Journal of Hydrology X最新文献

{"title":"Multivariate design storms reveal misrepresentation of flood hazard by traditional approaches","authors":"Tabea Cache,&nbsp;Hannes Müller-Thomy,&nbsp;Christian Onof,&nbsp;Simon M. Papalexiou,&nbsp;Nadav Peleg","doi":"10.1016/j.hydroa.2026.100231","DOIUrl":"10.1016/j.hydroa.2026.100231","url":null,"abstract":"<div><div>Design storms are widely used in hydraulic engineering to represent extreme precipitation and assess flood risk. Commonly used block-method design storms, such as Euler Type II, assign a single return period across all temporal scales of an event, neglecting dependencies in rainfall intensity across durations. This simplification may lead to unrealistic event representations. Here, we compare flood hazard from a traditional Euler Type II storm with storms generated using the multivariate REALISTIC framework, which accounts for joint return periods across durations. Using Zurich as a case study, we simulate 3-hour 100-year events and evaluate the resulting flood hazard with a hydrodynamic model. Results show that flood hazard is highly sensitive to the underlying intensity-duration profile. The block-method storm produces substantially higher flood depths and intensities than events whose profiles are consistent with observed joint return periods, which often result in limited flooding. These findings suggest that traditional design storms provide an incomplete estimation of flood hazard and highlight the need for multivariate approaches to support more robust risk assessments.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"31 ","pages":"Article 100231"},"PeriodicalIF":4.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149199719","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":"Response to Comment on “When are AI models ready for deployment? Reassessing Google’s global AI flood forecasting system through the lens of responsible modelling”","authors":"Kailong Li,&nbsp;Saman Razavi,&nbsp;Holger R. Maier,&nbsp;Markus Hrachowitz,&nbsp;Ehsan Nabavi,&nbsp;Natasha Harvey,&nbsp;Khaled Akhtar,&nbsp;Fisaha Unduche","doi":"10.1016/j.hydroa.2026.100230","DOIUrl":"10.1016/j.hydroa.2026.100230","url":null,"abstract":"<div><div>The Comment by Nearing et al. (2026) on our earlier work, which included an independent assessment of the performance of Google's global AI flood forecasting system, prompted us to further reflect on responsible AI and numerical modelling more broadly, particularly when models inform high-stakes decisions such as disaster-risk management. While substantial disagreements remain over how operational performance should be evaluated and interpreted, there is important common ground on the need for rigorous evaluation, clearer standards, and model intercomparison. We use this exchange to further discuss evaluation assumptions, communication of model performance, benchmarking, independent scrutiny, and standard-setting, with the aim of contributing to more robust and responsible assessment of models intended for operational decision support.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"31 ","pages":"Article 100230"},"PeriodicalIF":4.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149159088","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":"Global groundwater modeling: Proof-of-concept of 3D variably saturated flow simulation at kilometer resolution","authors":"Stefan Kollet ,&nbsp;Alexandre Belleflamme ,&nbsp;Laura Condon ,&nbsp;Muhammad Fahad ,&nbsp;Klaus Goergen ,&nbsp;Reed Maxwell ,&nbsp;Bibi Naz","doi":"10.1016/j.hydroa.2025.100213","DOIUrl":"10.1016/j.hydroa.2025.100213","url":null,"abstract":"<div><div>The proof-of-concept study presents, for the first time, the results of integrated numerical hydrological simulations at kilometer resolution on a global scale. Using available datasets and applying significant simplifications to the real terrestrial system, the model was informed with hydrofacies, soil texture and topographic slopes, and effective recharge at the upper boundary. A steady-state spin-up was performed, resulting in a 3D pressure head distribution of the water continuum from 60 m deep variably saturated groundwater to surface water. Relative saturation and diagnostic water table depth were examined, resolving variability over several orders of magnitude. In our opinion, the added value of the partial differential equation (PDE) based simulations outweighs the computational resources required, which are considerable. These simulations are possible, because of the advent of massively parallel, accelerator based supercomputer architectures and performance portable scientific software. While the current simulation results may not be reliable from the perspective of stakeholders at this stage of model development, the study demonstrates the feasibility of prognostic groundwater simulation at the global scale, and will stimulate future model improvements, including the quantification of uncertainties. Simultaneously, the study opens new avenues for future research in the context of hyper-resolution global Earth system modelling.