<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Earth Science | Hydrosphere section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/earth-science/sections/hydrosphere</link>
        <description>RSS Feed for Hydrosphere section in the Frontiers in Earth Science journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-14T16:19:29.618+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2026.1819926</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2026.1819926</link>
        <title><![CDATA[Study on hydraulic properties in a pore-fracture coupled model for predicting water inrush from mine floors]]></title>
        <pubdate>2026-05-11T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Lihong Shi</author><author>Weitao Liu</author><author>Dianrui Mu</author><author>Hongtao Li</author><author>Zhenguo Mao</author><author>Xiao Zhang</author>
        <description><![CDATA[The mining-induced failure zone is one of the main water flow channels. Due to the complexity of fracture distribution and groundwater flow in the fractures, calculating the water conductivity of fractures in the mining failure zone is a hot and difficult research topic at present. In order to simultaneously simulate the permeability of the complete rock mass of the floor and the hydraulic conductivity of the fractures, a numerical simulation model of pore-fracture porous media was established to study the influence of different fracture structure parameters in the coal seam floor on the hydraulic conductivity of the floor. The spatial distribution of water pressure and water flow velocity under the conditions of different fracture structure parameters and permeability was studied. The simulation results show that the floor pressure distribution is consistent with the actual situation. The distribution of water pressure at the fracture location presents a downward trend. The pressure gradient in the fracture is smaller than that in the fracture’s surrounding porous rock mass. The model can not only simulate and calculate the resistance of complex pore-fracture media to fluid, but also simplify the calculation, providing a new method for the simulation and calculation of the hydraulic conductivity of the floor fracture zone.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2026.1855848</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2026.1855848</link>
        <title><![CDATA[Editorial: Advances in GIS and remote sensing applications in the monitoring of regional hydrology, ecology and environment]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Xin Pan</author><author>Ziyu Lv</author><author>Kevin Tansey</author><author>Lisheng Song</author><author>Qian Sun</author><author>Yingbao Yang</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2026.1828346</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2026.1828346</link>
        <title><![CDATA[Mapping of groundwater protection zones using expert-driven and machine learning methods: a case study of Yulin City, China]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chaoqi Yang</author><author>Shimin Chen</author><author>Kaisar Ahmat</author><author>Zhiqun Deng</author><author>Osman Ilniyaz</author>
        <description><![CDATA[Groundwater protection is critical for sustainable water resource management, particularly in arid regions. However, current zoning methods show challenges such as data bias of expert-driven models and limited interpretability of machine learning models. To address these issues, using 16 hydrological datasets from Yulin City in northwest China, two methodological frameworks were constructed: one combining the traditional Analytic Hierarchy Process (AHP) with Geographic Information System (GIS), and the other combining machine learning methods with Principal Component Analysis (PCA) and Self-Organizing Map (SOM). Rather than proposing a novel hybrid model, this study establishes a comparative framework that serves as a prescriptive decision protocol: AHP-GIS provides a transparent, defensible basis for regulatory implementation, while PCA-SOM with SHAP analysis offers interpretable insights into data-driven patterns. The zoning results of these methods show high spatial consistency (81.10%) with some differences (18.90%). Both methods effectively captured medium to key protection zones, particularly in areas characterized by high groundwater yield, good water quality, and ecological sensitivity. SHAP analysis further explained methodological divergences: pollution resistance and mining intensity were the primary drivers of key protection zone in PCA-SOM (12.89%), contrasting with the expert-assigned priority to functional zone and water quality in AHP-GIS (20.06%). This dual-framework approach overcomes the limitations of individual methods by using AHP-GIS to address the black-box nature of machine learning for policy applications, while using PCA-SOM to counteract the subjective bias inherent in expert weighting. Comparisons reveal fundamental trade-offs between transparency and objectivity, pattern sensitivity, regulatory consistency and adaptability to complex spatial relationships. By providing a decision protocol for method selection based on specific management contexts, our findings offer actionable guidance for overcoming the limitations of current approaches.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2026.1806978</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2026.1806978</link>
