Paper Harvest Report
Date Range: 2026-01-13 to 2026-01-13
0 top-tier and 9 high-impact papers were selected out of 278 total publications (3.2%).
Today’s Highlights
Today’s research highlights the critical role of high-resolution meteorological forcing data, with evaluations of E-OBS and global products for large-sample hydrology and flux tower validation. Studies also reveal the significant memory effects of soil moisture and temperature on climate, and the compounding impacts of climate change and land use on soil erosion in sensitive regions like the Qinghai-Tibet Plateau. Emerging trends include the development of optimized spectral indices for improved vegetation and water mapping, advanced modeling techniques for agricultural water demand prediction, and novel methodologies for generating accurate stream Digital Terrain Models using UAV and LiDAR data. Furthermore, research is exploring the environmental implications of wastewater treatment, specifically the prevalence of antibiotic resistance genes, and the application of AI and IoT for sustainable agriculture in arid environments.
Part 2: High-Impact Journals + Topic Match (9 papers)
Peer-reviewed research articles from high-impact journals that match your topics.
Evaluating E-OBS forcing data for large-sample hydrology using model performance diagnostics
Authors: Franziska Clerc-Schwarzenbach, Thiago V. M. do Nascimento Journal: Hydrology and Earth System Sciences (2026-01-13) DOI: 10.5194/hess-30-119-2026
Matched Topics: hydrology
Abstract:
Abstract. For large-sample hydrological studies over large spatial domains, large-scale meteorological forcing data are often desired. For Europe, the EStreams dataset and catalogue satisfies this demand. In EStreams, the meteorological time series are obtained from the Ensemble Observation (E-OBS) product which is available for all of Europe. Due to the large spatial extent of this dataset, limitations and regional variations of data quality have to be expected when the dataset is compared to smaller-scale datasets, e.g., at national level. In this study, we compare the meteorological time series included for 2682 catchments in EStreams to eight smaller datasets (mostly CAMELS datasets). We assess how the different meteorological data impact the performance of a bucket-type hydrological m…
Evaluation of high-resolution meteorological data products using flux tower observations across Brazil
Authors: Jamie R. C. Brown, Ross Woods, Humberto Ribeiro da Rocha, Debora Regina Roberti, Rafael Rosolem Journal: Hydrology and Earth System Sciences (2026-01-13) DOI: 10.5194/hess-30-141-2026
Matched Topics: land surface model
Abstract:
Abstract. In the past decade, the scientific community has seen an increase in the number of global hydrometeorological products. This has been possible with efforts to push continental and global land surface modelling to hyper-resolution applications. As the resolution of these datasets increases, so does the need to compare their estimates against local in-situ measurements. This is particularly important for Brazil, whose large continental-scale domain results in a wide range of climates and biomes. In this study, high-resolution (0.1 to 0.25°) global and regional meteorological datasets are compared against flux tower observations at 11 sites across Brazil (for periods between 1999–2010), covering Brazil’s main land cover types (tropical rainforest, woodland savanna, various croplands…
The Memories of Soil Moisture and Soil Temperature Anomalies in Subsequent Soil Moisture and Soil Temperature in China
Authors: Yaoming Song, Haishan Chen, Lin Wang, Anning Huang, Wei Gu Journal: Journal of Geophysical Research: Atmospheres (2026-01-13) DOI: 10.1029/2025jd044117
Matched Topics: flood, land surface model
Abstract:
The memories of soil moisture (SM) and soil temperature (ST) modulate the effect of land surface on climate prediction on monthly and longer timescales. Based on a Lagrangian‐based understanding of SM and ST memories, this study explores the characteristics of SM and ST memories using ERA5‐Land reanalysis data, observations, land surface model and WRF model. The results show that SM and ST memories are longer in North China, Northeast China, Northwest China and Qinghai‐Tibet Plateau than in other regions of China, even exceeding 10 months. In the southern part of China, memories are short, approximately 0–6 months. SM memories have similar spatial distributions at different soil depths over 12 months, as do ST memories. The WRF‐simulated memories show generally consistent spatial patterns …
Impacts of climate change and land use dynamics on soil erosion in the Qinghai–Tibet plateau
Authors: Eslam Rashad, Yujie Liu, Zhaoyang Shi, Ahmed Refaee, Tao Pan Journal: Scientific Reports (2026-01-13) DOI: 10.1038/s41598-025-34550-x
Matched Topics: river, water management, climate change
Abstract:
Soil erosion (SE) caused by water is exacerbated by climate change and human activity, threatening water resources and ecological stability. The Qinghai-Tibet Plateau (QTP), with its unique ecosystem and river systems, is heavily influenced by sedimentation linked to water- induced SE. This study evaluates current SE patterns on the QTP and forecasts soil loss for 2050 and 2090 to identify priority areas for soil and water conservation. SE trends from 1985 to 2020 were conducted using the InVEST model. The CA-Markov model, in conjunction with CMIP6 climate projections, was employed to predict SE under varying land use and cover (LUCC) and climate change scenarios for the future. The results show that the average annual SE on the QTP stands at 20.02 t h⁻¹ ya⁻¹ from 1985 to 2020. Under the L…
Optimized spectral indices for global vegetation and water mapping using Sentinel-2
Authors: Charalambos Chrysostomou, Stelios P. Neophytides, Michalis Mavrovouniotis, Diofantos G. Hadjimitsis Journal: Scientific Reports (2026-01-13) DOI: 10.1038/s41598-025-34720-x
Matched Topics: surface water
Abstract:
