Ana Paula Barros

Clouds strongly impact net surface radiation, creating differential heating patterns, and trigger land breezes that in turn organize cloud development. These dial processes determine the location of persistent ecohydrological regimes at local scales. Aerosol–cloud interactions (ACI) directly impact the diurnal cycle of cloud optical depth, and therefore the surface energy budget; yet they are generally not explicitly represented in studies of land‐atmosphere interactions. To address this gap, a 3D Large‐Eddy Simulation model was modified to describe ACI in the gray zone. The goal is to simulate realistic cumulus cloud dynamics consistent with the spatial dynamics of land surface moisture and temperature, and associated boundary layer processes. The model is tested and evaluated using simulations at hectometer resolution with prescribed diurnal surface heating corresponding to idealized wet (low Bowen ratio [LBR]) and dry (high Bowen ratio [HBR]) surface boundary conditions. Expected cloud sensitivity is well captured: compared to LBR, HBR clouds have a significantly higher cloud base (about 1 km), up to 50% reduction in total cloud fraction, and cloudiness ends earlier by about 2 hr due to enhanced turbulence and lower moisture availability. Analyses of the evolution of 3D cloud drop size distributions show that the model captures the cloud life cycle, including aerosol activation, condensational growth, enhanced collision‐coalescence, and aerosol regeneration. The results show that resolved eddy‐scale (sub‐km) within‐cloud circulations modulate cloud microphysical structure by locally altering supersaturation at minute timescales. Successive aerosol regeneration yields coarser cloud condensation nuclei, producing a coarse‐aerosol mode, and impacting mid‐day cloud fraction and cloudiness persistence.

Precipitation estimation in mountainous regions is heavily reliant on remote sensing observations and analysis. We demonstrate that the most advanced global precipitation products underestimate extreme rainfall in mountainous regions. A study comprising over 10 000 flood-producing extreme rainfall events worldwide over the past 52 years (1973–2024) shows median underestimations of 100 mm and 225 mm for the top 5 % and 1 % extreme rainfall events, respectively. The spatial variability of rainfall is also significantly underestimated by a factor of 3 in the world’s mountains. These findings confirm the limited ability to monitor and quantify extreme rainfall in complex terrain. This work provides a novel path to systematically enhance precipitation estimates globally using a general hybrid physics–AI framework, even for the top 1 % of extreme events.

Precipitation estimation in mountainous regions is heavily reliant on remote sensing observations and analysis. We demonstrate that the most advanced global precipitation products underestimate extreme rainfall in mountainous regions. A study comprising over 10 000 flood-producing extreme rainfall events worldwide over the past 52 years (1973–2024) shows median underestimations of 100 mm and 225 mm for the top 5 % and 1 % extreme rainfall events, respectively. The spatial variability of rainfall is also significantly underestimated by a factor of 3 in the world’s mountains. These findings confirm the limited ability to monitor and quantify extreme rainfall in complex terrain. This work provides a novel path to systematically enhance precipitation estimates globally using a general hybrid physics–AI framework, even for the top 1 % of extreme events.

Precipitation estimation in mountainous regions is heavily reliant on remote sensing observations and analysis. We demonstrate that the most advanced global precipitation products underestimate extreme rainfall in mountainous regions. A study comprising over 10 000 flood-producing extreme rainfall events worldwide over the past 52 years (1973–2024) shows median underestimations of 100 mm and 225 mm for the top 5 % and 1 % extreme rainfall events, respectively. The spatial variability of rainfall is also significantly underestimated by a factor of 3 in the world’s mountains. These findings confirm the limited ability to monitor and quantify extreme rainfall in complex terrain. This work provides a novel path to systematically enhance precipitation estimates globally using a general hybrid physics–AI framework, even for the top 1 % of extreme events.

