Nakada et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-021-00440-9(2021) 8:46

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

High-resolution flow simulation in Typhoon
21, 2018: massive loss of submerged
macrophytes in Lake Biwa
Satoshi Nakada1*, Hiroki Haga2, Maho Iwaki1,2, Kohji Mabuchi1 and Noriko Takamura1

Abstract
The global activities of typhoons and hurricanes are gradually changing, and these storms can drastically affect lakeecosystems through the recession of submerged macrophytes that regulate the water quality in lakes. Using anechosounder, we captured the short-term, massive loss of submerged macrophytes attributed to the abnormal
fluctuation of the water level induced by the approach of a catastrophic super typhoon in the southern basin of
Lake Biwa, Japan. This paper investigates the physical processes responsible for the loss of vegetation using a highresolution circulation model in Lake Biwa as a pilot study area. The circulation model was coupled with dynamical
models of the fluid force and erosion acting on the vegetation. Our simulation successfully reproduced the waterlevel fluctuation and high-speed current (torrent) generated by the typhoon gale. The simulated results
demonstrated that the fluid force driven by the gale-induced torrent uprooted submerged macrophytes during thetyphoon approach and that this fluid force (rather than erosion) caused the outflow of vegetation. As a result, thisuprooting attributed to the fluid force induced the massive loss of submerged macrophytes in a large area of thesouthern basin, which might have increased primary production and reduced the stock of fish such as bluegill in
the lake. Our model can estimate the reduction in the macrophyte height within the range of − 1.3 to − 0.4 m,
suggesting that fluid forces greater than the time-averaged value (1.24 × 10−4 N) were available. Flow speeds ofapproximately 0.8 m/s might be the critical value that induces the fluid force acting on the uprooting of thesubmerged macrophytes. Our approach is practical for evaluating changes in lake environments attributed to themassive outflow of submerged macrophytes under various climate change scenarios.
Keywords: Flow simulation, Typhoon, Submerged macrophytes, Lake Biwa, Fluid force

1 Introduction
The global activities of tropical storms (typhoons andhurricanes), which can drastically affect wetland and lakeecosystems, are gradually changing. Moreover, tropicalstorms disturb the community structures of aquaticplants in tropical and temperate regions (Wang et al.
2016). For example, hurricanes Katrina and Rita eroded
the freshwater marshes of the Louisiana coastal wetlands
in 2005 and caused significant uprooting (Howes et al.,* Correspondence: nakada.satoshi@nies.go.jp
1
National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki
305-8506, Japan
Full list of author information is available at the end of the article

2010). Many lakes in these regions are within the strikezones of tropical storms, and they experience high interseasonal and interannual variations in rainfall and runoff(Havens et al. 2016a). Havens et al. (2016a) reported thecase of Lake Okeechobee and found that three hurricanes significantly reduced the coverage of submerged
aquatic vegetation to one-tenth and affected water quality and plankton dynamics in open water zones. Terrestrial loadings in periods of high rainfall could enhancedissolved color, reduce irradiance, increase water turnover rates that suppress blooms, and markedly alter the
ecosystems in lakes located in regions where oceanic cycles and their teleconnections result in decadal variation© The Author(s). 2021, corrected publication 2021. Open Access This article is licensed under a Creative Commons Attribution
4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, aslong as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence,and indicate if changes were made. The images or other third party material in this article are included in the article's Creative
Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative
Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need
to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/
licenses/by/4.0/.

(2021) 8:46

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in rainfall (Havens et al. 2016b). With model results predicting future warming of the tropical sea surface, theIntergovernmental Panel on Climate Change (IPCC
2007) reported that tropical cyclones will become more
intense, leading to more violent winds and rainfall.
Aquatic plants, particularly submerged macrophytes,
play an important role in aquatic environments by positively interacting with the water quality and ecosystemsof lakes. The submerged macrophytes in shallow eutrophic lakes can affect nutrient cycling, sediment–waterinteractions, water column irradiance, and phytoplankton blooms (Weisner et al. 1997). The vegetation of submerged macrophytes supplies a variety of ecosystem
functions, providing shelters for fish and aquatic invertebrates, restoring water quality, and regulating the oxygen
balance (Sood et al. 2012). Accordingly, recent studies
have focused on the phytoremediation and purification
effects of submerged macrophytes on nutrient-polluted
water (e.g., Sood et al. 2012; Yanran et al. 2012; Dhoteand Dixit, 2009), implying that coverage reductions ofaquatic plants could negatively affect the water quality inshallow lakes. To understand water quality fluctuations,the physical processes of the vegetation coveragechanges should be clarified; therefore, this study focuseson the processes in the short-term period that are as yet
unexplained.
The ecosystem in a shallow lake can change drasticallyduring the short-term approach of a storm coincident
with the recession of aquatic plants. Ji et al. (2018) reported that a lake disturbed by tropical storms experienced a regime shift from clear to turbid water due tolosing submerged vegetation. Heavy rainfall, flooding,and strong winds can also create suitable habitats foraquatic organisms and increase their diversity in regions
frequently affected by typhoons (Wang et al. 2008).
To understand the short-term changes taking place
within a lake ecosystem, we need to know the physicalprocesses that induce the large-scale recession of aquaticplants; unfortunately, no analysis has been performed toreinforce this knowledge. Damage to aquatic plants varies based on the life forms of the species, geologicalhabitat conditions, and tropical storm intensity (Wanget al. 2016). For example, tropical storms cause damage
to mangroves, including the loss of foliage, breakage oftrunks, and even uprooting of trees (Ellison 1998). Heavystorms have induced a shift to a permanent turbid state
by destroying vegetation (McKinnon and Mitchell 1994).
The most important potential impact of storms can be
through their effect on water level fluctuations (Scheffer
and van Nes 2007). However, little is known about theprocess by which submerged macrophytes disappear
during tropical storms. The issue of the recession processes of vegetation related to water level fluctuations isclearly far from resolved at short time scales, such as

