Gary C. Heathman’s research while affiliated with Agricultural Research Service and other places

The evaluation of agricultural practices through monitoring and modeling is necessary for the development of more effective conservation programs and policies. No-till and reduced-till are both agricultural conservation practices widely promoted for their proven ability to conserve water and reduce soil erosion. These conservation practices were used to evaluate the APEX (Agricultural Policy/Environmental Extender) model. Data from two tile-drained corn-soybean rotation fields located within the St. Joseph River watershed in northeast Indiana were collected and compared. Observed daily surface and subsurface tile flow, sediment load, and nutrient transport values were described and analyzed using a Wilcoxon signed-rank test. Among the nutrient variables examined, we compared soluble phosphorus (SP), total phosphorus (TP), soluble nitrogen (SN), and soluble nitrogen in tile (SN-tile). The results agree with previous findings identifying lower sediment and nutrient transport values in no-till compared to reduced-till (except for SP during the corn year). However, significantly lower values were only observed for sediment and SN-tile losses in the no-till system. The monitored variables were also used for calibration and validation of the APEX model. APEX calibration/validation evaluation scores were satisfactory for surface runoff for both tillage management simulations (R2 = 0.87/0.76 and NSE = 0.65/0.76 for no-till, and R2= 0.76/0.74 and NSE = 0.74/0.74 for reduced-till, in addition to other statistical analyses). Model performances in simulating sediment load, nutrient variables, and tile flow were relatively lower, yet satisfactory overall. APEX was an efficient tool for simulating most of the variables examined. However, the model presents limitations in simulating tile flow, and consequently nitrogen loss, from tile-drained systems. © 2014 American Society of Agricultural and Biological Engineers.

Soil erosion is one of the processes responsible for water and soil quality deterioration and is impacted by local soil and land cover conditions. One of the primary functions of land cover is to protect the soil and prevent land degradation by water and wind erosion [1]. Recent interest in biofuel energy production can compromise soil quality due to increased removal of crop residue to be used as source of biofuel feedstocks. Knowledge of the impact of human-induced changes to land cover is critical to developing ecosystem-based management approaches to address these issues.

Various studies have demonstrated that spectral indices derived from remotely sensed data can be used to quantify crop residue cover, if adequately calibrated using in situ data. This study evaluates the capability of the Normalized Difference Tillage Index (NDTI) derived from Advance Land Imager (ALI) relative to that of Landsat Thematic Mapper (TM) and the performance of the Cellulose Absorption Index (CAI) derived from Hyperion and airborne hyperspectral data acquired over central Indiana watersheds. A framework based on Cumulative Distribution Function (CDF) matching is also proposed to leverage the superior predictive capability of hyperspectral based indices to improve predictions of multispectral based indices over extended regions. ALI data consistently yielded crop residue models with lower root mean square error (RMSE) values than those developed using Landsat TM data. Hyperspectral based indices were generally superior in predictive capability to the NDTI based predictions. Observation operators derived from the CDF matching method were successful in scaling multiple data sets to achieve models with lower RMSE and improved predictive capability over the entire range of index values.

Soil erosion reduces crop productivity and creates negative impacts on water quality. Soil erosion by water has become a problem worldwide and as concerns about the environment continue to grow, soil erosion remains a very active area of scientific research. In this study, based on advanced remote sensing and Geographic Information Systems (GIS) technologies, the influences of precipitation, soil, topography and vegetation on soil erosion sensitivity are evaluated. An index system and the classification standard for soil erosion sensitivity assessment in the Tongbai–Dabie Mountainous area are established with soil erosion sensitivity being evaluated and analyzed in order to provide a scientific basis for controlling soil erosion and for making sound ecological engineering decisions. According to the regional conditions, sensitivity is classified into five levels: insensitive, mild, moderate, high and extreme. The distribution of erosion sensitivity for the region is analyzed and the various impacts are discussed. The results show that the sensitivity of the Tongbai–Dabie Mountainous area to soil erosion is relatively high, with 46.34% of the total area above the moderate level, and 44.30% and 9.36% rated at the mild and insensitive levels, respectively. In regards to the spatial distribution, the sensitivity levels decrease from south to north, with highly sensitive areas found mainly in the south in the areas of Jinzhai, Huoshan, Shangcheng, Yuexi, and Shucheng. The distribution of soil erosion sensitivity levels was very consistent with the intensity of soil erosion. Areas of high sensitivity are found to have severe areas of soil erosion, indicating that regional soil erosion is highly influenced by natural factors, although in some areas it is evident that the impact of human activities has played a significant role in exacerbating the problem. The results of this investigation serve to advance efforts to reduce the impacts soil erosion in the region and prevent further erosion in areas having high erosion sensitivities.

