Highlights No-tillage improved plant-available water (PAW) as compared to conventional tillage, especially in dry years. Soil structure showed 4.5 times higher impact on PAW than surface disturbance due to tillage. Consideration of changes in soil structure is important in modeling the effects of soil management. PALMS was found to be an effective model to assess the effects of changes in soil management on soil water. Abstract. Management practices such as no-tillage (NT) have the potential to provide many benefits, such as reduced runoff and soil erosion and increased infiltration and soil water holding capacity. Most hydrological models that are used to simulate the effects of soil management are built based on empirical relationships between management and hydrology outcomes, and they tend to ignore or oversimplify the effects of soil structure. However, soil structure is management dependent and is a driver of water movement and storage in soil. The goal of this study was to better understand the effects of differences in soil structure between NT and conventional tillage (CT) on field-scale hydrology and plant available water (PAW). This study employed in-field measurements of soil structure in NT and CT fields in the Texas Blackland Prairies and used the Precision Agricultural-Landscape Modeling System (PALMS), which can simulate the effects of differences in soil structure. Regression analysis was performed on simulated soil water to understand the relative contributions of variations in surface roughness and macropore properties due to tillage on PAW. Results from this study showed that NT accumulated 44.8, 20.4, and 5.7 cm more PAW than CT in the top 150 cm of the soil profile during the summer growing season in the years 2006, 2008, and 2011, respectively, all of which encountered considerable dry spells. It was also found that the changes of soil structure due to tillage had about 4.5 times more impact on PAW than surface roughness. This study highlights the benefits of adopting NT over CT and showcases the importance of considering soil structure in modeling the effects of soil management on PAW. Keywords: Macropores, No-till, Preferential flow, Surface disturbance, Surface roughness, Texas Blackland Prairies.

Core Ideas Time for water to fully drain from cracks and mesopores was 24 to 72 h. In moist, cracked soil, water moved to 60 cm after 2 h. On dry, cracked soil, water moved to >120 cm in <1.5 h. The mesopore infiltration module improved estimation of soil profile water content. At the pedon scale, the mesopore module improved ponding predictions. Water is preferentially conducted away from the soil surface through large cracks formed in shrink–swell soils, which complicates our ability to calculate the partitioning of water into infiltration and runoff. Preferential flow paths affect the hydrology of a landscape but often are not included in hydrology models. The Precision Agricultural‐Landscape Modeling System (PALMS) contains a Mesopore and Matrix (M&M) module that allows preferential flow and was tested on cracking soil at the pedon and small watershed scale for this study. Four irrigation events were conducted on 10‐m by 10‐m plots of a cracking soil, and volumetric water content (VWC) output for PALMS with and without the M&M module was compared with that measured by a neutron moisture meter. Additionally, measurements of VWC on a 4.4‐ha small watershed were compared with PALMS predictions. At both scales, the M&M module simulated water movement down the soil profile more quickly and eliminated unobserved ponding at the pedon scale relative to the PALMS matrix only. Simulations of water content of the soil profile were generally improved when the M&M module was used. Furthermore, PALMS M&M was relatively easy to parameterize using obtainable and physically relevant parameters, rendering it applicable to shrink–swell soils in a variety of systems.

Core Ideas Desiccation cracks make runoff and infiltration predictions difficult in expansive soils. A crack‐volume model based on water content and soil properties was developed. Crack volume was measured in situ using digital photography of excavated soil layers. The new model improved crack volume estimates over a model based on layer thickness. A critical gap in hydrology knowledge is predicting the partition of runoff and infiltration during rainfall events in shrink‐swell soils with desiccation cracks. Knowledge of surface cracking and crack volume is needed, but field measurements of these vertical soil cracks are time and labor intensive, and the results cannot be easily translated to another location. Our approach to predict soil crack volume at the pedon‐scale uses an existing soil shrinkage model, which has been modified to include soil water content and the coefficient of linear extensibility (COLE). To validate the model, measurements of soil layer thickness, water content, and crack volume were made for seven soils with COLE values from 0.01 to 0.17 m m –1 . Soil crack volume was estimated by filling cracks with a cement slurry and photographing excavated soil layers at the end of the study. Over two drying and wetting cycles, the relationship between soil layer thickness and water content was linear. The modified crack volume equation, using COLE and water content, was a better fit to cement‐estimated crack volume, r 2 from 0.06 to 0.61, than the existing shrinkage model. However, crack volume estimates by both models were six‐times higher than the cement‐estimated crack volume. It is possible the models of crack volume are including all changes to soil porosity from mega‐cracks to mesopores, while direct techniques only measure the larger‐scale cracks. The new, modified crack volume equation is a pedon‐validated equation that advances predictions of cracking extent in landscapes where shrink‐swell potential is variable in space.

