Paper Harvest Report

Date range: May 30, 2026

2 top-tier papers selected out of 52 total publications

Today’s Highlights

SWOT mission data can significantly correct biases in ICESat-2 lake-level time series, improving accuracy across over 81,000 global lakes and reservoirs and identifying spatial variability as a major source of measurement uncertainty. Meanwhile, a coupled WRF–Noah-MP study shows that dynamic root-water-uptake schemes reduce persistent warm and dry biases over the central United States by strengthening land–atmosphere feedback, highlighting how belowground processes propagate into regional precipitation.

Table of Contents

  1. Today’s Highlights
  2. Top-Tier Journal Papers
    1. The Potential for Leveraging SWOT‐Mapped Uneven Water Surface Elevations to Enhance ICESat‐2–Derived Lake Levels
    2. Root Dynamics Mitigate Warm and Dry Biases Over the Central United States
  3. Statistics
    1. Papers by journal
  4. Filtering Criteria

Top-Tier Journal Papers

The Potential for Leveraging SWOT‐Mapped Uneven Water Surface Elevations to Enhance ICESat‐2–Derived Lake Levels

Authors: Chi Hsiang Huang, Huilin Gao

Journal: Geophysical Research Letters · DOI: 10.1029/2025gl119771

Matched topics: reservoir

Satellite altimetry is advantageous for measuring water surface elevations (WSE) globally. However, biases of time series can be caused by uneven water surfaces, as nadir pointing measurements are often collected at different locations across a lake. This study demonstrates how two‐dimensional WSE difference maps derived from the SWOT mission can enhance ICESat‐2 WSE time series. First, the SWOT‐derived WSE difference maps showed strong agreement with in situ data over Lake Erie (R² = 0.79). For Lake Powell, this method improves the R² of the ICESat‐2 time series from 0.62 to 0.89. Furthermore, spatial variability in water surface, as estimated using SWOT data, accounts for 44% and 16% of the uncertainty in median WSE fluctuations in global lakes and reservoirs, respectively. By analyzing 81,133 lakes worldwide, this study identifies hotspots of bias and offers an advantage for integrating multi‐satellite altimetry data, facilitating more accurate and long‐term hydrological monitoring across diverse global landscapes.

Root Dynamics Mitigate Warm and Dry Biases Over the Central United States

Authors: Zhao Yang, Zhe Feng, Adam C. Varble, Koichi Sakaguchi, Colleen Kaul, Jerome D. Fast et al.

Journal: Geophysical Research Letters · DOI: 10.1029/2026gl121754

Matched topics: land surface model

The central United States frequently exhibits warm and dry biases in simulations of summertime conditions, a persistent feature that remains unresolved. While previous studies linked these biases to misrepresented surface energy exchanges, the role of belowground processes remains poorly understood. Here, we demonstrate that inadequate representation of root water uptake in land surface models contributes to this bias. Using both offline Noah‐MP and coupled WRF‐Noah‐MP simulations with static and dynamic root water uptake schemes, we show that the inclusion of dynamic root processes reduces 2‐m air temperature biases and enhances precipitation, primarily by increasing the rain rate of convective systems. Offline and coupled simulations further reveal that the cooling effects and precipitation increases are amplified through positive land‐atmosphere feedback, active only in the coupled model. These findings highlight an important role of root in modulating land‐atmosphere interactions and underscore the need to refine root‐zone processes to improve regional atmospheric simulations.

Statistics

Metric Count
Journals searched 11
Total papers fetched 52
Passed deterministic filter 4
After LLM relevance filtering 2
Rejected (not relevant) 2
AI for Science items picked 0

Papers by journal

Journal Papers
Geophysical Research Letters 2

Filtering Criteria

Topics: hydrology, hydrologic, hydraulic, watershed, streamflow, runoff, flood, drought, water resources, river, reservoir, dam, irrigation, groundwater, aquifer, evapotranspiration, precipitation, rainfall, snowmelt, glacier, ice sheet, sea level, storm surge, water quality, sediment, erosion, land surface model, earth system model, climate model, reanalysis, remote sensing, satellite, GRACE, SWOT, lidar, machine learning, deep learning, neural network, data assimilation

Fields: Environmental Science, Geography, Geology, Earth and Planetary Sciences, Agricultural and Biological Sciences