Journal of Hydrology X最新文献

{"title":"Precipitation-elevation relationship: Non-linearity and space–time variability prevail in the Swiss Alps","authors":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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":"","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":null,"pages":null},"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}

{"title":"Optimizing sensor location for the parsimonious design of flood early warning systems","authors":"","doi":"10.1016/j.hydroa.2024.100182","DOIUrl":"10.1016/j.hydroa.2024.100182","url":null,"abstract":"<div><p>Flood early warning systems (FEWS) are effective means for saving human lives from the devastating impacts of extreme hydrological events. FEWS relies on hydrologic monitoring networks that are typically expensive and challenging to design. This issue is particularly relevant when identifying the most cost-efficient number, type, and positioning of the sensors for FEWS that may be used to take decisions and alert the population at flood risk.</p><p>In this study, we focus on a widely recognized FEWS solution to analyze hydrological monitoring and forecasting performances expressed as discharge in various cross-sections of a drainage network. We propose and test a novel framework that aims to maximize FEWS performances while minimizing the number of sections that need instrumentation and suggesting optimal sensor placement to enhance forecasting accuracy. In the selected case study, we demonstrate through feature importance measure that only four sub-basins can achieve the same forecasting performance as the potential twenty-six cross-sections of the local hydrologic monitoring network. The operational dashboard resulting from our proposed framework can assist decision-makers in maximizing the performance and wider adoption of flood early warning systems across geographic and socio-economic scales.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000129/pdfft?md5=01c4d1773b11cc112bf5bb148fa011b1&pid=1-s2.0-S2589915524000129-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848523","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":"The role of regional water vapor dynamics in creating precipitation extremes","authors":"Seokhyeon Kim ,&nbsp;Conrad Wasko ,&nbsp;Ashish Sharma ,&nbsp;Rory Nathan","doi":"10.1016/j.hydroa.2024.100181","DOIUrl":"https://doi.org/10.1016/j.hydroa.2024.100181","url":null,"abstract":"<div><p>While sub-daily precipitation extremes cause flash flooding and pose risk to life, longer precipitation extremes threaten infrastructure such as water supply dams. Frequent storm or floods events replenish water supplies, ensuring the health of our ecosystems, while rarer larger storms or floods cause damage to property and life. These differing impacts depend on both storm rarity and duration and are largely dependent on coincident atmospheric water vapour. Using a novel metric that quantifies the extent of concurrency that exists between precipitation and total water vapour extremes, large regional variations are identified across the globe. Tropical regions such as Northeast Africa and South/East Asia consistently exhibit greater concurrency across all precipitation durations. In contrast, areas of the extra-tropics, such as the Mediterranean and Northwest Americas, show a rapid decline in concurrency with increasing duration. However, for rare events of long duration, non-tropical regions maintain high concurrency. With the link between climate change and increasing total water vapour well established, these results suggest that flood events will increase globally, with increases most apparent for longer and rarer events. This work underscores the need for tailored regional strategies in managing extreme precipitation and flood events in the future.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000117/pdfft?md5=29405f9ed81d96b1fc00aaa0fd37cba0&pid=1-s2.0-S2589915524000117-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540398","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":"Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins","authors":"John W. Fulton ,&nbsp;Nick G. Hall ,&nbsp;Laura A. Hempel ,&nbsp;J.J. Gourley ,&nbsp;Mark F. Henneberg ,&nbsp;Michael S. Kohn ,&nbsp;William Famer ,&nbsp;William H. Asquith ,&nbsp;Daniel Wasielewski ,&nbsp;Andrew S. Stecklein ,&nbsp;Amanullah Mommandi ,&nbsp;Aziz Khan","doi":"10.1016/j.hydroa.2024.100180","DOIUrl":"https://doi.org/10.1016/j.hydroa.2024.100180","url":null,"abstract":"<div><p>The magnitude and timing of extreme events such as debris and floodflows (collectively referred to as floodflows) in post-wildfire basins are difficult to measure and are even more difficult to predict. To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant radio communication network was leveraged to monitor flood wave velocities, to validate travel times, and to compliment observations from NEXRAD weather radar. The sensor ensemble (DEbris and Floodflow Early warNing System, DEFENS) was deployed in Waldo Canyon, Pike National Forest, Colorado, USA, which was burned entirely (100 percent burned) by the Waldo Canyon fire during the summer of 2012 (<span>MTBS, 2020</span>).