Development and evaluation of ArcGIS based watershed-scale L-THIA ACN-WQ system for watershed management

The Long-term Hydrologic Impact Assessment Model with Asymptotic Curve Number Regression Equation and Water Quality model (L-THIA ACN-WQ) has been developed to simulate streamflow as well as instream water quality using fewer parameters, compared to other watershed models. However, since model input parameters (i.e. hydraulic response unit (HRU) map, stream network, database (DB), etc.) should be built by user manually, it is difficult to use the model for a nonprofessional or environmental policy decision-maker. In addition, it is difficult to analyze model outputs in time and space because the model does not provide geographic information system (GIS) information for the simulation results. To overcome the limitations, an advanced version of L-THIA ACN-WQ system which is based on ArcGIS interface was developed in this study. To evaluate the applicability of the developed system, it was applied to the Banbyeon A watershed in which total maximum daily load (TMDL) has been implemented. The required model input datasets were automatically collected in the system, and stream flow, T-N and T-P pollutant loads were simulated for the watershed. Furthermore, flow duration curve (FDC) and load duration curve (LDC) were generated to analyze hot spot areas in the system through automatic processes included in the system. The system can establish the model input data easily, automatically provide the graphs of FDC and LDC, and provide hot spot areas which indicate high pollutant loads. Therefore, this system can be useful in establishing various watershed management plans. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). doi: 10.2166/ws.2017.176 om https://iwaponline.com/ws/article-pdf/18/4/1206/236791/ws018041206.pdf 9 Jichul Ryu Yong Seok Kim Water Pollution Load Management Research Division, National Institute of Environmental Research, Seogu, Incheon, Republic of Korea Won Seok Jang The Sustainability Innovation Lab at Colorado (SILC), University of Colorado Boulder, Boulder, CO 80303, USA Jonggun Kim Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843, USA Gwanjae Lee Kyoung Jae Lim (corresponding author) Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon, Gangwon, Republic of Korea E-mail: kjlim@kangwon.ac.kr Kwangsik Yoon Department of Rural & Bio-Systems Engineering, Chonnam National University, Gwangju, Republic of Korea


Modules for direct runoff, baseflow and channel routing
In the direct runoff module of the L-THIA ACN-WQ model, direct runoff is calculated for each hydrologic response unit (HRU) using 52 asymptotic curve number equations by land covers and hydrologic soil groups (Equation (1)), and the direct runoff reached to a stream is then estimated using Equation (2).
where Q 1 DR,HRU is the amount of direct runoff generated by an HRU for each day (mm), P is the rainfall (mm), S is the potential maximum retention (mm) and Adj_CN HRU,ACN is adjusted CN value determined from asymptotic CN regression equations.
where Q DR,HRU is the amount of direct runoff discharged to the main channel on a given day (mm), Q 1 DR,HRU is amount of direct runoff generated by the HRU on a given day (mm), Q stor is the direct runoff lagged from the previous day, DR lag is the direct runoff lag coefficient and TC is the time of concentration (h).
In the baseflow module, infiltration is calculated by Equation (3) transformed from the NRCS-CN method, and then the recharged water into aquifer and baseflow flowed into stream are estimated by Equations (4) and (5). Lastly, streamflow is calculated using the MUSKINGUM method that is widely used in hydrology for stream routing.
where F HRU,i is the amount of infiltration on a given day (mm), S is the is the potential maximum retention (mm), P is the rainfall (mm), Adj_CN HRU , ACN is the adjusted CN value determined from asymptotic CN regression equations and I a is the initial abstraction (mm).
where ω rcharg,HRU,i is the amount of recharge entering both aquifers on a given day (mm), BF delay is the delay time in aquifer recharge once the water infiltrates from the surface (days), F HRU,i is the amount of infiltration on the given day (mm) and ω rcharg,HRU,iÀ1 is the amount of recharge that enters the aquifers on the previous day (mm).
where Q BF,HRU,i is the baseflow into the main channel on a given day (mm), Q BF,HRU,iÀ1 is the baseflow into the main channel on the previous day, α BF is the baseflow recession constant, ω unconf,HRU is the amount of recharge entering the unconfined aquifer on the given day (mm), Δt is the time step (1 day), aqf is the amount of water  Weighting factor for Muskingum method 0.1-0.9 (1) constant value; (2) multiplied value.  stored in the unconfined aquifer on the given day (mm) and aqf thr is the threshold water level in the unconfined aquifer for baseflow contribution to the main channel to occur (mm).