        <title><![CDATA[Editorial: Monitoring and modeling of runoff and soil processes in river basins]]></title>
        <pubdate>2026-03-18T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Shailesh Kumar Singh</author><author>Holger Rupp</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2026.1733824</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2026.1733824</link>
        <title><![CDATA[Effects of water and sediment variations on estuarine channel evolution: mechanisms and morphological discrimination]]></title>
        <pubdate>2026-02-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jing Su</author><author>Yanjie Sun</author><author>Xiaolong Song</author>
        <description><![CDATA[This study employed physical experiments to simulate the morphological evolution of a meandering tail channel, a critical river-sea interaction zone, under varying flow and sediment conditions, with a focus on the Yellow River. Results demonstrate distinct evolutionary patterns: during sediment-feeding phases, the non-estuarine reach experiences deposition under low flows, leading to bed aggradation, channel widening, and mid-channel bar development, suggesting a potential shift toward a wandering pattern, while scour dominates under high flows, forming narrow, deep cross-sections. In the estuarine reach, a nascent Lambda-shaped delta forms under low flows, whereas high-flow conditions promote erosion and a W-shaped cross-section. After sediment feeding ceases, the non-estuarine reach maintains a wide, shallow form under low flows but undergoes intense scour under high flows, whereas the estuarine reach develops a multi-distributary fan-shaped deposit under low flows, with high flows triggering channel migration or avulsion. Experiments confirm that sediment transport profoundly influences channel morphology regardless of bed state, and particle size distribution of sediments correlates strongly with the degree of channel evolution. Based on these findings, the resistance law expression was refined, and a channel pattern discrimination method suitable for the lower Yellow River was proposed and validated with measured data, confirming its rationality and reliability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2026.1721642</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2026.1721642</link>
        <title><![CDATA[Validation of analog sensor measurements in hydrometeorological participatory monitoring in various tropical countries]]></title>
        <pubdate>2026-02-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fabian Mitze</author><author>Suzanne Robin Jacobs</author><author>Lutz Breuer</author><author>Jazmin Campos Zeballos</author><author>Fabia Codalli</author><author>Frank Paul Shagega</author><author>Björn Weeser</author>
        <description><![CDATA[As remote tropical mountain regions often lack open data and traditional methods of collecting hydrometeorological data are not always feasible, this study validates an alternative participatory monitoring approach for collecting hydrometeorological data in mountainous regions in Ecuador, Honduras and Tanzania. Volunteers used analog low-cost sensors to measure air temperature, relative humidity, rainfall and water level. The measurements were validated with photos taken alongside the measurements. Data from selected stations were additionally validated against automatic sensor data using different metrics, such as the mean absolute error (MAE). In addition, errors made by frequent and non-frequent participants were compared, assessing the performance of these two target groups. In the period between May 2023 and May 2025 a total of 2,982 observations were received, whereby the majority were submitted by frequent participants (84.4%). A comparison between frequent and non-frequent users showed that the former measured with higher accuracy. The comparison with automatic sensor data showed a correlation for all parameters ranging from 0.42 to 0.96. The best results in terms of accuracy were achieved for air temperature (MAE: 0.74 °C–1.65 °C) and water level (MAE: 0.04–0.08 m). On the other hand, a high deviation was found for relative humidity (MAE: 16.76%–31.69%). This deviation was corrected by applying linear regression, resulting in moderate deviation (MAE: 5.45%–9.50%). Rainfall had a MAE ranging from 2.55 to 3.10 mm. This was mainly attributed to the low measurement frequency and the limited capacity of the rain gauges. Overall, the study showed ambivalent results, where analog thermometers and water level gauges can be considered the most promising alternatives to automatic sensor measurements. However, the hygrometers only provided moderate measurement quality, while the rain gauges used were too small to cover all rainfall in the periods analyzed.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1692790</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1692790</link>