</p><p>Surface velocity, stage, and precipitation time series collected during the DEFENS deployment on 10 August 2015 were used to monitor and predict flood wave velocities and travel times as a function of stream discharge (discharge; streamflow). The 10 August 2015 event exhibited spatial and temporal variations in rainfall intensity and duration that resulted in a discharge equal to 5.01 cubic meters per second (m³/s). Discharge was estimated post-event using a slope-conveyance indirect discharge method and was verified using velocity radars and the probability concept algorithm. Mean flood wave velocities – represented by the kinematic celerity <span><math><mfenced><mrow><msub><mi>c</mi><mi>k</mi></msub><mo>=</mo><mn>2.619</mn><mspace></mspace><mi>m</mi><mi>e</mi><mi>t</mi><mi>e</mi><mi>r</mi><mi>s</mi><mspace></mspace><mi>p</mi><mi>e</mi><mi>r</mi><mspace></mspace><mi>s</mi><mi>e</mi><mi>c</mi><mi>o</mi><mi>n</mi><mi>d</mi><mo>,</mo><mspace></mspace><mi>m</mi><mo>/</mo><mi>s</mi><mo>±</mo><mn>0.556</mn><mspace></mspace><mi>p</mi><mi>e</mi><mi>r</mi><mi>c</mi><mi>e</mi><mi>n</mi><mi>t</mi></mrow></mfenced></math></span> and dynamic celerity <span><math><mfenced><mrow><msub><mi>c</mi><mi>d</mi></msub><mo>=</mo><mn>3.533</mn><mspace></mspace><mi>m</mi><mo>/</mo><mi>s</mi><mo>±</mo><mn>0.181</mn><mspace></mspace><mi>p</mi><mi>e</mi><mi>r</mi><mi>c</mi><mi>e</mi><mi>n</mi><mi>t</mi></mrow></mfenced><mi>a</mi><mi>n</mi><mi>d</mi><mspace></mspace><mi>t</mi><mi>h</mi><mi>e</mi><mi>i</mi><mi>r</mi><mspace></mspace><mi>u</mi><mi>n</mi><mi>c</mi><mi>e</mi><mi>r</mi><mi>t</mi><mi>a</mi><mi>i</mi><mi>n</mi><mi>t</mi><mi>i</mi><mi>e</mi><mi>s</mi></math></span> were computed. L-moments were computed to establish probability density functions (PDFs) and associated statistics for each of the at-a-section hydraulic parameters to serve as a workflow for implementing alert networks in hydrologically similar basins that lack data.</p><p>Measured flood wave velocities and travel times agreed well with predicted values. Absolute percent differences between predicted and measured flood wave velocities ranged from 1.6 percent to 49 percent","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000105/pdfft?md5=82fb8c468784981870183c41722a869b&pid=1-s2.0-S2589915524000105-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479214","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 the Thornthwaite Mather procedure for baseflow and groundwater storage predictions in sloping and mountainous regions","authors":"Feleke K. Sishu ,&nbsp;Seifu A. Tilahun ,&nbsp;Petra Schmitter ,&nbsp;Tammo S. Steenhuis","doi":"10.1016/j.hydroa.2024.100179","DOIUrl":"https://doi.org/10.1016/j.hydroa.2024.100179","url":null,"abstract":"<div><p>Hillslope aquifers regulate streamflow and are a critical potable and irrigation water source, especially in developing countries. Knowing recharge and baseflow is essential for managing these aquifers. Methods using available data to calculate recharge and baseflow from aquifers are not valid for uplands. This paper adapts the Thornthwaite and Mather (T-M) procedure from plains to sloping and mountainous regions by replacing the linear reservoir with a zero-order aquifer. The revised T-M procedure was tested over four years in two contrasting watersheds in the humid Ethiopian highlands: the 57 km² Dangishta with a perennial stream and the nine km² Robit Bata, where the flow ceased four months after the end of the rain phase. The monthly average groundwater tables were predicted with an accuracy ranging from satisfactory to good for both watersheds. Baseflow predictions were “very good” after considering the evaporation from shallow groundwater in the valley bottom during the dry phase in Dangishta. We conclude that the T-M procedure is ideally suited for calculating recharge, baseflow and groundwater storage in upland regions with sparse hydrological data since the procedure uses as input only rainfall and potential evaporation data that are readily available together with an estimate of the aquifer travel time.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000099/pdfft?md5=fcd021fe86a9e1229d0a54c3a5071e78&pid=1-s2.0-S2589915524000099-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140894818","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":"Snow depth time series Generation: Effective simulation at multiple time scales","authors":"Hebatallah Mohamed Abdelmoaty ,&nbsp;Simon Michael Papalexiou ,&nbsp;Sofia Nerantzaki ,&nbsp;Giuseppe Mascaro ,&nbsp;Abhishek Gaur ,&nbsp;Henry Lu ,&nbsp;Martyn P. Clark ,&nbsp;Yannis Markonis","doi":"10.1016/j.hydroa.2024.100177","DOIUrl":"https://doi.org/10.1016/j.hydroa.2024.100177","url":null,"abstract":"<div><p>Snow depth (SD) is a crucial variable of the water, energy, and nutrient cycles, impacting water quantity and quality, the occurrence of floods and droughts, snow-related hazards, and sub-surface ecological functions. As a result, quantifying SD dynamics is crucial for several scientific and practical applications. Ground measurements of SD provide information at sparse locations, and physical global model simulations provide information at relatively coarse spatial resolutions. An approach to complement this information is using stochastic models that generate time series of hydroclimatic variables, preserving their statistical properties in a computationally-effective manner. However, stochastic generation methods to produce SD time