<p>A critical consequence of agriculturally managed lands is
the <a></a>transport of nutrients and sediment to fresh water
systems, which is ultimately responsible for a range of adverse impacts on
human and environmental health. In the
U.S. alone, over half of streams and rivers are classified as impaired, with
agriculture as the primary contributor. To address deterioration of water
quality, there is a need for reliable tools and mathematical models to monitor
and predict impacts to water quantity and quality. Soil water content is a key
variable in representing environmental systems, linking and driving hydrologic,
climate, and biogeochemical cycles; however, the influence of soil water
simulations on model predictions is not well characterized, particularly for
water quality. Moreover, while soil moisture estimation is the focus of multiple
remote sensing missions, defining its potential for use in water quality models
remains an open question. The goal of this research is to test whether updating
model soil water process representation or model soil water estimates can
provide better overall predictive confidence in estimates of both soil moisture
and water quality. A widely-used ecohydrologic model, the Soil and Water
Assessment Tool (SWAT), was used to evaluate four objectives: 1) investigate
the potential of a gridded version of the SWAT model for use with similarly
gridded, remote sensing data products, 2) determine the sensitivity of model
predictions to changes in soil water content, 3) implement and test a more
physically representative soil water percolation algorithm, and 4) perform
practical data assimilation experiments using remote sensing data products,
focusing on the effects of soil water updates on water quality predictions.
With the exception of the first objective, model source code was modified to
investigate the relative influence and effect of soil water on overall model
predictions. Results suggested that use of the SWAT grid model was currently
not viable given practical computational constraints. While the advantages
provided by the gridded approach are likely useful for small scale watersheds
(< 500 km2), the spatial resolution necessary to run the simulation was too
coarse, such that many of the benefits of the gridded approach are negated.
Sensitivity tests demonstrated a strong response of model predictions to
perturbations in soil moisture. Effects were highly process dependent, where
water quality was particularly sensitive to changes in both transport and
transformation processes. Model response was reliant upon a default thresholding
behavior that restricts subsurface flow and redistribution processes below
field capacity. An alternative approach that removed this threshold and keyed
processes to relative saturation showed improvement by allowing a more
realistic range of soil moisture and a reduction of flushing behavior. This
approach was further extended to test against baseline satellite data
assimilation experiments; however, did not conclusively outperform the original
model simulations. Nevertheless, overall, data assimilation experiments using a
remote sensing surface soil moisture data product from the NASA Soil Moisture
Active/Passive (SMAP) mission were able to correct for a dry bias in the model
simulations and reduce error. Data assimilation updates significantly impacted flow
predictions, generally by increasing the dominant contributing flow process.
This led to substantial differences between two test sites, where landscape and
seasonal characteristics moderated the impact of data assimilation updates to
hydrologic, water quality, and crop yield predictions. While the findings
illustrate the potential to improve predictions, continued future efforts to
refine soil water process representation and optimize data assimilation with
longer time series are needed. The dependence of ecohydrologic model
predictions on soil moisture highlighted by this research underscores the
importance and challenge of effectively representing a complex,
physically-based process. As essential decision support systems rely on
modeling analyses, improving prediction accuracy is vital.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8309240 |
| Date | 15 August 2019 |
| Creators | Garett William Pignotti (6866696) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Evaluating_Impacts_of_Remote_Sensing_Soil_Moisture_Products_on_Water_Quality_Model_Predictions_in_Mixed_Land_Use_Basins/8309240 |
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