Paper Harvest Report
Date Range: 2026-01-24 to 2026-01-24
4 top-tier and 4 high-impact papers were selected out of 153 total publications (5.2%).
Today’s Highlights
This week’s research highlights advancements in hydrological modeling and monitoring, with a focus on integrating novel AI techniques and satellite data. Papers explore deep generative models for uncertainty quantification in time-series forecasting, and the potential of Large Language Models as calibration agents for hydrological models. Satellite observations are proving invaluable for tracking freshwater dispersal in large estuarine systems and for developing long-term soil moisture datasets crucial for agricultural drought monitoring in Africa. Furthermore, new methods are emerging for evaluating coastal water surface elevation with missions like SWOT, predicting river water quality using machine learning, and assessing urban resilience to sea-level rise, alongside analyses of climate-induced shifts in forest habitat suitability.
Part 1: Top-Tier Journals + Topic Match (4 papers)
Peer-reviewed research articles from top-tier journals that match your research topics.
Modeling Uncertainty With Engression: A Deep Generative Time‐Series Approach
Authors: Basil Kraft, Steven Stalder, William H. Aeberhard, Nicolás Harrington Ruiz, Nicolai Meinshausen et al. (7 authors) Journal: Geophysical Research Letters ⭐ (2026-01-24) DOI: 10.1029/2025gl120122
Matched Topics: runoff
Abstract:
Deep learning enables precise environmental predictions across spatial and temporal scales. However, reliable uncertainty estimation with generative capabilities remains crucial for actionable forecasting and simulation, yet robust quantification methods remain challenging. Recently, engression, a generative approach for model‐agnostic uncertainty quantification, has been proposed. We evaluate its feasibility for environmental time‐series modeling, specifically applying it to rainfall‐runoff prediction using long short‐term memory (LSTM) networks. As a benchmark, we use quantile regression, a generalization of the mean absolute error. In our analysis, we focus on the quality of the uncertainty estimates and realism of generated sequences, while including a comparison to point‐prediction pe…
Evaluating SWOT in the Coastal Zone: Comparisons With Tide Gauge and Airborne LiDAR in the Bristol Channel and Severn Estuary, UK
Authors: Youtong Rong, Paul Bates, Jeff Neal, Paul Bell, Christine Gommenginger et al. (22 authors) Journal: Geophysical Research Letters ⭐ (2026-01-24) DOI: 10.1029/2025gl116590
Matched Topics: river, surface water
Abstract:
Traditional nadir altimeters struggle with coastal water surface elevation (WSE) measurement and fine‐scale river‐estuary interactions, due to land‐water signal interference and their wide inter‐track spacing. The wide‐swath Surface Water and Ocean Topography (SWOT) mission, using a new Ka‐band radar interferometer, aims to address these issues by delivering 2D WSE measurements with unprecedented spatial resolution, accuracy, and precision. However, the mission’s effectiveness in coastal WSE retrieval and its error characteristics remain unverified. This study leverages gauge and airborne LiDAR data to validate SWOT’s WSE in the Bristol Channel and Severn Estuary. Assuming error‐free in situ data, SWOT ocean products exhibit a standard deviation of difference (STD) of 13 cm within a 3 km r…
Satellite Data Trace Seasonal Freshwater Dispersion in Hudson and James Bays
Authors: Atreya Basu, Simon Bélanger, Zou Zou A. Kuzyk, Rakesh K. Singh, Soham Mukherjee et al. (10 authors) Journal: Geophysical Research Letters ⭐ (2026-01-24) DOI: 10.1029/2025gl116442
Matched Topics: river, seasonal
Abstract:
Freshwater dispersal controls stratification, circulation, and biogeochemical processes in Hudson and James Bay (HJB); however, limited field observations hinder understanding of how different freshwater types drive change. This study uses Aqua‐Moderate Resolution Imaging Spectroradiometer and Soil Moisture and Ocean Salinity‐Microwave Imaging Radiometer using Aperture Synthesis data to distinguish and map river water (RW) and sea ice melt (SIM), the main freshwater types in HJB. Satellite‐derived RW and SIM agree with in situ tracers, filling field data gaps that support future freshwater research. Higher RW fractions up to 0.40 in southern HJB and a seasonal SIM decline from 0.35 to 0.00 in central HJB match observed patterns. RW spreads offshore in July, contracts in August, and forms a…
Large Language Models as Calibration Agents in Hydrological Modeling: Feasibility and Limitations
Authors: Zhanliang Zhu, Yehai Tang, Xiongpeng Tang, Jianyun Zhang, Chao Gao et al. (8 authors) Journal: Geophysical Research Letters ⭐ (2026-01-24) DOI: 10.1029/2025gl120043
Matched Topics: earth system model
Abstract:
Large language models (LLMs) represent the forefront of artificial intelligence, exhibiting substantial potential for advancing understanding and reasoning in complex Earth system sciences. Hydrological modeling, essential for water resource management and Earth system knowledge, predominantly relies on traditional calibration paradigms and remains largely unexplored with respect to leveraging recent advancements in LLMs. Five advanced LLMs (GPT‐4o‐mini, DeepSeek‐R1, DeepSeek‐V3, Llama‐4maverick, and llama‐70b) were systematically evaluated and compared against two benchmarks (SCE‐UA and NSGA‐III). Results indicate substantial variability: DeepSeek‐R1 achieved stable near‐optimal convergence within 200 iterations, significantly faster than SCE‐UA (>1,200) or NSGA‐III (>2,200), and delivere…
Part 2: High-Impact Journals + Topic Match (4 papers)
Peer-reviewed research articles from high-impact journals that match your topics.
A new, long-term root zone soil moisture dataset for operational agricultural drought monitoring over Africa
Authors: Ross I. Maidment, Tristan Quaife, Ewan Pinnington, Emily Black, Amsalework Ejigu et al. (7 authors) Journal: Scientific Data (2026-01-24) DOI: 10.1038/s41597-026-06585-w
Matched Topics: drought, land surface model
Abstract:
Abstract</jats:title> Quantifying root zone soil moisture (RZSM) is critical for assessing water availability to crops and identifying agricultural drought across Africa, with access to reliable and timely RZSM data essential for informed decision-making. While rainfall is frequently used to assess crop growing conditions, it alone may not reliably reflect crop water availability due to the impact of evapotranspiration on soil moisture and rainfall not always reflective of concurrent soil water content at rooting depth. To provide robust information on agricultural drought, this paper describes a new, operational RZSM dataset, called TAMSAT soil moisture (TAMSAT-SM), available from 1983-present at 0.25° spatial resolution. TAMSAT-SM is derived using the JULES land surface…
Water quality index prediction via a robust machine learning model using oxygen-related indices for river water quality monitoring
Authors: Amin Arzhangi, Sadegh Partani Journal: Scientific Reports (2026-01-24) DOI: 10.1038/s41598-026-36156-3
Matched Topics: river
Abstract:
Rivers face increasing pollution, requiring accurate water quality assessment tools. Existing indices like the Water Quality Index (WQI) often overlook the integration of oxygen-related parameters critical to aquatic health. Here, we develop a machine learning model using Support Vector Regression (SVR) to predict the Water Quality Index (WQIOIs) by integrating key oxygen-related parameters, including Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), and the reaeration coefficients (K1, K2). Applied to three rivers in Iran, the model demonstrated high accuracy, with a cross-validated R² > 0.95 and root mean squared error (RMSE) of 0.92 for the Haraz River and 1.41 for the Simineh River. Predictions showed strong correlation (r = 0.98) with standard indice…
Analysis of urban resilience assessment and spatiotemporal patterns in coastal cities under sea-level rise
Authors: Bing Liang, Guoqing Shi, Haozhe Wu, Sige Qu, Mark Wang et al. (6 authors) Journal: Scientific Reports (2026-01-24) DOI: 10.1038/s41598-026-37425-x
Matched Topics: river
Abstract:
With global climate warming, sea-level rise has become a significant challenge for coastal cities. A three-dimensional resilience model is developed based on resilience breadth, depth, and effectiveness, providing a comprehensive evaluation of urban resilience. Furthermore, the Mann-Kendall test (M-K test) was applied to analyze the spatiotemporal evolution of urban resilience, and a Grey Model (GM) was employed to forecast future resilience levels. Taking Shanghai as a case study, extensive data collection and processing were conducted for a thorough resilience assessment. The main findings include: (1)Shanghai’s urban resilience has shown a significant declining trend since 2011, with notable spatial disparities between central and peripheral districts.(2)The three-dimensional model reve…
Climate-induced shifts in habitat suitability of forest types and adaptation strategies in the Western Ghats of Tamil Nadu, India
Authors: Andimuthu Ramachandran, Mithilasri Manickavasagam, S. Hariharan, S. Kanmani, M. Mathan et al. (15 authors) Journal: Scientific Reports (2026-01-24) DOI: 10.1038/s41598-025-22362-y
Matched Topics: seasonal, climate change
Abstract:
Climate change poses a critical threat to forest ecosystems, particularly in biodiversity hotspots such as the Western Ghats of Tamil Nadu. This study aims to assess the current and future habitat suitability of dominant tree species representing evergreen, deciduous, and thorn forests using the MaxEnt species distribution model under the SSP2–4.5 climate scenario. A total of 240 geo-referenced occurrence points, along with 19 bioclimatic and topographic variables, were used to predict species-specific habitat changes for the near future (2021–2050), based on downscaled EC-Earth3 CMIP6 climate data. The model projects a significant decline in habitat suitability for evergreen (− 248.72 sq. km) and deciduous (− 720.21 sq. km) forests, while thorn forests are expected to expand by + 968.93 s…
Statistics
- Papers Published: 153 (research articles from tracked journals)
- Papers Selected: 8 (5.2%)
- Papers with Abstracts: 8/8 (100.0%)
- Semantic Scholar Coverage: 150/153 (98.0%)
- Not in S2: 3 papers (404 errors are normal for non-indexed content)
Papers by Journal
Scientific Reports (3/98)
Nature Communications (0/24)
Geophysical Research Letters (4/19)
Scientific Data (1/10)
Journal of Geophysical Research: Atmospheres (0/2)
Format: Journal Name (selected/published)
Selection Breakdown
- Part 1 (Top-tier + topics): 4
- Part 2 (High-impact + topics): 4
Filtering Criteria
Relevant Fields: engineering, environmental science, computer science, geology, geography
Topics: hydrology, hydrologic model, river, runoff, streamflow, reservoir, water management, flood, drought, seasonal, land surface model, climate change, hydropower, surface water, irrigation, earth system model