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"30 ","pages":"Article 100213"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926743","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":"Coherent changes in global high and low flows","authors":"Qi Huang ,&nbsp;Jan Seibert ,&nbsp;Yongqiang Zhang","doi":"10.1016/j.hydroa.2025.100212","DOIUrl":"10.1016/j.hydroa.2025.100212","url":null,"abstract":"<div><div>Streamflow shifts threaten water security under climate change. While gauged basins have been extensively studied, ungauged regions remain poorly characterized. Using high- and low-flow trends from 2,213 unregulated catchments, we predict four types of streamflow changes globally. From 1982 to 2018, 81.1% of studied regions exhibited coherent trends: 49.3% showed wetting (increased high and low flows), and 31.8% showed drying (decreased flows). The remaining areas experienced diverging (6.2%) or equalizing (12.6%) changes. Wetting dominated arid, snow, and polar climate regions, whereas drying prevailed in equatorial and temperate zones. Streamflow trends aligned with precipitation changes in 58.3% of areas, while 39.6% were also driven by non-precipitation factors, highlighting complex hydroclimatic interactions. Ongoing shifts demand enhanced water management and hazard adaptation.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"30 ","pages":"Article 100212"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718926","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":"Hydrological impacts of deforestation on flow dynamics of small watersheds in the Brazilian Cerrado – Amazon transition region","authors":"Hellen Kezia Almada ,&nbsp;Divino Vicente Silvério ,&nbsp;Christopher Neill ,&nbsp;Eddie Lenza de Oliveira ,&nbsp;Leonardo Maracahipes-Santos ,&nbsp;Marcia Nunes Macedo","doi":"10.1016/j.hydroa.2026.100214","DOIUrl":"10.1016/j.hydroa.2026.100214","url":null,"abstract":"<div><div>Understanding the effects of land use changes on hydrological cycling is essential for managing water resources. While deforestation is a known driver of hydrological regime shifts, few studies have assessed its impacts in the steep landscapes of the Brazilian Amazon-Cerrado transition, a region experiencing rapid deforestation and agricultural expansion. Here, we evaluate streamflow responses to land use and land cover changes using three years of field monitoring across eight watersheds (2.5 – 39 km²) in eastern Mato Grosso, Brazil. Our study watersheds spanned a gradient of slopes (2% – 8.6%) and native vegetation cover (10% – 80%). Annual and daily flows were consistently higher in deforested watersheds, and these areas also exhibited greater flow seasonality and stormflow peaks. Notably, deforested watersheds experienced reduced baseflow during the dry season, increasing water scarcity at a critical period of the year. These findings highlight the importance of considering terrain slope and seasonal dynamics, as analyses at annual scales may mask the dual impacts of deforestation – increased flood risk during the rainy season and exacerbated water scarcity during the dry season. Integrating slope, land cover, and multiyear data is therefore essential to support more effective water resource management in rapidly changing tropical landscapes.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"30 ","pages":"Article 100214"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173197","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":"When are AI models ready for deployment? Reassessing Google’s global AI flood forecasting system through the lens of responsible modelling","authors":"Kailong Li ,&nbsp;Saman Razavi ,&nbsp;Holger R. Maier ,&nbsp;Markus Hrachowitz ,&nbsp;Ehsan Nabavi ,&nbsp;Natasha Harvey ,&nbsp;Khaled Akhtar ,&nbsp;Fisaha Unduche","doi":"10.1016/j.hydroa.2026.100215","DOIUrl":"10.1016/j.hydroa.2026.100215","url":null,"abstract":"<div><div>The development of AI models is increasing at a rapid rate. However, when are they ready to be deployed in real-world operational settings? In this paper, we introduce a framework to support such assessments and apply it to Google’s recently released AI-based flood prediction system, which is claimed to achieve “reliability in predicting extreme riverine events” and provide “accurate and timely warnings” that are available “earlier and over larger and more impactful events in ungauged basins”. The system has been integrated into an operational early-warning platform producing open, real-time forecasts in more than 80 countries. While this development promises to usher in a new and exciting age in global flood forecasting, the supporting evidence relies heavily on several subjective choices, the implications of which have not been acknowledged or assessed. Here, we evaluate the consequences of these choices on claims of operational deployment readiness across four dimensions: predictive accuracy, forecast timeliness, the characterization of extreme events, and benchmarking against state-of-the-art models. Our assessment reveals that the system’s actual predictive accuracy is likely to be substantially lower than reported—particularly for extreme events—raising concerns about <em>responsible practices across modelling and publicity</em> in high-stakes applications. The deployment of the Google AI model therefore risks misinforming those who depend on its outputs for evacuation and preparedness decisions, particularly in less-developed countries such as those targeted by the enterprise, given its alarmingly high (&gt;90%) rates of false positives and false negatives. Beyond the immediate operational consequences, if left unaddressed, these outcomes may erode public trust in AI within hydrological sciences. We conclude by calling for greater transparency, accountability, and methodological rigor in the integration of AI into flood forecasting.