Proximal sensing, such as electromagnetic induction, has been used to map the spatial distribution of soil properties; however, the response of these instruments to short-interval variability associated with Vertisols has not been studied. Meter-scale circular landscape features called gilgai, which are associated with surface and subsurface variability, make sampling these soils difficult, especially if the surface has been plowed. The EM38 is an electromagnetic induction tool with a relatively small spatial footprint (1m2), which may be fine enough to identify subsoil variability associated with gilgai. In 2011, an EM38 survey was conducted for a 40 by 50m field with intact circular gilgai in the Texas Blackland Prairies. Soil properties including gravimetric water content, bulk density, inorganic C content, and electrical conductivity of the soil solution were measured. In 2012, half of the field was plowed, and another EM38 survey was conducted. The EM38 was able to locate subsurface variability in soil properties between microhighs and microlows under both intact and plowed conditions. Semi-variance of soil properties increased with increasing distance and reached maximum variance at 3m, corresponding to the average diameter of gilgai features. The overall variability across the study site decreased after plowing. Water content and inorganic C content were the primary soil properties that forced the response of the EM38 in these landscapes, and the partial correlation coefficient suggests the effects of water content and inorganic C content on EM38 response are independent. In calcareous Vertisols, the EM38 can be used to identify subsurface variability and may be useful in developing sampling schemes for Vertisol classification, soil sampling, and fine-scale digital soil mapping.

Though much has been done to understand proximally-sensed bulk apparent electrical conductivity (ECa) in agricultural soils, many of the soil properties identified to be mappable using these techniques, such as salinity and clay content, are not expected to drive ECa response in a non-saline Vertisol. In Vertisols, agrillipedoturbation creates meter-scale variability in soil moisture and chemical properties associated with gilgai features, and if developed from calcareous parent material, can exhibit meter and landscape scale variability in inorganic C content. The ability to map inorganic C may be especially useful in a Vertisol due to its strong correlation with shrink-swell potential. The overall goal of this project was to investigate the potential for mapping inorganic C using ECa surveys in a calcareous Vertisol, with the future goal of mapping shrink-swell potential on these landscapes. On a 40- by 50-m field with intact circular gilgai, ECa was mapped under both moist and dry soil conditions. Soil samples were taken for water content, clay content, inorganic C content, salinity, and depth to parent material. Under moist soil conditions, the strongest correlation to ECa was inorganic C content (r=−0.63), followed by water content (r=0.49); however, under dry conditions, only inorganic C content was significant (r=−0.60). In addition, ECa surveys and inorganic C samples were taken for two larger watersheds of 10 and 14ha. Again, inorganic C content was significantly and reliably correlated to ECa for both fields, and the resulting regression slopes and intercepts were not significantly different between watersheds, though the surveys were conducted at different times. Results suggest that ECa can be used to map inorganic C content in Vertisols weathered from calcareous parent materials, allowing for spatial inference of shrink-swell potential which may be useful in distributed hydrology modeling.

Publicly accessible, high-quality, long-term, satellite-based solar resource data is foundational and critical to solar technologies to quantify system output predictions and deploy solar energy technologies in grid-tied systems. Solar radiation models have been in development for more than three decades. For many years, the National Renewable Energy Laboratory (NREL) developed and/or updated such models through the National Solar Radiation Data Base (NSRDB). There are two widely used approaches to derive solar resource data from models: (a) an empirical approach that relates ground-based observations to satellite measurements and (b) a physics-based approach that considers the radiation received at the satellite and creates retrievals to estimate clouds and surface radiation. Although empirical methods have been traditionally used for computing surface radiation, the advent of faster computing has made operational physical models viable. The Global Solar Insolation Project (GSIP) is an operational physical model from the National Oceanic and Atmospheric Administration (NOAA) that computes global horizontal irradiance (GHI) using the visible and infrared channel measurements from the Geostationary Operational Environmental Satellites (GOES) system. GSIP uses a two-stage scheme that first retrieves cloud properties and then uses those properties in the Satellite Algorithm for Surface Radiation Budget (SASRAB) model to calculate surface radiation. NREL,more » the University of Wisconsin, and NOAA have recently collaborated to adapt GSIP to create a high temporal and spatial resolution data set. The product initially generates the cloud properties using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms [3], whereas the GHI is calculated using SASRAB. Then NREL implements accurate and high-resolution input parameters such as aerosol optical depth (AOD) and precipitable water vapor (PWV) to compute direct normal irradiance (DNI) using the DISC model. The AOD and PWV, temperature, and pressure data are also combined with the MMAC model to simulate solar radiation under clear-sky conditions. The current NSRDB update is based on a 4-km x 4-km resolution at a 30-minute time interval, which has a higher temporal and spatial resolution. This paper demonstrates the evaluation of the data set using ground-measured data and detailed evaluation statistics. The result of the comparison shows a good correlation to the NSRDB data set. Further, an outline of the new version of the NSRDB and future plans for enhancement and improvement are provided.« less

