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Table of contents

Manual calibration was applied to the model to yield correct magnitudes of salt ion concentration in soil water, groundwater, and stream water. Due to the predominance of SO 4 and Ca among salt ions in the regional system, targeted parameters were the solubility product of CaSO 4 precipitation—dissolution and the soil fraction of CaSO 4. The solubility product was increased from 0. Model results are tested against in-stream concentration of salt ions, soil salinity, groundwater concentration of salt ions, and groundwater salt ion mass loading to the Arkansas River.

Simulated EC e values are included in the comparison with field-measured EC e values if the simulated water content of the HRU soil layer is greater than 0.


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For 1 , the variations include uniform initial concentrations baseline model , random spatially variable concentrations, and initial concentrations equal to 0. For 2 , the variation included one simulation with no loading. Results for TDS at all five gaging stations are shown in Fig. However, comparing simulated and measured in-stream concentrations on a daily basis is generally a difficult challenge for watershed modelling. Simulated hydrographs for these sites are in Wei et al.

See Fig. TDS is the summation of the concentration of the eight salt ions. These minerals are not observed in NRCS soil surveys of the region and hence were not included in the baseline model. However, several model scenarios were run to investigate the influence of these minerals. Soil bulk fractions between 0. For example, using a fraction of 0. Applying spatially varying fractions across the watershed could provide the correct magnitude of in-stream concentrations of all ions at all stream sampling sites. Regardless, measured in-stream concentrations can provide key information as to the salt minerals present in the watershed, and differences between model output and field data highlight the need for better field survey data of salt mineral content in soils.


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For the baseline model, these fractions were set to 0. The in-stream concentrations in the two tributaries Fig. The two tributaries act as drainage channels for irrigation runoff and groundwater return flows, with much lower flows than the Arkansas River, and hence the in-stream concentrations are affected much more strongly by salt loadings from irrigation events and associated flow patterns. However, the overall trends and magnitude compare well to observed data.

This is shown in the plot of all salt ion data for Timpas Creek in Fig. The relationship for Crooked Arroyo yields an R 2 value of 0. This is particularly promising given that there is no specified upstream loading for the tributaries, and hence all salt mass within the stream system is due to surface runoff, lateral flow, and groundwater discharge.

Hence, comparing simulated and observed in-stream salinity concentrations in these two systems provides a strong test for the model. The summary of in-river salt concentration results is shown by a comparison of all salt ion data for the Rocky Ford Fig. Timpas Creek Fig. However, as the SWAT model often is used to estimate monthly in-stream loads rather than daily in-stream concentration, these results are promising regarding the use of SWAT to estimate in-stream salinity loadings.

The mass loading of total salt from the aquifer to the Arkansas River for each day of the — time period is shown in Fig. These unaccounted-for loadings include groundwater and thus provide an upper limit of in-stream salt loading from groundwater discharge. Results from a salt mass balance calculation on the Arkansas River are also plotted, showing the unaccounted-for TDS loadings groundwater, surface runoff, small inflows in the Arkansas River.

Groundwater salt results are shown by spatial maps and by comparison of frequency distributions. For all simulated results, only concentration values from days on which field samples were taken are included in the analysis. These maps are shown to provide an indication of the degree of spatial variation simulated by the model. Average concentration of field samples based on field samples from 82 monitoring wells shown in Fig.

In addition to a comparison of maximum and average values, comparison at various magnitude levels is performed using relative frequency plots shown in Fig. Results for SO 4 Fig. Note that simulated values were taken from each cultivated HRU, whereas the field surveys using the EM sensors were conducted in approximately fields. The average of observed values is 4.

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The average of the simulated values is 2. As seen from the frequency distribution in Fig. However, the overall magnitude and distribution of values approach the distribution of the measured values. Note that EM measurements have inherent uncertainty. In addition, some of the HRUs included in the analysis are fallow during this period — , which may lead to low soil salinity values that were not measured in the field survey.

The domain-wide salt balance is presented in Fig. A similar salt balance can be performed for each salt ion in the system. Of the salt entering the river, The fluctuations in simulated in-stream concentration, however, are larger than observed with the measured values. This is due to the manner in which SWAT simulates groundwater return flow, with a steady-state flow equation for each HRU that provides pulses of groundwater to streams rather than the multi-dimensional groundwater flow equation that provides physically based, spatially distributed diffuse flow through the aquifer towards the stream network.

