ABSTRACTStochastic weather generators create time series that reproduce key weather dynamics present in long-term observations. The dataset detailed herein is a large-scale gridded parameterization ...for CLImate GENerator (CLIGEN) that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations, thereby obtaining near-global availability of combined coverages. This dataset primarily covers countries north of 40° latitude with 0.25° spatial resolution. Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products. Precipitation parameters were statistically downscaled to estimate point-scale values, while point-scale temperature and solar radiation parameters were approximated by direct calculation from high-resolution datasets. Surrogate parameter values were used in some cases, such as with wind parameters. Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations. These parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 stations. Two sensitive parameters, monthly average storm accumulation and maximum 30-minute intensity, were shown have RMSE values of 1.48 mm and 4.67 mm hr−1, respectively. Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5% of ground-based parameterizations, effectively improving climate data availability.
CLIGEN is a stochastic weather generator that creates statistically representative timeseries of daily and sub-daily point-scale weather variables from observed monthly statistics and other ...parameters. CLIGEN precipitation timeseries are used as climate input for various risk-assessment modelling applications as an alternative to observe long-term, high temporal resolution records. Here, we queried gridded global climate datasets (TerraClimate, ERA5, GPM-IMERG, and GLDAS) to estimate various 20-year climate statistics and obtain complete CLIGEN input parameter sets with coverage of the African and South American continents at 0.25 arc degree resolution. The estimation of CLIGEN precipitation parameters was informed by a ground-based dataset of >10,000 locations worldwide. The ground observations provided target values to fit regression models that downscale CLIGEN precipitation input parameters. Aside from precipitation parameters, CLIGEN's parameters for temperature, solar radiation, etc. were in most cases directly calculated according to the original global datasets. Cross-validation for estimated precipitation parameters quantified errors that resulted from applying the estimation approach in a predictive fashion. Based on all training data, the RMSE was 2.23 mm for the estimated monthly average single-event accumulation and 4.70 mm/hr for monthly maximum 30-min intensity. This dataset facilitates exploration of hydrological and soil erosional hypotheses across Africa and South America.
Each year, terrestrial ecosystems absorb more than a quarter of the anthropogenic carbon emissions, termed as land carbon sink. An exceptionally large land carbon sink anomaly was recorded in 2011, ...of which more than half was attributed to Australia. However, the persistence and spatially attribution of this carbon sink remain largely unknown. Here we conducted an observation-based study to characterize the Australian land carbon sink through the novel coupling of satellite retrievals of atmospheric CO2 and photosynthesis and in-situ flux tower measures. We show the 2010-11 carbon sink was primarily ascribed to savannas and grasslands. When all biomes were normalized by rainfall, shrublands however, were most efficient in absorbing carbon. We found the 2010-11 net CO2 uptake was highly transient with rapid dissipation through drought. The size of the 2010-11 carbon sink over Australia (0.97 Pg) was reduced to 0.48 Pg in 2011-12, and was nearly eliminated in 2012-13 (0.08 Pg). We further report evidence of an earlier 2000-01 large net CO2 uptake, demonstrating a repetitive nature of this land carbon sink. Given a significant increasing trend in extreme wet year precipitation over Australia, we suggest that carbon sink episodes will exert greater future impacts on global carbon cycle.
Nutrient recycling is fundamental to sustainable agricultural systems, but few mechanisms exist to ensure that surplus manure nutrients from animal feeding operations are transported for use on ...nutrient-deficient croplands. As a result, manure nutrients concentrate in locations where they can threaten environmental health and devalue manure as a fertilizer resource. This study advances the concept of the “manureshed” – the lands surrounding animal feeding operations onto which manure nutrients can be redistributed to meet environmental, production, and economic goals. Manuresheds can be managed at multiple scales, for example, on farms with both animals and crops, among animal farms and crop farms within a county, or even among animal farms and crop farms in distant counties. With a focus on redistribution among counties, we classified the 3109 counties of the contiguous United States by their capacity to either supply manure phosphorus (P) and nitrogen (N) from confined livestock production (“sources”) or to assimilate and remove excess P and N via crops (“sinks”). Manure nutrient source counties were identified in 40 of the 48 states, with a substantial concentration in the southern US. Source counties for manure P greatly outnumbered source counties for manure N (390 vs. 100), and 99 of the 100 manure N source counties were also source counties for manure P. Conversely, sink counties for manure N outnumbered sink counties for manure P (2766 vs. 2317). We used the P balances of the source and sink counties to delineate four manuresheds dominated by various combinations of confined hog, poultry, dairy, and beef industries. The four manuresheds differed in the transport distances needed to assimilate excess manure P from their respective source areas (from 147 ± 51 km for a beef dominated manureshed to 368 ± 140 km for a poultry dominated manureshed), highlighting the need for systems-level strategies to promote manure nutrient recycling that operate across local, county, regional, and national scales.
•A "manureshed" encompasses the lands surrounding animal feeding operations onto which manure nutrients can be redistributed to meet environmental, production, and economic goals.•3109 U.S. counties were classified as sources or sinks of manure nutrients.•Manure nutrient source counties were found in 40 states.•Manuresheds were delineated for major animal industries.•Manureshed differed in transport distances from 147 km to 368 km.
