Earth and Space Science最新文献

{"title":"A Simple and Robust CryoSat-2 Radar Freeboard Correction Method Dedicated to TFMRA50 for the Arctic Winter Snow Depth and Sea Ice Thickness Retrieval","authors":"Hoyeon Shi,&nbsp;Rasmus Tonboe,&nbsp;Sang-Moo Lee,&nbsp;Gorm Dybkjær,&nbsp;Byung-Ju Sohn,&nbsp;Suman Singha,&nbsp;Fabrizio Baordo","doi":"10.1029/2024EA003715","DOIUrl":"https://doi.org/10.1029/2024EA003715","url":null,"abstract":"<p>CryoSat-2 has been successful in observing sea ice thickness from space by providing ice freeboard information. The initial estimate of the ice freeboard, called radar freeboard, is obtained by analyzing the observed waveform using a retracker. A series of corrections are needed to convert the radar freeboard to the ice freeboard. Those are the physical effects (e.g., changes in wave propagation speed and the distribution of scattering at snow and ice surfaces, etc.) and the bias of the retracker; however, traditionally, only the wave speed correction has been applied due to lack of enough information to perform the complete correction. Here, an alternative correction method for the CryoSat-2 radar freeboard derived using the Threshold First-Maximum Retracker Algorithm with a 50% threshold (TFMRA50) is proposed. Snow depth was used as a predictor for the correction, similar to the traditional wave speed correction, but the coefficients were empirically determined by performing a direct comparison of the radar freeboard from CryoSat-2 and the ice freeboard from airborne observations. Consequently, this new empirical correction treats the physical effects and the retracker bias as a whole, which have been difficult to separate in the retrieval process. In this paper, we demonstrate that the retrieval accuracy of snow and ice variables and the consistency of the two independent retrieval methods are improved when the new correction is applied. The result of this study emphasizes the importance of compatibility between the retracker and the freeboard correction method.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to Find Accurate Terrain and Canopy Height GEDI Footprints in Temperate Forests and Grasslands?","authors":"Vítězslav Moudrý,&nbsp;Jiří Prošek,&nbsp;Suzanne Marselis,&nbsp;Jana Marešová,&nbsp;Eliška Šárovcová,&nbsp;Kateřina Gdulová,&nbsp;Giorgi Kozhoridze,&nbsp;Michele Torresani,&nbsp;Duccio Rocchini,&nbsp;Anette Eltner,&nbsp;Xiao Liu,&nbsp;Markéta Potůčková,&nbsp;Adéla Šedová,&nbsp;Pablo Crespo-Peremarch,&nbsp;Jesús Torralba,&nbsp;Luis A. Ruiz,&nbsp;Michela Perrone,&nbsp;Olga Špatenková,&nbsp;Jan Wild","doi":"10.1029/2024EA003709","DOIUrl":"https://doi.org/10.1029/2024EA003709","url":null,"abstract":"<p>Filtering approaches on Global Ecosystem Dynamics Investigation (GEDI) data differ considerably across existing studies and it is yet unclear which method is the most effective. We conducted an in-depth analysis of GEDI's vertical accuracy in mapping terrain and canopy heights across three study sites in temperate forests and grasslands in Spain, California, and New Zealand. We started with unfiltered data (2,081,108 footprints) and describe a workflow for data filtering using Level 2A parameters and for geolocation error mitigation. We found that retaining observations with at least one detected mode eliminates noise more effectively than sensitivity. The accuracy of terrain and canopy height observations depended considerably on the number of modes, beam sensitivity, landcover, and terrain slope. In dense forests, a minimum sensitivity of 0.9 was required, while in areas with sparse vegetation, sensitivity of 0.5 sufficed. Sensitivity greater than 0.9 resulted in an overestimation of canopy height in grasslands, especially on steep slopes, where high sensitivity led to the detection of multiple modes. We suggest excluding observations with more than five modes in grasslands. We found that the most effective strategy for filtering low-quality observations was to combine the quality flag and difference from TanDEM-X, striking an optimal balance between eliminating poor-quality data and preserving a maximum number of high-quality observations. Positional shifts improved the accuracy of GEDI terrain estimates but not of vegetation height estimates. Our findings guide users to an easy way of processing of GEDI footprints, enabling the use of the most accurate data and leading to more reliable applications.