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    <title>Hydrology and Climate Change Article Summaries</title>
    <link>https://biblio.quintanasegui.com</link>
    <description>Latest scientific summaries</description>
    <lastBuildDate>Mon, 09 Mar 2026 06:29:48 +0000</lastBuildDate>
    
            <item>
                <title>Bahuguna et al. (2026) Integrating multidisciplinary approach to decipher the peculier incidence of burst of new water source and perpetual problem of ground subsidence in Joshimath, Uttarakhand</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1007_s11069-025-07737-8.html</link>
                <description><![CDATA[This study integrates a multidisciplinary approach to investigate the causes of a sudden water outburst and persistent ground subsidence in Joshimath, Uttarakhand. It identifies distinct failure mechanisms linked to heterogeneous paleo-slide debris and subsurface water saturation, proposing a comprehensive, site-specific slope management plan for the affected areas.]]></description>
                <pubDate>Mon, 09 Mar 2026 05:29:36 +0000</pubDate>
                <guid>10.1007_s11069-025-07737-8</guid>
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                <title>Wang et al. (2026) Subseasonal Ensemble Prediction of the 2024 Abrupt Drought-to-Flood Transition in Henan Province, China</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.3390_w18050635.html</link>
                <description><![CDATA[This study developed a three-dimensional method using soil moisture percentiles to identify and evaluate the spatiotemporal evolution of an abrupt drought-to-flood transition (ADFT) event in Henan Province, China, in 2024, using ECMWF S2S reforecasts. It found that while the ECMWF model captured the transition at a 1-week lead, its skill significantly decreased at a 2-week lead due to model errors and atmospheric circulation biases.]]></description>
                <pubDate>Mon, 09 Mar 2026 05:28:37 +0000</pubDate>
                <guid>10.3390_w18050635</guid>
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                <title>Devi et al. (2026) Assessment of Crop Water Requirements and Irrigation Scheduling under Climate Change Scenarios using the FAO-CROPWAT Model: A Review</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.9734_jsrr_2026_v32i34047.html</link>
                <description><![CDATA[This review synthesizes 22 studies applying the FAO–CROPWAT model to assess agricultural water demand and irrigation planning, confirming its reliability as a decision-support tool for climate-resilient water management amidst regional variability and projected increases in future irrigation requirements due to climate change.]]></description>
                <pubDate>Mon, 09 Mar 2026 05:28:17 +0000</pubDate>
                <guid>10.9734_jsrr_2026_v32i34047</guid>
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                <title>Ma et al. (2026) Response of sediment delivery ratio to water-sediment and riverbed boundary conditions during flood events in the lower yellow river since 2000</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1038_s41598-026-42616-7.html</link>
                <description><![CDATA[This study investigates the response of the sediment delivery ratio to water-sediment and riverbed boundary conditions in the Lower Yellow River since 2000, developing a theoretical equation that incorporates riverbed characteristics for improved accuracy in predicting sediment transport capacity during flood events. The findings highlight the crucial role of riverbed boundaries and offer practical recommendations for river management.]]></description>
                <pubDate>Mon, 09 Mar 2026 05:27:43 +0000</pubDate>
                <guid>10.1038_s41598-026-42616-7</guid>
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                <title>Li et al. (2026) Deriving phase-contingent dynamic drought-limited water levels: An adaptive framework for managing megadrought evolution</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1016_j.ejrh.2026.103309.html</link>
                <description><![CDATA[This study develops an adaptive framework to derive dynamic, phase-contingent Drought-Limited Water Levels (DLWLs) for managing megadroughts in reservoirs, addressing the limitations of static thresholds. It demonstrates that a supervised Random Forest model, anchored in physically constrained hydrological benchmarks, reliably classifies drought severity across four evolutionary phases, enabling improved, resilient reservoir operation.]]></description>
                <pubDate>Mon, 09 Mar 2026 05:27:13 +0000</pubDate>
                <guid>10.1016_j.ejrh.2026.103309</guid>
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                <title>Xu et al. (2026) Time-lag and cumulative drought effects decouple vegetation sensitivity from damage risk in the upper Yangtze River basin</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1016_j.ecolind.2026.114739.html</link>
                <description><![CDATA[This study analyzed vegetation response to drought in the upper Yangtze River basin (1990-2022) using NDVI and multi-scale SPEI, developing a composite drought sensitivity index and quantifying loss risk with a Copula-Bayes framework, revealing that drought sensitivity does not always align with actual vegetation loss probability.]]></description>
                <pubDate>Mon, 09 Mar 2026 05:26:52 +0000</pubDate>
                <guid>10.1016_j.ecolind.2026.114739</guid>
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                <title>Batungwanayo et al. (2026) Compound drought stressors drive vegetation decline in the African Great Lakes region: a multiscale causal analysis</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1088_2752-5295_ae4cc1.html</link>
