POTENTIAL MARKET IMPACTS OF SOYBEAN RUST

Crop Production/Industries,

Data Assimilation to extract Soil Moisture Information from SMAP Observations

This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE) by 0.005 m(exp 3) m(exp 3) and 0.001 m(exp 3) m(exp 3), respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m(exp 3) m(exp 3), but increased the root zone bias by 0.014 m(exp 3) m(exp 3). Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to skill degradation in other land surface variables

Utilization of Steel Slag in Blind Inlets for Dissolved Phosphorus Removal

Blind inlets are implemented to promote obstruction-free surface drainage of field depressions as an alternative to tile risers for the removal of sediment and particulate phosphorus (P) through an aggregate bed. However, conventional limestone used in blind inlets does not remove dissolved P, which is a stronger eutrophication agent than particulate P. Steel slag has been suggested as an alternative to limestone in blind inlets for removing dissolved P. The objectives of this study were to construct a blind inlet with steel slag and evaluate its ability to remove dissolved P, nitrogen (N), and herbicides. A blind inlet was constructed with steel slag in late 2015; data from only 2018 are reported due to inflow sampling issues. The blind inlet removed at least 45% of the dissolved P load and was still effective after three years. The dissolved P removal efficiency was greater with higher inflow P concentrations. More than 70% of glyphosate and its metabolite, and dicamba were removed. Total N was removed in the form of organic N and ammonium, although N cycling processes within the blind inlet appeared to produce nitrate. Higher dissolved atrazine and organic carbon loads were measured in outflow than inflow, likely due to the deposition of sediment-bound particulate forms not measured in inflow, which then solubilized with time. At a cost similar to local aggregate, steel slag in blind inlets represents a simple update for improving dissolved P removal

Performance of a Ditch-Style Phosphorus Removal Structure for Treating Agricultural Drainage Water with Aluminum-Treated Steel Slag

Several structural, treatment, and management approaches exist to minimize phosphorus (P) transport from agricultural landscapes (e.g., cover cropping and conservation tillage). However, many of these practices are designed to minimize particulate P transport and are not as effective in controlling dissolved P (DP) losses. Phosphorus removal structures employ a P sorption material (PSM) to trap DP from flowing water. These structures have been successful in treating surface runoff by utilizing aluminum (Al)-treated steel slag, but subsurface tile drainage has never been tested with this material. The goal of this study was to evaluate the performance and economics of a ditch-style P removal structure using Al-treated steel slag for treating agricultural subsurface drainage discharge. The structure treated subsurface drainage water from a 4.5 ha agricultural field with elevated soil test P levels. Overall, the structure removed approximately 27% and 50% of all DP and total P (TP) entering the structure, respectively (i.e., 2.4 and 9.4 kg DP and TP removal). After an initial period of strong DP removal, the discrete DP removal became highly variable. Flow-through analysis of slag samples showed that the slag used to construct the structure was coarser and less sorptive compared to the slag samples collected prior to construction that were used to design and size the structure. Results of this study highlight the importance of correctly designing the P removal structures using representative PSMs

POTENTIAL MARKET IMPACTS OF SOYBEAN RUST

Paper and PowerPoint Presentatio

Understanding temporal stability: a long-term analysis of USDA ARS watersheds

The U.S. Department of Agriculture’s Agricultural Research Service (USDA-ARS) maintains seven in situ soil moisture networks throughout the continental United States, some since 2002. These networks are crucial for understanding the spatial and temporal extent of droughts in their historical context, parameterization of hydrologic models, and local agricultural decision support. However, the estimates from these networks are dependent upon their ability to provide reliable soil moisture information at a large scale. It is also not known how many network stations are sufficient to monitor watershed scale dynamics. Therefore, the objectives of this research were to: (1) determine how temporally stable these networks are, including the relationships between various sensors on a year-to-year and seasonal basis, and (2) attempt to determine how many sensors are required, within a network, to approximate the full network average. Using data from seven in situ, it is concluded that approximately 12 soil moisture sensors are sufficient in most environments, presuming their locations are distributed to capture the hydrologic heterogeneity of the watershed. It is possible to install a temporary network containing a suitable number of sensors for an appropriate length of time, glean stable relationships between locations, and retain these insights moving forward with fewer sensor resources

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE) by 0.005 m 3 m − 3 and 0.001 m 3 m − 3 , respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m 3 m − 3 , but increased the root zone bias by 0.014 m 3 m − 3 . Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to skill degradation in other land surface variables

Assessment of the SMAP Level-4 surface and root-zone soil moisture product using in situ measurements

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real-time) and provides 3-hourly, global, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root-zone) soil moisture measurements for 43 (17) reference pixels at 9-km and 36-km grid-cell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root-zone) soil moisture at 401 (297) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 cu m/cu m or better. The ubRMSE for L4_SM surface (root-zone) soil moisture is 0.038 cu m/cu m (0.028 cu m/cu m) at the 9-km scale and 0.034 cu m/cu m (0.024 cu m/cu m) at the 36-km scale. The L4_SM estimates improve (significantly at the 5 level for surface soil moisture) over model-only estimates, which have a 9-km surface (root-zone) ubRMSE of 0.043 cu m/cu m (0.031 cu m/cu m) and do not benefit from the assimilation of SMAP brightness temperature observations. Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions