IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Research article
erd

Impact Evaluation of Climate Change on Disaster Risk ofForested Watershed Rivers in Snowy Regions using SWAT+SHOTARO KIKUCHI
Graduate School of Agro-Environmental Science, Tokyo University of Agriculture, JapanSARVESH MASKEY
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan

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

SERGIO AZAEL MAY CUEVAS
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan

MAKOTO OBA
Center for Climate Change Adaptation, National Institute for Environmental Studies, 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 1 October 2023

Accepted 13 December 2023(*Corresponding Author)

Abstract In this study, we analyzed the reduction in snowfall and snowmelt under climatechange scenarios in a cold snowy region. Also, we clarified the causal relationship between
deforestation and disaster risk in watershed management in Mishima Town, Fukushima
Prefecture. We used SWAT+ to conduct simulations; the SWAT+ model of Mishima town
was built in a previous study, and its reproducibility was verified by NSE and RMSE bycorrecting parameters and using auto-calibration. The latest set of models from the CMIP6,
also used in the 6th Impact Assessment Report of the IPCC and statistically downscaled
scenarios in Japan developed by the Center for Climate Change Adaptation, National Institutefor Environmental Studies, was adapted to simulate climate change scenarios. The Emission
Scenarios are based on RCP8.5, which has been used in future climate change discussions(i.e., the assumption that greenhouse gases will continue to increase without any global
warming countermeasures). We simulated the end of the 21st century situation. As the resultof the simulation, the RCP8.5 scenario showed a significant decrease in snowfall andsnowmelt, with only one day of snowfall of RCP8.5 from January to March. The potential
for early spring snowmelt water availability of RCP8.5 at the end of the 21st century could
have been much higher. Additionally, we conducted a simulation of deforestation. We
assumed that 5% of the forested area became agricultural land due to deforestation.
Simulation results indicated that the surface flow under deforestation conditions was 145.1mm, a 7% increase over the surface flow of the original forest prior to deforestation (135.1mm). Heavy and long-lasting rainfall showed no difference in water storage function between
the two cases, but 5% deforestation was shown to increase river discharge at the beginningof a rainfall event.
Keywords SWAT+, climate change, snowmelt, runoff, predictionⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

INTRODUCTION
Climate change significantly impacts the increase in average annual temperatures and fluctuations inrainfall and snowfall, resulting in more frequent natural disasters worldwide. The IPCC Sixth
Assessment Report (2021) states that a 2°C rise in temperature would lead to a 14% intensificationof rainfall intensity. On the other hand, land use change due to deforestation is known to increase
runoff and affect flood risk, with 5.2 million hectares deforested each year (2000-2010), mostly indeveloping countries in Africa, Latin America, and Southeast Asia, where agricultural activities
account for about 80% of the loss (Synthesis Report for REDD+ Policymakers, 2012). The impactsof climate change are also significant in Japan, where mountain disasters and floods are becoming
more frequent and severe, and the importance of the water source recharge and flood mitigation
functions of forests is increasing (Gunma Prefecture Basic Plan for Forest and Forestry 2021- 2030).
Since an increase in average temperature due to climate change is expected to significantly impact
the amount of snowfall and snowmelt and affect the availability of water for agriculture in early
spring in snowy and cold regions, future predictions based on climate change scenarios are essential.
Still, the assessment of the impact of climate change on water resources at the small watershed levelhas not been sufficiently verified.
In addition, although previous studies have examined the relationship between land use change
due to urban development and other factors and disaster risk (Qingmu et al., 2017) and the flood
control effects of agricultural land (Yoshikawa et al., 2009), there are few studies on the relationshipbetween deforestation and additional agricultural land.
OBJECTIVE
In this study, we analyzed the effects of climate change on snowfall and snowmelt and the impact of
deforestation on making agricultural land in the Hashigo River in Mishima town, Fukushima
prefecture, a forested area with a cold and snowy climate.
We used SWAT+ to conduct simulations, the SWAT+ model of Mishima town was built in aprevious study (Kikuchi et al., 2022), and its reproducibility was verified by NSE and RMSE bycorrecting parameters and using auto-calibration.
METHODOLOGY
Study Site
The study site is the Hashigo River watershed in Mishima Town in Fukushima Prefecture (Fig.1).
The Hashigo River is a tributary of the Tadami River. The watershed is located at latitude 37.50 o37.47 o, longitude 139.62 o -139.63 o, and range of altitude 350-370 m. The watershed area is 2.85
km2, covered mainly by mountain forests (Table 1). 0.4 % of paddy fields are located along the mainriver. The annual mean daily temperature, the minimum, and maximum daily temperatures are
10.6°C and 6.9°C, snowfall is from December to February, and snowmelt is from March to April.
Rainfall is relatively heavy during the rainy season from June to July, and typhoons are more frequentfrom September to October. Annual precipitation was 1,340 mm in 2020.
Table 1 Land-use of the target watershed
Watershed area