Modules for pollutant loads and water quality
The water quality module calculates pollutant loads for direct runoff and baseflow reached to stream using the improved  The programming language has been widely used for general purpose as a high-level programming language. Recently, it was chosen for the ArcGIS platform as a scripting language.
ArcPy is a site-package of Python that helps to build successful ArcGIS scripting modules. Its goal is to create the script tools for performing geographic data analysis, data conversion, data management, and map automation using Python. ArcPy provides access to geoprocessing tools, additional functions, classes, and modules that allow the user to create simple or complex processes conveniently and easily (Dutta et al. ).
ArcPy, which is a very efficient programming language in GIS interface, has been used in various GIS applications. It has been used (Alam ) to extract sub-catchment, outfall, function, and conduit information in urban drainage modeling. In the third part, basic information of stream networks and reach properties (e.g. length, width, depth, slope, and Manning's n value of reach segment) for flow routing are automatically generated from GIS-based stream networks and watershed information in the project folder in this step (Figure 2(c)).
In the fourth step, the core engine of L-THIA ACN-WQ is run with input data sets for hydrology and water quality simulation at a watershed (Figure 2(d)).
Lastly, two options are provided in this part. To generate      The target water quality standard for the Banbyeon A watershed was designated based on 'drinking water (Ia)' standard by the MOE, Korea (Table 4; MOE ). The target water quality of TN was assumed as that of biological oxygen demand (BOD) (MOE ) since the water quality standard of TN has not been established for this watershed.
The target water quality of TN and TP were determined as 2.0 mg/L and 0.04 mg/L, respectively. The flow conditions were categorized into the low-flow (95%), dry-condition (75%), mid-range flow (50%) and high flow (15%).
In addition, subbasins were ranked based on the amount of pollutant loads per unit area and visualized based on pollutant ranking at subbasins.

RESULTS AND DISCUSSION
Development of ArcGIS based watershed-scale L-THIA

ACN-WQ system
The ArcGIS based watershed-scale L-THIA ACN-WQ system was developed using ArcPy programming language   to provide user-friendly interface of watershed-scale L-THIA ACN-WQ model, which does not provide any kind of user interface to construct input data and pre-process the data.
Using the automated GIS-based system, users can easily create HRU maps using GIS input data (e.g. DEM, soil, land cover, and stream) (Figure 4),

FDC and LDC analysis
The streamflow and pollutant loads simulated with the calibrated parameters (Tables 5 and 6)  Ranking subbasins based on pollutant load per unit area The system ranked subbasins based on the pollutant loads per unit area. In this study, the pollutant loads (BOD, TN, and TP) were ranked in the ArcGIS interface (fifth option in the watershed-scale L-THIA ACN-WQ system toolbar), and the pollutant load ranking map was generated to provide the hot spot subbasin to decision makers. Subbasins which are discharging more nutrients were ranked (Figure 10), and the five subbasins (#25, #27, #24, #23, and #22 for TN, and in #7, #24, #27, #25, and #22 for TP) were selected as the hot spot subbasins for nutrient in the Banbyeon A watershed.
It was found that paddy, upland, and orchard occupy the majority of land uses in subbasins #24, #25, and #27. For these subbasins, nutrients discharged from agricultural areas need to be reduced by establishing appropriate agricultural related BMPs.
With the post-processing module (Mapping of Hot Spot Are option in fifth menu) of the watershed-scale L-THIA ACN-WQ system, the TN and TP loading per unit area maps were generated ( Figure 11) with the ranking information. Figure 11 showed that pollutant loads were greater in agriculture dominant subbasins located in downstream areas.