Paper Harvest Report

Date Range: 2026-01-31 to 2026-01-31

Summary

Papers Published: 139 (research articles from tracked journals)
Papers Selected: 3 (2.2%)
Papers with Abstracts: 3/3 (100.0%)
Semantic Scholar Coverage: 137/139 (98.6%)
- Not in S2: 2 papers (404 errors are normal for non-indexed content)

Papers by Journal

Scientific Reports                                 (1/79)
Nature Communications                              (0/35)
Geophysical Research Letters                       (1/13)
Journal of Geophysical Research: Atmospheres       (0/6)
Scientific Data                                    (0/4)
Journal of Advances in Modeling Earth Systems      (0/1)
Earth's Future                                     (1/1)

Format: Journal Name (selected/published)

Selection Breakdown

Part 1 (Top-tier + topics): 1
Part 2 (High-impact + topics): 2

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

Part 1: Top-Tier Journals + Topic Match (1 papers)

Peer-reviewed research articles from top-tier journals that match your research topics.

On the Role of Sea Surface Temperature in the 16 April 2024 Rainstorm Over the United Arab Emirates

Authors: Basit Khan, Subrota Halder, Zouhair Lachkar, Francesco Paparella, Olivier Pauluis Journal: Geophysical Research Letters ⭐ (2026-01-31) DOI: 10.1029/2025gl118215

Matched Topics: river

Abstract:

This study examines how anomalously high sea surface temperatures (SSTs) in the Arabian Sea and surrounding gulfs contributed to the record‐breaking rainfall (250 mm ) over the United Arab Emirates (UAE) on 16 April 2024, with the greatest impacts in Dubai, Al‐Ain, and Abu Dhabi. Numerical modeling and satellite observations were used to examine the atmospheric and oceanic conditions leading to this extreme event. Sensitivity experiments show that warm SST anomalies enhanced moisture supply and intensified convection, producing larger and more intense convective storms. The warmer SSTs increased both moisture availability and storm intensity. The sensitivity model runs show higher precipitable water across much of the domain in the week preceding 16 April. Results indicate that, while at…

Part 2: High-Impact Journals + Topic Match (2 papers)

Peer-reviewed research articles from high-impact journals that match your topics.

Transition in Global Basins From Precipitation‐Dominated to Evaporative Demand‐Dominated Meteorological Drought: Past Patterns and Future Projections

Authors: Jiachen Ji, Chiyuan Miao, Jinlong Hu, Jiajia Su, Shidie Chen et al. (9 authors) Journal: Earth’s Future (2026-01-31) DOI: 10.1029/2025ef007492

Matched Topics: river, drought

Abstract:

Meteorological drought, one of the most destructive natural hazards, is driven by both precipitation deficits and high evaporative demand. While precipitation has traditionally been considered the dominant driver, recent studies suggest an increasing influence of evaporative demand. However, it remains uncertain whether these findings represent individual regional cases or a boarder emerging global trend. To address this, we developed a systematic framework using a standardized precipitation evapotranspiration index (SPEI) variant experiment to attribute drought drivers across 292 major global basins. Our historical analysis (1970–2024) reveals a widespread global transition: 48.1% of the global basin area (6.43 × 10 7 km 2 ) transitioned from precipitation‐dominated to evaporativ…

River extraction from high-resolution remote sensing images based on non-uniform sampling and semi-supervised learning

Authors: Kun Wang, Lin Han, Liangzhi Li Journal: Scientific Reports (2026-01-31) DOI: 10.1038/s41598-026-38167-6

Matched Topics: river

Abstract:

Accurate river extraction is crucial for agricultural irrigation, water conservancy planning, and flood warning. To mitigate the issues of excessive detail loss and scarcity of labeled data in existing encoder-decoder networks, we propose a non-uniform sampling method combined with graph-based semi-supervised learning to leverage unlabeled data effectively. The method samples more points in high-frequency regions (e.g., river edges) and fewer in low-frequency regions, followed by bilinear interpolation for feature fusion. Experimental results on the Gaofen-2 dataset demonstrate that our method improves Unet, Linknet, and DeeplabV3 by 0.9, 1.5, and 1.6% in accuracy, and by 1.7, 2.9, and 1.9% in IoU, respectively. With semi-supervised learning, using all unlabeled data boosts pixel accuracy …