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003709","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TANAGER: Design and Validation of an Automated Spectrogoniometer for Bidirectional Reflectance Studies of Natural Rock Surfaces","authors":"Melissa Rice,&nbsp;Kristiana Lapo,&nbsp;Kathleen Hoza,&nbsp;Ed Cloutis,&nbsp;Mike Kraft,&nbsp;Sean Mulcahy,&nbsp;Dan Applin,&nbsp;Samantha Theuer","doi":"10.1029/2024EA003686","DOIUrl":"https://doi.org/10.1029/2024EA003686","url":null,"abstract":"<p>Laboratory measurements of reflectance spectra of rocks and minerals at multiple viewing geometries are important for interpreting spacecraft data of planetary surfaces. However, efficiently acquiring such measurements is challenging, as it requires a custom goniometer that can accommodate multiple, bulky samples beneath a movable light source and detector. Most spectrogoniometric laboratory work to date has focused on mineral mixtures and particulates, yet it is also critical to characterize natural rock surfaces to understand the influence of texture and alteration. We designed the Three-Axis N-sample Automated Goniometer for Evaluating Reflectance (TANAGER) specifically to rapidly acquire spectra of natural rock surfaces across the full scattering hemisphere. TANAGER has its light source and the spectrometer's fiber optic mounted on rotating and tilting arcs, with a rotating azimuth stage and six-position sample tray, all of which are fully motorized and integrated with a Malvern PanAnalytical ASD FieldSpec4 Hi-Res reflectance spectrometer. Using well-characterized color calibration targets, we have validated the accuracy and repeatability of TANAGER spectra. We also confirm that the system introduces no discernible noise or artifacts. All design schematics and control software for TANAGER are open-source and available for use and modification by the larger scientific community.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003686","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep-Learning Based Causal Inference: A Feasibility Study Based on Three Years of Tectonic-Climate Data From Moxa Geodynamic Observatory","authors":"Wasim Ahmad,&nbsp;Valentin Kasburg,&nbsp;Nina Kukowski,&nbsp;Maha Shadaydeh,&nbsp;Joachim Denzler","doi":"10.1029/2023EA003430","DOIUrl":"https://doi.org/10.1029/2023EA003430","url":null,"abstract":"<p>Highly sensitive laser strainmeters at Moxa Geodynamic Observatory (MGO) measure motions of the upper Earth's crust. Since the mountain overburden of the laser strainmeters installed in the gallery of the observatory is relatively low, the recorded time series are strongly influenced by local meteorological phenomena. To estimate the nonlinear effect of the meteorological variables on strain measurements in a non-stationary environment, advanced methods capable of learning the nonlinearity and discovering causal relationships in the non-stationary multivariate tectonic-climate time series are needed. Methods for causal inference generally perform well in identifying linear causal relationships but often struggle to retrieve complex nonlinear causal structures prevalent in real-world systems. This work presents a novel <i>model invariance-based causal discovery</i> (CDMI) method that utilizes deep networks to model nonlinearity in a multivariate time series system. We propose to use the theoretically well-established Knockoffs framework to generate in-distribution, uncorrelated copies of the original data as interventional variables and test the model invariance for causal discovery. To deal with the non-stationary behavior of the tectonic-climate time series recorded at the MGO, we propose a regime identification approach that we apply before causal analysis to generate segments of time series that possess locally consistent statistical properties. First, we evaluate our method on synthetically generated time series by comparing it to other causal analysis methods. We then investigate the hypothesized effect of meteorological variables on strain measurements. Our approach outperforms other causality methods and provides meaningful insights into tectonic-climate causal interactions.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023EA003430","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Observing System Simulation Experiments Exploring Potential Spaceborne Deployment Options for a Differential Absorption Radar Measuring Marine Surface Pressures","authors":"N. C. Privé,&nbsp;Matthew McLinden,&nbsp;Bing Lin,&nbsp;G. M. Heymsfield,&nbsp;Xia Cai,&nbsp;Steven Harrah","doi":"10.1029/2024EA003791","DOIUrl":"https://doi.org/10.1029/2024EA003791","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>A new technology for remote measurements of marine surface pressure has been proposed, employing a V-band differential absorption radar and a radiometric temperature sounder to calculate the total column atmospheric mass. Observing System Simulation Experiments (OSSEs) are performed to evaluate the potential impact of Spaceborne Marine Surface Pressure (SMSP) on Numerical Weather Prediction. These experiments build on prior efforts (Privé, McLinden, et al., 2023, https://doi.org/10.16993/tellusa.3254), but with an updated version of the OSSE framework and with more sophisticated simulation of the SMSP observations and a longer experiment period. Several different instrument configurations are compared, including both scanning and non-scanning orbits. SMSP impacts are calculated for analysis quality and forecast skill, and a forecast sensitivity observation impact tool is employed to place SMSP observations in context with the global observing network. The effects of rain contamination on observation quality are explored. Different magnitudes of simulated SMSP observation error are tested in the context of data assimilation to show the range of potential behaviors. Overall, SMSP observations are found to be most beneficial in the southern hemisphere extratropics, with statistically significant forecast improvements for the first 72 hr of the forecast. A constellation of four non-scanning SMSP satellites is found to outperform a single scanning instrument with a 250 km wide swath.