The National Renewable Energy Laboratory (NREL), University of Wisconsin, and National Oceanic Atmospheric Administration are collaborating to investigate the integration of the Satellite Algorithm for Shortwave Radiation Budget (SASRAB) products into future versions of NREL's 4-km by 4-km gridded National Solar Radiation Database (NSRDB). This paper describes a method to select an improved clear-sky model that could replace the current SASRAB global horizontal irradiance and direct normal irradiances reported during clear-sky conditions.

Because of spectral shifts from instrument to instrument in the operational NOAA satellite imager longwave infrared channels, the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) has developed a single-channel land surface temperature (LST) algorithm based on the observed 11-m radiances, numerical weather prediction data, and radiative transfer modeling that allows for consistent results from the Geostationary Operational Environmental Satellite-I/L (GOES-I/L), GOES-M-P, and Advanced Very High Resolution Radiometer (AVHRR)/1 through 3 sensor versions. This approach is implemented in the real-time NESDIS processing systems [GOES Surface and Insolation Products (GSIP) and Clouds from AVHRR Extended (CLAVR-x)], and in the Pathfinder Atmospheres-Extended (PATMOS-x) climate dataset. An analysis of the PATMOS-x LST against that derived from the upwelling broadband longwave flux at each Surface Radiation Network (SURFRAD) site showed that biases in PATMOS-x were approximately 1 K or less. The standard deviations of the PATMOS-x minus SURFRAD LST biases are generally 2.5 K or less at all sites for all sensors. Using the PATMOS-x minus SURFRAD LST distributions to validate the PATMOS-x cloud detection, the PATMOS-x cloud probability of correct detection values were shown to meet the GOES-R specifications for all sites.

A new set of reflectance calibration coefficients has been derived for channel 1 (0.63 m) and channel 2 (0.86 m) of the Advanced Very High Resolution Radiometer (AVHRR) flown on the National Oceanic and Atmospheric Administration (NOAA) and European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar orbiting meteorological satellites. This paper uses several approaches that are radiometrically tied to the observations from National Aeronautics and Space Administration's (NASA's) Moderate Resolution Imaging Spectroradiometer (MODIS) imager to make the first consistent set of AVHRR reflectance calibration coefficients for every AVHRR that has ever flown. Our results indicate that the calibration coefficients presented here provide an accuracy of approximately 2% for channel 1 and 3% for channel 2 relative to that from the MODIS sensor.

The over three-decade-long data record from the Advanced Very High Resolution Radiometer (AVHRR) is ideal for studies of the Earth's changing climate. However, the lack of on-board calibration requires that the solar channels be recalibrated after launch. Numerous calibration studies have been conducted, but significant differences remain among the calibrations. This study is one effort to outline a path towards consensus calibration of the AVHRR solar channels. The characteristics of the polar orbiting satellites bearing the AVHRRs, the AVHRR instruments and data are described as they are related to calibration. A review of past and current calibration studies is also presented and examples of their lack of consensus shown. A list of consensus items is then provided that, if followed by the AVHRR calibration community, should bring the various calibration methods to within the small percent difference required for long-term climate detection.

