IJERD – International Journal of Environmental and Rural Development (2022) 13-2

Research article
erd

Estimation of Long-term River Discharge in Forested
Watershed in Snowy Region by SWATSHOTARO KIKUCHI
Graduate School of Agriculture, Tokyo University of Agriculture, Japan

HIROMU OKAZAWA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan
Email: h1okazaw@nodai.ac.jp

SARVESH MASKEY
Faculty of Regional Environment Science, Tokyo University of Agriculture, JapanSERGIO AZAEL MAY CUEVAS
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan

MAKOTO OBA
Department of Design for Social System and Living Environment,
Tohoku Institute of Thechnology, JapanSHOGO NAKAMURA
Fukushima Regional Collaborative Research Center,
National Institute for Environmental Studies, JapanSEIJI HAYASHI
Fukushima Regional Collaborative Research Center,
National Institute for Environmental Studies, Japan
Received 31 January 2022

Accepted 18 July 2022(*Corresponding Author)

Abstract Utilizing a hydrological model for analyzing the hydrological cycle throughout a river
basin is an effective method to assess the impacts of climate change on water resource
management, flood control, and agriculture. Although there are various hydrological models

developed, in this study, Soil and Water Assessment Tool plus (SWAT+) is used as it is
widely used and predicts the impacts of land use management in watershed management.
SWAT+ is a complex quasi−physically based water quality model relying on numerous
input files and parameters, thus this poses a great challenge when attempting to set up themodel manually, and there is a lack of information regarding the validation of SWAT+'s of
performance for snow accumulation and melting processes. The objective of this study is toestimate long-term streamflow in forested watershed in snowy region using SWAT+, and toverify the accuracy of the estimation and to confirm the improvement of the accuracy byadjusting parameters. In order to improve the accuracy of simulation, “the saturated
hydraulic conductivity of soil layer” and “the available water capacity of soil layer” were
adjusted for parameter of soil moisture content, moreover, we adjusted parameter oftemperature of “snowfall” and “snowmelt”. Finally, “the time of lateral flow travel” which
is difficult to measure was calibrated using the auto-calibration of SWAT+. As the results,
it was difficult to achieve high accuracy in predicting river discharge with the default
parameters of SWAT+, but some months (May-Oct) could be accurately predicted after
adjusting parameters using measured data and conducting the auto-calibration. On the other
hand, simulations during snowfall and snowmelt term (Dec-Mar) were less accurate andneed to set more detailed conditions.
Keywords SWAT, long-term river discharge, snowmelt, runoff, prediction,Ⓒ ISERD

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INTRODUCTION
With rising temperatures by climate change will threatens to disturb this balance by altering thefraction of precipitation falling as snow and the timing of snowmelt, which may have profoundeffects on food production (Qin et al., 2020). Utilizing a hydrological model for analyzing thehydrological cycle throughout a river basin is an effective method to assess the impacts of climatechange on water resource management, flood control, and agriculture. SWAT (Soil and Water
Assessment Tool) developed by USDA-ARS is a hydrological cycle model that has been usedworldwide for more than 20 years (Williams et al., 2008). In order to face present and future challenges
in SWAT code has undergone major modifications over the past few years, resulting in SWAT+, acompletely revised version of the model (Bieger et al., 2016).
SWAT+ is a complex quasi−physically based water quality model relying on numerous input files
and parameters to represent hydrologic, climatic, water quality, management, plant, and soil processes
within a watershed, thus this poses a challenge when attempting to set up the model manually (Qi and
Grunwald, 2005). Moreover, although snowmelt hydrology is an important subcomponent andprediction of snow accumulation and melting processes are one of the great challenges for SWAT+ (Qiand Grunwald, 2005), but there is a lack of information regarding the validation of SWAT+'s snowmelt
season performance (Wang and Melesse, 2005), especially studies of SWAT+ in forested watershed insnowy region are currently lacking.
OBJECTIVE
The objective of this study is to estimate long-term streamflow in forested watershed in snowy
region using SWAT+, and to verify the accuracy of the estimation and to confirm the improvementof the accuracy by adjusting parameters. For this, in this study, SWAT+ was used to simulate thedaily flow in the Hashigo River in Mishima town, Fukushima Prefecture which is a forested areawith a cold and snowy climate. The several parameters were adjusted based on measured data, andthe SWAT+ automatic parameter calibration method was conducted to improve the accuracy ofmodel. The calculated river discharge was verified by comparing with the observed.
METHODOLOGY
General Description of Study Site
The study site is the Hashigo River watershed in Mishima Town in Fukushima Prefecture (Fig.1).
Hashigo River is a tributary of the Tadami River. The watershed locates at latitude 37.50o - 37.47o,longitude 139.62o-139.63o and range of altitude 350-370 m. Watershed area is 2.85 km2, mostly
covered by mountainous forest (Table 1). The watershed has 0.4% of paddy field which is locatedalong the main river. Annual mean daily temperature, minimum and maximum daily temperature is
10.6°C, 6.9°C and 15.3°C. The snowfall period is from December to February, and the snowmelt
period is from March to April. Rainfall is relatively heavy during the rainy season from June toJuly, and typhoons are more frequent from September to October. Annual precipitation (20192020) was 1,632 mm in 2019 and 1,340 mm in 2020.
Table 1 Land-use of the target watershed
Watershed area
(km2)
2.85