        <title><![CDATA[Hydrochemical characteristics and exchange dynamics between surface water and groundwater in an arid river basin]]></title>
        <pubdate>2026-01-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yi Xiao</author><author>Cai Ren</author><author>Ji Zhang</author><author>Jianyu Huo</author><author>Yanfei Zhang</author><author>Wenjun Wang</author><author>Haojie Chen</author><author>Jiawen Yu</author><author>Aihua Long</author>
        <description><![CDATA[The Weigan River Basin, located in the arid region of northwest China, faces severe water scarcity. The complex interactions between surface water and groundwater pose a critical challenge for accurately assessing total water resources. To elucidate the exchange mechanisms and fluxes, this study employs a comprehensive analytical approach integrating hydrochemistry, stable isotopes (δ18O, δD), and Bayesian mixing models (MixSIAR). Hydrochemical analysis reveals a patterned spatial evolution of water chemistry characteristics within the basin. Mountainous surface waters predominantly exhibit a HCO3·SO4-Ca·Mg type, controlled by the weathering of carbonate and silicate rocks. Groundwater chemistry evolves along the flow path from an HCO3·Cl-Na·Ca type to an HCO3·SO4-Na·Ca type, revealing groundwater recharge from surface water rich in SO42-. In the plains, groundwater undergoes further evaporation and concentration, cation exchange adsorption, and human activities, eventually discharging into surface water and causing elevated Na+ levels in rivers. Based on these insights, MixSIAR model quantification reveals a clear and statistically significant spatiotemporal transformation pattern. In mountainous sections (Heizi River, Karasu River, Tairweichuk River, upper reaches of both the Muzhati River and Weigan River), surface water serves as the primary groundwater recharge source (dry period contribution: 59%–70%; wet period contribution: 54%–59%). Conversely, in the plain areas of the lower reaches of both the Muzhati and Weigan Rivers, groundwater replenishes surface water (dry period contribution: 53%–55%; wet period contribution: 56%–63%). Seasonally, surface water contribution during the dry period is on average 7.6% higher than during the wet period. In contrast, groundwater contribution in the plain region is on average 5.5% higher during the wet period than during the dry period. Through a research approach combining geochemical tracing and quantitative modeling, this study not only reveals the water cycle patterns in the Weigan River basin but also provides quantifiable scientific basis for precise simulation and management of water resources in arid inland river basins.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1740170</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1740170</link>
        <title><![CDATA[Spatiotemporal variability of hydrogen and oxygen stable isotopes in the Han River Basin and the regional hydrological implication]]></title>
        <pubdate>2026-01-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Na Wu</author><author>Ya-Ni Yan</author><author>Jun-Wen Zhang</author><author>Mei-Li He</author><author>Dong Zhang</author><author>Yu-Cong Fu</author><author>Gui-Shan Zhang</author><author>Zhi-Qi Zhao</author>
        <description><![CDATA[Characterization of the spatiotemporal variability of stable isotopes (δ18O and δD) in the surface water of the Han River Basin (HRB) is critical for tracing basin-scale hydrological cycle processes, identifying moisture-source dynamics, and optimizing water resource management. Through systematic sampling and analysis of hydrogen and oxygen isotopes in the mainstream, tributary, groundwater, and rainwater of the HRB, we investigated the spatial and seasonal variation in the isotopic composition of water bodies in this area. The Local Meteoric Water Line (LMWL): δD = 7.72δ18O + 11.55 indicates that the study area is influenced by atmospheric precipitation and exhibits evaporative fractionation. The seasonal variation is closely related to the circulation effect and evaporative fractionation. The summer water isotope values (δ18O: −8.2‰, δD: −52.5‰) were