After decades of research into the subject, preferential flow in soils still plagues those of us who hope to provide better solutions to society's natural resource management conundrums. To that end, simulation models that strike the balance between simplicity and robustness are a high priority. We present the two-domain, Mesopore and Matrix (M&M), water-infiltration module based on soil structure for the Precision Agricultural-Landscape Modeling System (PALMS), by combining laminar flow of water through interaggregate slits with water movement from slits into aggregates using Darcy's law. The M&M model is based on new assumptions using soil aggregate geometry that allow the model to be more easily parameterized for landscapes with varying soil properties. The vertical and horizontal arrangements of aggregates and the slit width (2B(Ped)(theta)) are based on cubic geometry, where the width of the cubes represents the width (w(ped)(theta,z)) of soil structural units, which depends on soil water content (theta) and depth (z). The M&M module can be parameterized so that mesopore infiltration resembles that of the Richards equation as a starting point for the preferential-flow parameterization. Using field measurements of ped size and in situ mesopore volume to calculate B-ped(B) and w(ped)(theta,z), the parameters from the Richards equation starting point can be modified to capture mesopore effects. PALMS was run using this approach with and without M&M for the 1996 frost-free growing season (March 1 to Nov. 1). While both models slightly under-predicted 1.4 m depth drainage compared with independently measured field data from the same period, PALMS with the M&M module increased estimated drainage by 32% in response to the largest 1996 storm event (similar to 100 mm in 2 days) and 11% seasonally (627 mm cumulative precipitation) over the original PALMS model using the Green and Ampt approach. For the 2008 growing season, a period of unusually high rainfall rates (820 mm annual precipitation), PALMS with M&M simulated a 57% increase in drainage response to the season's largest storm (similar to 200 mm in 2 days) and a 20% increase in seasonal drainage compared to PALMS with the Green and Ampt equations. For both 1996 and 2008 seasons, the drainage response to large storms predicted by PALMS with M&M occurred much more rapidly (hours as opposed to days) than PALMS with the Green and Ampt approach. (C) 2009 Elsevier B.V. All rights reserved.

This study was conducted to evaluate the overall performance of the Precision Agricultural‐Landscape Modeling System (PALMS) for calculating runoff and soil loss under cropped conditions. The PALMS model uses a lognormal distribution of saturated hydraulic conductivity across the fields to simulate typical soil heterogeneity within soil texture classes. Runoff and soil loss data were collected in three farm fields for a total of 75 runoff events during 2 yr under six cropping scenarios (alfalfa [Medicago sativa L.] and corn [Zea mays L.] no‐tilled, corn and soybean [Glycine max (L.) Merr.] moldboard plowed, and alfalfa and corn chisel plowed). For individual storms, calculated runoff and sediment loss from PALMS were compared with corresponding measurements for each farm during the entire cropping season. The coefficient of determination (r2) between runoff calculations and measurements was 0.84. The r2 between soil loss calculations and measurements with the storm‐by‐storm simulations during both seasons was 0.78. Based on these continuous simulation results, the PALMS calculations appear to have lower relative errors with large events than small events, a desirable result because large events are most important in assessing the environmental consequences of management practices.

Two of the most important limitations when predicting soil movement are the natural complexity and the spatial heterogeneity of the processes. Soil erosion can vary significantly across short distances as a function of local soil properties and microtopography; but regardless of this, many erosion models assume homogeneity in topography and soil characteristics. The objective of this research was to develop a method for estimating soil loss from agricultural fields that is faithful to the complex topography and spatial heterogeneity common to managed landscapes. Sediment loss for individual storms was achieved by linking soil detachment–deposition equations adapted from the Water Erosion Prediction Project (WEPP) model to the existing water‐flow subroutine in the Precision Agricultural‐Landscape Modeling System (PALMS). In PALMS, sediment was routed appropriately in a two‐dimensional grid, defining the pathways taken by the eroded material. Usually PALMS works on a grid‐cell size of 5 to 20 m and simulates runoff and soil erosion patterns as affected by slope, soil texture, anisotropic surface roughness, soil consolidation, canopy cover, and tillage interactions with topography. In this study, PALMS and WEPP were used to simulate the sediment transport on an idealized field with a complex hillslope profile. Both models showed a consistent soil loss pattern and only minor differences in transport capacities. The models were also compared with data from actual erosion plots, where both runoff and soil loss were predicted with similar errors for both PALMS and WEPP. To illustrate the capability of PALMS, it was applied to a field with complex topography.

This work describes a simple, passive sampling system for measuring runoff, sediment, and chemical losses from typical agricultural fields. The sampler consists of a 5 to 7 m wide runoff collector connected to a series of multislot divisors. These divisors split the flow into aliquots, providing a continuous sampling during the runoff event. Divisors were located in a wooden box below ground level. With an adequate pump, this system can operate in fields with a slope gradient as low as 2%, and can stay in the field during winter to record first snowmelt-generated runoff. A radio transmitter reports by telemetry the occurrence and magnitude of any runoff event, and indicates when the system should be sampled and emptied. This article includes a description of the equipment, advantages, and disadvantages based on 2 yr of operation, and examples of data collected.