2

Nitrate pollution in streams is a significant environmental challenge, affecting water quality and ecosystem health. Hydrological connectivity, the way water moves through the landscape, governs nitrate transport and transformation, but measuring it across entire catchments remains a challenge. Spatial patterns of soil moisture can provide key insights into catchment-scale hydrological connectivity, yet they have not been widely integrated into nitrate prediction models. In this study, we developed a deep learning framework trained on daily streamflow data and SMAP-HydroBlocks, a high-resolution satellite-based soil moisture data set. We tested whether deep learning models can predict nitrate concentrations using spatial soil moisture patterns and applied Explainable AI (XAI) methods to identify which areas contribute most to the model predictions. Our findings reveal that soil moisture patterns significantly improve nitrate predictions, with near-stream areas having the highest predictive power. This study highlights the potential of AI-driven approaches to provide spatially detailed insights into nitrate transport, which could support better water quality management and help identify nutrient pollution hotspots at fine spatial scales.

Improving the accuracy of land surface models (LSMs) is crucial for reducing uncertainties in climate change projections. Parameter data assimilation (DA), which fine-tunes model parameters to better match observed data, is key to enhancing LSM performance. However, the complexity of LSMs poses challenges for global optimization. Advances in computational power, novel data sets, and machine learning (ML) offer promising solutions to improve LSMs. ML can streamline the DA process, handling large data sets and reducing computational demands. This article discusses the progress made in LSM parameter estimation and the challenges faced by the community. We then discuss how ML can help address these challenges and outline future priorities. International collaboration, fostered by initiatives like the Analysis, Integration and Modeling of the Earth System Land DA Working Group and the International Land Model Forum, is essential for accelerating progress, facilitating knowledge exchange, and developing standardized methods for more accurate climate modeling.

The Brazilian Amazon Forest is experiencing alterations in water fluxes driven by agriculture expansion and climate change, which …

High Mountain Asia (HMA) plays a crucial role in Asian hydrology—its vast snow and glacier-covered landscape significantly influences …

We pair survey responses with GPS, crop-cut and CHIRPS benchmarks for 640 maize fields in Zambia to ask what the gaps between farmer …

The Pantanal, the world’s largest tropical wetland, experienced unusual drying in 2000–2021, but the causes are poorly understood. …

The Optical TRApezoid Model (OPTRAM) has been extensively utilized to map high-resolution surface soil moisture (top 0–5 cm) using …

Brazil holds 16% of the world’s freshwater, yet its water systems are still not fully understood. To understand how rainfall and rivers interact across the country, we studied the hydrologic signatures of 735 catchments. Through catchment classification we identified six major water behavior groups, mostly based on how wet or dry the regions are. In drier areas, climate drives streamflow; in wetter ones, soil and land features matter more. This helps map and predict when and where floods or droughts are likely to happen and it also guides smarter water storage and usage in vulnerable regions. Ultimately, this research supports better planning for a changing climate.

This study aims to better understand flash droughts worldwide and their varying characteristics and impacts. We surveyed the experiences of people affected by flash droughts and then examined a wide range of literature, including non-English and nonacademic sources. This helped us understand how flash droughts can differ from those commonly studied in the United States and China. We identified and described five types of flash droughts, some of which may not be detected by current drought measurement methods. It is crucial to include all types of flash droughts in drought monitoring systems and management plans, as they are expected to become more common due to global warming. We can then better prepare for and reduce the impacts of this growing threat.

Mycotoxin contamination of corn is a pervasive problem that negatively impacts human and animal health and causes economic losses to …