Estimating and Classifying Spatial and Temporal

Distributions of Flow Conditions for Fish Habitats

by Using Geostatistical Approaches with Measured

Flow and Fish Data

Advisor ： Lin Yu-Pin , PH.D

Presenter ： Wang Cheng-Long

1Environmental and Landscape Ecological Lab

Outline• Introduction• Material & Methods• Results• Discussion & Conclusion• References

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Introduction

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• Streams are heterogeneous environments where organisms exhibit patchy distributions on a spatially and temporally variable physical arena

• To improve the accuracy of flow conditions judgment, the habitat model identifying the flow conditions are clearly illustrated by four key variables: water depth, water velocity, substrate composition and in-stream cover.

Velocity, depth, river cross-sections, slope, substrate…etc

Empirical methodFroude Number

method

Flow conditions (i.e. Pool, Riffle, Slack, Run)

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• This study objectives:

(1)Simulate the velocity, depth and fish probability in the investigating reaches.

(2)Estimate the flow condition requirement of S. japonicus, in order to find the preference of S. japonicus in reach scale.

(3)Discover the relation between the classifications of flow conditions and S. japonicus in the seasonal variations.

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Material & Methods

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• Datuan stream (Fig. 1) has a

total length of 14.5 km, and

we choose four sections,

which are investigated by

50m. in winter and spring.• Indicator species and measuring equipment

S. Japonicus reproduces during the summer in the mid and upstream sections of rivers, then they return to the fresh water upstream approximately 6 months after hatching.

Study area

Fish collection was using the backpack electroshocker. Velocity and depth was measured by propeller-type current meter

50m

Fig. 1 Study area

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• Kriging estimating water velocity and water depth is to calculate experimental variograms.

• The kriging estimation variance can be calculated by adopting the Lagrange method to minimize the estimation variance based on non-biased constraints.

Kriging estimation

)(

1

2)]()([)(2

1)(

hn

iiiuu xuhxu

hnh

N

iiuuikriging xx

100

2 )(

(1)

(2)

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• The Kriging estiamtion is divided into two sections :

(1)Ordinary Kriging is used to interpolating

velocity/depth value in the unsampling sites.

(2)Indicator Kriging is applied to estimating the fish probability in the reach.

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• Flow conditions of estimated velocity and water depth values are classified by Froude number (Jowett, 1993) and the empirical method (Wong, 2000).

Flow classification methods

gd

VFr

Flow

patternsRiffle Pool Run Slack

V(m/s) > 0.3 < 0.3 > 0.3 < 0.3

D(m) < 0.3 > 0.3 > 0.3 < 0.3

Flow patterns Froude number (Fr.)

Pool Fr < 0.18

Run Fr = 0.18 ~ 0.41

Riffle Fr > 0.41

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The overlapping of the fish probability and flow condition maps.

+

fish probability mapflow condition maps Fig. 2 overlapped mapping

Fish probability

Flow condition

GIS exhibition Using the combination of flow condition maps and GIS, it could be easy to show the relation between the flow condition and fish probability in topology in Datuan stream .

Results

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Empirical method in winter

Pool and run occupied the stream area in reach 1,2 and 3

Run appeared at the most area of reach 4, but some pools were distributed in the middle section.

50mFig. 3a Fig. 3b Fig. 3c Fig. 3d

Reach (1) (2) (3) (4)

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Reach 1 in winter had only one type of flow conditions (pool). The area ratio of pool is reduced except that of the probability of 0~0.2.

Run was the only flow condition in reach 2 (Fig. 3b). In addition, the variation of area ratio was increased by the raising of probability.

Run occupied the area of reach 3 (Fig. 3c), the area of run was increased except for the probability interval of 0.8~1.

The area of run was greater than the other flow condition (pool). It means that run was one of the suitable habitats for S. japonicus.