</p>\u0000 </section>\u0000 </div>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Structure of Field-Aligned Current Systems as Inferred From the Multiscale Minimum Variance Analysis","authors":"Costel Bunescu","doi":"10.1029/2024EA003708","DOIUrl":"https://doi.org/10.1029/2024EA003708","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Auroral field-aligned currents (FACs) have an intrinsic complexity caused by the superposition of contributions from a broad spectrum of scales and diversity of locations. The complex FAC systems are investigated by using the multiscale minimum variance analysis. This technique provides a scale based decomposition of the FAC systems by identifying the constituting FAC elements as well as their structure. At the basis, the analysis exploits the scale dependence of the eigenvalues of the magnetic field variance matrix. The scale decomposition along the transversal (latitudinal) direction results from the scale derivative of the maximum eigenvalue. The complementary information from the scale derivative of the minimum eigenvalue helps to infer the full structure of each FAC element by providing estimates of the FAC length (longitudinal) scale. The scale derivative of minimum and maximum eigenvalues are used to identify FAC signatures associated to different types of aurora (e.g., highly extended, finite arcs, gradient regions) as well as to characterize the influence of the crossing location with respect to the FAC structures (e.g., near edge crossings). The multiscale analysis is applied to simulated FACs and to spacecraft observations made by the Swarm mission. The use with real world data illustrates the power of this analysis, whose full benefits for magnetosphere-ionosphere coupling investigations are yet to be explored.</p>\u0000 </section>\u0000 </div>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003708","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Weather Forecast Accuracy Through the Integration of WRF and BP Neural Networks: A Novel Approach","authors":"Zeyang Liu,&nbsp;Jing Zhang,&nbsp;Yadong Yang,&nbsp;Yaping Wang,&nbsp;Wangjun Luo,&nbsp;Xiancun Zhou","doi":"10.1029/2024EA003613","DOIUrl":"https://doi.org/10.1029/2024EA003613","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>In the past century, scholars from both domestic and international communities have delved into the study of numerical weather prediction models to promptly understand meteorological factors and mitigate the impacts of extreme weather events on humanity. Effective and precise prediction models enable the forecasting of meteorological conditions in the upcoming days, empowering individuals to implement proactive measures to minimize the adverse effects of extreme weather (Liang et al., 2021). The WRF (Weather Research and Forecasting) modeling system is commonly used for forecasting meteorological elements. However, uncertainties terribly hamper the correctness of the forecasting results. To this end, the present study was conducted to build a secondary model on the basis of the WRF forecast model. The WRF-BPNN model was proposed for verification after constructing the network, the temperature vertical profile and the mixing ratio vertical profile were predicted, and the results on the validation set were tested. The results showed that the WRF-BPNN model could effectively predict the temperature profile and mixing ratio profile, presenting better performance than the traditional WRF model.</p>\u0000 </section>\u0000 </div>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Worldwide Rocket Launch Emissions 2019: An Inventory for Use in Global Models","authors":"Tyler F. M. Brown,&nbsp;Michele T. Bannister,&nbsp;Laura E. Revell,&nbsp;Timofei Sukhodolov,&nbsp;Eugene Rozanov","doi":"10.1029/2024EA003668","DOIUrl":"https://doi.org/10.1029/2024EA003668","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>The rate of rocket launches is accelerating, driven by the rapid global development of the space industry. Rocket launches emit gases and particulates into the stratosphere, where they impact the ozone layer via radiative and chemical processes. We create a three-dimensional per-vehicle inventory of stratospheric emissions, accounting for flight profiles and all major fuel types in active use (solid, kerosene, cryogenic and