Journal of Hydrology X最新文献

{"title":"Charting uncertain waters: Insights into objective function formulations under future uncertainty","authors":"Jiajia Huang ,&nbsp;Wenyan Wu ,&nbsp;Michael Leonard ,&nbsp;Ye Wang","doi":"10.1016/j.hydroa.2025.100209","DOIUrl":"10.1016/j.hydroa.2025.100209","url":null,"abstract":"<div><div>Optimal management of water resources is challenging due to uncertainty in future conditions. One promising approach is to directly incorporate future uncertainty into objective function formulations of optimization problems, enabling system performance evaluation across multiple potential conditions. However, this creates additional uncertainties as both the choice of objective function formulation and the plausible future conditions included in optimization are subjective. Given the inherent uncertainty in plausible future conditions, it is highly unlikely that future conditions included in optimization can cover all conditions that might occur. Therefore, identifying objective function formulations that perform well regardless of future uncertainties is crucial; however, it has not been formally explored. In this study, the performance of different objective function formulations under both expected (i.e., similar to conditions used in optimization) and unexpected (i.e., vastly different from conditions used in optimization) future conditions is investigated using a real-world case study. Results reveal that percentile and expected-value-based formulations generally perform consistently under both expected and unexpected conditions, whereas extreme-case-based formulations can lead to highly variable results depending on the actual conditions that will be realized in the future. Finally, variance-based formulations offer the greatest consistency across all conditions but may lead to compromised performance under favorable conditions.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"28 ","pages":"Article 100209"},"PeriodicalIF":3.1,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219403","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":"Fuzzy-based input method for uncertainty quantification in a deterministic model comparison with ChatGPT for peak flow prediction","authors":"Zhonghao Zhang,&nbsp;Caterina Valeo","doi":"10.1016/j.hydroa.2025.100208","DOIUrl":"10.1016/j.hydroa.2025.100208","url":null,"abstract":"<div><div>ChatGPT, a generative AI, is applied and compared to the PCSWMM hydrological model for modelling peak flow in a small watershed in the runoff period of April to September. A new approach for fuzzy mathematical representation of rainfall and peak-flow errors was developed to lead to a fuzzy based GPT model and fuzzy based PCSWMM model. This led to fuzzy output for both models and a more appropriate application of both models given data errors and large language model structure. Training and validation were conducted with an approximately 25/75 split of the data and again using a 75/25 data split. Evaluation metrics were used to compare model performance under the different data-split scenarios. Calibrated and validated PCSWMM outperformed GPT in the 25/75 data split but ChatGPT 4o mini’s generation outperformed PCSWMM in the 75/25 split and with comparable validation metrics and an application that was less onerous than when using PCSWMM. The fuzzy-based error analysis showed that for both models, a fuzzy-based approach produced more interpretable and reasonable results than either original model. Moreover, the trade-off between coverage (uncertainty range) and precision for GPT‑4o mini model’s fuzzy output at high membership levels (∝-cut) demonstrated enhanced predictive performance under data‑scarce conditions.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"28 ","pages":"Article 100208"},"PeriodicalIF":3.1,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219292","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":"Reservoir storage flash droughts in India are driven by human interventions","authors":"Rajesh Singh ,&nbsp;Vimal Mishra","doi":"10.1016/j.hydroa.2025.100207","DOIUrl":"10.1016/j.hydroa.2025.100207","url":null,"abstract":"<div><div>Reservoir storage flash droughts (RFDs), characterized by the rapid decline in reservoir storage, and conventional (long-term) reservoir storage droughts (RDs) impact water availability, hydropower generation, and agricultural activities. However, the mechanism and drivers of flash and conventional reservoir storage droughts in India remain unexplored. Using daily observations of reservoir storage, we identify RFDs and RDs in 81 major reservoirs in India during the 2000–2023 period. 46 out of 81 reservoirs are dominated by upstream climate as reservoir storage trends are driven by changes and variability in upstream precipitation, while the remaining 35 reservoirs are identified as human-dominating reservoirs. RFDs occur more frequently in human-dominating reservoirs than climate-dominating, especially in small reservoirs. About 70 % of RFDs in climate and human-dominating reservoirs are caused by sudden release to meet increased water demands in the downstream regions. Additionally, upstream precipitation deficit and downstream water demand control RDs, while downstream water demands can solely drive RFDs. Unlike reservoir storage trends, reservoir storage droughts are mostly linked with downstream water demands. We highlight the role of climate and human interventions in reservoir storage/droughts in India.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"28 ","pages":"Article 100207"},"PeriodicalIF":3.1,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907622","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":"Adapting to future changes using smart stormwater storage systems to preserve flow regimes","authors":"Ruijie Liang ,&nbsp;Mark A. Thyer ,&nbsp;Holger R. Maier ,&nbsp;Graeme C. Dandy ,&nbsp;Emily Z. Berglund","doi":"10.1016/j.hydroa.2025.100206","DOIUrl":"10.1016/j.hydroa.2025.100206","url":null,"abstract":"<div><div>Worldwide, stormwater systems are increasingly stressed due to increased rainfall and runoff caused by climate change and urbanization. Traditional static strategies for addressing these challenges, including increasing infrastructure capacity, are often inadequate as they are not suited to dealing with large uncertainties. In contrast, adaptive strategies, such as smart real-time control (RTC), are suited to dealing with such uncertainties, as they are able to respond to future changes as they occur. However, existing RTC approaches are not truly adaptive, as they require information on future rainfall. In this paper, we modify an existing RTC approach that does not require such information so that it is able to match desired outflow hydrographs in the face of changing inflow hydrographs. The utility of the proposed Target Flow Control for Hydrographs (TFC-H) approach is demonstrated by simulating its ability to achieve desired target flow hydrographs for multiple future worlds of a simplified lot-scale system, in which peak flows increase from 7 % to 95 % and storm volumes increase from 25 % to 57 %. The results show that use of the TFC-H approach effectively maintains the desired target outflow hydrograph with less than 5 % error for this wide range of “future worlds”. Importantly, unlike other RTC approaches, the TFC-H approach is able to adapt without any knowledge/predictions of future rainfall/inflow hydrographs. This clearly demonstrates the potential of the TFC-H approach to enable existing stormwater systems to adapt to future changes.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"28 ","pages":"Article 100206"},"PeriodicalIF":3.1,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330508","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":"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}