significantly higher than those in spring (δ18O: −8.7‰, δD: −58.0‰) and autumn (δ18O: −8.6‰, δD: −56.6‰). This pattern can be attributed to two main factors: first, the moisture derived from the Western Pacific during summer exhibits inherently heavier isotopic composition (δ18O: −3.75‰, δD: −18.5‰); second, intensified evaporative fractionation further enriches heavy isotopes in surface waters. Across the Han River Basin, the spatial pattern of δ18O values follows an “increase-decrease-increase” trend from the Hanzhong Basin to the Qin-Ba Mountains, then to the middle and lower reaches. This trend is primarily controlled by the shifting dominance of three factors: groundwater discharge, tributary inputs, and direct precipitation. This study, for the first time, reveals that the seasonal variations of stable isotopes in surface water of the HRB are driven by circulation effect, providing a new isotopic tracing basis for hydrological analysis of watersheds in monsoon regions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1705085</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1705085</link>
        <title><![CDATA[Theoretical analysis and application of the telluric electric field frequency selection method for shallow groundwater exploration]]></title>
        <pubdate>2025-12-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Danqi Wang</author><author>Tianchun Yang</author><author>AbdulGaniyu Isah</author><author>Qin Qin</author><author>Maoyue Zhu</author>
        <description><![CDATA[The telluric electric field frequency selection method (TEFSM) measures horizontal electric field components at discrete frequencies of naturally occurring electromagnetic (EM) fields. Developed as an extension of magnetotellurics (MT) and audio-frequency magnetotellurics (AMT), TEFSM offers potential for shallow groundwater exploration, yet its underlying mechanisms and practical effectiveness remain underexplored. Here, we combine theoretical analysis, forward modeling, and field validation to assess its performance. A conductive sphere model subjected to magnetotelluric and stray current fields was used to compute secondary surface responses, revealing low-potential anomalies directly above the target. The anomaly amplitude decreases with increasing burial depth and decreasing sphere radius. Field validation under the Rural Drinking Water Safety Project in Guangxi Province, China, involved 131 TEFSM-guided wells drilled to depths of up to 142.8 m. Of these, 114 yielded >1 m3/h, corresponding to an ∼87% success rate. The close agreement between simulations and field outcomes demonstrates that TEFSM reliably detects shallow conductive structures and is an effective tool for groundwater exploration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1672749</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1672749</link>
        <title><![CDATA[Assessing the impact of global change on flood dynamics and rice submergence susceptibility in the Kilombero floodplain, Tanzania]]></title>
        <pubdate>2025-12-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mark Tuschen</author><author>Stefanie Steinbach</author><author>Hnin Phyu Sin</author><author>Mariele Evers</author>
        <description><![CDATA[Climate and land use change are increasingly altering the water balance and flood dynamics of East African wetlands. In Tanzania’s Kilombero floodplain, rice cultivation relies on seasonal flooding, which is becoming more variable and intense due to climate and land use change. While floodwater is essential for rice cultivation, prolonged submergence poses a threat to yields and regional food security. However, it remains unclear how catchment-scale hydrological changes translate into floodplain-scale flood dynamics and submergence risks for rice crops. To address this, we developed a HEC-RAS 2D hydrodynamic model of the Kilombero floodplain, simulating future flood dynamics under climate change (RCP 4.5 and 8.5) and land use change scenarios. We assessed the susceptibility of rice crops to prolonged submergence by integrating flood model outputs with physiological traits of rice plants. Results show that high-emission scenarios (RCP 8.5) and extensive land conversion to rice cultivation in the floodplain significantly increase areas prone to prolonged rice crop submergence compared to baseline conditions and moderate-emission scenarios (RCP 4.5). Rice plant height was the dominant factor influencing submergence susceptibility. Our findings highlight the importance of integrating hydrodynamic modelling with crop characteristics to inform adaptive rice variety selection and agricultural planning in the context of global change.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1607597</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1607597</link>