                <description><![CDATA[This study identifies the direct drivers of compound drought impacts on vegetation productivity in the African Great Lakes Region (AGLR) over 25 years, revealing that combined vapor pressure deficit (VPD) and soil moisture (SM) deficits are the primary causal factors, leading to a 15% greater greenness decline in croplands and shrublands compared to forests.]]></description>
                <pubDate>Thu, 05 Mar 2026 05:30:18 +0000</pubDate>
                <guid>10.1088_2752-5295_ae4cc1</guid>
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                <title>Merlo et al. (2026) Tracking shifts in European drought hotspots</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1088_1748-9326_ae4ca7.html</link>
                <description><![CDATA[This study develops novel impact-based Combined Drought Indices (iCDIs) using a machine learning framework to directly link hydroclimatic drivers to remotely sensed vegetation stress across Europe. The iCDIs outperform traditional indices and project a significant northward shift in future drought impacts, identifying Central Europe as an emerging hotspot, contrary to conventional views.]]></description>
                <pubDate>Thu, 05 Mar 2026 05:29:48 +0000</pubDate>
                <guid>10.1088_1748-9326_ae4ca7</guid>
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            <item>
                <title>Sun et al. (2026) Land use and land cover change intensified soil moisture drought: evidence from CMIP6-LUMIP</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1088_1748-9326_ae4ca5.html</link>
                <description><![CDATA[This study quantifies the long-term impacts of historical land use and land cover change (LULCC) on global soil moisture drought (SMD) characteristics from 1901-2014, finding that LULCC significantly intensifies SMD over more than half of the global land area by altering biophysical processes that deplete soil water storage.]]></description>
                <pubDate>Thu, 05 Mar 2026 05:29:24 +0000</pubDate>
                <guid>10.1088_1748-9326_ae4ca5</guid>
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                <title>Kwon et al. (2026) Synergistic impact of simultaneously assimilating radar- and radiometer-based soil moisture retrievals on the performance of numerical weather prediction systems</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.5194_hess-30-1261-2026.html</link>
                <description><![CDATA[This study evaluates the synergistic impact of simultaneously assimilating radar-based (ASCAT) and radiometer-based (SMAP) soil moisture retrievals into the Korean Integrated Model (KIM) using a weakly coupled data assimilation system. The findings demonstrate that multi-sensor soil moisture assimilation leads to more balanced and improved analyses and forecasts of specific humidity, air temperature, and precipitation compared to single-sensor assimilation.]]></description>
                <pubDate>Thu, 05 Mar 2026 05:28:45 +0000</pubDate>
                <guid>10.5194_hess-30-1261-2026</guid>
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                <title>Zhang et al. (2026) Long-Term Evolution of Permafrost across the Qinghai-Tibet Plateau: Perspectives from Multi-Model Ensembles and Machine Learning</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1175_jcli-d-25-0473.1.html</link>
                <description><![CDATA[This study combined CMIP6 data with machine learning models to project permafrost extent and maximum seasonal soil freeze depth (SFD) across the Qinghai-Tibet Plateau (QTP) from 2025 to 2100 under various SSP scenarios. Results indicate continuous permafrost degradation into seasonally frozen ground, with SFD declining significantly, and specific high-risk zones identified, with the Deep Neural Network (DNN) model demonstrating superior performance.]]></description>
                <pubDate>Thu, 05 Mar 2026 05:28:27 +0000</pubDate>
                <guid>10.1175_jcli-d-25-0473.1</guid>
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                <title>Ravazzolo et al. (2026) Towards integrated short-term Rain-on-Grid modeling and long-term RUSLE estimates for improved erosion susceptibility assessment in the Oltrepò Pavese hills of Northern Italy</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1016_j.ejrh.2026.103290.html</link>
                <description><![CDATA[This study evaluates the complementary use of the empirical Revised Universal Soil Loss Equation (RUSLE) and a two-dimensional Rain-on-Grid (RoG) hydrodynamic model for erosion susceptibility assessment in Northern Italy. The models showed over 50% spatial overlap in identifying erosion-prone areas, with RoG better reproducing event-based erosion zones and RUSLE capturing land-cover effects, offering a practical integrated framework for data-scarce catchments.]]></description>
                <pubDate>Thu, 05 Mar 2026 05:27:51 +0000</pubDate>
                <guid>10.1016_j.ejrh.2026.103290</guid>
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            <item>
                <title>Bi et al. (2026) A 0.1° monthly potential evapotranspiration dataset based on the optimal models over global vegetation zones</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1038_s41597-026-06956-3.html</link>
                <description><![CDATA[This study developed a global 0.1° monthly potential evapotranspiration (PET) dataset for 1992–2022 by calibrating and selecting optimal PET models (Priestley-Taylor and Milly-Dunne) using observations from 124 eddy covariance sites, aiming to reduce uncertainties in existing PET products.]]></description>
                <pubDate>Thu, 05 Mar 2026 05:27:18 +0000</pubDate>
                <guid>10.1038_s41597-026-06956-3</guid>
            </item>
            
            <item>
                <title>Eliades et al. (2026) Forests in a semi-arid climate die with a memory: satellite signals predict forest mortality years after drought</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1007_s11676-026-02016-z.html</link>
