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    <title>Hydrology and Climate Change Article Summaries</title>
    <link>https://biblio.quintanasegui.com</link>
    <description>Latest scientific summaries</description>
    <lastBuildDate>Tue, 21 Jul 2026 08:08:51 +0000</lastBuildDate>
    
            <item>
                <title>Jin et al. (2026) Improving FY-4B Satellite Precipitation Retrieval over Coastal Complex Terrain of Eastern China: Deep Learning Approaches with Multi-Source Underlying Surface Data</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.3390_rs18142397.html</link>
                <description><![CDATA[This study develops a deep learning framework to improve precipitation retrieval from FY-4B satellite data by integrating underlying-surface information. The findings demonstrate that incorporating topographic and land-cover data enhances precipitation detection, with the degree of improvement depending on the specific neural network architecture used.]]></description>
                <pubDate>Tue, 21 Jul 2026 05:50:50 +0000</pubDate>
                <guid>10.3390_rs18142397</guid>
            </item>
            
            <item>
                <title>Lu et al. (2026) A Thermal Infrared Remote Sensing Model for Diagnosing Winter Wheat Water (Triticum aestivum L.) Stress by Integrating Angular Effects and Kernel-Driven Models</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.3390_plants15142201.html</link>
                <description><![CDATA[This study investigates the directional effects of canopy temperature in winter wheat using UAV thermal imagery and employs a kernel-driven model to retrieve isotropic temperature for more accurate Crop Water Stress Index (CWSI) estimation.]]></description>
                <pubDate>Tue, 21 Jul 2026 06:02:21 +0000</pubDate>
                <guid>10.3390_plants15142201</guid>
            </item>
            
            <item>
                <title>Liu et al. (2026) SPEI-Based Drought Frequency, Intensity, and Duration from CMIP6 Models Under SSP1-2.6 and SSP5-8.5 Across Tropical–Subtropical Monsoon Asia</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.3390_w18141745.html</link>
                <description><![CDATA[This study evaluates future drought risk in tropical-subtropical monsoon Asia using CMIP6 models, concluding that while low-emission scenarios (SSP1-2.6) can stabilize moisture regimes, high-emission scenarios (SSP5-8.5) will lead to pervasive intensification and expansion of drought.]]></description>
                <pubDate>Tue, 21 Jul 2026 05:37:41 +0000</pubDate>
                <guid>10.3390_w18141745</guid>
            </item>
            
            <item>
                <title>Mohamed et al. (2026) Climate Change and Climatic Water Balance in Brandenburg (Germany): Seasonal Drying and Hydro-Climatic Stress Under Multi-Model Climate Projections to 2100</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.3390_w18141737.html</link>
                <description><![CDATA[This study evaluates projected changes in climatic water balance and hydro-climatic stress in Brandenburg, Germany, concluding that rising temperatures and evaporative demand will lead to a robust increase in growing-season drying by 2100.]]></description>
                <pubDate>Tue, 21 Jul 2026 05:23:09 +0000</pubDate>
                <guid>10.3390_w18141737</guid>
            </item>
            
            <item>
                <title>Zhang et al. (2026) Unraveling the Responses of Gross Primary Productivity to Multiple Drought Types Across China Using Multi-Source Remote Sensing Data</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.3390_agronomy16141361.html</link>
                <description><![CDATA[This study evaluates the impact of four different drought indices on Gross Primary Productivity (GPP) across China from 2003 to 2020, identifying the Temperature Condition Index (TCI) as the primary driver of GPP variations.]]></description>
                <pubDate>Tue, 21 Jul 2026 04:56:11 +0000</pubDate>
                <guid>10.3390_agronomy16141361</guid>
            </item>
            
            <item>
                <title>Fattahi et al. (2026) Deep Learning LSTM-Based Model for Predicting SPI and SPEI Drought Indices</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1061_nhrefo.nheng-2561.html</link>
                <description><![CDATA[The study develops a deep learning LSTM-based model to predict the Standard Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI) in two Iranian watersheds, demonstrating superior accuracy and dynamic property preservation compared to traditional time-series models.]]></description>
                <pubDate>Wed, 15 Jul 2026 04:58:26 +0000</pubDate>
                <guid>10.1061_nhrefo.nheng-2561</guid>
            </item>
            