storm periods. This lack of knowledge exists because no
biological data that would be useful for analyzing thedisappearance of submerged macrophytes are available
both before and after a typhoon strike.
As a consequence, a simulation coupled with flow anddynamical models on high-resolution grids is needed toanalyze the dissipation of submerged macrophytes; inaddition, observational data are needed to describe theloss of vegetation. The use of a high-resolution flow
model can be a powerful approach for simulating themassive loss of submerged macrophytes. To date, various models have been proposed for simulating lake circulation (e.g., Akitomo et al. 2009), water temperatureand the thermocline (e.g., Koue et al. 2018), dissolved
oxygen (Kitazawa et al. 2010), primary production (Satoet al. 2011), and other features at seasonal and interannual time scales; nevertheless, the grid resolutions ofthese models are insufficient for representing the detailed flow field with a focus on submerged macrophytes.
In this context, previous studies have proposed a theoretical framework and bulk models for the fluid force acting on submerged macrophytes (e.g., Luhar and Nepf
2011; Hayashi and Konno 2007). Fortunately, we succeeded in collecting the observational plant height datathat are necessary for capturing the massive loss of submerged macrophytes by conducting echosounder surveying on a regular schedule.
This paper investigates the short-term, massive loss ofsubmerged macrophytes as a result of the approach of acatastrophic super typhoon in Lake Biwa, Japan, which istaken as a pilot study area (Fig. 1). To investigate theprocesses that caused the outflow of vegetation, we assume that the outflow can be attributed to the uprootingof submerged macrophytes induced by the erosion of
the bed and fluid force acting on the vegetation body.
Therefore, we proposed two dynamical models, namely,
erosion and fluid force models, focusing on the habitatsof the submerged macrophytes in the lake. In the firstmodel, the outflow can be induced by erosion generated
by a strong current or torrent (Fig. 2a). In the second
model, the outflow can be induced by the fluid force
generated by the torrent (Fig. 2b). Then, a numerical
fluid simulation was coupled with an evaluation of thefluid force and erosion models to identify the key physical factors governing this massive loss. The erosion andfluid force may be influenced by wind waves and river
runoff, which will be discussed in Section 4.

Nakada et al. Progress in Earth and Planetary Science

2 Study area
Lake Biwa (Fig. 1) is the largest lake in Japan (with a volume of 27.5 km3), providing water resources for approximately 14.5 million people in the Kansai region (e.g., theShiga, Kyoto, Osaka, and Hyogo regions), including
some megacities. The lake, which has a surface area ofNakada et al. Progress in Earth and Planetary Science(2021) 8:46

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Fig. 1 Maps of the a pressure distribution when Typhoon 21 approached Lake Biwa and the typhoon course (red line and dots), with the blue
square indicating the area around Lake Biwa shown in Fig. 1b, b wind vectors and speeds (colors) based on the GPV-MSM reanalysis dataset at
15:00 on 4 September, and c topography and bathymetry around and in Lake Biwa, respectively. The black and gray squares indicate the gauge
stations operated by the Ministry of Land, Infrastructure, Transport and Tourism of Japan (MLIT) and the Lake Biwa Branch Office of the National
Institute for Environmental Studies (LaBBO-NIES), respectively. White squares denote the Ogoto offshore comprehensive automatic observation
station (OOCAOS) in the southern basin of Lake Biwa. d Bathymetric maps for the analysis in the southern basin. The black solid lines indicate thetransect lines for the echosounding observations to measure the vegetation height of the submerged macrophytes

670 km2, is located on the central island of the Japanesearchipelago, Honshu. Lake Biwa consists of two basins, adeep northern basin (area-mean depth of 44 m,

maximum depth of 104 m) and a shallow southern basin(area-mean depth of 4 m); the latter, which represents
our study field, has a surface area of 51.6 km2 (HagaNakada et al. Progress in Earth and Planetary Science(2021) 8:46

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Fig. 2 Conceptual diagrams of the two processes responsible for the vegetation outflow induced by the high-speed current. a Erosion (case 1)
evaluated by the nondimensional bed shear stress and the sediment flux from the bed to the lake water. b Fluid force (case 2) represented by
the resultant of the buoyancy and drag forces. The variables depicted in the diagram are explained further in the text2006). The lake water is generally provided by more than
100 rivers in the watershed (3174 km2) and is discharged
through a single outlet (Kawanabe et al. 2012), namely,
the Seta River in the south, under the control of a weirthat regulates the runoff and water level of the lake. In
recent years, extensive beds of submerged macrophytes
have developed in the southern basin. Over the last two
decades, submerged macrophytes covered over 90% of
the area of the southern basin (Haga and Ishikawa
2016).

macrophytes described in Section 2. We simulated thelarge-scale changes in the lake water level induced by
Typhoon 21 using an unstructured-grid ocean modeland atmospheric reanalysis data. Then, the shear stressand fluid force acting on flexible plants were calculated
based on bed shear stress theory and a hydraulic resistance model, respectively. The bed shear stress theory
used for the erosion model is later described in Subsection 3.1.3, the fluid force model is explained in Subsection 3.1.4, and the model’s input datasets are
summarized in Table 1.

3 Methods and data
3.1 Model

We evaluated the key factors of the above processes byfollowing the procedure of the flow chart shown in Fig.
3; we coupled a circulation model with two dynamical
models to represent the outflow of submerged

Fig. 3 Flowchart of the erosion and fluid force evaluation based on
the simulated results of the FVCOM (center gray-shaded square)using the observed datasets (left, gray-shaded group) andmeteorological reanalysis datasets (upper white square)

3.1.1 Lake circulation model

In the simulation part of the flowchart (Fig. 3), we
used the prognostic, unstructured-grid, finite-volumecommunity ocean model (FVCOM) (Chen et al.
2003). This finite-volume approach ensures the conservation of mass and heat necessary to reproduce
the key physical processes under a dominant change
in the water level of the lake. The FVCOM uses anunstructured, terrain-following triangular grid, leading
to improved flexibility in adjusting the grid resolution(10–60 m) to fit the irregular coastal geometry andbathymetry of Lake Biwa (Fig. 4). A σ-z mixed coordinate system consisting of five constant-thickness (0.5m) layers in the surface layer and a 10-sigma layer
below the surface was employed in the vertical dimension to represent realistic temperature stratification (Nakada et al. 2017).
Considering the observed large fluctuation in the waterlevel induced by Typhoon 21 (explained in detail in Section 4.2), the simulation was conducted for a week following the approach of Typhoon 21 to the lake on 4
September 2018. The ocean simulations with a timeinterval of 0.2 s began from an initial state of rest andlasted for 2 weeks with a start date of 29 August 2018.

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Table 1 The list of specifications for the datasets used in the simulation and analysis
Dataset

Usage

Data
sourceCategoryTimeinterval

Data period

Observational
location

Lake watertemperature

Initial condition of simulation

LBERI

Observation Monthly

28 Aug., 2018

Fig.1cBathymetry

Initial condition of simulation

LBERI

Observation Compiration of the various dataset

Fig.1cWater plant height

Input for fluid force
calculation

LBM

Observation Monthly

9 Aug. & 5 Sep.,
2018

Fig.1dMoisture content

Input for erosion calculation

LBM

Observation snapthot

9-11 Sep, 2002

Fig.5

Meteorological data

Input for simulation

JMA

Re-analysis

hourly29-12 Sep., 2018

Fig.1bRiver runoff

Input for simulation

MLIT

Estimation

hourly2-5 Sep., 2018

Fig.1aWater level

Validation for simulation

MLIT&NIES

Observation hourly

2-5 Sep., 2018

Fig.1cWind speed

Validation for simulation

JWA

Observation hourly

2-6 Sep., 2018

Fig.1cChlorophyll-a

Data analysis

JWA

Observation hourly

2-5 Sep., 2018

Fig.1c3.1.2 Input data to the circulation model

Four input datasets, namely, bathymetry, watertemperature, runoff, and meteorological reanalysis datasets, were used in the simulation, as shown in the flowchart (Fig. 3) and summarized in Table 1. We usedbathymetry data with a 50 m horizontal resolution (Fig.
1c and d) distributed by the Lake Biwa Environmental
Research Institute (LBERI) of Shiga Prefecture as the input data to the simulation and interpolated these data to
the unstructured grids in the FVCOM (Fig. 4). The insitu temperature data observed on 28 August at Imaduoki-Chuou (Fig. 1c) obtained by LBERI were used as theinitial water temperature conditions in the simulation
and were homogeneously input to our model
horizontally.
The hourly mean river discharges from the Seta River
used in the simulation (Fig. 1d) were observed at the
Seta River Weir operated by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan,
Kinki Regional Development Bureau Biwako Office. We
considered the river runoff from five major watershedareas and observed outflow runoff from the Seta River inthe simulation (Fig. 4a). The daily mean river discharges
were estimated from the precipitation, observed waterlevels around Lake Biwa, and outflow runoff from the
Seta River and were linearly interpolated as the hourly
input data (Fig. 7).
Reanalysis meteorological Grid Point Value datasets of
the Meso-Scale Model (GPV-MSM) produced by theJapan Meteorological Agency (JMA) were downloaded
from the server of the Research Institute for the Sustainable Humanosphere (http://database.rish.kyoto-u.ac.jp/
arch/jmadata/) and input into the FVCOM as the meanhourly meteorological data including air pressure at theland surface, air temperature at 2 m, precipitation, cloud
cover, relative humidity, and wind velocity at 10 m. Eachmeteorological datapoint mi was inter/extrapolated toeach computational grid as the averaged value, mi = ∑mGPVw/ ∑ w (Nakada and Isoda 2000), where mGPV isfrom the GPV-MSM datasets, and w is a Gaussian
weight function defined by w = exp(D2/rG2). Here, D isthe distance between the data and computational grid
point, and rG(= 20 km) is the e-folding horizontal scale(influence of the Gaussian radius), which is nearly equal
to four times the mesh size of GPV-MSM (~ 5 km).
3.1.3 Erosion estimation

The erosion induced by bed flow can excavate the sediment on the lake bottom, leading to the uprooting ofsubmerged macrophytes. A conceptual model (Fig. 2a)
exhibiting the concepts underlying the calculation of thenondimensional bed shear stress, sediment resuspension,and the flux of sediment from the lakebed to the lakewater was employed in similar simulations (e.g., Nakada
et al. 2018). The nondimensional bed shear stress, Rτ, ateach computational grid was obtained from the following equations (Umita et al. 1988):
Rτ ¼ τ b =τ e −1

ð1Þ

τ b ¼ ρw u2

ð2Þ

τ e ¼ 0:79τ y0:94

ð3Þ

where τb is the bed shear stress, and τe is the critical
shear stress based on the empirical formula (Otsubo andMuraoka 1985). When τb > τe, or in other words, Rτ > 0,
bed sediment can be transported from the seabed into
the lake water. Here, ρw (1000 kg m−3) is the density of
qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
lake water, and u ¼ gn2 U 2 =D1=3 is the bottom friction
velocity, which is determined by the gravitational acceleration constant g (9.8 m s−2), the roughness coefficientn (0.025), the current speed U, and the total depth D (=h+H). Here, h is the water depth, and H is the waterlevel. τy = 1.494 × 106ω−2.452 is the yield value (Nakano
et al. 1991), which is determined by the moisture ratioNakada et al. Progress in Earth and Planetary Science(2021) 8:46

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Fig. 4 Maps of the unstructured triangular grids in the FVCOM for a the whole basin of Lake Biwa and (b) the southern basin. The total numbersof triangular cells and nodes are 284,569 and 144,037, respectively. The colors indicate that the horizontal resolution of the grids (measured by
the side length of each triangle) is 10–60 m and increases up to 10–20 m in the southern basin, for example, in c Yamanoshita Bay, d theKarasuma Peninsula, e the Yabase region, and three small islands in the northern basinof the bed sediment ω = W/(1 − W) or the moisture content W (Fig. 5). Values of W were produced by inter/extrapolation to the computational grid using a Gaussianfunction based on sediment observations by the Lake
Biwa Museum in 2002 (Haga et al. 2006). The southernbasin has a maximum W of 81% and higher W values
than the areas around the Karasuma Peninsula (Fig. 1c)and the entrance of the Seta River (W < 60%). The instantaneous erosion (or resuspended sediment flux)
from the bed E(t) (kg m−2 s−1) of each grid in the model
domain of the southern basin (Fig. 4b) was calculatedusing the following empirical equation (see Umita et al.
1988; Nakano et al. 1991; Otsubo and Muraoka 1985 forthe physical parameters used in the equation):E ðt Þ ¼ MRτ 2:3

ð4Þ

where M (= 1.6×10−5) is the resuspension constant (kgm−2 s−1), and t is an arbitrary time in the model. The
total erosion induced by bed flow is calculated asfollows:

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submerged macrophytes (Hayashi and Konno 2007). (1)The blades of the submerged macrophytes, such as Vallisneria, were modeled as isolated, buoyant, inextensible
elastic stems with a constant representative width (b =0.01 m), thickness (tb = 0.001 m), and density (ρv = 160
kg m−3). These parameters were used as the typicalmacrophyte model in the experiments of Hayashi and
Konno (2007). (2) The horizontal velocity (U) was uniform over depth and had a steady flow on each computational grid with fine scales (~ 10 m); the dominanthydrodynamic force was form drag, and unsteady flows,
such as those induced by surface waves, were not considered. (3) Viscous skin friction was negligible. Formdrag, which is derived from the velocity normal to theblade surface, was represented using a standard quadratic law. The drag force acting on a blade length
equivalent to the plant height hv is:1
F D ¼ ρw C D bhv U 2 cos2 θ
2Fig. 5 Map of the moisture content (%) of the lakebed of thesouthern basin. Colored dots indicate the observational site moisture
contents observed in September 2002 (Haga et al. 2006)

ET ¼

Z te

Here, ρw (1000 kg m−3) is the density of lake water,and CD (1.7) is the drag coefficient (Hayashi and Konno
2007). The blade length is given by the observed macrophyte height shown in Section 4.1. Hayashi and Konno(2007) evaluated the local bending angle θ (Fig. 2b) of
the blade relative to the vertical (0 ° ≤ θ ≤ 90°, where θ =0° denotes an upright posture) based on laboratory experiments. Following their blade model based on theforce balance, as shown in Fig. 5b, the drag force is:F D ¼ F B sinθE ðt Þdt

ð5Þ

ts

Regardless of the presence of the submerged macrophytes, the erosion model assumes a plain lakebed characterized only by the water content. In fact, submerged
macrophytes suppress the speed of bed flow and lead todamping erosion, which is not represented in this model.
Therefore, the erosion model used in the present analysis can have the most dominant effect on the lakebed.
3.1.4 Fluid force acting on submerged macrophytes

The fluid force acting on the blades of the submerged
macrophytes can be equivalent to the tension force acting on the root of the submerged macrophytes with abending angle at the lake bottom (Fig. 2b). If the tension
force exceeds the critical value of the force with which
the lakebed supports the aquatic plants, the plants areuprooted. To evaluate the fluid force, we employed thegeneral theoretical model for buoyant, flexible macrophyte blades in flow (Fig. 2b) (e.g., Luhar and Nepf2011). In this paper, we employed a few simplifying assumptions without the flow-induced reconfiguration of
buoyant, flexible vegetation to evaluate the outflow of

ð6Þ

ð7Þ

Here, the vertical buoyancy force is:F B ¼ ðρw −ρv Þgt b bhv

ð8Þ

and g (9.8 m s−2) is the gravitational acceleration constant. The fluid force acting on the blade induced by theflow and buoyancy is:F F ¼ F B cosθ

ð9Þ

From Eqs. (6) and (7):1
F B sinθ ¼ ρw C D bhv U 2 cos2 θ:2ð10ÞSolving Eq. (10), we can obtain the following solution:
qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ð11Þ
sinθ ¼ − F B þ F B 2 þ ψ 2 =ψsﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ cosθ ¼ F B 2
1 þ ð F B =ψ Þ2 −1 =ψð12ÞHere, ψ = ρwCDbhvU2. Therefore, the fluid force can bederived using the simulated current velocity U and theobserved macrophyte height hv:

Nakada et al. Progress in Earth and Planetary ScienceF F ¼ FB

rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ
22ð

1 þ ð F B =ψÞ2 −1Þ=ψ

(2021) 8:46

ð13ÞIn this fashion, we calculated the fluid force FF at eachmodel time step and evaluated the outflow of the submerged macrophytes from the bed.
3.2 Data
3.2.1 Plant height data

We used the plant height data of submerged macrophytes derived by echosounding surveys conducted
on a regular schedule by Lake Biwa Museum (LBM)
in the pretyphoon period (on 9 and 10 August 2018)
and in the posttyphoon period (on 5, 6, and 12 September). The observation configuration was composed of a BioSonics MX Echosounder system(BioSonics Inc.), which was used to measure theplant heights, and a differential-GPS (d-GPS) WU800
device (Japan Radio, Co., Ltd., Tokyo, Japan) for positioning. The transducer of the echosounder was installed in the bottom of a vessel hull and positioned
0.3 m underwater. The echosounding surveys, whichwere capable of covering the vegetation distribution
in the southern basin, were conducted along 30 longitudinal transects spaced at intervals of 500 m at a
vessel speed of 9–13 km/h (5–7 knots), as shown in
Fig. 1d.
The raw data derived by the echosounder were processed by Visual Habitat (BioSonics Inc.) to detect thelake bottom and submerged macrophytes based on thedefault detection configuration, although we manually
modified the configuration as appropriate. The detection
limit for plant heights was set to 0.1 m. The processed
data, formatted as comma-separated values (CSV), wereinterpolated by inter/extrapolation into the triangulargrids of the FVCOM (Fig. 4) using a Gaussian function(Nakada and Isoda 2000). The grid datasets were used to
calculate the difference in the plant heights between the
pre- and posttyphoon periods and to estimate the fluid
force acting on the macrophytes using the plant heighthv based on Eqs. (6–9).
3.2.2 Water level and chlorophyll-a data

Figure 1c shows the observation stations operated by
the MLIT, the Japan Water Agency (JWA), and theLake Biwa Branch Office of the National Institute forEnvironmental Studies. In situ water level data wereused to verify the simulated water level to ensure thereproducibility of the model. The MLIT releases realtime, observational data on the water level at gauge
stations. The water level data were downloaded fromthe website of the Water Information System run by
the MLIT (http://www1.river.go.jp/), with an analysis
period corresponding to the span of the simulated

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data to allow a comparison of the in situ data withthe simulated water level data. We used meteorological data, such as wind, water level, andchlorophyll-a, and water quality data observed at the
Ogoto offshore comprehensive automatic observation
station operated by the JWA. Due to the potential forthe biofouling of in-water instrumentation, the sensors were maintained by the JWA at regular semimonthly intervals. We also collected hourly waterlevel data at three stations (Fig. 1c) using HOBO
U20L-02 water level loggers (Onset Computer Corporation, USA).

4 Results
4.1 Massive loss of submerged macrophytes

Figure 6 displays the observed height of the submerged
macrophytes on 9 August 2018, as measured by the onboard echosounder, which indicates that there was asubstantial area of submerged macrophytes in the southern basin. The submerged macrophyte beds (36.4 km2)
covered 68% of the area of the southern basin, and thearea-mean height was 0.54 m.
The approach of Typhoon 21 might have induced
the large-volume outflow of submerged macrophytes
and short-term blooms of phytoplankton over a large
area of the southern basin. On 4 September 2018,
category 5 super Typhoon 21 (Jebi) approached Lake
Biwa at 15:00 JST (Fig. 1a) and induced a south-tosoutheast gale in the surface layer (Fig. 1b). After thetyphoon’s approach, the submerged macrophyteheight on 5 September significantly decreased
throughout the whole basin (area-mean height 0.23m), with a 58% height reduction compared to thearea-mean height before Typhoon 21; in particular,
the vegetation height was nearly zero in the southernarea of the basin. A map of the difference in macrophyte height (Fig. 6c) shows an average decrease in
the submerged macrophyte height (0.31 m) and a reduction in the coverage area of 18.8 km2, which constituted 53% of the southern basin. The area in which
the height decreased accounted for 35.2 km2, which
was 97% of the area of the inherent habitat of thesubmerged macrophytes. Furthermore, significant decreases in height occurred in three areas: the northcentral area of the basin, the nearshore area along theeastern coast, and the estuarine area around the old
Kusatsu River. After the reduction in the area of submerged macrophytes, on 5 September, the concentration of chlorophyll-a in the southern basin nearly
doubled relative to the concentration during the pretyphoon period between 1 and 3 September (Fig. 7),
although the peaks on 4 September might imply benthic algal resuspension.

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Fig. 6 Distribution of the macrophyte height (m) in the southern basin of Lake Biwa on 9 August 2018 (a) and on 5 September (b). c Temporalchange in the macrophyte height derived by subtracting the height on 5 September (b) from the height on 9 August (a)4.2 Model validation
4.2.1 Wind data used in the simulation

Figure 8a shows the time series of the area-averaged
wind vector over the model domain of the southernbasin of Lake Biwa. A southeasterly wind was dominant
at noon on 3 September; this condition changed from astrong southerly wind to a southwesterly wind when thetyphoon approached Lake Biwa. A map of the windspeeds and vectors is illustrated in Fig. 8b at 15:00 JST
on 4 September (red line in Fig. 8a); when Typhoon 21

was approaching, Lake Biwa was characterized by astrong southerly wind with a homogeneous horizontal
distribution over the lake. The wind speed increased
northward and exceeded 28 m/s in the northern area ofthe southern basin.
Figure 8c shows a scatter plot between the GPVMSM-predicted wind speed and the in situ wind speed
observed at the Ogoto offshore observation station (Fig.
1c) during the period of 3–6 September, although somedata are missing at 15:00 and 16:00 JST on 4 September.
The comparison between the observed and predicted
wind speeds in this scatterplot suggests that the observed speed was approximately 2 times greater than thepredicted speed, leading to the derivation of a wind estimation formula (modified wind Wmod = 1.89WMSM) fromthe predicted wind WMSM with a high correlation coefficient (R = 0.92). Following the derived formula, we
modified the wind speed input into the simulation.
4.2.2 Water level fluctuation

Fig. 7 Temporal variations in the total river runoff into/from Lake
Biwa and the chlorophyll-a concentration observed at the OOCAOS
located in the southern basin during the period of 2–5 September.
The outflow was observed in the Seta River

Figure 9a shows the time series of the observed and simulated water levels during the typhoon approach period
on 4 September. Both time series at the southern observational site (S2: Mihogasaki) indicate that the predominant decreases (approximately 1 m) in the water level at
S2 were induced by typhoon winds. On the other hand,
the water levels of both time series at the northern observational site (N3: Katayama) increased by approximately 20 cm when the water level at the southern site
was at a local minimum. The increases at N3 andNakada et al. Progress in Earth and Planetary Science(2021) 8:46

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Fig. 8 a Vectors of the area-averaged wind speed derived from the hourly GPV-MSM reanalysis dataset in the southern basin during the periodof 2–6 September 2018. Red shading indicates the date of the Typhoon 21 approach around Lake Biwa. b Map of the wind vectors and speeds(colors) interpolated using the GPV-MSM reanalysis dataset at 15:00 JST on 4 September. c Scatter plots showing the observed and estimatedwind speeds during the period of 2–6 September

decreases at S2 started at 14:00 when the wind changed
from the southeasterly wind (Fig. 8) to the southsouthwesterly gale (~ 30 m/s) corresponding to the longaxis direction of the southern basin (Fig. 1). This explains why the predominant decrease at S2 and higher
peak at N3 were induced by the gale.
A comparison between the observed and simulated
water levels at all observational sites (Fig. 9b) results in ahigh correlation coefficient (R = 0.94, N = 246) and a
low calculation error (RMSE = 12.15 cm, BIAS = − 5.35cm) compared to the magnitude of the water levelchange. This suggests that the calculated water levels of
the simulation results are reasonably reproducible. Thus,
the FVCOM simulation was capable of qualitatively reproducing the water level fluctuations induced by typhoon winds with low calculation error. The calculation
error could be attributed to the estimation errors of thelocal wind owing to the coarse horizontal resolution of
the GPV-MSM, leading to reduced reproducibility of thegravity wave influencing the water level fluctuations. A
numerical experiment with the case of no wind correction (using only WMSM) was also conducted (Fig. 9a),yielding a clearly smaller peak at N3 and trough at S2.
The case with no consideration of the air pressure on
the lake surface shows little difference compared to thecontrol run (not shown). These results suggested thatthe observed marked peaks at N3 and trough at S2 weregenerated by the gale-induced torrent attributed to thewind setup.

4.3 Simulated results
4.3.1 Gale-induced torrent

Figure 10 shows three snapshots of the simulated waterlevel distribution (color shading) and surface flow field(vectors) in the southern basin during the typhoon approach, indicating the generation of a gale-induced torrent. The northward torrent induced by the southsoutheast wind exceeded 1 m/s in the northern area during 15:00–16:00 JST but was greater than the flow speed
in the southern area. The water level was reduced by approximately 1.2 m due to the massive water transport associated with the northward torrent; consequently, thewater level in the southern area was significantly lower
than that in the northern area. On the other hand, after
the south winds of the typhoon diminished (Fig. 11), thewater level recovered to approximately 0 m during 17:
00–19:00 JST. The temporal variation in the surface currents indicates that the southward backwash currents
returned from the northern basin, and their speed
exceeded 1 m/s in the northern area; moreover, thespeed of these currents in the northern area was greaterthan that in the southern area at 18:00 JST, when southward water transport was dominant.

Nakada et al. Progress in Earth and Planetary Science(2021) 8:46

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0.71 m × water area of the southern basin of 51.6 km3)
between the southern and northern basins. Three peaksin the bottom speed were visible at 14:00, 15:20, and 16:
40 JST during the typhoon approach period (12:00–18:
00 JST); the maximum peak (0.25 m/s) of the bottom
flow was reached first, and its magnitude was approximately one-third that of the maximum peak of the surface flow. The temporal variation in the surface flow
speed was similar to that of the depth- and areaaveraged flow speed in the southern basin (blue solidline), suggesting that the surface velocity field represented the flow dynamics in the southern basin. In contrast, the temporal variation after 20:00 indicates that
small fluctuations occurred after the typhoon departed
from the area.

4.3.2 Erosion

Fig. 9 a Temporal variation reflected in snapshots taken every ten
minutes of the observed (colored dots) and simulated (colored lines)
water levels in the cases with/without wind correction at the
Mihogasaki (red) and Katayama (blue) gauge stations corresponding
to S2 and N2, respectively, as shown in Fig. 1. b Scatter plots
showing the observed and simulated water levels during the periodof 14:00–19:00 JST on 4 September. The colors of the dots indicate
the eleven gauge stations (N1–N6, S1–S5), as shown in Fig. 1

Figure 12 shows maps of the maximum speeds andvectors of the surface and bottom currents. The horizontal pattern of the maximum current speed at the surface
indicates that the northward current was dominant during the typhoon period. The maximum speed of thenorthern area was greater than that of the southern area
and was particularly dominant but localized in the narrow strait around the Biwako-ohashi Bridge. The patternof the maximum speed of the bottom current shows thatthe southward current was dominant in the direction
opposite to that of the surface flow pattern. The dominant maximum bottom speed was also limited in the most
northern area near the Biwako-ohashi Bridge.
Figure 13 shows the time series of the area-averaged
water level (black solid line) in the southern basin (southof 35° 7.8′ N); a prominent negative peak of the waterlevel is visible at 16:10. The area-averaged flow speed at
the surface shows two prevalent peaks of 0.83 and 0.51
m/s at 15:10 and 17:20 JST, respectively; these apparent
fluctuations suggest the to-and-fro transport of a large
volume of 3.64 × 1010 m3 (= the water level difference of

Figure 14 shows the maps used to evaluate the shear
stress and erosion induced by the torrent at the bed.
The map of the critical shear stress (Fig. 14a) derived by
Eq. (3) shows the highest values in the northeastern areaaround the coast of the Karasuma Peninsula and in thesouthern area near the Ohmi-ohashi Bridge. These areas
have a relatively low water content of less than 40% (Fig.
5); in particular, the area near the Ohmi-ohashi Bridge
has an even lower water content of less than 20%. Figure
14b shows a map of the time-averaged bed shear stressduring the period of 2–5 September obtained by Eq. (2);this map indicates high stress (> 0.5 N/m2) in the northern area, particularly in the strait area near the Biwakoohashi Bridge. These areas presented a greater bed stressthan the areas exhibiting the dominant speed of the bottom flow (Fig. 12). Moreover, the values in the southernarea were substantially smaller, ranging from 0 to 0.4 N/
m2, except for the higher values at the head of thesouthern outlet flowing into the Seta River.
Based on the results of the critical and bed shear
stresses (Fig. 14a and b), we derived a map of the timeaveraged nondimensional bed shear stress Rτ (Fig. 14c)
during the period of 2–5 September on the basis of Eq.
(1). The Rτ value was nearly zero and displayed a similar
pattern to that of the bed shear stress (Fig. 14b), with alarger Rτ in the northern area, particularly in the strait
area. Hence, the bed shear stress associated with substantial erosion is expected to be small.
As a result of the nondimensional bed shear stress distribution, we derived a map of the total or timeintegrated erosion ET defined by Eq. (5) during theperiod of 2–6 September (Fig. 14d). This map indicatesthat the value of the erosion was nearly zero, except for
the higher erosion (> 5 cm) in the northernmost strait
area. This suggests that most erosion was limited to thenorthernmost strait area according to the distribution

Nakada et al. Progress in Earth and Planetary Science(2021) 8:46

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Fig. 10 Snapshots of maps of the simulated water level (color) and surface velocity field (vector) during the period in which Typhoon 21
approached Lake Biwa (14:00-16:00 JST) on 4 September 2018. The inset panels on the bottom right display the observed wind velocities (bluevectors) around Lake Biwa at each time; these data were provided by BiwakoDAS (https://koayu.eri.co.jp/Biwadas_Summary/)

pattern of Rτ (Fig. 14c); this distribution is similar to thearea with a high bed shear stress (Fig. 14b). Consequently, the magnitude of the bottom flow speed determined the magnitude of the nondimensional bed shear
stress related to erosion.

4.3.3 Fluid force

We estimated the distribution of the fluid force FF using
the flow velocity based on Eq. (13) and created maps ofthe maximum FF and time-averaged magnitude of thefluid force FF during the period of 2–6 September

Fig. 11 The same data as in Fig. 10 but for the period when Typhoon 21 departed Lake Biwa (17:00–19:00 JST)Nakada et al. Progress in Earth and Planetary Science(2021) 8:46

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Fig. 12 Maps of the maximum velocity fields (vectors) and speeds (colors) for the surface (left) and bottom (right) currents simulated by the
FVCOM during the period of 14:00–19:00 JST on 4 September 2018

(Fig. 15). The spatial pattern of the time-averaged FF was
similar to that of the maximum FF; both patterns presented larger values in the north-central area, in the western coastal area, and in the estuarine area of the old
Kusatsu River. These patterns mirrored the spatial patternof the decrease in height of submerged macrophytes (Fig.
2c), suggesting that the fluid force induced by the torrent
could have uprooted the submerged macrophytes with thegreatest heights and washed them out.
Figure 16 shows the time series of the area-averaged
magnitude of the fluid force FcF and the nondimensional
bed shear stress Rbτ in the southern basin. The temporalvariation in FcF was also predominant during the typhoon approach period and showed three peaks,

Fig. 13 Simulated time series of the water level (solid black line), thedepth-averaged flow (solid blue line), the surface flow (dashed line),
and the bottom flow speed (fine line) averaged over the area of thesouthern basin during the period from 0:00 JST on 4 September to
9:00 JST on 5 September

although the first peak was small. The variation in FcF
was closer to that in the surface flow than to that in thebottom flow and remained large after the typhoon approach (Fig. 13). The temporal variation in Rbτ exhibited
three peaks during the typhoon approach period; thesepeaks corresponded to those of the bottom flow speed(Fig. 13), illustrating that the magnitude of Rbτ was dominated by the magnitude of the bottom flow speed. Subsequently, the magnitude of Rbτ rapidly diminished after thethird peak and decreased to nearly zero after 18:00 JST.
Figure 17 shows the relationship between the timeaveraged fluid force FF (Fig. 15) and the observed change
in the water plant height (Δhv) using the values at eachmodel grid (Fig. 6). Each averaged value was calculated by
the data sorted with a bin of Δhv=0.1 m. A close linear relationship is visible within the range of (Δhv) from − 1.3 to− 0.4 m, corresponding to the area of the marked decreasein height (Fig. 6). The regression line (Log 10 ð FF Þ ¼ −0:81
Δhv −4:23 ) was derived with a high correlation (R2 =0.94), indicating that the reliable range of Δhv can be
estimated from the calculated fluid force. When thedecreased height is the minimum limit (Δhv = − 0.4m), Log 10 ð FF Þ took the value − 3.91 calculated fromthe regression and the averaged fluid force (1.24×10−4
N). This suggests the limitations of the fluid force
model and indicates that within the range Log 10 ð FF Þ< − 3.91, it might be difficult to estimate the decreased height (Δhv) from the simulated FF. The

Nakada et al. Progress in Earth and Planetary Science(2021) 8:46

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Fig. 14 Maps of a the critical shear stress, b the time-averaged bed shear stress, c the time-averaged nondimensional bed shear stress, and d thetime-integrated erosion from the lakebed during the period 0:00–23:50 JST on 4 September

maximum fluid force (3.62×10−2 N) when Δhv = − 0.4 m
can also be derived, which corresponds to flow speeds of
0.8 ±0.5 (standard deviation) m/s. This speed can be thecritical value to induce the fluid force acting on the
uprooting of the submerged macrophytes.

5 Discussion
This paper highlights that the fluid force (rather thanthe erosion) driven by a gale-induced torrent during theapproach of a typhoon at Lake Biwa uprooted thesubmerged macrophytes therein, leading to the massive
outflow of those macrophytes from a large area of thesouthern basin of Lake Biwa. These results demonstratethat a previously proposed theoretical framework (e.g.,
Luhar and Nepf 2011) and analytical model (Hayashi
and Konno 2007) are applicable even to the high flow
speed range of 1 m/s or greater for calculating the fluid
force acting on submerged macrophytes. Our approach
can contribute to assessing the damage to and erosion
risk of aquatic populations in various lakes (Chiu et al.

Nakada et al. Progress in Earth and Planetary Science(2021) 8:46

Page 15 of 19

Fig. 15 Maps of the maximum (left) and time-averaged (right) fluid forces acting on the submerged macrophytes during the period of 0:00–
23:50 JST on 4 September2017), particularly in lakes with aquatic macrophyte habitats, during storm strikes worldwide (Short et al. 2016).
In particular, based on an evaluation considering violent,high-intensity typhoon strikes projected under climatechange scenarios (Mei and Xie 2016), we can estimate
the frequency at which submerged macrophytes will experience massive losses in the future. These findings are
expected to lead to an understanding of lake ecosystems
and the creation of adaptation strategies to cope withprojected climate change with respect to short-term
events attributed to typhoon strikes (Jeppesen et al.
2017).
The massive loss of submerged macrophytes could decrease the fish stock. In fact, the bluegill (Lepomis
macrochirus) in Lake Biwa, the population of which isFig. 16 Simulated time series of the nondimensional bed shear
stress (solid line) and fluid force acting on the submerged
macrophytes (broken line) averaged over the area of the southernbasin during the period from 0:00 on 4 September to 9:00 JST on
5 September

decreasing interannually with an overall rapidly decreasing trend, reached a minimum level in 2019, as shownby the fish catch data archived on the Shiga Prefecture
website (https://www.pref.shiga.lg.jp/ippan/
kankyoshizen/biwako/307015.html). This may be attributable to the short-term, typhoon-induced, drastic decrease in the submerged macrophyte bed since
submerged macrophytes can provide a habitat for invertebrates and act as feeding sites and refugia for bluegill(Miller et al. 2018). There were similar events due to thetyphoon approach on 16 September 1961 (Typhoon 18,
Nancy) but no evidence for the short-term, typhooninduced, drastic decrease in the submerged macrophytes
because the submerged macrophytes were luxuriant after
1997. Cross and McInerny (2005) reported that thesechanges in submerged plant cover and its detritus substrates can primarily explain the spatial variation in bluegill abundance. According to Schriver et al. (1995),
changes in the abundance of submerged macrophytes
can impact fish-zooplankton-phytoplankton interactionsin shallow eutrophic lakes.
The reproducibility of our simulation results depends
on the accuracy of the wind and current velocity data,
which is a limitation of our approach. We evaluated theaccuracy of the simulated water level compared with theobserved water level (RMSE: 0.12 m); this value falls
within 9% of the water level amplitude (approximately
1.4 m) of both the observed and the simulated fluctuations, suggesting sufficient reproducibility in analyses ofthe fluctuations generated by a typhoon gale. Note that

Nakada et al. Progress in Earth and Planetary Science(2021) 8:46

Fig. 17 The relationship between the time-averaged fluid force (Fig.
15) and height changes of the submerged macrophytes (Fig. 6). The
upper panel shows the number of sample data counted in all gridsof the southern basin with a bin of 0.1 m for the height changes ofthe submerged macrophytes. The lower panel shows the plots of
the averaged values for the fluid forces and height changes (blackdots) with the standard deviation (bars) using the sample data

the accuracy of the wind estimation at the lake surface
can substantially contribute to the reproducibility of our
flow simulation and depends on the observational errorof the wind observations. Both the wind speed and thewind direction can significantly affect the fetch in thesouthern basin and the intensity of the gale-induced torrent. The accuracy of our simulation could decline without the validation of the GPV-MSM-predicted windbased on observational data of the lake surface wind.
The fluid force model used in the simulation is simple
because the fluid force is determined by two variables(input data) for the simulated current speed driven
mainly by the lake surface wind and the observed plantheight. We verified the validity of the simulated results
based on observational water level data, suggesting thata limitation of our model is that the accuracy of the simulated current velocity is unknown. The current velocity
must be observed in the areas with submerged macrophyte beds for a comparison with the simulated velocity
to improve the accuracy of the simulated speed. The
fluid force model adopted in this study is simple andconventional, but our results suggested that the model
can be used to estimate the horizontal distribution ofPage 16 of 19

aquatic plants uprooted and washed out by gale-induced
torrents during extreme events, such as when typhoons
approach. This approach can be practical for evaluatingchanges in lake environments attributed to the massive
outflow of submerged macrophytes under various climate change scenarios, although the fluid force and flow
speed should be verified by observational data in futurestudies.
Wind-induced waves may also contribute to waveinduced currents, generating fluid forces and bed shear
stress generating erosion, resulting in macrophyte
uprooting. We evaluated the representative wave height
Hw(= 0.5 m) and period Tw(= 2.2 s) during the typhoon
approach period of 14:00–19:00 JST on 4 September
based on the simple Sverdrup-Munk-Bretschneider(SMB) method (Bretschneider 1970) using the observed
wind speeds (14.5–19.6 m/s) and supposing typicalfetches (3–4 km). Considering the area-mean depth D(=
4 m) of the southern basin and wavelength Lw = gTw2/(2π) (= 7.5 m), we obtained the scale criterion D/Lw>1/2,
indicating that the wind wave induced by the typhoon
gale was a deep-water wave. The orbital of the deepwater wave is exponentially decreased, and its halfexcursion near the bottom requires approximatelye−DHw as well as the orbital velocity. As a result, we derived the typical wave-induced current Ub = 2πe−DHw/
Tw(= 0.03 m/s) near the bottom based on a similar calculation (Dufois et al. 2008). The current speed of Ub
was less than that of the torrent-induced current, asshown in Fig. 12, suggesting that the effect of wind
waves is negligible. Therefore, wind-induced waves andwave-induced currents can secondarily contribute toerosion and fluid forces.
The bed flow may scour the sediments around thesubmerged macrophytes, although the simulated bed
flow was low in the results of this study. The scouring depth S is primarily determined by the flow speed
and the diameter (Sumer and Fredsøe 2001). Classically, the scouring depth is empirically determined as
S/D = 1.3, where D is the diameter of the cylinder. A
more accurate S within the available range S/D < 2.7
can be estimated from the speed of the bed flow if it
is known (e.g., Qi and Gao 2014). Assuming that D is
the typical stem width of submerged macrophytes (b= 0.01 m) in this study, within S/D < 2.7, S is calculated to be a maximum 2.7 cm smaller than the typical root depth of submerged macrophytes (> 6 cm).
This result suggests that uprooting induced by thisscouring effect was negligible.
This study illustrates that a high-resolution grid of several tens of meters is highly suitable to reproduce thecurrent velocity induced by a typhoon strike. Our model
is composed of grids with a high horizontal resolution(10–60 m), as shown in Fig. 6, which corresponds to the(2021) 8:46