        <title><![CDATA[Sediment origins in the Catamayo-Chira Transboundary Basin: impacts on Poechos Reservoir capacity under ENSO influence]]></title>
        <pubdate>2025-09-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Maria S. Dunin-Borkowski</author><author>Marina Farías de Reyes</author><author>Fausto W. Asencio</author><author>Jorge Demetrio Reyes-Salazar</author><author>Pablo Ochoa-Cueva</author>
        <description><![CDATA[The Poechos Reservoir, which began operations in 1976 with an initial water storage capacity of 885 hm3, has undergone severe sedimentation. By 2018, bathymetric surveys from the Chira–Piura Special Project (PEChP), the institution responsible for its operation and maintenance, reported an accumulated volume of 520 hm3, representing a 58.8% loss in storage. This situation raises concerns about long-term water security and sediment source dynamics. The present study aims to quantify the total mass and annual origin of sediment inflows to the reservoir. The study analyzed the Transboundary Catamayo–Chira Basin for the period 2001–2017, selected according to data availability: MODIS vegetation cover mosaics (available since February 2000), PISCOp precipitation datasets from SENAMHI (available until mid-2018), and annual reservoir bathymetries from PEChP (available until 2018). Sediment supply was estimated using the sediment delivery ratio (SDR) model implemented in the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST 3.13.0) software and validated against bathymetric measurements. Results show that the basin supplies an annual median of 6.91 × 106 t yr−1, a value consistent with 6.53 × 106 t yr−1, derived from bathymetric data for the same period. Eastern sub-basins dominated contributions, with Macará (2.34 × 106 t yr−1), Quiroz (1.98 × 10⁶ t yr⁻¹), and Catamayo (1.50 × 106 t yr−1) accounting for 84.3% of the load, while Alamor and La Solana contributed only 0.65 and 0.18 × 106 t yr−1, respectively. However, the 2017 El Niño–Southern Oscillation (ENSO) event altered this pattern: basin-wide supply surged to 34.92 × 106 t, with western sub-basins contributing more than half of the total, including a 57-fold increase from La Solana. These findings demonstrate that sediment supply is strongly controlled by climatic variability, with ENSO events shifting the spatial dominance of sediment sources. The predominance of eastern sub-basins under normal conditions contrasts with the episodic but extreme contributions from western sub-basins during El Niño. This highlights the need for adaptive management strategies that combine vegetation cover restoration with basin-wide monitoring, especially in semi-arid Andean systems where reservoir capacity is critical for water security.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1679849</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1679849</link>
        <title><![CDATA[Correction: Editorial: Contributions to river plastic monitoring across scales, volume II]]></title>
        <pubdate>2025-09-25T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Marcel Liedermann</author><author>Daniel González-Fernández</author><author>Freija Mendrik</author><author>Lauren Biermann</author><author>Tim H. M. van Emmerik</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1551218</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1551218</link>
        <title><![CDATA[Decomposing land surface total water storage in the Indus, Ganges, and Brahmaputra basins]]></title>
        <pubdate>2025-09-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>A. A. Prusevich</author><author>R. B. Lammers</author><author>D. S. Grogan</author><author>S. Zuidema</author><author>D. M. Meko</author><author>D. R. Rounce</author><author>R. Hock</author><author>I. Velicogna</author>
        <description><![CDATA[IntroductionThe goal of this study is to decompose the influence of specific hydrologic reservoirs in the Earth’s critical zone that interact to create observed total water supply (TWS) anomalies in the highly altered and densely populated Indus, Ganges, and Brahmaputra drainage basins. Understanding the contributions to TWS anomalies can help find potential solutions for the sustainability of human water supply.MethodsWe compare changes in the macroscale hydrology of three important High Mountain Asian drainage basins through seasonal and long-term trends in TWS. Statistical time-series analysis of nine individual TWS components modeled by a hydrologic model are used to simulate water storage terms.ResultsLong-term TWS trends look similar across the study basins, we find that the drivers and causes of trends and their seasonal variability are fundamentally different in each basin. TWS declines in the Indus and Ganges watersheds are primarily driven by the depletion of aquifers (67% and 76%, respectively) due to irrigated land expansion and