Geology topics

Liwang Ma

Publications and source records attributed to Liwang Ma.

10 recordsLinked to original sources

USGS2017

Crop modeling applications in agricultural water management

This article introduces the fourteen articles that comprise the “Crop Modeling and Decision Support for Optimizing Use of Limited Water” collection. This collection was developed from a special session on crop modeling applications in agricultural water management held at the 2016 ASABE Annual International Meeting (AIM) in Orlando, Florida. In addition, other authors who were not able to attend the 2016 ASABE AIM were also invited to submit papers. The articles summarized in this introductory article demonstrate a wide array of applications in which crop models can be used to optimize agricultural water management. The following section titles indicate the topics covered in this collection: (1) evapotranspiration modeling (one article), (2) model development and parameterization (two articles), (3) application of crop models for irrigation scheduling (five articles), (4) coordinated water and nutrient management (one article), (5) soil water management (two articles), (6) risk assessment of water-limited irrigation management (one article), and (7) regional assessments of climate impact (two articles). Changing weather and climate, increasing population, and groundwater depletion will continue to stimulate innovations in agricultural water management, and crop models will play an important role in helping to optimize water use in agriculture.

Transactions of the ASABE↗

USGS2015

Corn stover harvest increases herbicide movement to subsurface drains: RZWQM simulations

BACKGROUND Crop residue removal for bioenergy production can alter soil hydrologic properties and the movement of agrochemicals to subsurface drains. The Root Zone Water Quality Model (RZWQM), previously calibrated using measured flow and atrazine concentrations in drainage from a 0.4 ha chisel-tilled plot, was used to investigate effects of 50 and 100% corn ( Zea mays L.) stover harvest and the accompanying reductions in soil crust hydraulic conductivity and total macroporosity on transport of atrazine, metolachlor, and metolachlor oxanilic acid (OXA). RESULTS The model accurately simulated field-measured metolachlor transport in drainage. A 3-yr simulation indicated that 50% residue removal decreased subsurface drainage by 31% and increased atrazine and metolachlor transport in drainage 4 to 5-fold when surface crust conductivity and macroporosity were reduced by 25%. Based on its measured sorption coefficient, ~ 2-fold reductions in OXA losses were simulated with residue removal. CONCLUSION RZWQM indicated that if corn stover harvest reduces crust conductivity and soil macroporosity, losses of atrazine and metolachlor in subsurface drainage will increase due to reduced sorption related to more water moving through fewer macropores. Losses of the metolachlor degradation product OXA will decrease due to the more rapid movement of the parent compound into the soil.

Pest Management Science↗

USGS2015

Simulating maize yield and bomass with spatial variability of soil field capacity

Spatial variability in field soil properties is a challenge for system modelers who use single representative values, such as means, for model inputs, rather than their distributions. In this study, the root zone water quality model (RZWQM2) was first calibrated for 4 yr of maize ( Zea mays L.) data at six irrigation levels in northern Colorado and then used to study spatial variability of soil field capacity (FC) estimated in 96 plots on maize yield and biomass. The best results were obtained when the crop parameters were fitted along with FCs, with a root mean squared error (RMSE) of 354 kg ha –1 for yield and 1202 kg ha –1 for biomass. When running the model using each of the 96 sets of field-estimated FC values, instead of calibrating FCs, the average simulated yield and biomass from the 96 runs were close to measured values with a RMSE of 376 kg ha –1 for yield and 1504 kg ha –1 for biomass. When an average of the 96 FC values for each soil layer was used, simulated yield and biomass were also acceptable with a RMSE of 438 kg ha –1 for yield and 1627 kg ha –1 for biomass. Therefore, when there are large numbers of FC measurements, an average value might be sufficient for model inputs. However, when the ranges of FC measurements were known for each soil layer, a sampled distribution of FCs using the Latin hypercube sampling (LHS) might be used for model inputs.

Agronomy Journal↗

USGS2014

Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States

BACKGROUND Complex environmental models are frequently extrapolated to overcome data limitations in space and time, but quantifying data worth to such models is rarely attempted. The authors determined which field observations most informed the parameters of agricultural system models applied to field sites in Nebraska (NE) and Maryland (MD), and identified parameters and observations that most influenced prediction uncertainty. RESULTS The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration. CONCLUSIONS Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty. RESULTS: The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration. CONCLUSIONS: Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty.