This study presents a physical-statistical Snow Water Equivalent (SWE) retrieval framework in forested areas using dual-frequency X- and Ku-band SAR airborne measurements. The methodology builds on previous work coupling snow hydrology and microwave propagation and backscatter models and introduces a parameterization of microwave propagation and scattering within the forest canopy based on the Water Cloud Model (WCM) modified to account for canopy closure effects. The retrieval framework was applied to SnowSAR measurements from four flights over Grand Mesa, Colorado and its performance was evaluated against snow pit observations and LiDAR snow depth measurements. Prior distributions of snowpack properties were generated using a multilayer snow hydrology model (MSHM) forced with Numerical Weather Prediction (NWP) analysis data. SAR measurements were spatially averaged to 90 m and 30 m resolution for retrieval. Prior distributions of vegetation and ground parameters were initialized using Ku-HH measurements, with effective soil and vegetation parameters estimated for frozen conditions. Soil parameters were estimated in open areas and spatially interpolated to nearby forested areas using ordinary kriging. SWE and snow depth retrievals for forested pixels at 90 m resolution were considered successful by accepting a relative residual backscatter (RRB) tolerance in the Bayesian optimization up to 30 % for individual pixels with incidence angles between 30°–50° along SnowSAR flight paths. Successful retrievals capture both the mean and spatial variance of snowpack properties across the Grand Mesa plateau consistent with the LiDAR survey with RRB generally below 5 %. Validation against collocated LiDAR snow depth and snow pit SWE measurements from the SnowEx ’17 campaign show a root mean square error (RMSE) of 0.033 m (< 8 % of maximum SWE for pits) and improved spatial patterns compared to snow hydrology predictions driven by NWP alone. The errors are larger in pixels with mixed land-cover (e.g., forest-grassland boundaries, land-margins of frozen ponds and lakes, and mixed forest) due to increased uncertainty in the estimation of vegetation parameters. Absolute relative differences (ARD) between LiDAR snow depth and SAR snow depth retrievals are below 10 % for 62 % of the forested pixels at 90 m resolution, and the fraction of successful retrievals increases to 82 % for ARD < 20 %. Retrievals at 30 m resolution achieve dramatic improvement with 78 % of the retrievals for ARD < 10 % due to reduced mixed land-cover uncertainty. These results demonstrate the feasibility of dual-frequency Bayesian SWE retrieval in forested landscapes by combining physical modeling with remote sensing at high spatial resolution enabled by SAR.

This study presents a physical-statistical Snow Water Equivalent (SWE) retrieval framework in forested areas using dual-frequency X- and Ku-band SAR airborne measurements. The methodology builds on previous work coupling snow hydrology and microwave propagation and backscatter models and introduces a parameterization of microwave propagation and scattering within the forest canopy based on the Water Cloud Model (WCM) modified to account for canopy closure effects. The retrieval framework was applied to SnowSAR measurements from four flights over Grand Mesa, Colorado and its performance was evaluated against snow pit observations and LiDAR snow depth measurements. Prior distributions of snowpack properties were generated using a multilayer snow hydrology model (MSHM) forced with Numerical Weather Prediction (NWP) analysis data. SAR measurements were spatially averaged to 90 m and 30 m resolution for retrieval. Prior distributions of vegetation and ground parameters were initialized using Ku-HH measurements, with effective soil and vegetation parameters estimated for frozen conditions. Soil parameters were estimated in open areas and spatially interpolated to nearby forested areas using ordinary kriging. SWE and snow depth retrievals for forested pixels at 90 m resolution were considered successful by accepting a relative residual backscatter (RRB) tolerance in the Bayesian optimization up to 30 % for individual pixels with incidence angles between 30°–50° along SnowSAR flight paths. Successful retrievals capture both the mean and spatial variance of snowpack properties across the Grand Mesa plateau consistent with the LiDAR survey with RRB generally below 5 %. Validation against collocated LiDAR snow depth and snow pit SWE measurements from the SnowEx ’17 campaign show a root mean square error (RMSE) of 0.033 m (< 8 % of maximum SWE for pits) and improved spatial patterns compared to snow hydrology predictions driven by NWP alone. The errors are larger in pixels with mixed land-cover (e.g., forest-grassland boundaries, land-margins of frozen ponds and lakes, and mixed forest) due to increased uncertainty in the estimation of vegetation parameters. Absolute relative differences (ARD) between LiDAR snow depth and SAR snow depth retrievals are below 10 % for 62 % of the forested pixels at 90 m resolution, and the fraction of successful retrievals increases to 82 % for ARD < 20 %. Retrievals at 30 m resolution achieve dramatic improvement with 78 % of the retrievals for ARD < 10 % due to reduced mixed land-cover uncertainty. These results demonstrate the feasibility of dual-frequency Bayesian SWE retrieval in forested landscapes by combining physical modeling with remote sensing at high spatial resolution enabled by SAR.

