Eric F. Wood

Atmospheric evaporative demand is a key metric for monitoring agricultural drought. Existing ways of estimating evaporative demand in drought indices do not faithfully represent the constraints imposed by land surface characteristics and become less accurate over nonuniform land surfaces. This study proposes incorporating surface vegetation characteristics, such as vegetation dynamics data, aerodynamic parameters, and physiological parameters, into existing potential-evapotranspiration (PET) methods. This approach is implemented across the continental United States (CONUS) for the period from 1981–2017 and is tested using a recently developed drought index, the Standardized Precipitation–Evapotranspiration Index (SPEI). We show that activating realistic maximum surface conductance and aerodynamic conductance could improve the prediction of soil moisture dynamics and drought impacts by 29 %–41 % on average compared to more simple, widely used methods. We also demonstrate that this is especially effective in forests and humid regions, with improvements of 86 %–89 %. Our approach only requires a minimal amount of ancillary data while allowing for both historical reconstruction and real-time drought forecasting. This offers a physically meaningful yet easy-to-implement way to account for vegetation control in drought indices.

Atmospheric evaporative demand is a key metric for monitoring agricultural drought. The existing ways of estimating evaporative demand in drought indices do not faithfully represent the constraints of land surface characteristics and become less accurate over non-uniform land surfaces. This study proposes incorporating surface vegetation characteristics, such as vegetation dynamics data, aerodynamic and physiological parameters, into existing potential evapotranspiration (PET) methods. This approach is implemented over the Continental United States (CONUS) for the period of 1981–2017 and is tested in a recently developed drought index the Standardized Precipitation Evapotranspiration Index (SPEI). We show that activating realistic maximum surface and aerodynamic conductance could improve prediction of soil moisture dynamics and drought impacts by 29 % on average compared to the widely used simple methods, especially effective in the forests and humid regions. Surface characteristics that have a strong influence on the performance of the SPEI are mainly driven by leaf area index (LAI). Our approach only requires the minimum amount of ancillary data, while permitting both historical reconstruction and real-time forecast of drought. This offers a physically meaningful, yet easy-to-implement way to account for the vegetation control in drought indices.

Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site‐specific studies, little is known about how SM varies locally (1–100‐m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long‐standing challenge in hydrology. We reveal the striking variability of local‐scale SM across the United States using SMAP‐HydroBlocks — a novel satellite‐based surface SM data set at 30‐m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local‐scale complexity yields a remarkable and unique multi‐scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1‐km resolution, with complete loss expected at the scale of current state‐of‐the‐art SM monitoring and modeling systems (1–25 km resolution).

Soil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil moisture varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrievals and a few regional in-situ networks. Here, 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. SMAP-HB performed substantially better than the current state-of-the-art SMAP products, showing a median temporal correlation of 0.73 ± 0.13 and a median Kling-Gupta Efficiency of 0.52 ± 0.20. The largest benefit of SMAP-HB is, however, the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products. The SMAP-HB dataset is available via zenodo and at https://waterai.earth/smaphb.

We used environmental metrics developed from multi‐source satellite observations to quantify the global influence of El Niño‐Southern Oscillation (ENSO) events on surface wetting and drying anomalies, and their impact on vegetation health. The environmental metrics included a microwave surface wetness index (ASWI) incorporating near‐surface atmospheric vapor pressure deficit (VPD), volumetric soil moisture (VSM), and land surface fractional water cover (FW) derived from Advanced Microwave Scanning Radiometer (AMSR) observations, and the vegetation health index (VHI) derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) observations. The combined ASWI and VHI analysis reveals complex ENSO related impacts on the distribution of water availability to plant communities, and variable vegetation sensitivity to associated drought and pluvial events. A delayed VHI response to changes in surface wetness (up to 3.4 months) was observed, whereby the ASWI may provide an effective forecast predictor of climate impacts on vegetation health. The intense 2015/2016 El Niño event coincided with strong ASWI and VHI latitudinal correspondence (R ≥ 0.73). The cascading impacts of climate anomalies on water cycle components and vegetation were further investigated over ENSO‐sensitive sub‐regions including Amazonia, Australia, southern Africa, and the South American Paraná delta region. The ASWI component information linked the effect of drought and pluvial events on vegetation health to underlying changes in surface water inundation, soil moisture and atmospheric moisture deficits. The new satellite‐based assessments reveal the global complexity of ENSO‐related impacts on surface water storages, and the influence of these climate and hydrologic perturbations on ecosystem productivity.

Skillful seasonal climate forecasts can support decision making in water resources management and agricultural planning. In arid and semi‐arid regions, tailoring reliable forecasts has the potential to improve water management by using key hydroclimate variables months in advance. This article analyses and compares the performance of two common approaches (empirical and hybrid dynamical‐statistical) in seasonal climate forecasting over a drought‐prone area located in Southwest Asia including Iran. Empirical models are framed as a baseline skill that hybrid models need to outperform. Both approaches provide probabilistic forecasts of precipitation and temperature using canonical correlation analysis to provide forecasts at 0.25° resolution. Empirical models are developed based on the large‐scale observed atmosphere–ocean patterns for forecasting using antecedent climate anomalies as predictors, while the hybrid approach makes use of model output statistics to correct systematic errors in dynamical climate model forecast outputs. Eight state‐of‐the‐art dynamical models from the North American Multi‐Model Ensemble project are analysed. Individual models with the highest goodness index are weighted to develop seven different hybrid dynamical‐statistical Multi‐model Ensembles. In this study, (October–December) and (January–February) are considered as target seasons which are the most important periods within the water year for water resource allocation to the agriculture sector. The results show that the hybrid approach has improved performance compared to the raw general circulation models and purely empirical models, and that the performance of the hybrid models is season‐dependent. Seasonal forecasts of precipitation (temperature) have a higher skill in OND (JFM). In addition, in most cases, Multi‐model Ensemble (MME) is more skillful than the empirical models and outperforms individual dynamical models. However, the best individual model might be as skillful as the MME given the target season and region of interest.