Land-use (%)

River length(km2)

ForestGrassland

Paddy field

(km)

2.85

98.4

1.2

0.4

2.5

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Fig. 1 Location of target watershed (Hashigo River)
SWAT+
Utilizing a hydrological model for analyzing the hydrological cycle throughout a river basin is aneffective method to assess the impacts of climate change on water resource management, flood
control, and agriculture. SWAT (Soil and Water Assessment Tool) developed by USDA-ARS is ahydrological cycle model used worldwide for over 20 years (Williams et al., 2008). To face present
and future challenges in SWAT code has undergone significant modifications over the past few years,
resulting in SWAT+, a completely revised version of the model (Katrin et al., 2016). SWAT+ is amodel that analyzes water movement by subdividing the target watershed into subwatersheds called
HRUs (hydrologic runoff units) and can predict river discharge and water quality for an unspecified
watershed with few input parameters. The data collection is shown in Table 2, and data sets of
SWAT+ are shown in Table 3. The time interval for the calculations in this study was set to days. The
warm-up period was set from 1st February 2019 to 31st December 2019, and the simulation period
was set from 1st January 2020 to 31st December 2020. Some parameters were adjusted and enteredfrom actual measurements.
Table 2 Data collection
Data
Elevation
data

SourceGeospatial Information Authority of Japan
(GSI)

Note

Land use

The Japan Aerospace Exploration Agency(JAXA) and Earth Observation ResearchCenter (EORC)

10m mesh <Land use: 12 categories>.
Land use was confirmed by foot survey, andsome obviously different land uses werecorrected （Fig. 1 B）

Soil map

The National Institute of Agrobiological
Sciences (NIAS)’s basic land classification
survey data (Shapefile, 1:200,000)

Shapefile (polygon)

Soil
component

Field survey data (17/Dec/2021)

Soil moisture content, EC, pH, specific gravity,
permeability coefficient, particle size distribution

Weather data

National Institute for Environmental Studies ofJapan (NIES)

10 min Data: River discharge (m3/s)
Daily data: rainfall, temperature (max., min.,avg.), relative humidity, solar radiation, wind
speed

Raster data (10 m mesh)（Fig. 1 A）

Table 3 Data set of SWAT+ model
Item

Contents

Version- SWAT+ Editor 2.0.4

Period- Warm-up：2019/2/1-2019/12/31

Time interval- Daily data

Input parameters- The following parameters are adjusted and input from actual measured values
Permeability (soil k), maximum water capacity (awc), clay, silt, sand, rock, EC, clay, silt,
sand, rock.
- Snowfall temperature: 2.51°C, Melting snow temperature: 2.71°C- Evapotranspiration calculation: Penman-Monteith method- Calculation of surface flow: Curve Number method

Calculation
methodⒸ ISERD

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Simulation：2020/1/1-2020/12/31

IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Climate Change Scenario
In this study, we generated a hypothetical dataset for the year 2090 by adjusting the data of 2020
using the variability parameters (e.g., temperature increases) from published climate change
scenarios. As the publish methods, We adopted the latest set of models from the CMIP6 (Coupled
Model Intercomparison Project 6th), which is also used in the 6th Impact Assessment Report of theIntergovernmental Panel on Climate Change (IPCC) and adapted statistically downscaled scenarios
NIES2020 in Japan developed by the center for climate change adaptation, National Institute forEnvironmental Studies (NIES)（Ishizaki, 2021）. The Emission scenarios use the Representative
Concentration Pathway (RCP) scenarios, introduced as part of the IPCC's Special Report on
Emissions Scenarios (SRES). There are several RCP scenarios, but in this study, RCP8.5, whichassumes no action on global warming and continued greenhouse gas emissions, was selected. The
hypothetical dataset as input for the SWAT+ model to simulate how climate change will impact
various factors: snowfall, snowmelt, and flow, comparing conditions between 2020 and 2090.
Table 4 Basic information on climate change scenario
Name of climate scenario

NIES2020

Climate model

MIROC6

Climate parameters

Max. and mini. temperature, precipitation, total solar radiation, relative humidity

Emission scenarios

RCP8.5

Forecast period

2090

Table 5 Data prediction of RCP 8.5 for 2090 and 2020 in Fukushima
SSP585-MIROC6-NIES2020
for Fukushima

2090
Jan.

Feb.

2020
Mar.

Jan.

Feb.

Mar.

Max temperature
Min temperature

6.2
-2.2

7.5
-2.1

11.4
1.2

3.0
-4.9

3.5
-5.1

8.1
-1.7

Relative humidity
Solar radiation

70.3
6.8

67.1
9.5

70.1
13.1

73.6
7.1

72.2
9.7

71.8
12.7

Precipitation

103.3

77.1

129.0

90.3

73.2

107.7

Deforestation Situation
In Mishima Town, a renewable energy project using woody biomass resources is being consideredwith the establishment of the Regional Recycling and Symbiosis Promotion Council. On the other
hand, flood risk from deforestation needs to be adequately assessed. According to studies by the
National Institute for Environmental (Ooba et al., 2017), the upper limit of forest use is estimated to
be 1/50 (2%) due to the limitation of age class composition. In this study, the upper limit of forest
use, 2%, was exceeded, and a case was assumed in which 5% of the forest area became agriculturalland due to logging. From the standpoint of convenience in transporting timber, the area of
deforestation was assumed to be two downstream watersheds (Fig. 1 Watershed 3 and 4).
RESULTS AND DISCUSSION
Climate Change Scenario
The simulation results based on the observed data for 2020 (Original) and the 2090 climate change
scenario (RCP8.5) are shown in Fig. 2. The river discharge of January is more significant for RCP8.5
because precipitation is more considerable for RCP8.5. On the other hand, after mid-February, theoriginal data tends to have more significant river discharge.
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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

1

400

Precipitation(mm)River Discharge(m3/s)

ORG RCP8.5

0.1

0.01
January 2020

500

February 2020

March 2020

Fig. 2 River discharge of original (2020) and RCP8.5 (2090)
The results of average river discharge, snowmelt, snowfall, evapotranspiration, days of snowfall,and days of snowmelt from January to March are shown in Table. 6. River discharges of RCP 8.5 arehigher than the originals, except in March. After mid-February, the river discharge of RCP8.5
temporarily decreased a little, but there is no significant difference between RCP8.5 and the originalin terms of monthly river discharge. On the other hand, the original from January to March had 78.8mm of snowfall and 93.4 mm of snowmelt, while RCP 8.5 had 5.7 mm of both snowfall and snowmelt.
In addition, RCP 8.5 has only 6-7% of the original snowfall and snowmelt, and RCP 8.5 has only
one snowfall and snowmelt day. The above results indicate a significant decrease in snowfall andsnowmelt in RCP 8.5 in 2090. The evapotranspiration of RCP 8.5 during January-March is 19.48mm, which is about 1.37 times higher than the original (14.19 mm). The impact of climate changeon the flow rate indicates that there is no significant difference between RCP8.5 and the original(2020) due to increased rainfall. The results indicate that while changes in river discharge may not
be immediately apparent due to increased rainfall in the RCP 8.5 scenario, the alarming decrease insnowfall and snowmelt could have far-reaching consequences for water resources management andthe ecosystem.
Table 6 Simulation results of the original (2020) and RCP8.5 (2090)
Original
Average River discharge (m3/s)
Snowmelt (mm)Snowfall (mm)Evapotranspiration (ET) (mm)Snowfall days
Snowmelt days