                <description><![CDATA[This study investigates the relationship between satellite-derived vegetation indicators and meteorological drought indices to understand tree mortality mechanisms in semi-arid Cypriot forests, revealing that severe drought conditions trigger mortality and that vegetation response is linked to multi-year climate memory effects, with indicator effectiveness varying by species and post-mortality stage.]]></description>
                <pubDate>Thu, 05 Mar 2026 05:26:54 +0000</pubDate>
                <guid>10.1007_s11676-026-02016-z</guid>
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            <item>
                <title>Phommavong et al. (2026) Integrated Approach to Modelling and Forecasting Water Balance under Climate Change in Southern Laos Using Hydrological Model</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1142_s2345748126500028.html</link>
                <description><![CDATA[This study investigates the water balance dynamics in southern Laos from 2018 to 2022, revealing a high infiltration capacity (63.5%) due to sandy loam soils, significant runoff (30%) during monsoons, and low evapotranspiration (6.4%), providing crucial insights for regional water management.]]></description>
                <pubDate>Tue, 03 Mar 2026 15:44:34 +0000</pubDate>
                <guid>10.1142_s2345748126500028</guid>
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            <item>
                <title>Chuphal et al. (2026) Development of Gridded Root-Zone Soil Moisture Product for India, 1981–2024</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1038_s41597-026-06940-x.html</link>
                <description><![CDATA[This study developed a high-resolution (0.05°), long-term (1981–2024) daily root-zone soil moisture dataset for India using a hybrid modeling and machine learning approach, providing a crucial resource for drought monitoring and agricultural planning.]]></description>
                <pubDate>Tue, 03 Mar 2026 15:44:19 +0000</pubDate>
                <guid>10.1038_s41597-026-06940-x</guid>
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                <title>Small et al. (2026) The 1957–1976 Summertime Drought Gap in the Southeastern United States</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1353_sgo.2026.a984028.html</link>
                <description><![CDATA[This paper examined drought frequency in the Southeastern United States from 1931–2024, identifying an exceptional 20-year drought-free period (1957–1976) concurrent with below-average temperatures and a negative Atlantic Multidecadal Oscillation phase, suggesting a rare climatological event unlikely to reoccur.]]></description>
                <pubDate>Tue, 03 Mar 2026 15:43:49 +0000</pubDate>
                <guid>10.1353_sgo.2026.a984028</guid>
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            <item>
                <title>Shahnazi et al. (2026) A novel implementation of a decomposition-enhanced hybrid GWO–KELM model with LUBE for constructing prediction intervals of groundwater drought</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1007_s12145-026-02093-y.html</link>
                <description><![CDATA[This study developed a novel decomposition-enhanced hybrid Grey Wolf Optimizer (GWO)–Kernel Extreme Learning Machine (KELM) model with Lower–Upper Bound Estimation (LUBE) for multi-horizon point and interval forecasting of groundwater drought (Standardized Groundwater Index, SGI). The Variational Mode Decomposition (VMD)–GWO–KELM model consistently outperformed other approaches, especially for short-term forecasts, providing reliable and sharp prediction intervals.]]></description>
                <pubDate>Tue, 03 Mar 2026 15:43:36 +0000</pubDate>
                <guid>10.1007_s12145-026-02093-y</guid>
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            <item>
                <title>Zhang et al. (2026) Multi-source precipitation fusion for hydrological models: Correction and metrics importance analysis</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1016_j.ejrh.2026.103291.html</link>
                <description><![CDATA[This study developed a lightweight Absolute Distance Inverse Weighting (ADIW) framework to merge eight precipitation datasets, evaluating the merged product's performance and bias-corrected versions through hydrological simulations using HYPE and VIC models in the Ganjiang River Basin. The ADIW+Linear Regression (LR) approach demonstrated optimal hydrological performance, with Relative Bias (RB) and Mean Absolute Error (MAE) identified as key metrics controlling hydrological reliability.]]></description>
                <pubDate>Tue, 03 Mar 2026 15:43:07 +0000</pubDate>
                <guid>10.1016_j.ejrh.2026.103291</guid>
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            <item>
                <title>Bani et al. (2026) Application of artificial intelligence-based modelling to investigate spring streamflow predictability under ENSO and IOD forcing</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1007_s40808-026-02743-6.html</link>
                <description><![CDATA[This study developed Artificial Neural Network (ANN) models, driven by lagged El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) indices, to forecast spring streamflow in Victoria, Australia. The ANN models consistently and substantially outperformed traditional Multiple Linear Regression (MLR) across diverse catchments, demonstrating enhanced predictive accuracy and better representation of nonlinear climate-streamflow interactions.]]></description>
                <pubDate>Tue, 03 Mar 2026 15:42:24 +0000</pubDate>
                <guid>10.1007_s40808-026-02743-6</guid>
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