Soil moisture plays a significant role in determining the amount of energy exchange between the atmosphere and the earth's surface and is highly variable in space and time. Temporal stability analysis (TSA) is a statistical approach for describing the persistence of spatial patterns and characteristic behavior of soil moisture. Using TSA, this study is aimed at determining the adequacy of long term point-scale surface and subsurface soil moisture (θv) measurements in representing field and watershed scale averages that serve as in situ ground truth locations for remotely sensed soil moisture calibration and validation programs, as well as applications for hydrologic modeling. In two agricultural fields, twenty temporary frequency-domain reflectometry (FDR) soil moisture sensors, spaced 70 m apart, were installed at depths of 5 and 20 cm in each field with measurements transmitted every 30 min from June 29 through September 21, 2010. Soil moisture data were also obtained from FDR sensors permanently installed at depths of 5 and 20 cm at seven sites located within the USDA, Upper Cedar Creek Watershed (UCCW) monitoring network in northeastern Indiana. Additionally, meteorological data (i.e., rainfall, air temperature, humidity) were obtained from existing UCCW network weather stations. Spatiotemporal analysis revealed persistent patterns in surface soil moisture and identified sites that were temporally stable at both study scales. However, soil water patterns differed between preferred states (wet/dry) and were primarily controlled by lateral and vertical fluxes. At the field scale, locations that were optimal for estimating the area-average water contents were different from the permanent sensor locations. However, minimum offset values could be applied to the permanent sensor data to obtain representative field average values of surface θv. TSA of 20 cm θv showed little correlation with surface θv TSA results in terms of comparable stable sites or vertical transferability at either scale. The results are of relevance for the interpretation, scaling, or in describing the variability of coarser resolution soil moisture data such as that retrieved from remotely sensed active and passive microwave platforms and in terms of modeling field and watershed scale soil moisture based on point measurements.

Scale difference between in situ and remotely sensed soil moisture observations and model grid size has been an issue for validation of remote sensing data, soil moisture data assimilation and calibration of hydrologic models. This study aims to link two different scales of soil moisture estimates by upscaling single point measurements to field averages for representing field-scale agricultural areas (similar to 2 ha) located within the Upper Cedar Creek Watershed in northeastern Indiana. Several statistical methods, mainly focusing on cumulative distribution function (CDF) matching, are tested to upscale point measurements to spatially representative soil moisture. These transforming equations are termed observation operators. The CDF matching is found to be the most successful upscaling method followed by the mean relative difference method using temporally stable point measurements. This study also tests the temporal and spatial (horizontal and vertical) transferability of the different observation operators. Results indicate that the two observation operators from the CDF matching approach and the mean relative difference method using a temporally stable location are transferable in space, but not in time. Rainfall characteristic is most likely the dominant factor affecting the success of observation operator transferability. In addition, the CDF matching approach is found to be an effective method to deduce spatial variability (standard deviation) of soil moisture from single point measurements.

Data assimilation techniques have been proven as an effective tool to improve model forecasts by combining information about observed variables in many areas. This article examines the potential of assimilating surface soil moisture observations into a field-scale hydrological model, the Root Zone Water Quality Model, to improve soil moisture estimation. The Ensemble Kalman Filter (EnKF), a popular data assimilation technique for nonlinear systems, was applied and compared with a simple direct insertion method. In situ soil moisture data at four different depths (5, 20, 40, and 60 cm) from two agricultural fields (AS1 and AS2) in northeastern Indiana were used for assimilation and validation purposes. Through daily update, the EnKF improved soil moisture estimation compared with the direct insertion method and model results without assimilation, having more distinct improvement at the 5 and 20 cm depths than for deeper layers (40 and 60 cm). Local vertical soil property heterogeneity in AS1 deteriorated soil moisture estimates with the EnKF. Removal of systematic bias in the forecast model was found to be critical for more successful soil moisture data assimilation studies. This study also demonstrates that a more frequent update generally contributes in enhancing the open loop simulation; however, large forecasting error can prevent more frequent update from providing better results. In addition, results indicate that various ensemble sizes make little difference in the assimilation results. An ensemble of 100 members produced results that were comparable with results obtained from larger ensembles. Copyright © 2011 John Wiley & Sons, Ltd.