In the WRR 50th Anniversary Special Collection, Lettenmaier et al. (2015, https://doi.org/10.1002/2015WR017616 ) provided a timely account of how satellite remote sensing evolved from a niche capability in hydrology into a mainstream driver of hydrologic discovery, as the community shifted from imagery toward validated Earth Observation (EO) products and open‐access data systems. In this Commentary for the WRR 60th Anniversary Special Collection, we revisit and extend that synthesis and argue that the next step is not just more EO data, but a more accountable remote sensing hydrology. The challenge is to integrate remote sensing more directly into hydrologic science questions in ways that ground networks alone cannot provide consistently across scales. We center this Commentary on a core question: How can EO be used, credibly and consistently, to quantify where water is stored, how it moves, and how the hydrologic cycle is changing, in ways that improve hydrologic understanding and decision value beyond what ground networks alone can provide? We summarize key advances since 2015 that sharpen water‐cycle observability, expand analysis‐ready data systems and computation, and accelerate hybrid inference, while highlighting persistent challenges that continue to limit credible hydrologic inference near thresholds and extremes. We conclude with a science‐driven agenda focused on closing observability gaps, improving record coherence, advancing uncertainty‐aware inference, strengthening end‐to‐end traceability from measurements to societal impacts, and sustaining climate‐quality EO records for hydrologic science in a changing climate, while recognizing that these advances will depend on continuity of observing capability and credible follow‐on mission strategies.

This study presents a physical-statistical Snow Water Equivalent (SWE) retrieval framework in forested areas using dual-frequency X- and Ku-band SAR airborne measurements. The methodology builds on previous work coupling snow hydrology and microwave propagation and backscatter models and introduces a parameterization of microwave propagation and scattering within the forest canopy based on the Water Cloud Model (WCM) modified to account for canopy closure effects. The retrieval framework was applied to SnowSAR measurements from four flights over Grand Mesa, Colorado and its performance was evaluated against snow pit observations and LiDAR snow depth measurements. Prior distributions of snowpack properties were generated using a multilayer snow hydrology model (MSHM) forced with Numerical Weather Prediction (NWP) analysis data. SAR measurements were spatially averaged to 90 m and 30 m resolution for retrieval. Prior distributions of vegetation and ground parameters were initialized using Ku-HH measurements, with effective soil and vegetation parameters estimated for frozen conditions. Soil parameters were estimated in open areas and spatially interpolated to nearby forested areas using ordinary kriging. SWE and snow depth retrievals for forested pixels at 90 m resolution were considered successful by accepting a relative residual backscatter (RRB) tolerance in the Bayesian optimization up to 30 % for individual pixels with incidence angles between 30°–50° along SnowSAR flight paths. Successful retrievals capture both the mean and spatial variance of snowpack properties across the Grand Mesa plateau consistent with the LiDAR survey with RRB generally below 5 %. Validation against collocated LiDAR snow depth and snow pit SWE measurements from the SnowEx ’17 campaign show a root mean square error (RMSE) of 0.033 m (< 8 % of maximum SWE for pits) and improved spatial patterns compared to snow hydrology predictions driven by NWP alone. The errors are larger in pixels with mixed land-cover (e.g., forest-grassland boundaries, land-margins of frozen ponds and lakes, and mixed forest) due to increased uncertainty in the estimation of vegetation parameters. Absolute relative differences (ARD) between LiDAR snow depth and SAR snow depth retrievals are below 10 % for 62 % of the forested pixels at 90 m resolution, and the fraction of successful retrievals increases to 82 % for ARD < 20 %. Retrievals at 30 m resolution achieve dramatic improvement with 78 % of the retrievals for ARD < 10 % due to reduced mixed land-cover uncertainty. These results demonstrate the feasibility of dual-frequency Bayesian SWE retrieval in forested landscapes by combining physical modeling with remote sensing at high spatial resolution enabled by SAR.

Key Points: The editors thank the 2025 peer reviewers!

The exponential growth of artificial intelligence (AI) has driven the rapid global expansion of data centers, raising serious concerns about their environmental impact—particularly water use. While national and global water consumption by data centers may seem modest compared to other users, their localized impacts can be significant—especially in regions already facing water stress or drought. This commentary examines the multi‐faceted water footprint of data centers, encompassing direct cooling, electricity generation, and supply chain water demands. It highlights major gaps in transparency around how much water data centers use, which undermine effective regulation, innovation, and community planning. To ensure the sustainable growth of digital infrastructure and the preservation of water resources, comprehensive monitoring and public disclosure of water use are essential. Equally important are resilient water infrastructure planning and stronger collaboration between industry and communities.