Journal of Hydrology X最新文献

{"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}

{"title":"Revisiting model complexity: Space-time correction of high dimensional variable sets in climate model simulations","authors":"Cilcia Kusumastuti ,&nbsp;Rajeshwar Mehrotra ,&nbsp;Ashish Sharma","doi":"10.1016/j.hydroa.2024.100193","DOIUrl":"10.1016/j.hydroa.2024.100193","url":null,"abstract":"<div><div>Multivariate bias correction (BC) models are well-known to correct more statistical attributes in climate model simulations. However, their inherent complexity and excessive parameters can introduce higher uncertainty into future climate simulations. In contrast, univariate BC models, with fewer parameters, are limited to correcting certain attributes. An issue that has not been investigated in-depth is the impact of<!--> <!-->an increased number of variables in the multivariate BC has on the bias-corrected climate models’ stability. This study compares the performance of a multivariate BC approach, Multivariate Recursive Nested Bias Correction (MRNBC), and a univariate BC approach, Continuous Wavelet-based Bias Correction (CWBC), as the number of variables to be corrected increases, known as the “curse of dimensionality” (CoD). The analysis uses high-resolution climate model outputs for both current and future simulations of sea surface temperature and precipitation in the Niño 3.4 region. Results show both BC models effectively correct current climate biases. As the number of variables increases, CWBC remains robust and produces sensible future simulations, while MRNBC’s complexity leads to deterioration in standard deviations and spatial cross-correlation. CWBC, based on univariate correction, is<!--> <!-->relatively unaffected by the CoD.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100193"},"PeriodicalIF":3.1,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650978","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":"Effects of model complexity on karst catchment runoff modeling for flood warning systems","authors":"Paul Knöll ,&nbsp;Ferry Schiperski ,&nbsp;Antonia Roesrath ,&nbsp;Traugott Scheytt","doi":"10.1016/j.hydroa.2024.100194","DOIUrl":"10.1016/j.hydroa.2024.100194","url":null,"abstract":"<div><div>Severe flood events are deemed more frequent in the near future with a changing climate. Headwater catchments, especially when karstified, exhibit a pronounced susceptibility to swift and substantial responses to precipitation events, leading to flooding. In this study, a karstified headwater catchment in SW Germany is investigated, focusing on gaining insights into the key processes controlling its discharge behavior. Intensive fieldwork was conducted and a variety of field data were collected and analyzed to determine the general system behavior during low flow and flood events. Field insights reveal a groundwater borne streamflow generation with a subsurface catchment largely differing from the surface catchment. Episodic and sporadic springs were identified as crucial contributors to stream flow generation.</div><div>The study was undertaken to evaluate the viability of simulating streamflow for flood warning using a lumped modeling approach at a sub-daily temporal scale, since lumped models are widely used for karst spring discharge modeling. Based on field data observations, a comparative analysis of different model structures was undertaken, aiming at assessing the required degree of model complexity for representing catchment runoff generation as well as the relevant system features and properties. In order to find an adequate model structure, a total of 21 models with varying degree of complexity were set up and run. Both, subsurface and surface catchment limits were considered. Results show that the hydrograph of the whole catchment can be represented by a rather simple lumped model in the present case under two prerequisites: (1) input needs to represent the groundwater catchment emphasizing the groundwater borne nature of flow and (2) the models need to allow for direct runoff, as the sporadic springs observed in the field contribute significant discharge to streamflow during flood events. It is revealed that it seems valid to start modeling with a relatively simple storage model as long as key processes in the catchment are represented. The general feasibility of such a simple modeling approach in this complex catchment encourages its feasibility in other headwater catchments.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"26 ","pages":"Article 100194"},"PeriodicalIF":3.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707302","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":"Quantifying the economic value of a national hydrometric network for households","authors":"Kush Thakar ,&nbsp;Neil Macdonald ,&nbsp;Karyn Morrissey","doi":"10.1016/j.hydroa.2024.100192","DOIUrl":"10.1016/j.hydroa.2024.100192","url":null,"abstract":"<div><div>This study reports the results of a Choice Experiment to quantify households’ willingness-to-pay for river gauging programmes in Scotland. The hydrometric network is operated and maintained by the Scottish Environment Protection Agency (SEPA), Scotland’s principal environment regulator, a non-department public body of the Scottish Government. Results from mixed logit and latent class modelling show that most households (‘Hydrometric Maximisers’ − around 70 %) have significant, positive willingness-to-pay values for river gauging programmes, but a minority (‘Hydrometric Satisficers’ − around 30 %) do not view this as a major public policy priority. On average, hydrometric data collection delivers non-market benefits worth £84,625,562 to the Scottish economy, with a minimum economic Benefit-to-Cost ratio of 25:1. This is in addition to the infrastructure value and any private returns made by commercial users of the data. The findings demonstrate that traditional approaches to assessing the benefits of hydrometric networks often underestimate their value. The research also highlights the importance of public information campaigns and household engagement initiatives to increase awareness of hydro-meteorological services, and to develop the business case more fully for public investment in environmental observation networks.