Forest98.4

Land-use (%)
Grass land
Paddy field
1.2
0.4

River length
(km)
2.5

Data Analysis Using SWAT+
SWAT creates objects called Hydrologic Response Units (HRUs), which are lumped areas within asubbasin with a combination of land use, soil type, and slope, etc., for simulating all hydrologicⒸ ISERD

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processes occurring in the landscape (Bieger et al.,2016). SWAT+ also introduces Landscape Units(LSUs), which allow for the separation of lowland (wetland) processes from upland processes. The
use of LSUs allows for a more detailed classification of HRUs compared to SWAT and increases
the number of HRUs. (Kakarndee et al., 2020).

Fig. 1 Location of target watershed (Hashigo River)
The modeling process adopts the water balance method, which is expressed as follows (1).

(1)
Where SWt is the final soil water content (mm), SW0 is the initial soil water content on day(mm), t is the time (day), Rday is the amount of precipitation on day (mm), Qsurf is the amount of
surface runoff on day (mm), Ea is the amount of evapotranspiration on day (mm), Wseep is theamount of water entering the unsaturated zone from the soil profile on day (mm), Qgw is the amountof return flow on day (mm).
The subdivision of the watershed enables the model to reflect differences in evapotranspiration for various crops and soils. Runoff is predicted separately for each HRU and routed toobtain the total runoff for the watershed. This increases accuracy and gives a much better physicaldescription of the water balance (Neitsch et al., 2009). The surface runoff is estimated using the
NRCS Curve Number (CN) method (2).
(2)
Where Qsurf is the surface runoff or rainfall excess (mm), Rday is the rainfall depth for the day(mm), Ia is the initial abstraction (surface storage, interception, and infiltration prior to runoff)(mm), S is the retention parameter (mm).
The retention parameter depends on topography (slope), soil, land-use, management practices,and changes with the time due to soil water content (3).
(3)Ⓒ ISERD

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Where CN is the NRCS curve number corresponding to antecedent water content, soil
infiltration, land-use and land management conditions (Kakarndee et al., 2020).
Evaluation of the Simulated Data
The Root Mean Squared Error (RMSE) is used to indicate the degree of fit between thesimulation and observed values (4).
(4)
Where EV is error variance (squared of observation value minus simulation value) and n is
number of observation days.
The Nash-Sutcliffe Efficiency (NSE) is a normalized statistic that determines the relative
magnitude of the residual variance compared to the measured data variance.
(5)
Where Qsim is the simulated value (m3/s), Qobs is the observed value (m3/s), and Qave is theaverage of observed value for the term of simulation (m3/s).
Qsim and Qobs are respectively the simulated and observed streamflow at time step (m3/s), P
and Q are the corresponding means of the simulated and observed streamflow over the entire
period (m3/s), and n is the total number of streamflow data points.
Description of Spatial Data
The spatial data used in SWAT+ are Digital Elevation Model (DEM), soil and land-use data. 10
meters resolution elevation data obtained from the Geospatial Information Authority of Japan (GSI)
was used for DEM. Soil maps were obtained from the National Institute of Agrobiological Sciences(NIAS)’s basic land classification survey data (Shapefile, 1:200,000). The default parameters in
SWAT+ database was used for soil parameters. The High-Resolution Land Use and Land Cover
Map of Japan (10 m grid size) prepared by the Japan Aerospace Exploration Agency (JAXA) and
Earth Observation Research Center (EORC) was used for the land-use data. A 5% threshold area
was used to define HRUs applying a 5% threshold means that land-uses, soils and slope ranges
whose areas are less than 5% of the subbasin area are eliminated from HRU formation within eachsubbasin. A total of 123 HRUs were created using these spatial data, 45 of which were defined as
actual HURs.
Description of Temporal Data
Daily data for discharge of river (measured at 10-minute intervals) and rainfall, maximum andminimum temperature, relative humidity, solar radiation, and wind speed were measured
automatically by networked weather station. The meteorological data are for the years February
2019 to December 2020. In this study, Penman-Monteith was used as the calculation method forevapotranspiration (Okazawa and Takeuchi, 2016).
RESULTS AND DISCUSSION
Simulation of Daily River Discharge
The SWAT+ model was built from the above data. In the first calculation, the default parameters of
SWAT database were used for the parameters, and the calculation period was set from FebruaryⒸ ISERD108