Results in Fig. Results also indicate the importance of including salt mass in applied irrigation water, as it accounts for approximately half of salt leaching to the aquifer. Finally, results show the importance of including precipitation—dissolution in the module, as this process is a large component of the salt balance. Without including this process, the module would severely under-predict salt ion concentrations throughout the watershed, demonstrating the need to include each salt ion individually as opposed to modelling salinity as a conservative solute in the system.

The effect of initial salt ion concentrations and upstream salt ion mass loading is summarized by the time series charts in Fig. There are only small differences between using uniform or HRU-variable initial concentrations for soil water and groundwater. Any differences are readily resolved during the warm-up period.

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Hence, to facilitate model use we recommend that uniform initial concentrations be used. For this watershed, salt loading to the streams is principally from groundwater, and if soil water and groundwater are not provided with initial salt ion concentrations, the groundwater salt ion loading to subbasin streams is small compared to the baseline simulation. As downstream flow and in-stream salt loading are affected by groundwater loading, these areas e. Not including upstream salt ion loading at Catlin Dam has a stronger effect on the Rocky Ford site Fig.

This is due to Las Animas being much farther downstream, and hence there is much more groundwater salt ion loading to the streams that can make up for the salt not included at the upstream end of the Arkansas River at Catlin Dam.

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Overall, any point sources of in-stream salt should be added, unless only downstream areas are targeted for baseline simulations and best management practice investigation. The effect of neglecting point sources of in-stream salt decreases as the groundwater loading component of total salt yield increases. The importance of including equilibrium chemistry in the salt transport module is demonstrated by the results shown in Fig.


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    • Clearly, in-stream concentrations are much too low for the simulation without the SEC module for the Timpas Creek and Las Animas sites. This is due to the neglect of salt mineral dissolution, which in the actual system transfers salt mass from the soil and aquifer material to soil water and groundwater, thereby increasing the loading of salt to the stream network. For the Rocky Ford site, the scenarios yield similar results due to the location of the site being close to the upstream end of the modelled region, and thus in-stream concentrations are not affected by groundwater and surface runoff salt loadings to the river.

    For this system, and likely most watersheds, equilibrium chemistry must be included to establish the correct magnitude of salt loading and concentrations. The measured TDS values are also shown. The salinity module of SWAT differs from other salinity models in that it accounts for salt loading for each major hydrologic pathway in a watershed setting stream, groundwater, lateral flow, surface runoff, tile drain flow , for each major salt ion, subject to chemical equilibrium reactions precipitation—dissolution, complexation, cation exchange.

    As such, it can be used to estimate baseline salt loading within a watershed and also explore the impact of land management and water management scenarios to mitigate soil salinity, groundwater salinity, and surface water salinity. The model, however, does not simulate physically based, spatially distributed groundwater flow and solute transport with an accurate depiction of water table elevation and groundwater head gradient, and thus the trends in groundwater salt loading to streams may not be accurate see Fig.

    This study presents a new watershed-scale salt ion fate and transport model by developing a salinity module for the SWAT model. The module accounts for salt loading for each major hydrologic pathway in a watershed setting stream, groundwater, lateral flow, surface runoff, tile drain flow , for each major salt ion SO 4 , Ca, Mg, Na, K, Cl, CO 3 , HCO 3. The module also accounts for principal equilibrium chemistry reactions precipitation—dissolution, complexation, cation exchange. Model results are tested against in-stream salt ion concentration, groundwater salt ion concentration, soil salinity, and groundwater salt loading to the Arkansas River.

    The model can be used to assess baseline salinity conditions in a watershed and to explore land and water management strategies aimed at decreasing salinization in river basins. Such strategies may include on-farm management, lining irrigation canals to reduce saline canal seepage, dry-drainage practices, and reduction of volumes of applied irrigation water. Due to the simulation of soil water salt ion concentrations and SWAT's simulation of crop growth, the salinity module can also be used to investigate the effect of these strategies on crop yield.

    Although this study applied the model to an irrigated area, the model can be applied to non-irrigated areas as well. The code consists of the original SWAT files, with six additional files for the salinity module. The code can also be sent via request from Ryan Bailey at rtbailey colostate.

    STK prepared the solution chemistry algorithms for the salinity module.

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    The authors wish to thank two anonymous reviewers for their suggestions which greatly improved the manuscript. Abbaspour, K. Arabi, M. Bailey, R.