Gary C. Heathman

Assimilation of surface soil moisture to estimate profile soil water content

Abstract The use of surface soil water content data as additional input for the Root Zone Water Quality Model in modeling profile soil water content was investigated at four field sites in the Little Washita River Experimental Watershed in south central Oklahoma, coincident with the Southern Great Plains 1997 (SGP97) Hydrology Experiment. Modeled soil water profile estimates were compared to field measurements made periodically during the same time period using a field calibrated time-domain reflectometry (TDR) system. The model was first run in the normal mode with inputs of initial conditions and upper boundary conditions of measured rainfall intensities and daily mean meteorological variables that determined evapotranspiration (ET). Soil hydraulic properties used in the model were estimated from limited soils data information, since in practical terms this is usually the case. Moreover, in our earlier study even the complete description of hydraulic properties based on laboratory and field measurements did not improve the results over average profile estimates using only limited input data. The model runs were then repeated with the daily simulated soil water content in the surface 0–5 cm layer being replaced by 0–5 cm measured soil water content. This process of forcing measured surface water content as additional model input is called direct insertion data assimilation. The simulated profile soil water contents, with and without data assimilation, were compared with TDR-measured profiles to a depth of 60 cm. Gravimetric surface soil water content was measured during SGP97 from June 18 to July 16, 1997 and used as a surrogate for remotely sensed surface moisture data. Data assimilation of surface soil moisture improved model estimates to a depth of 30 cm at all sites. Of particular significance, with data assimilation, model estimates more closely matched the measured dynamic fluctuations of soil moisture in the top 30 cm in response to rainfall events. There was no significant improvement in soil water estimates below the 30 cm depth. This may indicate that data assimilation of surface soil moisture tends to compensate for any errors in model simulations emanating from: (1) errors in the measurement of rainfall intensities or in using 5-min averaged rainfall intensities as done here; (2) errors in using daily average values of meteorological variables that determine ET in a daily ET model; (3) errors in determining hydraulic properties of the surface soil by either laboratory methods or more simple techniques; (4) errors due to the spatial variability of soil hydraulic properties not properly represented in the model.

Crop Residue Modeling and Mapping Using Landsat, ALI, Hyperion and Airborne Remote Sensing Data

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.

Evaluation of the AnnAGNPS Model for Atrazine Prediction in Northeast Indiana

The Annualized Agricultural Nonpoint Source (AnnAGNPS) pollution model was developed for simulation of runoff, sediment, nutrient, and pesticide losses from ungauged agricultural watersheds. This article describes the first documented calibration and validation of AnnAGNPS for prediction of atrazine loading. Here, the model was applied to the 707 km2 Cedar Creek watershed (CCW) and the 45 km2 Matson Ditch sub-catchment (MDS), which are predominantly (>85%) agricultural, with major crops of corn and soybeans. Atrazine herbicide is of significant concern, as the St. Joseph River is the source of drinking water for the city of Fort Wayne, Indiana, with Cedar Creek being the main tributary. Major objectives were to evaluate the ability of AnnAGNPS to simulate runoff and atrazine concentrations in uncalibrated, calibrated, and validation modes. In an uncalibrated mode, flow discharge predictions by AnnAGNPS were satisfactory at the CCW scale but could be improved through calibration. Flow discharge for both CCW and MDS could be well matched with observed values during model calibration and validation. AnnAGNPS predictions of atrazine concentrations in runoff water were very poor, and it was impossible to improve the results through any type of calibration. Inspection of the model source code revealed a unit conversion error in the runoff value being input to the pesticide routine, which when corrected greatly improved the results. The corrected AnnAGNPS model code could be satisfactorily calibrated and validated for predictions of atrazine concentrations in the MDS, but not in the CCW where only coarse measured data were available.

Application of remote sensing observations as APEX model input for estimating soil erosion

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.