Page 17 of 19

longitudinal resolution of the echosounder observation system used to measure the vegetation height ofthe submerged macrophytes (Fig. 1d). This resolution
can contribute to a direct comparison between theobserved heights of the submerged macrophytes andestimated fluid force and erosion acting on the submerged macrophytes. A reasonable calculation of thefluid force and erosion requires a high horizontal
resolution using a realistically simulated water leveland flow in the southern basin. In comparison, amodel using a coarser spatial resolution than that ofour model experienced difficulty simulating the highspeed currents generated by an atmospheric disturbance and was therefore insufficient (Nakada et al.
2014). To date, the grid resolutions of previously
proposed models have been greater than 0.5 km because the main purpose of those models was to reproduce the lake circulation associated with theseasonal thermocline and the interannual variationsin the lake water temperature (e.g., Akitomo et al.
2009; Koue et al. 2018). We first employed a flow
model with a regular 0.5 km grid to simulate thegale-induced current, but the reproducibility of thewater level was insufficient for our analyses (notshown).
The massive outflow of submerged macrophytes induced by storm strikes can drastically change the lakeecosystem and environment and should be investigated with respect to climate change scenarios (Jeppesen et al. 2017). A southwest wind greater than 20
m/s blew during the study period for longer than 3 h(Fig. 8), inducing a northward torrent and volumetrictransport from the southern basin to the northernbasin (Figs. 10 and 11). We confirmed that the concentration of chlorophyll-a in the southern basin after
the typhoon approach nearly doubled relative to theconcentration during the pretyphoon period between
1 and 3 September (Fig. 3). This suggests that shortterm blooms of toxic cyanobacteria harmful algal
bloom (HAB) species might occur over a large areaof the southern basin once the turbidity has declined,
as indicated by Havens et al. (2016a). Typhoons will
be more intense and violent under the future projected climate of East Asia (Mei and Xie 2016), which
could increase the possibility of events that generate avariety of consequences, such as massive losses andrapid increases in HABs. Similar problems can beshared among the many global lakes with submerged
macrophyte vegetation. We suggest the analysis of typhoon strikes focusing on various lakes at a worldwide scale based on regional climate change scenarios
to enable the exploration of possible drastic changes
in lake environments triggered by the massive loss ofsubmerged macrophytes.

6 Conclusions
This study examined the massive loss of submerged
macrophytes induced by a super typhoon and investigated the physical processes responsible for the outflow
of vegetation using a high-resolution flow model. The
simulated results demonstrated that the fluid force
driven by a gale-induced torrent uprooted the submerged macrophytes during the typhoon approach; thisfluid force (rather than erosion) was the mechanism responsible for the removal of macrophytes. As a result,these processes induced the massive loss of submerged
macrophytes over the large area of the southern basin of
Lake Biwa.
The proposed high-resolution flow model will be essential for evaluating the short-term, massive losses induced by typhoon strikes, although the reproducibility ofthe simulation depends on the accuracy of the winddata. The massive outflow of submerged macrophytes
induced by storm strikes can drastically change the ecosystem and environment of a lake, leading to a possible
reduction in the fish stock and an increase in primary
production. Our simulation successfully reproduced theabnormal fluctuation of the water level generated by thetyphoon gale. The model accuracy of our flow simulation using high-resolution grids was acceptable for the
fluid force analysis and did not affect our conclusion.
We hope that such drastic changes attributed to themassive outflow of submerged macrophytes will be investigated based on climate change scenarios.

Nakada et al. Progress in Earth and Planetary Science

Abbreviations
FVCOM: Finite-volume community ocean model; LBERI: Lake Biwa
Environmental Research Institute; GPV-MSM: Grid Point Value datasets of the
Meso-Scale Model; LBM: Lake Biwa Museum; CSV: Comma-separated values;
JMA: Japan Meteorological Agency; MLIT: Ministry of Land, Infrastructure,
Transport and Tourism; JWA: Japan Water Agency; JST: Japan Standard Time;
HAB: Harmful algal bloom

Acknowledgements
This work was supported by a grant from the Collaborative Research Fundfrom Shiga Prefecture “Studies on conservation and ecosystem managementof Lake Biwa” under the Japanese Grant for Regional Revitalization. We thankDr. C. Jiao, LBERI, for providing the bathymetry grid data of Lake Biwa. We
deeply appreciate Mr. K. Hatano, JWA, for providing the observational
meteorological data, such as wind and air pressure. In this research, we used
the supercomputer of ACCMS, Kyoto University. We thank the ResearchInstitute for Sustainable Humanosphere, Kyoto University, for providing the
input datasets of GPV-MSM. We deeply appreciate two anonymous reviewers
and the editor for their fruitful comments.

Authors’ contributions
SN mainly contributed to the writing of the paper, modeling, simulation, anddata analysis. HH contributed to the observation of the plant height ofmacrophytes by an echosounder. MI contributed to the in situ datacollection and analysis of the water level time series. KM contributed to thein situ data collection of the water level time series at various sites. NT
mainly contributed to the study design of this paper and many discussions.
The authors read and approved the final manuscript.

Nakada et al. Progress in Earth and Planetary Science(2021) 8:46

Funding
This work was supported by the Collaborative Research Fund from Shiga
Prefecture “Studies on conservation and ecosystem management of Lake
Biwa” under the Japanese Grant for Regional Revitalization.
Availability of data and materials
Please contact author for data requests.

Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1
National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki
305-8506, Japan. 2Lake Biwa Museum, 1091 Oroshimo-cho, Kusatsu, Shiga
525-0001, Japan.
Received: 22 December 2020 Accepted: 4 July 2021

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