hypergolic). In 2019, stratospheric (15–50 km) rocket launch emissions were 5.82 Gg <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>C</mi>\u0000 <mi>O</mi>\u0000 </mrow>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${mathrm{C}mathrm{O}}_{2}$</annotation>\u0000 </semantics></math>, 6.38 Gg <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${mathrm{H}}_{2}$</annotation>\u0000 </semantics></math>O, 0.28 Gg black carbon, 0.22 Gg nitrogen oxides, 0.50 Gg reactive chlorine and 0.91 Gg particulate alumina. The geographic locations of launch sites are preserved in the inventory, which covers all active launch sites in 2019. We also report the emissions data from contemporary vehicles that were not launched in 2019, so that users have freedom to construct their own launch activity scenarios. A subset of the inventory—stratospheric emissions for successful launches in 2019—is freely available and formatted for direct use in global chemistry-climate or Earth system models.</p>\u0000 </section>\u0000 </div>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near Real-Time Earthquake Monitoring in Texas Using the Highly Precise Deep Learning Phase Picker","authors":"Yangkang Chen,&nbsp;Alexandros Savvaidis,&nbsp;Daniel Siervo,&nbsp;Dino Huang,&nbsp;Omar M. Saad","doi":"10.1029/2024EA003890","DOIUrl":"https://doi.org/10.1029/2024EA003890","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Artificial intelligence (AI) seismology has witnessed enormous success in a variety of fields, especially in earthquake detection and <i>P</i> and <i>S</i>-wave arrival picking. It has become widely accepted that DL techniques greatly help routine seismic monitoring by enabling more accurate picking than traditional pickers like STA/LTA. However, a completely automatic AI-driven earthquake monitoring framework has not been reported due to the concerns of potential false positives using DL pickers. Here, we propose a novel AI-facilitated near real-time monitoring framework using a recently developed deep learning (DL) picker (EQCCT) that has been deployed in the Texas seismological network (TexNet). For the West Texas area, TexNet's seismic monitoring relies on the EQCCT picker to report earthquake events. For earthquakes with a magnitude above two, the picks are further validated by analysts to output the final TexNet catalog. Due to the fast-increasing seismicity caused by continuing oil&amp;gas production in West Texas, this AI-facilitated framework significantly relieves the workload of TexNet analysts. We show the mean absolute error (MAE) of automatic magnitude estimation for the magnitude-above-two earthquakes is smaller than 0.15 in West Texas and MAEs of hypocenter locations within 2.6 km in both distance and depth estimates. This research provides more evidence that DL pickers can play a fundamental role in daily earthquake monitoring.</p>\u0000 </section>\u0000 </div>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Land and Atmospheric Drivers of the 2023 Flood in India","authors":"Anuj Prakash Kushwaha,&nbsp;Hiren Solanki,&nbsp;Urmin Vegad,&nbsp;Shanti Shwarup Mahto,&nbsp;Vimal Mishra","doi":"10.1029/2024EA003750","DOIUrl":"https://doi.org/10.1029/2024EA003750","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Floods in India are recurring natural disasters resulting from extreme precipitation during the summer monsoon season (June–September). The recent flood in North India in July 2023 caused substantial damage to lives, agriculture, and infrastructure. However, what led to the 2023 North India flood and the role of atmospheric and land drivers still need to be examined. Using in situ observations, satellite data, and ERA5 reanalysis combined with hydrological and hydrodynamical modeling, we examine the role of land and atmospheric drivers in flood occurrence and its impacts. Extreme precipitation in a large region during 7–10 July 2023 created favorable conditions for the flood in the hilly terrains and plains of north India. More than 300 mm of precipitation fell in just 4 days, which was eight times higher than the long-term average (2001–2022). Anomalously high moisture transport over northern India was recorded on 7 July 2023, making atmospheric conditions favorable for intense landfall. Increased column water vapor and specific humidity at different pressure levels confirmed the continuous moisture presence before the extreme rainfall that caused floods in northern India from 7 to 12 July 2023. Atmospheric and land (high antecedent soil moisture) conditions contributed to a more than 200% rise in streamflow at several gauge stations. Satellite-based flood extent shows a considerable flood inundation that caused damage in the Sutlej and Yamuna River basins. Our findings highlight the crucial role of the favorable land and atmospheric conditions that caused floods and flash floods in north India in July 2023.</p>\u0000 </section>\u0000 </div>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}