Jan.
0.086
52.80
38.30
0.65
7
10

Feb.
0.067
32.80
32.70
1.80
6
10

Mar.
0.050
7.80
7.80
11.74
5
4

Total
0.068
93.40
78.80
14.19
18
24

Jan.
0.098
0.00
0.00
1.13
0
0

RCP 8.5
Feb.
Mar.
0.069
0.049
5.70
0.00
5.70
0.00
3.17
15.18
1
0
1
0

Fig. 3 River discharge of the simulation of deforestationⒸ ISERD

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Total
0.075
5.70
5.70
19.48
1
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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Deforestation Situation
In the one-year river discharge data for 2020 (Fig. 3), days with particularly high daily river discharge(July 12th, July 16th, and August 1st) were extracted and compared between the original anddeforestation. there were no significant differences for July 16th and August 1st, but July 12 thincreased the daily river discharge by a factor of 1.66. Heavy and long-lasting rainfall showed nodifference in water storage function between the two cases, but 5% deforestation was shown toincrease river discharge at the beginning of a rainfall event. The result of the deforestation situationis shown in Table 7. Surface flow of deforestration was 145.1 mm, 7% increase over surface flow of
the t original (135.1 mm). The ET of deforestration was 395.9 mm, 0.3% increase over ET of theoriginal (394.7 mm). The result of surface flow also indicates that the water storage function of thewatershed is declining due to the decrease in forest area. This effect was particularly pronounced at
the beginning of precipitation events, indicating that the presence of forests role which moderate
early-stage discharge.
Table 7 Simulation results of the original (2020) and deforestation 5%
Surface flow

Evapotranspiration(ET)

Annual averageRiver discharge
July 12th
July 16th

Aug. 1st

Original

135.1 mm/y

394.7 mm/y

0.083 m3/s/y

0.50

1.13

2.24

Deforestation 5%

145.1 mm/y

395.9 mm/y

0.084 m3/s/y

0.83

1.09

2.24

CONCLUSION
Herein, we analyzed the effects of snowfall and snowmelt (from January to March) in a small
watershed in Mishima, Fukushima Prefecture. The results of the climate change simulations indicate
a significant reduction in snowfall and snowmelt in RCP8.5 (2090) within CMIP6 when comparedto the original simulation results for 2020. If climate change were the RCP8.5 scenario, agriculturaluse of spring snowmelt would be problematic by the end of the 21st century. However, there would
be no significant difference in river discharge because of increased precipitation. However, climatechange mitigation measures need to take into account that the water storage function of forests
through snowfall and snowmelt will disappear. In addition, it may not show significant changes inthe short term, but the loss of snowfall and snowmelt and the associated changes in forest water
storage functions by snow raise concerns about the long-term sustainability of water resources andecosystems.
The results of the deforestation simulation showed a 7% increase in annual surface runoff and areduction in water storage function during the first rainy season. It is crucial to consider the need forthe water storage function of forests in combination with the effects of climate change anddeforestation. Both climate change scenarios and land use change simulations are few in small
watershed studies. However, local governments need to update their water resource management
based on climate change scenarios, and in small watersheds, water balance simulations based on
climate change scenarios should be more necessary. Although only runoff analysis was conducted inthis study, inundation analysis based on calculated river flow data would provide a more accurate
assessment of disaster risk.
ACKNOWLEDGEMENTS
This research was supported by the Fukushima Regional Collaborative Research Center (FRECC) in
the National Institute of Environmental Studies (NIES) and by the Tokyo University of Agriculture
as the Doctoral Research Grant Program of the Tokyo NODAI Research Institute, and JSPS
KAKENHI (JP20K06351).

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