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100192"},"PeriodicalIF":3.1,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650977","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":"Private sensors and crowdsourced rainfall data: Accuracy and potential for modelling pluvial flooding in urban areas of Oslo, Norway","authors":"Kay Khaing Kyaw ,&nbsp;Emma Baietti ,&nbsp;Cristian Lussana ,&nbsp;Valerio Luzzi ,&nbsp;Paolo Mazzoli ,&nbsp;Stefano Bagli ,&nbsp;Attilio Castellarin","doi":"10.1016/j.hydroa.2024.100191","DOIUrl":"10.1016/j.hydroa.2024.100191","url":null,"abstract":"<div><div>Cloudbursts and extreme rainstorms pose a growing threat to urban areas. Accurate rainfall data is essential for predicting inundations and urban flooding. Private weather stations are becoming increasingly common, and their spatial distribution roughly follows population density. This makes them a valuable source of crowdsourced data for high-resolution rainfall fields in urban areas. We evaluated the performance of private rain gauges in two recent pluvial flood events in Oslo. We also explored the potential use of private rain gauge data in inundation models. Our results indicate that private sensors have excellent rain detection capabilities, but they tend to underestimate the reference value on average by approximately 25%. However, bias-corrected crowdsourced rainfall data can produce significantly more accurate inundation maps than those generated from official rain gauges, if compared with maps resulting from bias-corrected weather radar. Overall, our study highlights the potential of utilizing crowdsourced rainfall data from private sensors for accurately representing pluvial flooding in urban areas. These findings have significant implications for improving flood prediction and mitigation strategies in vulnerable urban settings.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100191"},"PeriodicalIF":3.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650976","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":"A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks","authors":"Matteo Guidicelli ,&nbsp;Kristoffer Aalstad ,&nbsp;Désirée Treichler ,&nbsp;Nadine Salzmann","doi":"10.1016/j.hydroa.2024.100190","DOIUrl":"10.1016/j.hydroa.2024.100190","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Our understanding of the impact of climate change on water availability and natural hazards in high-mountain regions is limited due to the spatial and temporal scarcity of ground observations of precipitation and snow. Freely available, satellite-based information about the snowpack is currently mainly limited to indirect measurements of snow-covered area or very coarse-scale snow water equivalent (SWE), but only for flat areas in lowlands without vegetation cover. Novel space-based laser altimeters, such as ICESat-2, have the potential to provide high-resolution snow depth data in worldwide mountain regions where no ground observations exist. However, these space-based laser altimeters come with spatial gaps between ground tracks, obtained without repetition at a give location. To overcome these drawbacks, here, we present a combined probabilistic data assimilation and deep learning approach to reconstruct spatio-temporal SWE from observations of snow depth along ground tracks, imitating ICESat-2 tracks in view of a potential future global application.&lt;/div&gt;&lt;div&gt;Our approach is based on assimilating SWE and snow cover information in a degree-day model with an iterative ensemble smoother (IES) which allows temporally reconstructing SWE along hypothetical ground tracks separated by 3 km. As input, the degree-day model uses daily precipitation and downscaled air temperature from the ERA5 reanalysis. A feedforward neural network (FNN) is then used for spatial propagation of the daily mean and standard deviation of the updated SWE ensemble members obtained from the IES. The combined IES-FNN approach provides uncertainty-aware spatio-temporally continuous estimates of SWE.&lt;/div&gt;&lt;div&gt;We tested our approach in the alpine Dischma valley (Switzerland) using high-resolution snow depth maps obtained from photogrammetric techniques mounted on airplanes and unmanned aerial system observations. Our results show that the IES-FNN model provides reliable estimates at a resolution of approximately 100 m. Even assimilating only one SWE observation during the year (combined with satellite-based melt-out date estimates) produces satisfying results when evaluating the IES-FNN SWE reconstructions on independent dates and smaller (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;&lt;&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;4 km&lt;sup&gt;2&lt;/sup&gt;) areas: mean absolute error of 86 mm (78 mm) at Schürlialp (Latschüelfurgga) for average SWE of 180 mm (254 mm), and average spatial linear correlation with the reference SWE of 0.51 (0.48). However, the assimilated SWE observation must not be too early in the accumulation season or too late in the melt season when the snowpack is starting or ending to accumulate or melt, respectively. Smaller distances between ground tracks (1500 m and 500 m) show improved performance of the IES-FNN approach in space, with no significant improvement in terms of temporal reconstruction.&lt;/div&gt;&lt;div&gt;Applying the IES-FNN approach to e.g., real ICESat-2 data, remains challenging due to t","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100190"},"PeriodicalIF":3.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441862","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":"A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to Characterize compound flood risk","authors":"Adam Nayak ,&nbsp;Pierre Gentine ,&nbsp;Upmanu Lall","doi":"10.1016/j.hydroa.2024.100189","DOIUrl":"10.1016/j.hydroa.2024.100189","url":null,"abstract":"<div><div>Traditional approaches to flood risk management assume flood events follow an independent, identically distributed (i.i.d.) random process from which static risk measures are computed. Modern risk accounting strategies also consider nonstationarity or long-term trends in the mean and moments of the associated flood probability distributions. However, few approaches consider how extreme hydroclimatic events cluster in both space and time, compounding damage risks. Here we introduce a compound flood risk simulator that models and conditionally forecasts future variability in regional flooding events that cluster in time, given trends and oscillations in a variable climate signal. A modular, novel integration of wavelet signal processing, nonstationary time series forecasting, k-nearest neighbor (KNN) bootstrapping, multivariate copulas, and modified Neyman-Scott (NS) event clustering process provides users the ability to model interannual and sub-annual clustering of flood risk. Our semi-parametric flood generator specifically targets the clustered temporal dynamics of jointly modeled flood intensity, duration, and frequency over a finite future period of a decade or more, thereby providing a foundation for adaptation approaches that integrate temporally clustered flood risk into planning, response and recovery.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100189"},"PeriodicalIF":3.1,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427389","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}