Maryl;Nebraska↗

USGS2014

Effects of tillage and application rate on atrazine transport to subsurface drainage: Evaluation of RZWQM using a six-year field study

Well tested agricultural system models can improve our understanding of the water quality effects of management practices under different conditions. The Root Zone Water Quality Model (RZWQM) has been tested under a variety of conditions. However, the current model's ability to simulate pesticide transport to subsurface drain flow over a long term period under different tillage systems and application rates is not clear. Therefore, we calibrated and tested RZWQM using six years of data from Nashua, Iowa. In this experiment, atrazine was spring applied at 2.8 (1990–1992) and 0.6 kg/ha/yr (1993–1995) to two 0.4 ha plots with different tillage (till and no-till). The observed and simulated average annual flow weighted atrazine concentrations (FWAC) in subsurface drain flow from the no-till plot were 3.7 and 3.2 μg/L, respectively for the period with high atrazine application rates, and 0.8 and 0.9 μg/L, respectively for the period with low application rates. The 1990–1992 observed average annual FWAC difference between the no-till and tilled plot was 2.4 μg/L while the simulated difference was 2.1 μg/L. These observed and simulated differences for 1993–1995 were 0.1 and 0.1 μg/L, respectively. The Nash–Sutcliffe model performance statistic (EF) for cumulative atrazine flux to subsurface drain flow was 0.93 for the no-till plot testing years (1993–1995), which is comparable to other recent model tests. The value of EF is 1.0 when simulated data perfectly match observed data. The order of selected parameter sensitivity for RZWQM simulated FWAC was atrazine partition coefficient > number of macropores > atrazine half life in soil > soil hydraulic conductivity. Simulations from 1990 to 1995 with four different atrazine application rates applied at a constant rate throughout the simulation period showed concentrations in drain flow for the no-till plot to be twice those of the tilled plot. The differences were more pronounced in the early simulation period (1990–1992), partly because of the characteristics of macropore flow during large storms. The results suggest that RZWQM is a promising tool to study pesticide transport to subsurface drain flow under different tillage systems and application rates over several years, the concentrations of atrazine in drain flow can be higher with no-till than tilled soil over a range of atrazine application rates, and atrazine concentrations in drain flow are sensitive to the macropore flow characteristics under different tillage systems and rainfall timing and intensity.

Agricultural Water Management↗

USGS2012

Root zone water quality model (RZWQM2): Model use, calibration and validation

The Root Zone Water Quality Model (RZWQM2) has been used widely for simulating agricultural management effects on crop production and soil and water quality. Although it is a one-dimensional model, it has many desirable features for the modeling community. This article outlines the principles of calibrating the model component by component with one or more datasets and validating the model with independent datasets. Users should consult the RZWQM2 user manual distributed along with the model and a more detailed protocol on how to calibrate RZWQM2 provided in a book chapter. Two case studies (or examples) are included in this article. One is from an irrigated maize study in Colorado to illustrate the use of field and laboratory measured soil hydraulic properties on simulated soil water and crop production. It also demonstrates the interaction between soil and plant parameters in simulated plant responses to water stresses. The other is from a maize-soybean rotation study in Iowa to show a manual calibration of the model for crop yield, soil water, and N leaching in tile-drained soils. Although the commonly used trial-and-error calibration method works well for experienced users, as shown in the second example, an automated calibration procedure is more objective, as shown in the first example. Furthermore, the incorporation of the Parameter Estimation Software (PEST) into RZWQM2 made the calibration of the model more efficient than a grid (ordered) search of model parameters. In addition, PEST provides sensitivity and uncertainty analyses that should help users in selecting the right parameters to calibrate.

Transactions of the ASABE↗

USGS2010

Analysis of elevation changes detected from multi-temporal LiDAR surveys in forested landslide terrain in western Oregon

We examined elevation changes detected from two successive sets of Light Detection and Ranging (LiDAR) data in the northern Coast Range of Oregon. The first set of LiDAR data was acquired during leafon conditions and the second set during leaf-off conditions. We were able to successfully identify and map active landslides using a differential digital elevation model (DEM) created from the two LiDAR data sets, but this required the use of thresholds (0.50 and 0.75 m) to remove noise from the differential elevation data, visual pattern recognition of landslideinduced elevation changes, and supplemental QuickBird satellite imagery. After mapping, we field-verified 88 percent of the landslides that we had mapped with high confidence, but we could not detect active landslides with elevation changes of less than 0.50 m. Volumetric calculations showed that a total of about 18,100 m3 of material was missing from landslide areas, probably as a result of systematic negative elevation errors in the differential DEM and as a result of removal of material by erosion and transport. We also examined the accuracies of 285 leaf-off LiDAR elevations at four landslide sites using Global Positioning System and total station surveys. A comparison of LiDAR and survey data indicated an overall root mean square error of 0.50 m, a maximum error of 2.21 m, and a systematic error of 0.09 m. LiDAR ground-point densities were lowest in areas with young conifer forests and deciduous vegetation, which resulted in extensive interpolations of elevations in the leaf-on, bare-earth DEM. For optimal use of multi-temporal LiDAR data in forested areas, we recommend that all data sets be flown during leaf-off seasons.