series exclusively do not exist in the literature. Here, we apply a stochastic model to produce synthetic daily SD time series trained by 448 stations in Canada. We show that the model captures key statistical properties of the observed records, including the daily distributions of zero and non-zero SD, temporal clustering (i.e., autocorrelation), and seasonal patterns. The model also excelled in capturing the observed higher-order L-moments at multiple temporal scales, with biases between simulated and observed L-skewness and L-kurtosis within (<span><math><mrow><mo>-</mo></mrow></math></span>0.1, +0.1) for 93.0 % and 98.3 % of the stations, respectively. The stochastic modelling approach introduced here advances the generation of SD time series, which are needed to develope Earth-system models and assess the risk of snowmelt flooding that lead to severe damage and fatalities.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000075/pdfft?md5=7dd215d7d33cfa6261fe765a3f1374cd&pid=1-s2.0-S2589915524000075-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351808","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":"What can we learn from long hydrological time-series? The case of rainfall data at Collegio Romano, Rome, Italy","authors":"Elena Volpi,&nbsp;Corrado P. Mancini,&nbsp;Aldo Fiori","doi":"10.1016/j.hydroa.2024.100176","DOIUrl":"https://doi.org/10.1016/j.hydroa.2024.100176","url":null,"abstract":"<div><p>In this work, we explore the statistical behavior of one of the longest rainfall time-series in Italy and in the world, covering the period 1782–2017. Some standard and innovative statistical tools are applied to test the variability and change of the process across all values (in average, but also in terms of extremes) and scales (from days to years). An oscillation pattern occurs across all the time scales, from years to decades, limited by the sample length. It implies that there are no particular periods of variability, apart from seasonality, and no statistically significant trends, such that the process can be fully characterized in terms of the Hurst coefficient. Despite its exceptional length, the dataset is still insufficient to adequately capture the complex behavior of rainfall over the time scales, especially with regards to extremes, and to separate anthropogenically induced change from natural variability based on the data alone. Our findings suggest that samples of limited length do not allow robust statistical predictions, raising concerns about statistical analyses based on a limited dataset, even a relatively large one.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000063/pdfft?md5=73bd5ea9024f01d7e7728873f97364c1&pid=1-s2.0-S2589915524000063-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342198","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":"Remote Sensing Technologies for Unlocking New Groundwater Insights: A Comprehensive Review","authors":"Abba Ibrahim ,&nbsp;Aimrun Wayayok ,&nbsp;Helmi Zulhaidi Mohd Shafri ,&nbsp;Noorellimia Mat Toridi","doi":"10.1016/j.hydroa.2024.100175","DOIUrl":"10.1016/j.hydroa.2024.100175","url":null,"abstract":"<div><p>This study examined recent advances in remote sensing (RS) techniques used for the quantitative monitoring of groundwater storage changes and assessed their current capabilities and limitations. The evolution of the techniques analyses spans from empirical reliance on sparse point data to the assimilation of multi-platform satellite measurements using sophisticated machine learning algorithms. Key developments reveal enhanced characterisation of localised groundwater measurement by integrating coarse-resolution gravity data with high-resolution ground motion observations from radar imagery. Notable advances include improved accuracy achieved by integrating Gravity Recovery and Climate Experiment (GRACE) and Interferometric Synthetic Aperture Radar (InSAR) data. Cloud computing now facilitates intensive analysis of large geospatial datasets to address groundwater quantification challenges. While significant progress has been made, ongoing constraints include coarse spatial and temporal resolutions limiting basin-scale utility, propagation of uncertainties from sensor calibrations and data merging, and a lack of systematic validation impeding operational readiness. Addressing these limitations is critical for continued improvement of groundwater monitoring techniques. This review identifies promising pathways to overcome these limitations, emphasising standardised fusion frameworks for satellite gravimetry, radar interferometry, and hydrogeophysical techniques. The development of robust cloud-based modelling platforms for multi-source subsurface information assimilation is a key recommendation, highlighting the potential to significantly advance groundwater quantification accuracy. This comprehensive review serves as a valuable resource for water resource and remote sensing experts, providing insights into the evolving landscape of methodologies and paving the way for future advancements in groundwater storage monitoring tools.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000051/pdfft?md5=8f88ec3649903e752b30ff12ec455f17&pid=1-s2.0-S2589915524000051-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140269646","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}