water overuse. The Brahmaputra lower aquifer water use stress, and its TWS drop is mostly due to the melting of glaciers, the highest rate over all three basins. The Ganges and Brahmaputra have a quasi-monotonic decline of TWS, and the Indus basin exhibits a non-monotonic trend line of TWS due to different stages of its aquifer depletion relevant to aquifer water accessibility limited by well depth thresholds. Seasonal variability is primarily controlled by soil moisture saturation, shallow groundwater levels, reservoir storage, and snow accumulation for the Ganges and Brahmaputra basins. The Indus is driven by high mountain storage of snow and glaciers.DiscussionThe combination of hydrologic modeling and gravity observations show the effectiveness of identifying the critical components that make up TWS. Understanding the spatially heterogeneous drivers of observed TWS decline allows us to translate satellite observations into policy-relevant information. Because this functionality is built within a process-based hydrological model, future projections can illuminate those aspects of the hydrological cycle that require additional attention by decision makers to ensure adequate water resources are available for all.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1617125</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1617125</link>
        <title><![CDATA[Seasonal and diurnal groundwater fluctuations linked to environmental and drought variability for the Kermit dune field, Chihuahuan Desert, West Texas, United States]]></title>
        <pubdate>2025-08-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alix Fournier</author><author>Caleb Fox</author><author>Steven L. Forman</author>
        <description><![CDATA[Water availability is limited in the northern Chihuahua Desert due to increasing aridity and anthropogenic disturbance. This study investigates the groundwater fluctuations in the shallow water table of the Kermit dune field, West Texas, United States, to assess the aquifer’s response to climate variability and human impact. The Kermit dune field’s aquifer may contribute up to 9% of the regional Pecos Valley Aquifer’s annual recharge. Groundwater levels were monitored in three piezometers between 2021 and 2024 in both shallow central dune areas and at the downflow transition to sand sheet deposits. Statistical analyses, using linear regression, ANOVA, and mixed effects models, revealed that central groundwater (1–3 m deep) responds to precipitation with peak rise at a 3-day lag post-rainfall, while no recharge signal was detected for deeper groundwater (5–7 m deep), likely due to the thicker vadose zone and denser vegetation cover of high water-use plants, up to 80 L/day. Both areas seemed influenced by daylight duration and exhibited consistent daily cycles (5–8 mm), suggestive of evapotranspiration influence. Over the monitoring period, groundwater levels declined by ∼1.2 m on average, likely exacerbated by the formation of a dredge pond associated with mining operations. Additionally, consistently elevated electric conductivity (EC) measured post oil or produced water spill in 2022 indicated potential long-term groundwater contamination. These results highlighted the vulnerability of the shallow unconfined Kermit dunal aquifer to climate changes and anthropogenic disturbance, with implications for regional water sustainability and land surface stability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1609778</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1609778</link>
        <title><![CDATA[Exploring the use of new data assimilation technologies to map groundwater quality vulnerability in a large alluvial aquifer]]></title>
        <pubdate>2025-07-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wes Kitlasten</author><author>Catherine Moore</author><author>John Doherty</author>
        <description><![CDATA[Integrity of simulator-based Bayesian analysis requires adequate representation of prior parameter probabilities, and quantification and reduction of posterior predictive uncertainties through history-matching. In many groundwater management contexts, hydrogeological complexity and long numerical model run times can render both of these tasks difficult. We present three new technologies that can make simulator-based Bayesian analysis that is undertaken in complex hydrogeological environments more effective and more tractable. These are demonstrated using a case study where groundwater head, streamflow and groundwater age data are assimilated in order to assess groundwater vulnerability to anthropomorphic deterioration of its quality. Bayesian analysis begins by generating ensembles of realizations of hydraulic property and other parameters used by a multi-layer groundwater model. The first technology supports this first step, by ensuring