Reliable mapping of vegetation and surface water from satellite imagery remains challenging, as common spectral indices can saturate at high biomass, show limited sensitivity across ecosystems, and confuse targets with soil, shadows, or built-up surfaces. We present two indices, the Symbolic Regression Vegetation Index (SRVI) and the Symbolic Regression Water Index (SRWI), discovered with a data-driven symbolic regression framework applied to Sentinel-2 Level-2A reflectance and guided by ESA WorldCover labels. Expressions were evolved from physically interpretable building blocks using non-linear combinations of visible, NIR, and SWIR bands. Indices were derived on a spectrally complex Mediterranean site and evaluated on eleven independent regions spanning diverse biomes. Performance was a…
Hybrid PSO-SVM and symbolic regression model for agricultural water demand prediction
Authors: Hong Lv, Yuting Zhao, Wei Wang, Kai Hou, Xiaokang Zheng Journal: Scientific Reports (2026-01-13) DOI: 10.1038/s41598-026-34995-8
Matched Topics: irrigation
Abstract:
Agricultural water use is a key link in ensuring food security and the sustainable utilization of water resources. Understanding its evolutionary mechanisms holds significant theoretical and practical importance. This study focuses on Bayannur City, employing the Particle Swarm Optimization–Support Vector Machine (PSO-SVM) model to identify the primary controlling factors of agricultural water demand and using symbolic regression to construct a predictive equation. The results reveal a bidirectional guiding mechanism in agricultural water use: it is driven by factors such as Effective Irrigated Area and Grain Sown Area, and inhibited by High-Efficiency Irrigation Rate, Water Stress Index, and Agricultural Water Price, which demonstrates a linear restraining effect. The prediction model ind…
Development of a stream DTM generation methodology using UAV-based SfM and LiDAR point cloud
Authors: Jaejun Gou, Hyeokjin Lee, Jinseok Park, Seongju Jang, Nakyung Lee et al. (6 authors) Journal: Scientific Reports (2026-01-13) DOI: 10.1038/s41598-026-35473-x
Matched Topics: river
Abstract:
Filtering ground points and generating accurate Digital Terrain Models (DTMs) are crucial for hydrological and ecological modeling in riverine environments. This paper proposes a methodology for DTM generation in stream areas by integrating point clouds from a UAV-based structure from motion (SfM) and light detection and ranging (LiDAR) while applying ground filtering algorithms. The study area, the Bokha Stream in Icheon City, South Korea, was surveyed using Zenmuse L1 (LiDAR) and Phantom 4 multispectral (SfM). Water and non-water areas were classified using the normalized difference water index (NDWI), and three ground filters, cloth simulation filter (CSF), progressive triangular irregular network (PTIN), and simple morphological filter (SMRF), were applied to remove vegetation and erro…
Prevalence of antibiotic resistance gene in different wastewater treatment systems and effluent-irrigated soils through metagenomic analysis
Authors: Hua Fang, Miao Pu, Ailier jiang, Fuer haiti, Yubei Liu et al. (8 authors) Journal: Scientific Reports (2026-01-13) DOI: 10.1038/s41598-026-35758-1
Matched Topics: seasonal, irrigation
Abstract:
Wastewater treatment systems (WWTS) are considered to be the main source of antibiotic resistance genes (ARGs) spreading into the environment. In this study, samples were collected from WWTS influent, biological treatment tank effluent, and recycled water treatment plant (RTP) effluent during summer and winter, followed by metagenomic sequencing. The study investigated the differences in antibiotic resistance gene transfer between two typical wastewater treatment plants (WWTPs) processes and the impact of recycled water irrigation on ARG dissemination in soil. The WWTS (HD and MD) adopting two combined processes of “Adsorption-Biodegradation Process(AB )+ Anaerobic-Anoxic-Oxic Process(AAO)” and “AAO + Membrane Bioreactor(MBR)” as the research objects for the first time.The primary ARGs typ…
AI-enabled smart farming framework for sustainable date palm cultivation in arid regions using machine learning and IoT integration
Authors: Marran Al Qwaid, Md Tanjil Sarker, Sarowar Morshed Shawon, H.T. Zubair Journal: Scientific Reports (2026-01-13) DOI: 10.1038/s41598-026-36106-z
Matched Topics: irrigation
Abstract:
Sustainable agriculture in arid regions faces critical challenges due to water scarcity, high temperatures, and inefficient traditional farming practices. This study presents an AI-enabled smart farming framework for optimizing date palm (Phoenix dactylifera) cultivation through the integration of Machine Learning (ML) and Internet of Things (IoT) technologies. A structured multimodal dataset comprising biometric features palm height, trunk diameter, and leaf number, environmental parameters soil moisture, temperature, and humidity, and categorical attributes variety and health status was analyzed to classify palm health and support data-driven irrigation management. Four ML algorithms Random Forest (RF), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), and Support Vector …
Statistics
- Papers Published: 278 (research articles from tracked journals)
- Papers Selected: 9 (3.2%)
- Papers with Abstracts: 9/9 (100.0%)
- Semantic Scholar Coverage: 262/278 (94.2%)
- Not in S2: 14 papers (404 errors are normal for non-indexed content)
Papers by Journal
Scientific Reports (6/183)
Nature Communications (0/49)
Nature (0/11)
Proceedings of the National Academy of Sciences (0/10)
Geophysical Research Letters (0/7)
Scientific Data (0/6)
Hydrology and Earth System Sciences (2/3)
Journal of Geophysical Research: Atmospheres (1/3)
Nature Geoscience (0/2)
Bulletin of the American Meteorological Society (0/1)
Nature Reviews Earth & Environment (0/1)
Earth System Dynamics (0/1)
Geoscientific Model Development (0/1)
Format: Journal Name (selected/published)
Selection Breakdown
- Part 1 (Top-tier + topics): 0
- Part 2 (High-impact + topics): 9
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