Fig 3a. Reach (1)

Fig 3b. Reach (2)

Fig 3c. Reach (3)

Fig 3d. Reach (4)

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Froude Number method in winter

In Fig. 3e and f, the result was close to Fig. 3a and b, but part of run occurred in reach 1.

Pool and riffle also occupied the stream area in reach 3. The case differed from the result in the empirical method

Run still appeared at the most area of reach 4, but the range of pool

distribution is more widely in the Froude

number method

50mFig. 3e Fig. 3f Fig. 3g Fig. 3h

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The type of Fig. 3e and f was similar to Fig. 3a and b with the difference of the appearance of run in reach 1 and 2.

There was a difference between the two classifications. Run (Fig. 3c) was replaced with pool and riffle (Fig. 3g), and the areas of pool and riffle were increased except for the probability of 0.8~1.

The type of Fig. 3h was identical to Fig. 3d, but the area of pool was larger.

Fig 3e. Reach (1) Fig 3f. Reach (2)

Fig 3g. Reach (3) Fig 3h. Reach (4)

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Empirical method in spring

The major flow conditions were pool in reach 1 and riffle in reach 2. And reach 2 had high heterogeneity of the flow.

Run and riffle had the most two great area in the reach 3 and 4. The flow condition in reach 4 had high heterogeneity as same as which in reach 2.

50mFig. 4a Fig. 4b Fig. 4c Fig. 4d

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Pool owned the most of area in reach 1, but some area belonged to riffle.

The type of Fig. 4b was similar with Fig. 3b, riffle was the most important flow condition in reach 2, and the area ratio of it increased.

Reach 3 was shared with run and riffle (Fig. 4c). The result was similar with Fig. 3c, but the area of riffle in spring was widely spread.

Reach 4 had mixed flow conditions (pool, riffle, run, slack) (Fig. 4d)

Fig 4a. Reach (1)

Fig 4b. Reach (2)

Fig 4c. Reach (3) Fig 4d. Reach (4)

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Froude Number method in spring

The downstream was mainly categorized as pool and run. Riffle (Fig. 4b) was easy to be identified with run (Fig. 4f) in reach 2.

Run covered the most part of reach 3

There was a quite difference between

the two flow classifications.

The result in the Froude number

method was as same as the empirical rule method; besides, part

of pool and riffle were inlayed in

reach 4.50m

Fig. 4e Fig. 4f Fig. 4g Fig. 4h

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In reach 1, the area of pool decreased when the probability increased (Fig. 4e).Reach 2 had a mixed flow conditions which was similar to Fig. 3f, and run still owned the largest area in this reach (Fig. 4f).

In reach 3, run is almost the only flow condition, and the area of run increased except for the situation in the highest probability (0.8~1) (Fig. 4g)

There were three flow conditions (run, pool, riffle) in reach 4, especially, run got the most of area (Fig. 4h)

Fig 4e. Reach (1) Fig 4f. Reach (2)

Fig 4g. Reach (3)

Fig 4h. Reach (4)

Discussion

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(1) We combined the two classifications and kriging estimation in order to predicts the variability of stream

conditions in a fish community, and discover its impact on the distribution in temporal scale.

(2) Two key factors, current velocity and stream depth, are the most two important factors in the habitat preference of fish. The pool/riffle series are usually related with rank erosion and the type of substrate.

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(3) The result of the two classifications were not identical, especially in areas from downstream to middle stream under construction, and the classifications may also lose their accuracy due to the artificial disturbances.

(4)Base on the fish’s life cycle (shelter, reproduction, food source), the result shows that the empirical method is more appropriate for Datuan stream than the Froude number method.

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Conclusion

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(1)These results not only describe the abundance and heterogeneity of S. japonicus from downstream to upstream, but also quantify the area ratio of the combination of fish probability and flow conditions in each reach.

(2) These outcomes reduce the cost in time and money, then provide ecological information for engineers to river restoration, which supply the suitable habitats for the life- cycle of S. japonicus.

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~~~ Thanks for your attention ~~~

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