            <item>
                <title>Kushwaha et al. (2026) Human‐Induced Evapotranspiration in Indian Subcontinental River Basins</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1029_2025jd046274.html</link>
                <description><![CDATA[The study quantifies human-induced evapotranspiration (H-ET) across 12 major Indian river basins from 2003 to 2020, revealing that neglecting anthropogenic water use leads to significant overestimations of available water resources.]]></description>
                <pubDate>Thu, 18 Jun 2026 08:00:09 +0000</pubDate>
                <guid>10.1029_2025jd046274</guid>
            </item>
            
            <item>
                <title>Huang et al. (2026) Satellite soil moisture as an additional observational constraint for machine learning-based irrigation water use modeling</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1088_1748-9326_ae7e0c.html</link>
                <description><![CDATA[This study demonstrates that a cell-wise machine learning framework combined with satellite soil moisture data significantly improves the estimation of high-resolution (9 km) monthly irrigation water use across the conterminous United States compared to conventional pooled learning methods.]]></description>
                <pubDate>Thu, 18 Jun 2026 07:49:11 +0000</pubDate>
                <guid>10.1088_1748-9326_ae7e0c</guid>
            </item>
            
            <item>
                <title>Filippucci et al. (2026) Tracking Summer Greenland Blocking: The Upstream Pathway Shapes Historical Extremes and Future Change</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1002_joc.70472.html</link>
                <description><![CDATA[This study employs a novel Lagrangian tracking tool, `blocktrack`, to analyze summer Greenland atmospheric blocking (GB) in ERA5 reanalysis and CMIP6 models, identifying distinct types of blocking events and their future projections.]]></description>
                <pubDate>Thu, 18 Jun 2026 07:29:44 +0000</pubDate>
                <guid>10.1002_joc.70472</guid>
            </item>
            
            <item>
                <title>Yu et al. (2026) Rossby wave-modulated orbital precipitation anomalies in the Asia-Pacific region</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1038_s41467-026-74368-3.html</link>
                <description><![CDATA[The study identifies a banded precipitation anomaly across the Asia-Pacific region driven by planetary Rossby waves excited by seasonal deep convection over the Indo-Pacific Warm Pool (IPWP) under orbital precessional forcing.]]></description>
                <pubDate>Thu, 18 Jun 2026 06:48:17 +0000</pubDate>
                <guid>10.1038_s41467-026-74368-3</guid>
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            <item>
                <title>Zhang et al. (2026) Land‐Feedbacks‐Driven Dry‐Hot Mutual Reinforcement Extends Global Compound Drought‐Heatwave Durations</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1029_2026jd046466.html</link>
                <description><![CDATA[The study identifies a mutual reinforcement loop between temperature and drought (T-D and D-T processes) that prolongs the duration of compound drought and heatwave events (CDHE) beyond the influence of atmospheric dynamics.]]></description>
                <pubDate>Thu, 18 Jun 2026 05:17:55 +0000</pubDate>
                <guid>10.1029_2026jd046466</guid>
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            <item>
                <title>谭哲兴 et al. (2026) Winter Extreme Precipitation Over the Western Tibetan Plateau: Circulation Patterns and Underlying Mechanisms</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1002_joc.70466.html</link>
                <description><![CDATA[This study identifies three distinct weather regimes associated with regional extreme precipitation events (REPEs) over the western Tibetan Plateau, all of which are driven by Western disturbances (WDs) and modulated by global teleconnections.]]></description>
                <pubDate>Thu, 18 Jun 2026 07:39:55 +0000</pubDate>
                <guid>10.1002_joc.70466</guid>
            </item>
            
            <item>
                <title>Adeniyi et al. (2026) Drought influence on carbon assimilation and water use efficiency in Mediterranean ecosystems</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1038_s41598-026-54809-1.html</link>
                <description><![CDATA[This study evaluates the sensitivity of various satellite-derived indicators to drought in Mediterranean ecosystems, concluding that the Crop Water Stress Index (CWSI) is the most rapid indicator of physiological stress, preceding declines in productivity and greenness.]]></description>
                <pubDate>Mon, 15 Jun 2026 05:51:22 +0000</pubDate>
                <guid>10.1038_s41598-026-54809-1</guid>
            </item>
            
