Peer-Review Publications

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 …

We introduce Version 2 of our widely used 1-km Köppen-Geiger climate classification maps for historical and future climate conditions. …

Land use and cover change (LUCC) in Brazil encompass a complex interplay of diverse factors across different biomes. Understanding …

Smallholder agriculture is critical for current and future food security, yet quantifying the sources of smallholder yield variance …

We assessed how advanced soil properties maps can improve soil moisture modeling using a Land Surface Model. The results showed that using contemporary soil properties maps and pedotransfer functions to estimate soil properties improved soil moisture modeling performance, especially when soil properties data is available at different soil layers.

Sub-Saharan Africa (SSA) has the climatic and biophysical potential to grow the crops it needs to meet rapidly growing food demand; …

In this study we show how a surrogate model can accurately predict the spatial structure of Land Surface Model’s (LSM) hydrological output fields. For a target level of complexity, the optimal LSM tile configuration was determined using a multi-objective Pareto efficiency analysis. We found that approximately 100 tiles can effectively reproduce a quasi-fully distributed LSM setup with 1% of the computational cost. This method provides a path forward to efficiently determine the optimal tile configurations for LSMs while simultaneously considering the spatial heterogeneity and spatial accuracy of hydrologic processes.

This study provides a framework for direct integration of soil moisture observations collected from soil sensors and satellite imagery into process-based crop models for improving the representation of agricultral systems. The performance of this framework was evaluated across 19 site-years for crop yield, NDVI, soil moisture, tile flow drainage and nitrate leaching.

Drought management is currently informed by a variety of approaches, mostly responding to drought crisis when it happens. Toward more effective and integrated drought management, we introduce a conceptual drought diagnosis framework inspired by diagnostic concepts from the field of medicine. This framework comprises five steps: 1. Initial diagnostic assessment; 2. Diagnostic testing; 3. Consultation; 4. Communication of the diagnosis; and 5. Treatment and prognosis. This framework can help drought managers to be more proactive in enabling drought-affected regions to become more drought resilient in the future.

We introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. Its largest benefit of SMAP-HB is the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products.

Although there have been significant advances in river routing and sub-grid heterogeneity (i.e., tiling) schemes in Earth system models over the past decades, there has yet to be a concerted effort to couple these two concepts. This paper aims to bridge this gap through the development of a two-way coupling between tiling schemes and river networks in the HydroBlocks land surface model. The scheme is implemented and tested over a 1 arc degree domain in Oklahoma, United States.

Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.

We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.

We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) …

Soil Moisture (SM) is a direct measure of agricultural drought. While there are several global SM indices, none of them directly use SM …

Helicopters can experience brownout when flying close to a dusty surface. The uplifting of dust in the air can remarkably restrict the …

Given the varying manifestations of climate change over time and the influence of climate perceptions on adaptation, it is important to …

Accurate crop yield forecasts before harvest are crucial for providing early warning of agricultural losses, so that policy-makers can …

We present new global maps of the Köppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day …

This study represents the most comprehensive global-scale precipitation dataset evaluation to date. We evaluated 13 uncorrected precipitation datasets using precipitation observations from 76 086 gauges, and 9 gauge-corrected ones using hydrological modeling for 9053 catchments. Our results highlight large differences in estimation accuracy, and hence, the importance of precipitation dataset selection in both research and operational applications.

Dry conditions in 2013–16 in much of the western United States were responsible for severe drought and led to an exceptional fire …

Given widespread Amazonian deforestation, numerous studies have focused on how the regional hydrological cycle - in terms of …