Journal of Hydrology X最新文献

{"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":"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}

{"title":"Global analysis of forest tipping points leading to changing water cycle dynamics","authors":"Marisol Domínguez-Tuda ,&nbsp;Hugo A. Gutiérrez-Jurado","doi":"10.1016/j.hydroa.2024.100187","DOIUrl":"10.1016/j.hydroa.2024.100187","url":null,"abstract":"<div><div>Forest cover loss is increasing at unprecedented rates, affecting the hydrologic systems of major freshwater-producing regions of the world. However, quantification of the tipping points of forest cover loss before hydrologic changes manifest and their impact in water yield and climatic conditions has remained elusive. In this study, we aim to systematically document the critical thresholds of tree cover loss leading to changing hydrologic functioning within regions that experienced extensive drought, fire, or clear-cutting events spanning different climates during the period from 2001 to 2016. Using the Hydrologic Sensitivity Index based on Budyko’s curve, we analyzed the changes in hydrologic responses to climate variability as landcover changes across the affected forests. Critical thresholds were derived by fitting Richard’s Curve function to the observed relationship between growing sensitive area and tree cover loss. Our analysis reveals decrease in water yields and warming trends during the early stages of tree cover loss in tropical forests (c = 16 %), with negative anomalies observed in rainforests of Central Africa and Maritime Southeast Asia. Boreal forests also show low thresholds (c = 18 %) with a strong tendency toward a warmer climate state and no clear tendency in water yields. Mixed forests show moderate thresholds (c = 25 %) with unclear water yield and climate trends. Conversely, Temperate forests exhibit the most resilience to hydrologic regime shifts with high critical thresholds of tree cover loss (c = 46––54 %), but a rapid alteration once their threshold is surpassed resulting primarily in increased water yields and a shift toward cooler climate conditions. As the potential for additional tree cover loss heightens, due to expected increases in the frequency and intensity of droughts and wildfires, the analyses presented provide a quantitative framework to monitor and assess the impacts of changing forest cover conditions on the water cycle behavior of some of the largest freshwater producing regions of the world.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100187"},"PeriodicalIF":3.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427388","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":"Simulating the emergence of institutions that reverse freshwater salinization: An agent-based modeling approach","authors":"Kingston Armstrong ,&nbsp;Yinman Zhong ,&nbsp;Shantanu V. Bhide ,&nbsp;Stanley B. Grant ,&nbsp;Thomas Birkland ,&nbsp;Emily Zechman Berglund","doi":"10.1016/j.hydroa.2024.100188","DOIUrl":"10.1016/j.hydroa.2024.100188","url":null,"abstract":"<div><div>Salt concentration in global freshwater supplies has increased steadily, leading to the Freshwater Salinization Syndrome (FSS). To curb the FSS, stakeholders can self-organize to develop institutions, or a set of rules that limit salt emissions. This research develops an agent-based modeling framework to explore how institutions reverse the FSS. Property owners are represented as agents that apply rules of behavior to apply salt to deice pavement in response to winter weather, vote on institutions, and comply with or defect from institutions. Salt enters the soil-groundwater system through infiltration, which is modeled using a transit time distribution approach. Results demonstrate that stable institutions lead to positive economic outcomes for stakeholders, based on their ability to apply salt during winter events and access high-quality drinking water. Simulations are analyzed to explore institutions, or limits to the application of salt, that emerge based on the interactions of stakeholders as they agree on salt application limits, the intensity of monitoring for defectors, and sanctions. Institutions that emerge effectively limit the concentration of salt in drinking water. The emergence of stable institutions low rates of innovation among stakeholders, and the concentration of salt in groundwater exceeds standards due to high rates of defection among stakeholders. This research demonstrates how self-organized institutions can lead to sustainable application strategies that reverse the FSS.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100188"},"PeriodicalIF":3.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324020","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":"Precipitation-elevation relationship: Non-linearity and space–time variability prevail in the Swiss Alps","authors":"Lionel Benoit ,&nbsp;Erwan Koch ,&nbsp;Nadav Peleg ,&nbsp;Gregoire Mariethoz","doi":"10.1016/j.hydroa.2024.100186","DOIUrl":"10.1016/j.hydroa.2024.100186","url":null,"abstract":"<div><p>The relationship between mean daily precipitation and elevation is often regarded as linear and positive, resulting in simple “precipitation lapse rate” equations frequently employed to extrapolate daily rainfall from a single weather station over a large area. We examine the precipitation-elevation relationship in the Swiss Alps using a combination of weather radar and rain gauge data to test this common assumption, challenging it by fitting a two-segment piecewise linear model with a mid-slope break-point as an alternative. By examining data stratified by catchment, season, and weather type, we assess the space–time variability of the precipitation-elevation relationship. We conclude that a non-linear and non-stationary model seems necessary to capture the variability of the observed precipitation-elevation relationship. Based on our findings, we suggest that the simplified precipitation lapse rate concept is misleading and should be reconsidered in hydrological applications, emphasizing the need for a more realistic representation of precipitation variability over time and space.