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2019 to December 2020. The calculation results are shown in Fig. 2. The peak river dischargeduring the rainfall event is higher than Observed value. In addition, the river discharge duringsnowmelt events (From February to April in 2019 and 2020) is lower than observed data, and thesnowmelt rate is not properly reproduced.
Precipitation

Simulation (Default)

Observed

1000

0

100

10
200

1
300

0.1
400

Precipitation(mm)River Discharge(m3/s)

100

0.01
500

0.001

01/12/2020

01/11/2020

01/10/2020

01/09/2020

01/08/2020

01/07/2020

01/06/2020

01/05/2020

01/04/2020

01/03/2020

01/02/2020

01/01/2020

01/12/2019

01/11/2019

01/10/2019

01/09/2019

01/08/2019

01/07/2019

01/06/2019

01/05/2019

01/04/2019

01/03/2019

600
01/02/2019

0.0001

Fig. 2 River discharge of the first simulation results
Simulation after Adjusting Parameters
Comparing the first simulation and observed value, most of the data between December and March
was rainfall, and snowfall and snowmelt were not properly reproduced, thus the parameter ofsnowmelt and snowfall temperature needed to be adjusted. Using the meteorological data which
was maximum temperature, average temperature, and snow depth collected at 10-minute intervals
by a networked weather station, the snowfall temperature (fall_tmp) and snowmelt (melt_tmp)
were calculated to replace the default parameters (fall_tmp: 1.0°C, melt_tmp: 0.5°C). The snowfall
temperature was set to the average temperature (2.51°C) on days when there was snowfall(November and March within 2019-2020). The snowmelt temperature was set to the maximum
temperature (2.71°C) on days when there was no rain (during December-February 2019-2020) andthe snow depth decreased by at least 1 cm.
The peak river discharge of surface runoff in the first simulation was higher than observed
data, and it was assumed that parameter of infiltration was not properly recognized in default
settings. Therefore, the default parameter which were the saturated hydraulic conductivity of soil
layer (soil_k) and the available water capacity of soil layer (awc) were adjusted using the soil data
surveyed by the National Institute of Agrobiological Sciences of Japan. The data was input for the
first layer (soil_k: 159.84 mm/hr, awc: 0.70 mm/mm) and the second layer (soil_k: 25.20 mm/hr,
awc: 0.67 mm/mm).
Simulation After Auto Calibration
In addition to the adjustment of the above parameters, a calibration was conducted using the
SWAT+ auto-calibration. The auto-calibration uses the observed value to correct the parameters
and improves the value of NSE, RMSE and MSE by repeating the simulation. The auto-calibration
can be selected from NSE, RMSE and MSE. In this study, NSE was selected for the autocalibration. The parameters calibrated for the auto-calibration are those that are difficult to measureand input. In this study, the time of lateral flow travel (lat_time) was calibrated because the time lag
between rainfall and groundwater retention time or direct runoff is difficult to measure. The resultsof adjusting the parameters and the results of automatic calibration of lat_time are shown in Fig. 3.

