Peer-Review Publications

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 …