by watershed uniqueness, suggesting that even a global DL model should be tailored to individual watersheds for optimal performance.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"26 ","pages":"Article 100198"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}