Soil moisture is an intrinsic state variable that varies considerably in space and time. From a hydrologic viewpoint, soil moisture controls runoff, infiltration, storage and drainage. Soil moisture determines the partitioning of the incoming radiation between latent and sensible heat fluxes. Although soil moisture may be highly variable in space and time, if measurements of soil moisture at the field or small watershed scale are repeatedly observed, certain locations can often be identified as being temporally stable and representative of the an area average. This study is aimed at determining the adequacy of long term point-scale surface soil moisture measurements in representing local field scale averages which may ultimately serve as in situ locations for the calibration and validation of remotely sensed soil moisture. Experimental data were obtained by frequency-domain reflectometry (FDR) sensors permanently installed in two agricultural fields, AS1 and AS2 (2.23 and 2.71 ha, respectively) at a depth of 5 cm. Twenty additional FDR sensors, spaced 35 m apart, were installed horizontally at a depth of 5 cm in each field with automated data collection being transmitted every 30 min from July 15 through September 20, 2009. Additionally, meteorological data were obtained from existing weather stations in each field. The FDR sensors revealed persistent patterns in surface soil moisture within each field and identified sites that were temporally stable. The locations that were optimal for estimating the area-average field water contents were different from the permanent sensor locations in both fields. Permanent sensor data showed approximately 4 and 10% mean relative differences for fields AS1 and AS2, respectively, with relatively large standard deviations. Thus, minimum offset values could be applied to the temporally stable field sites to obtain representative field average values of surface soil moisture. However, use of permanent sensor data for offset estimates gave poor results. These findings are of relevance for applications of geospatial surface soil moisture data assimilation in hydrologic modeling when only point-scale observations are available, as well as, remotely sensed surface soil moisture calibration and validation studies.

This paper aims to investigate how surface soil moisture data assimilation affects each hydrologic process and how spatially varying inputs affect the potential capability of surface soil moisture assimilation at the watershed scale. The Ensemble Kalman Filter (EnKF) is coupled with a watershed scale, semi-distributed hydrologic model, the Soil and Water Assessment Tool (SWAT), to assimilate surface (5. cm) soil moisture. By intentionally setting inaccurate precipitation with open loop and EnKF scenarios in a synthetic experiment, the capability of surface soil moisture assimilation to compensate for the precipitation errors were examined. Results show that daily assimilation of surface soil moisture for each HRU improves model predictions especially reducing errors in surface and profile soil moisture estimation. Almost all hydrological processes associated with soil moisture are also improved with decreased root mean square error (RMSE) values through the EnKF scenario. The EnKF does not produce as much a significant improvement in streamflow predictions as compared to soil moisture estimates in the presence of large precipitation errors and the limitations of the infiltration-runoff model mechanism. Distributed errors of the soil water content also show the benefit of surface soil moisture assimilation and the influences of spatially varying inputs such as soil and landuse types. Thus, soil moisture update through data assimilation can be a supplementary way to overcome the errors created by inaccurate rainfall. Even though this synthetic study shows the potential of remotely sensed surface soil moisture measurements for applications of watershed scale water resources management, future studies are necessary that focus on the use of real-time observational data.

To better assess the impacts of conservation buffers and grasslands on water quality at large spatial scales, development and integration of novel approaches are crucial to ensure that these land management practices are functioning properly and meeting their original goals. Recent developments in remote sensing technology have greatly enriched the availability of geospatial data that can be used in hydrological modeling to assess the potential hydrological response of conservation practices over larger areas. A methodology was developed using the object-based image analysis approach with Landsat-5 TM imagery of the year 2005 and thematic layers of streams to quantify conservation buffers and grasslands (OBIA-2005). The OBIA-2005 land cover data was used in the Soil and Water Assessment Tool hydrologic model to assess the impacts of vegetative conservation practices on total phosphorus (TP) loads. The model was calibrated and validated for discharge and TP loads in the Cedar Creek Watershed (CCW) in northeast Indiana. In general model efficiency for streamflow values was within acceptable statistical ranges. While calibration of TP loads was satisfactory for the total contributing area of two nested catchments within the upper CCW. Vegetative buffers of 30.5m and 61m combined with conservation grasslands generated from the OBIA-2005 resulted in a large reduction of TP loads as compared to no practices. The results also showed that including conservation grassland alone reduced TP loads by less than 2%. However, the combination of these practices with the width of edge-of-field buffer strips module of the SWAT model achieved the largest TP loads reduction. These findings demonstrate that improved representation of vegetative conservation practices in geospatial land cover data sets are more effective in assessing the impacts of conservation buffers and grasslands on water quality through hydrologic modeling.