Environmental & Engineering Geoscience↗

USGS2010

Predicting unsaturated zone nitrogen mass balances in agricultural settings of the United States

Unsaturated zone N fate and transport were evaluated at four sites to identify the predominant pathways of N cycling: an almond [ Prunus dulcis (Mill.) D.A. Webb] orchard and cornfield ( Zea mays L.) in the lower Merced River study basin, California; and corn–soybean [ Glycine max (L.) Merr.] rotations in study basins at Maple Creek, Nebraska, and at Morgan Creek, Maryland. We used inverse modeling with a new version of the Root Zone Water Quality Model (RZWQM2) to estimate soil hydraulic and nitrogen transformation parameters throughout the unsaturated zone; previous versions were limited to 3-m depth and relied on manual calibration. The overall goal of the modeling was to derive unsaturated zone N mass balances for the four sites. RZWQM2 showed promise for deeper simulation profiles. Relative root mean square error (RRMSE) values for predicted and observed nitrate concentrations in lysimeters were 0.40 and 0.52 for California (6.5 m depth) and Nebraska (10 m), respectively, and index of agreement ( d ) values were 0.60 and 0.71 ( d varies between 0 and 1, with higher values indicating better agreement). For the shallow simulation profile (1 m) in Maryland, RRMSE and d for nitrate were 0.22 and 0.86, respectively. Except for Nebraska, predictions of average nitrate concentration at the bottom of the simulation profile agreed reasonably well with measured concentrations in monitoring wells. The largest additions of N were predicted to come from inorganic fertilizer (153–195 kg N ha −1 yr −1 in California) and N fixation (99 and 131 kg N ha −1 yr −1 in Maryland and Nebraska, respectively). Predicted N losses occurred primarily through plant uptake (144–237 kg N ha −1 yr −1 ) and deep seepage out of the profile (56–102 kg N ha −1 yr −1 ). Large reservoirs of organic N (up to 17,500 kg N ha −1 m −1 at Nebraska) were predicted to reside in the unsaturated zone, which has implications for potential future transfer of nitrate to groundwater.

Journal of Environmental Quality↗

USGS2010

Soil-test N recommendations augmented with PEST-optimized RZWQM simulations

Improved understanding of year-to-year late-spring soil nitrate test (LSNT) variability could help make it more attractive to producers. We test the ability of the Root Zone Water Quality Model (RZWQM) to simulate watershed-scale variability due to the LSNT, and we use the optimized model to simulate long-term field N dynamics under related conditions. Autoregressive techniques and the automatic parameter calibration program PEST were used to show that RZWQM simulates significantly lower nitrate concentration in discharge from LSNT treatments compared with areas receiving fall N fertilizer applications within the tile-drained Walnut Creek, Iowa, watershed (>5 mg N L-1 difference for the third year of the treatment, 1999). This result is similar to field-measured data from a paired watershed experiment. A statistical model we developed using RZWQM simulations from 1970 to 2005 shows that early-season precipitation and early-season temperature account for 90% of the interannual variation in LSNT-based fertilizer N rates. Long-term simulations with similar average N application rates for corn (Zea mays L.) (151 kg N ha-1) show annual average N loss in tile flow of 20.4, 22.2, and 27.3 kg N ha -1 for LSNT, single spring, and single fall N applications. These results suggest that (i) RZWQM is a promising tool to accurately estimate the water quality effects of LSNT; (ii) the majority of N loss difference between LSNT and fall applications is because more N remains in the root zone for crop uptake; and (iii) year-to-year LSNT-based N rate differences are mainly due to variation in early-season precipitation and temperature. Copyright ?? 2010 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

Journal of Environmental Quality↗

USGS2009

Soil nitrogen balance under wastewater management: Field measurements and simulation results

The use of treated wastewater for irrigation of crops could result in high nitrate-nitrogen (NO3-N) concentrations in the vadose zone and ground water. The goal of this 2-yr field-monitoring study in the deep silty clay loam soils south of Dodge City, Kansas, was to assess how and under what circumstances N from the secondary-treated, wastewater-irrigated corn reached the deep (20-45 m) water table of the underlying High Plains aquifer and what could be done to minimize this problem. We collected 15.2-m-deep soil cores for characterization of physical and chemical properties; installed neutron probe access tubes to measure soil-water content and suction lysimeters to sample soil water periodically; sampled monitoring, irrigation, and domestic wells in the area; and obtained climatic, crop, irrigation, and N application rate records for two wastewater-irrigated study sites. These data and additional information were used to run the Root Zone Water Quality Model to identify key parameters and processes that influence N losses in the study area. We demonstrated that NO3-N transport processes result in significant accumulations of N in the vadose zone and that NO3-N in the underlying ground water is increasing with time. Root Zone Water Quality Model simulations for two wastewater-irrigated study sites indicated that reducing levels of corn N fertilization by more than half to 170 kg ha-1 substantially increases N-use efficiency and achieves near-maximum crop yield. Combining such measures with a crop rotation that includes alfalfa should further reduce the accumulation and downward movement of NO3-N in the soil profile. Copyright ?? 2009 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

Journal of Environmental Quality↗