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100186"},"PeriodicalIF":3.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000166/pdfft?md5=5476d2fbeeb8f8e6e4cfbc3ca95b6d74&pid=1-s2.0-S2589915524000166-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242256","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":"How much X is in XAI: Responsible use of “Explainable” artificial intelligence in hydrology and water resources","authors":"Holger Robert Maier ,&nbsp;Firouzeh Rosa Taghikhah ,&nbsp;Ehsan Nabavi ,&nbsp;Saman Razavi ,&nbsp;Hoshin Gupta ,&nbsp;Wenyan Wu ,&nbsp;Douglas A.G. Radford ,&nbsp;Jiajia Huang","doi":"10.1016/j.hydroa.2024.100185","DOIUrl":"10.1016/j.hydroa.2024.100185","url":null,"abstract":"<div><p>Explainable Artificial Intelligence (XAI) offers the promise of being able to provide additional insight into complex hydrological problems. As the “<em>new kid on the block</em>”, these methods are embraced enthusiastically and often viewed as offering something radically new and different. However, upon closer inspection, many XAI approaches are very similar to more “<em>traditional</em>” methods of “<em>interrogating</em>” existing models, such as sensitivity or break-even analysis. In fact, the approach of developing data-driven models to obtain a better understanding of hydrological processes to inform the development of more physics-based models is as old as hydrology itself. Consequently, rather than being considered a new approach, XAI should be viewed as part of a long-standing tradition, and XAI methods part of an ever-expanding hydrological modelling toolkit, rather than a silver bullet. Critically, there needs to be shift from focusing on how to best <em>eXplain</em> what AI models have learnt (i.e., the X component of XAI) to developing models that are able to capture relationships that are contained within the data in a robust and reliable fashion (i.e., the AI component of XAI), as there is little value in explaining AI-derived relationships if these do not reflect underlying hydrological processes. However, this is often not the case due to a focus on maximising the predictive ability of AI models “<em>at all costs</em>”, not uncommonly resulting in large models that often have thousands or even millions of parameters that are not well defined. Consequently, these models generally <em>do not</em> capture underlying hydrological processes in a robust and reliable fashion. Finally, there is also a need to stop thinking about XAI as a purely technical approach, but a socio-technical approach that views XAI as a process that can assist with solving problems that are situated within broader social and political contexts.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100185"},"PeriodicalIF":3.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000154/pdfft?md5=a863cf9a0b87f3655a76e2ff3d7113af&pid=1-s2.0-S2589915524000154-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230736","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":"Characterization of the urban heat Island effect from remotely sensed data based on a hierarchical model","authors":"Lucas Ford ,&nbsp;Dingbao Wang ,&nbsp;Mukesh Kumar ,&nbsp;A. Sankarasubramanian","doi":"10.1016/j.hydroa.2024.100184","DOIUrl":"10.1016/j.hydroa.2024.100184","url":null,"abstract":"<div><p>This study attempts to statistically characterize the Urban Heat Island Intensity (UHII) (<span><math><mrow><mi>Δ</mi><mi>T</mi></mrow></math></span>) for 55 cities under three climate regimes – arid, snow and temperate – across the US. The study uses remotely sensed data products, daily temperature from MODIS and daily evapotranspiration from SSEBop model, to calculate the urban–rural difference in daily-mean temperature and daily-mean evapotranspiration (<span><math><mrow><mi>Δ</mi><mi>T</mi></mrow></math></span> and <span><math><mrow><mi>Δ</mi><mi>E</mi><mi>T</mi></mrow></math></span> respectively) for the selected cities. By developing a hierarchical model that explains UHII using temporally-varying <span><math><mrow><mi>Δ</mi><mi>E</mi><mi>T</mi></mrow></math></span> and spatially-varying urban morphometric characteristics (total urban area and percentage impervious area) available for each city, we find that 89% of the spatio-temporal variability in annual <span><math><mrow><mi>Δ</mi><mi>T</mi></mrow></math></span> can be explained. The relationship between <span><math><mrow><mi>Δ</mi><mi>T</mi></mrow></math></span> and <span><math><mrow><mi>Δ</mi><mi>E</mi><mi>T</mi></mrow></math></span> is found to be negative indicating increased difference in daily means of ET (<span><math><mrow><mi>Δ</mi><mi>E</mi><mi>T</mi></mrow></math></span>) result in increased difference in daily means of temperature (<span><math><mrow><mi>Δ</mi><mi>T</mi></mrow></math></span>) between urban and rural paracels The variation of <span><math><mrow><mi>Δ</mi><mi>T</mi></mrow></math></span> per unit <span><math><mrow><mi>Δ</mi><mi>E</mi><mi>T</mi></mrow></math></span> is found to be highest in arid and snowy environments and smallest in temperate environments in the south-southeast US. The relation between <span><math><mrow><mi>Δ</mi><mi>T</mi></mrow></math></span> and <span><math><mrow><mi>Δ</mi><mi>E</mi><mi>T</mi></mrow></math></span> is negative for most cities, except Madison (WI) and Sacramento (CA), across the US. Both the selected urban morphometric properties are found to be statistically significant in explaining the spatial variability in UHII, but the difference in urban–rural difference in evapotranspiration is the primary driver for UHII.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100184"},"PeriodicalIF":3.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000142/pdfft?md5=2495ac0366cac1f2041cee53bac8c93f&pid=1-s2.0-S2589915524000142-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142162990","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}