•Chihuahuan, Sonoran and Mojave Deserts vary in precipitation amount and seasonal distribution.•Due to hydrologic losses, ET was a better metric of ecosystem-available water than ...precipitation.•Ecosystem water use efficiency (GEP/ET) did not differ between winter and summer.•Due to lower respiration, winter seasons were critical for net carbon uptake.•Reduced 21st century winter precipitation reduced the carbon sink ∼6.8TgCyr1 in these 3 deserts.
Global-scale studies suggest that dryland ecosystems dominate an increasing trend in the magnitude and interannual variability of the land CO2 sink. However, such model-based analyses are poorly constrained by measured CO2 exchange in open shrublands, which is the most common global land cover type, covering ∼14% of Earth’s surface. Here we evaluate how the amount and seasonal timing of water availability regulate CO2 exchange between shrublands and the atmosphere. We use eddy covariance data from six US sites across the three warm deserts of North America with observed ranges in annual precipitation of ∼100–400mm, annual temperatures of 13–18°C, and records of 2–8 years (33 site-years in total). The Chihuahuan, Sonoran and Mojave Deserts present gradients in both mean annual precipitation and its seasonal distribution between the wet-winter Mojave Desert and the wet-summer Chihuahuan Desert. We found that due to hydrologic losses during the wettest summers in the Sonoran and Chihuahuan Deserts, evapotranspiration (ET) was a better metric than precipitation of water available to drive dryland CO2 exchange. In contrast with recent synthesis studies across diverse dryland biomes, we found that NEP could not be directly predicted from ET due to wintertime decoupling of the relationship between ecosystem respiration (Reco) and gross ecosystem productivity (GEP). Ecosystem water use efficiency (WUE=GEP/ET) did not differ between winter and summer. Carbon use efficiency (CUE=NEP/GEP), however, was greater in winter because Reco returned a smaller fraction of carbon to the atmosphere (23% of GEP) than in summer (77%). Combining the water-carbon relations found here with historical precipitation since 1980, we estimate that lower average winter precipitation during the 21st century reduced the net carbon sink of the three deserts by an average of 6.8TgC yr1. Our results highlight that winter precipitation is critical to the annual carbon balance of these warm desert shrublands.
Models like the Rangeland Hydrology and Erosion Model (RHEM) are useful for estimating soil erosion, however, they rely on input parameters that are sometimes difficult or expensive to measure. ...Specifically, RHEM requires information about foliar and ground cover fractions that generally must be measured in situ, which makes it difficult to use models like RHEM to produce erosion or soil risk maps for areas exceeding the size of a hillslope such as a large watershed. We previously developed a deep learning emulator of RHEM that has low computational expense and can, in principle, be run over large areas (e.g., over the continental US). In this paper, we develop a deep learning model to estimate the RHEM ground cover inputs from remote sensing time series, reducing the need for extensive field surveys to produce erosion maps. We achieve a prediction accuracy on hillslope runoff of R2≈0.9, and on soil loss and sediment yield of R2≈0.4 at 66,643 field locations within the US. We demonstrate how this approach can be used for mapping by developing runoff, soil loss, and sediment yield maps over a 1356 km2 region of interest in Nebraska.
•We developed a deep learning model estimating the ground cover inputs of the RHEM.•Litter and Shrubs are the most and Bio Crusts is the least accurate estimations.•We achieved an R2≈0.9 for runoff and ≈0.4 for soil loss by the estimated covers.
Thermal inertia is a physical property of soil at the land surface related to water content. We developed a method for estimating soil thermal inertia using two daily measurements of surface ...temperature, to capture the diurnal range, and diurnal time series of net radiation and specific humidity. The method solves for soil thermal inertia assuming homogeneous 1-D diffusion of heat near the land surface. The solution uses a boundary condition taken as the maximum likelihood estimate of ground heat flux made by a probabilistic uncertainty model of the partitioning of net radiation based on the theory of maximum entropy production (MEP model). We showed that by coupling the 1-D diffusion and MEP models of energy transfer at the land surface, the number of free parameters in the MEP model can be reduced from two (P — soil thermal inertia and I — thermal inertia of convective heat transfer to the atmosphere) to one (P is defined by I). A sensitivity analysis suggested that, for the purpose of estimating thermal inertia, the coupled model should be parameterized by the ratio P/I. The coupled model was demonstrated at two semi-arid sites in the southwest United States to estimate thermal inertia and these thermal inertia values were used to estimate soil moisture. We found 1) parameterizing the MEP model with a constant annual P/I value resulted in surface flux estimates which were similar to those made when daily P and I parameters were derived directly from measurements of ground heat flux (Nash-Sutcliffe efficiency>0.95); 2) estimates of P made using the coupled model were superior to those made using the diffusion model with a common linear approximation of the ground heat flux boundary condition; and 3) thermal inertia was a better predictor of soil moisture in moderately wet conditions than in dry conditions due to a lack of sensitivity of thermal inertia to changes in soil moisture at low moisture contents.
► Thermal inertia is estimated with coupled diffusion and maximum entropy models. ► A single parameter is required: the ratio of soil to atmosphere thermal inertia. ► More accurate than a diffusion model with linearized boundary condition. ► Soil moisture estimates are limited by sensitivity of thermal inertia to moisture.
During the 21st century, human–environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled ...systems. Human–environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi‐resolution information for analysis. Climate change, land‐use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high‐density, and high‐resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) for Socio‐Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross‐site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio‐environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data.
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