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Observed

Adjusted Prameter

Auto-Calibration

01/12/2020

01/11/2020

01/10/2020

01/09/2020

01/08/2020

01/07/2020

01/06/2020

01/05/2020

01/04/2020

01/03/2020

01/06/2019

01/02/2020

600
01/01/2020

0.001

01/12/2019

500

01/11/2019

0.01

01/10/2019

400

01/09/2019

0.1

01/08/2019

300

01/07/2019

1

01/05/2019

200

01/04/2019

10

01/03/2019

100

01/02/2019

100

River Discharge(m3/s)0Precipitation(mm)Precipitation
1000

Fig. 3 River discharge of the simulation after calibration of parameters
The river discharge after the calibration within February to April was closer than the firstsimulation to the observed data. The calibration slowed the decrease in the river discharge after the
peak of flow and increased the correlation between simulated and observed values
Accuracy of the Obtained Result
The NSE for each month were calculated from the simulation results of the default parameter,adjusted parameter, and auto-calibration. The results are shown in Fig. 4, where the NSE is anevaluation index that takes into account the variability, and the closer the value is to 1, the highest
accuracy. Within Snowfall season (Dec-Feb) and snowmelt season (Mar-Apr), the value of NSE islow, indicating low accuracy. The accuracy of the adjusted parameter and auto-calibration wereshown to be improved over the default parameter in NSE. The accuracy is relatively good during
the season when there was not much rainfall, while the accuracy tends to decrease from thesnowfall season to the snowmelt season (Apr-Dec). The results of calculating the NSE for whole
period are -0.70 for default parameter, -0.02 for adjusted parameter, and 0.27 for auto-calibration.
The RMSE for each month were calculated from the simulation results of the default parameter,adjusted parameter, and auto-calibration.
1.0

0.0
-1.0

NSE

-2.0
-3.0
-4.0
-5.0
-6.0
-7.0

Default

Adjusted

Auto-Calibration

Dec-20Nov-20

Oct-20

Sep-20Aug-20Jul-20Jun-20May-20

Apr-20Mar-20Feb-20Jan-20Dec-19Oct-19Sep-19Aug-19Jul-19Jun-19

May-19Apr-19Mar-19Feb-19

Nov-19-8.0

Fig. 4 The monthly NSE of river discharge for each simulation
The results are shown in Fig. 5. The best value of RMSE is 0, which is an evaluation index
that emphasizes the difference between the actual value and the observed value. During rainfall
events, the value of RMSE is high and the accuracy is evaluated to be low. During low rainfall
events, the river discharge is low and the accuracy is better because the difference between
observed and predicted values become smaller. The RMSE of auto-calibration has lower values
overall and is rated at higher accuracy compared to the other simulations. The RMSE for the whole
period was calculated to be 0.21 for the Default parameter, 0.16 for the adjusted parameter, andⒸ ISERD

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0.14 for the auto-calibration.
In the simulation with default parameters, NSE is negative in most months. On the other hand,by adjusting parameters and conducting auto-calibration, some months showed NSE are more than
0.5. RMSE was improved even when auto-calibration was conducted based on NSE. NSE duringsnowfall and snowmelt (Dec-Mar) is lower than other term, even with parameter adjustments andauto-calibration. It was shown that except for the snowfall and snowmelt season, reproducibility of
the river discharge can be secured.
0.8
Default

Adjusted

Auto-Calibration

RMSE

0.6

0.4

0.2

0.0
Dec-20Nov-20

Oct-20

Sep-20Aug-20Jul-20Jun-20May-20

Apr-20Mar-20Feb-20Jan-20Dec-19

Nov-19Oct-19Sep-19Aug-19Jul-19Jun-19

May-19Apr-19Mar-19Feb-19Fig. 5 The monthly RMSE of river discharge for each simulation
CONCLUSION
The accuracy of SWAT+ in forested snowfall areas was verified by simulating river discharge inthe Hashigo river in Mishima town, Fukushima prefecture. According to the results, although it wasdifficult to secure high reproducibility in simulations using default parameters, the accuracy ofsimulations could be improved by adjusting parameters and conducting auto-calibration. In thisstudy, soil data could not be measured, thus the accuracy of the simulation would be improved byusing the actual measured soil data. RMSE was improved even when auto-calibration was
performed based on NSE in this study, on the other hand, it is necessary to select the autocalibration criteria among NSE, RMSE, and MSE according to the purpose because eachcalibration criteria have different characteristic. It is quite difficult to ensure reproducibility ofsimulations during the snowfall and snowmelt season compared to other because of the complexityof conditions such as different elevations and slopes, thus to ensure reproducibility of snowfall andsnowmelt, setting more detailed conditions are needed for prediction using SWAT+. Although thesimulation period was from February 2019 through December 2020, weather and flow data willcontinue to be measured to verify the accuracy of the simulation.
ACKNOWLEDGEMENTS
This research was supported by the Fukushima Regional Collaborative Research Center (FRECC)
in the National Institute of Environmental Studies (NIES) and by Tokyo University of Agriculture
as the 2021 Doctoral Research Grant Program of the Research Institute for Agricultural and Life
Sciences, and the Strategic Research Project.
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