that respect for complex hydrogeology is embodied in nonstationary representations of hydraulic properties, as well as in stochasticity of so-called “hyperparameters” which govern their spatially variable geostatistics. The second and third technologies support data assimilation in two different ways, both of which are numerically cheap. One of these options, Ensemble Space Inversion (ENSI) requires adjustment of parameter fields in order for model outputs to match field measurements. The other option, Data Space Inversion (DSI) avoids parameter field adjustment through construction of direct statistical linkages between model-generated counterparts to field measurements and groundwater predictions of management interest. This statistical model is then history-matched in lieu of the numerical model. Deployment of both of these strategies at our case study site yields similar results. They reveal the likely existence of young water at depth over large parts of a regional aquifer system. This has repercussions for the quality of extracted water, and for land management in recharge areas.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1601615</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1601615</link>
        <title><![CDATA[Future variation and uncertainty source decomposition in deep learning bias-corrected CMIP6 global extreme precipitation historical simulation]]></title>
        <pubdate>2025-07-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaohua Xiang</author><author>Yongxuan Li</author><author>Xiaoling Wu</author><author>Zhu Liu</author><author>Lei Wu</author><author>Biqiong Wu</author><author>Chuanxin Jin</author><author>Zhiqiang Zeng</author>
        <description><![CDATA[Global circulation models (GCMs) serve as pivotal tools in climate science research. Despite their critical role in understanding and predicting climate change, GCMs often exhibit significant discrepancies with observational data due to systematic and random errors, which has driven the progress of bias correction (BC) techniques. This study explores a bias correction approach based on convolutional neural networks (CNNs) to improve the accuracy of Expert Team on Climate Change Detection and Indices (ETCCDI) extreme precipitation indices calculated from the Coupled Model Intercomparison Project Phase Six (CMIP6) daily predictions. Specifically, this research employs historical period data (1950–2014) for eight ETCCDI extreme precipitation indices from 10 GCMs to train eight individual CNN-based bias correction models, using the HadEX3 reference dataset for evaluation. All corrected data showing mean absolute percentage error (MAPE) were consistently reduced to below 0.1. Subsequently, these well-trained models are further utilized to predict ETCCDI extreme precipitation for the future under four Shared Socioeconomic Pathway (SSP) scenarios, and the projections of extreme precipitation changes are investigated across global continents. In addition, this study endeavors to separate and quantify three different components of uncertainty (model uncertainty, scenario uncertainty, and internal variability) associated with ETCCDI extreme precipitation indices and evaluate the impact of bias correction on uncertainty variation. The results indicate that CNNs are effective in correcting historical precipitation extremes. In the future period, extreme precipitation shows an increasing trend in general. The degree of change in R10mm is relatively small and reaches its peak in the medium term, whereas the variation in Rx1day is more pronounced and increases over time. Further analysis reveals that model uncertainty is the predominant source of uncertainty in ETCCDI extreme precipitation indices, accounting for more than 80% of total uncertainty. Implementation of CNNs as a BC method could significantly reduce model uncertainty but at the cost of increasing the proportion of scenario uncertainty and internal variability. This research not only highlights the potential of the CNN-based deep learning technique in enhancing the accuracy and reliability of extreme precipitation predictions but also provides insights into uncertainty decomposition and variation to better understand various sources of uncertainty within climate projections.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1636075</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1636075</link>
        <title><![CDATA[Editorial: Contributions to river plastic monitoring across scales, volume II]]></title>
        <pubdate>2025-06-30T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Marcel Liedermann</author><author>Daniel González-Fernández</author><author>Freija Mendrik</author><author>Lauren Biermann</author><author>Tim H. M. van Emmerik</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1543497</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1543497</link>