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"30 ","pages":"Article 100215"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147538516","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":"A generalized design flood estimation framework under stationary and non-stationary scenarios","authors":"C. Awasthi ,&nbsp;S.A. Archfield ,&nbsp;A. Sankarasubramanian","doi":"10.1016/j.hydroa.2025.100210","DOIUrl":"10.1016/j.hydroa.2025.100210","url":null,"abstract":"<div><div>With the ever-ongoing debate over the death of stationarity in flood time series, developing methods for flood frequency analysis that can address both stationarity and non-stationarity simultaneously has become increasingly important for design flood estimation. Existing non-stationary flood frequency analysis (NFFQ) methods are either limited to providing time-varying conditional flood quantile estimates or lack closed-form expression to estimate the design flood over the planning period. We propose a novel framework, MM-NFFQ, that introduces marginal moments (MM) estimation techniques to provide a closed-form expression to estimate design flood under non-stationarity. We demonstrate MM-NFFQ using the LP3 distribution for estimating conditional moments, but in principle, it can work for any 3-parameter distribution. We first show the proposed MM-NFFQ collapses to stationary flood frequency analysis analytically using synthetic data and then demonstrate the MM-NFFQ approach for two basins exhibiting non-stationarity in their flood time series. We further extend the analysis to selected 40 basins across CONUS and find that arid basins exhibit higher deviation from stationarity. Thus, the proposed MM-NFFQ framework can estimate traditional flood frequency curves for both, stationary and non-stationary flood processes, and can also be utilized to analyze the changes in conditional moments and marginal moments over different planning horizons.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"30 ","pages":"Article 100210"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147419828","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":"Water pricing with permits","authors":"Michael Neugart,&nbsp;Vahideh Sotoudehmollashahi","doi":"10.1016/j.hydroa.2026.100217","DOIUrl":"10.1016/j.hydroa.2026.100217","url":null,"abstract":"<div><div>Water scarcity is a problem that is likely to become more prevalent in many parts of the world in the near future. Changing water pricing schemes to reduce households’ water demand could be one policy to address the problem. Given households’ price-inelastic water demand, such an approach would likely require substantial price increases to reduce water demand. We propose an alternative pricing scheme that issues water-use rights (permits) to households at prices that cover a water supplier’s costs. These permits can be traded at the end of the year: households with excess permits are redeemed, while households in need of additional permits must purchase them. We show that, in such a setting, households’ opportunity cost considerations can curtail water consumption and yield lower water bills than a policy that increases volumetric rates.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"30 ","pages":"Article 100217"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797582","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":"Improving annual streamflow estimates using Budyko parameterization driven by catchment attributes","authors":"Habtamu Tamiru,&nbsp;Mesfin M. Mekonnen,&nbsp;Mukesh Kumar","doi":"10.1016/j.hydroa.2026.100216","DOIUrl":"10.1016/j.hydroa.2026.100216","url":null,"abstract":"<div><div>The Budyko framework is widely used to infer long-term water-energy partitioning. Its original uniform parameters limited accuracy, while locally calibrated formulations are difficult to apply in ungauged basins. This study develops a machine-learning (ML)-based parameterization linking Budyko parameters to catchment attributes, enabling annual estimates of evapotranspiration (ET) and streamflow in gauged and ungauged basins. Using 671 CAMELS catchments, we optimized parameters for six Budyko-type equations with the Shuffled Complex Evolution (SCE) algorithm, then adjusted them to catchment attributes using a ML model. The resulting attribute-based parameterization improved evaporative-index estimates relative to both SCE-based calibration and a calibrated SAC-SMA model. Applied to test catchments, it achieved a mean KGE of 0.67 for annual streamflow, outperforming calibrated SAC-SMA (0.48) and site-specific Budyko predictions (0.61). Results highlight a scalable approach for annual water-budget estimation in data-limited regions.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"30 ","pages":"Article 100216"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147538517","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":"2025-11-01","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}