Soil moisture is highly variable in space and time, and deficits (i.e., droughts) play an important role in modulating crop yields. Limited hydroclimate and yield data, however, hamper drought impact monitoring and assessment at the farm field scale. This study demonstrates the potential of using field-scale soil moisture simulations to support high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field scale. We present a multiscale modeling approach that combines HydroBlocks – a physically based hyper-resolution land surface model (LSM) – with machine learning. We used HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3 h 30 m resolution. These simulations, along with remotely sensed vegetation indices, meteorological data, and descriptors of the physical landscape (related to topography, land cover, and soils) were combined with district-level maize data to train a random forest (RF) model to predict maize yields at district and field scales (250 m). Our model predicted yields with an average testing coefficient of determination (R²) of 0.57 and mean absolute error (MAE) of 310 kg ha⁻¹ using year-based cross-validation. Our predicted maize losses due to the 2015–2016 El Niño drought agreed well with losses reported by the Food and Agriculture Organization (FAO). Our results reveal that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Soil moisture was also a more effective indicator of drought impacts on crops than precipitation, soil and air temperatures, and remotely sensed normalized difference vegetation index (NDVI)-based drought indices. This study demonstrates how field-scale modeling can help bridge the spatial-scale gap between drought monitoring and agricultural impacts.

Impacts of climate change on floods have been recently suggested to be more consistently seen in flood timing (or flood seasonality) as opposed to flood magnitude and frequency. Changes in flood timing can threaten the finely tuned water resource management systems and, if poorly understood, can alter flood risks in unpredictable ways. Nevertheless, patterns of global flood timing trend remain elusive. Whether climate change has played a significant role in shifting flood timing worldwide also remains unknown.Here we obtained an unprecedented set of discharge records from tens of thousands of global gauges and model-reconstructed naturalized discharge at ~3 million river reaches to delineate flood timing trend across the global river networks from 1980 to 2019. Hydroclimate drivers possibly causing these trends, including maximum precipitation, antecedent soil moisture, and snowmelt timing, are also investigated to disentangle climate change signals on floods. We found that the flood timing has been significantly earlier over the lower Mississippi, the Amur and the Amazon river basins, as well as large parts of the high-latitude Northern Hemisphere. Significant later floods are observed over the Yangtze and the lower Congo river basins, and the southeast Asia. However, ascribing these flood timing shifts to changing climate is not as obvious as previously suggested, implying the need for further research on this topic.

Drought is the most threatening natural hazard for agriculture. Between 1983 and 2003, drought led to a cumulative agriculture production loss of 166 billion U.S. dollars globally, thus monitoring and forecasting capabilities are essential for adaptation and preparedness. Soil moisture simulations play an indispensable role in reconstructing historic drought conditions and predicting future scenarios. However, there is a spatial scale gap between the resolution of soil moisture-based drought indices (10–25 km) and typical farm field sizes (1–2 ha). This spatial-scale gap hampers drought indices’ applicability for capturing and monitoring flash and local-scale agricultural droughts, particularly over heterogeneous landscapes and smallholder farming. This work presents a novel approach that uses hyper-resolution modeling and machine learning to identify droughts, characterize their topologies, and evaluate detection rates. We present a case-study for Zambia, where we simulated the root zone soil moisture at a daily 30-m resolution between 1981–2018 using the HydroBlocks land surface model. Using these simulations, we computed a weekly percentile-based drought index, defining it as in drought when the index dropped below the 20th percentile. Given the space and time location of the drought conditions, we applied a Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm to determine space- and time-connected clusters/events. We analyzed the topology of 7712 drought events, with a minimum 10-km2 coverage and two weeks duration. Our results showed that 88% were flash droughts (lasting less than one month), 82% were local events (less than 1,000 km2), and 62% were local flash droughts happening during the growing season (October–May). We performed a synthetic spatial scaling analysis to compute the change in detection rate across spatial resolutions. When considering drought conditions over at least 1,000-km2, our results showed that 10–50 km spatial resolution data missed 19 to 44 % of drought conditions captured with 30-m resolution data. This work demonstrates how current capabilities are likely underestimating droughts, and it highlights the urgent need to monitor and forecast droughts at a high spatial resolution. Such refined data can critically benefit local-scale drought mitigation and food security policy design.

Spatial variability of river network drainage density (D_d) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability ofD_dworldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimateD_dbased on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors andD_dare trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improvedD_dpatterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro–Vector) should enable full global modeling of river system processes at fine spatial resolutions.