        <title><![CDATA[Causes of watershed drought analyzed using explainable deep learning: a case study of the Fenhe River Basin]]></title>
        <pubdate>2025-06-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zixuan Chen</author><author>Xikun Wei</author><author>Guojie Wang</author><author>Yifan Hu</author><author>Haonan Liu</author><author>Jinman Zhang</author><author>Shuang Zhou</author><author>Zengbao Zhao</author><author>Yushan Liu</author>
        <description><![CDATA[This study predicted daily-scale drought for the Fenhe River (FHR) Basin and applied the explainable artificial intelligence (XAI) method to the model’s prediction results. Daily-scale drought prediction can provide more timely and detailed drought information, while deep learning interpretable methods can help understand the impact of different predictors on droughts and improve the credibility of the model. The standardized antecedent precipitation evapotranspiration index (SAPEI) was selected as an index for evaluating drought conditions. Five classical deep learning prediction models, namely, long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional long short-term memory (biLSTM) networks, transformer (TFR), and informer (IFR), were applied in the experiment, and the performance of each model was comprehensively evaluated. The results of the test set show that all models make effective predictions of droughts in the FHR Basin, with a Pearson correlation coefficient (R) higher than 0.75. BiLSTM performs better in short-term prediction, while TFR and IFR are better at long-term prediction. The results of the deep learning interpretable model show that, aside from the strong influence of the SAPEI itself in the prediction process, the mean temperature (TM) has the greatest influence among the auxiliary predictors, followed by precipitation (PRE) and relative humidity (RHU), with potential evapotranspiration (PET) being the weakest. Our work emphasizes the importance of timely warnings of drought and the role of XAI in the development of artificial intelligence.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1612208</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1612208</link>
        <title><![CDATA[Groundwater recharge in a steep mountain slope and its implications for slope stability: Åknes rockslide (Norway)]]></title>
        <pubdate>2025-06-18T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Clara Sena</author><author>Ioannis Papadimitrakis</author><author>Alvar Braathen</author><author>Andreas Aspaas</author><author>Gustav Pless</author><author>Anniina Kittilä</author><author>Carlos Miraldo Ordens</author>
        <description><![CDATA[The Åknes rockslide lies in a steep mountain slope, dipping on average 30 to 35° towards Sunnylv Fjord, Western Norway. As part of the early-warning system implemented for this rockslide, hydraulic heads have been continuously monitored since 2007. Four multi-level boreholes established in 2017–2018 provided an unprecedent dataset to better understand groundwater recharge in such geological setting. Hydraulic-head timeseries reveal high and opposing trends of up to 3.5 and −6.3 m/year, which could be related to the continuous alteration of the geometry and permeability of the water-carrying fracture network, due to rockmass creeping and widening of tension fractures. Deeper than 80 m below ground, hydraulic heads change from underpressured conditions in Spring to overpressured conditions in Autumn. The seasonal peak in hydraulic heads, coinciding with overpressured conditions, is a major concern in an eventual acceleration of the rockslide. Water infiltration is favoured in vertical fracture zones and local topographic depressions, such as the backscarp, while the connectivity of the sub-vertical fractures allows infiltrating water to reach the water table at 33–78 m depth, contributing to groundwater recharge. Water is available for infiltration in periods with near frost- and snow-free ground (May to November), with considerably higher amounts of water from May to June due to higher snowmelt rates. These results provide a better understanding of the hydrological regime and recharge processes in a steep mountain slope and their implications for the management of unstable slopes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/feart.2025.1595943</guid>
        <link>https://www.frontiersin.org/articles/10.3389/feart.2025.1595943</link>
        <title><![CDATA[Baltic hydro-climatic data: a regional data synthesis for the baltic sea drainage basin]]></title>
        <pubdate>2025-06-10T00:00:00Z</pubdate>
        <category>Data Report</category>
        <author>Mohanna Zarei</author><author>Georgia Destouni</author>
        <description></description>
      </item>
      </channel>
    </rss>