            <item>
                <title>Fibbi et al. (2026) Spatial Interpolation of Meteorological Variables with Daymet4-r2: A Self-Calibrating Algorithm for Complex Terrains</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.3390_w18121461.html</link>
                <description><![CDATA[The study develops and evaluates two real-time adaptations of the Daymet V4 interpolation method (Daymet4-r1 and Daymet4-r2) in Tuscany, demonstrating that a global optimization approach (Daymet4-r2) significantly improves the accuracy of gridded meteorological variables.]]></description>
                <pubDate>Wed, 17 Jun 2026 05:48:08 +0000</pubDate>
                <guid>10.3390_w18121461</guid>
            </item>
            
            <item>
                <title>Lalić et al. (2026) Tracking Seasonal Transitions Using a Meteorological Seasonality Index</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1002_joc.70447.html</link>
                <description><![CDATA[The study introduces the Normalised Daily Temperature Range (NDTR), a physically based index that defines seasonal boundaries based on atmospheric regime transitions rather than fixed calendar dates, significantly reducing intra-seasonal meteorological variability.]]></description>
                <pubDate>Tue, 16 Jun 2026 05:16:28 +0000</pubDate>
                <guid>10.1002_joc.70447</guid>
            </item>
            
            <item>
                <title>Raclavská et al. (2026) Optimising Soil Hydraulic Behaviour Through Combined Cellulose and Biochar Amendments: Implications for Climate-Smart Agriculture</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.3390_agriculture16121304.html</link>
                <description><![CDATA[This study evaluates the effects of waste paper cellulose and biochar on soil hydraulic behavior, finding that while cellulose increases total water storage, biochar improves water retention stability.]]></description>
                <pubDate>Wed, 17 Jun 2026 05:58:01 +0000</pubDate>
                <guid>10.3390_agriculture16121304</guid>
            </item>
            
            <item>
                <title>Sabut et al. (2026) Distinguishing drought and flash drought: definitions, processes, and consequences</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.1016_j.jhydrol.2026.135851.html</link>
                <description><![CDATA[This review synthesizes the distinctions between conventional droughts (CDs) and flash droughts (FDs), highlighting differences in their onset speeds, physical drivers, predictability, and systemic impacts.]]></description>
                <pubDate>Sun, 14 Jun 2026 05:49:40 +0000</pubDate>
                <guid>10.1016_j.jhydrol.2026.135851</guid>
            </item>
            
            <item>
                <title>Tuzzi et al. (2026) Estimation of Leaf Area Index and Vegetation Fractional Cover in SBG-TIR Configuration Using SCOPE Simulated Data and Sentinel-2 Images</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.3390_rs18121931.html</link>
                <description><![CDATA[This study evaluates machine learning approaches to retrieve Vegetation Fractional Cover (FC) and Leaf Area Index (LAI) using the limited VNIR bands of the upcoming SBG-TIR mission. The Gaussian Process Regression (GPR) model proved most effective, demonstrating high accuracy and strong agreement with Sentinel-2 biophysical products.]]></description>
                <pubDate>Sat, 13 Jun 2026 07:37:22 +0000</pubDate>
                <guid>10.3390_rs18121931</guid>
            </item>
            
            <item>
                <title>Wu et al. (2026) DLMSR-Transformer-MoE: A novel method for synchronous retrieval of land surface temperature and emissivity</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.17632_f9sf94wvd4.1.html</link>
                <description><![CDATA[The study introduces DLMSR-Transformer-MoE, a deep learning method designed for the synchronous retrieval of land surface temperature (LST) and land surface emissivity (LSE) using multi-channel thermal infrared brightness temperatures.]]></description>
                <pubDate>Sat, 13 Jun 2026 07:27:43 +0000</pubDate>
                <guid>10.17632_f9sf94wvd4.1</guid>
            </item>
            
            <item>
                <title>Wu et al. (2026) An ANN-Derived Model for Estimating Hourly Storm Patterns with Daily Precipitation Based on Climate Change-Induced Rainstorms</title>
                <link>https://biblio.quintanasegui.com/summaries/2026/10.3390_w18121432.html</link>
                <description><![CDATA[The study develops the SM_ESP_HRDY model, utilizing Artificial Neural Networks (ANN) to estimate hourly storm patterns from daily rainfall data, achieving high accuracy particularly for 2-day and 3-day events.]]></description>
                <pubDate>Sat, 13 Jun 2026 05:29:25 +0000</pubDate>
                <guid>10.3390_w18121432</guid>
            </item>
            
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