がって、衛星観測データにより、全世界の湖沼および貯水池における貯水量変化の季節変化・年々変化の検

出は可能であり、長期・連続的な貯水量変化の全球規模でのモニタリングの実現は十分に可能であると考え

られる。

5. まとめと今後の課題 本研究では、TERRA/MODIS 衛星画像および TOPEX/POSEIDON 衛星高度計データを用いて、全世界

の合計 34 地点の湖沼および貯水池の貯水量変化の長期変化を推定した。その結果、衛星による貯水量変

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TanaGuri

ChadVolta

Mweru

WoodsHuron

Vanern

Powell

Nasser

Malawi

Khanka

Kariba

Kainji

Gaoyou

Turkana

HovsgolZeyskoye

Victoria

Chiquita

Aral SeaWinnebago

Tonle Sap

Tanganyik

SaltonSea

Mangueira

Diefenbak

Cocibolca

Chardarin

RybinskoyeKiyevskoyeIjsselmeer

Cabora Bassa

Kremenshukoye

-180°

-180°

-150°

-150°

-120°

-120°

-90°

-90°

-60°

-60°

-30°

-30°

0°

0°

30°

30°

60°

60°

90°

90°

120°

120°

150°

150°

180°

180°

-60° -60°

-30° -30°

0° 0°

30° 30°

60° 60°

図 2 モニタリング対象地点（全 34 地点：湖沼 19 地点，貯水池 15 地点） (地点名の下線は、以下でグラフが示されている湖沼・ダム貯水池を意味する)

□ ダム貯水池

△ 湖沼

Huron

-50-30-10103050

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

⊿V

(km

3）

Cocibolca

-6-4-12479

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

⊿V

(km

3）

Guri

-40

-25

-10

5

20

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

⊿V

(km

3）

Kremenshugskoye

-4-20246

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

⊿V

(km

3）

Chardarinskoye

-4

-2

0

2

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

⊿V

(km

3）

Gaoyou

-0.004-0.003-0.002-0.0010.0000.001

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

⊿V

(km

3）

Nasser

-30

0

30

60

90

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

⊿V

(km

3）

Turkana

-10

0

10

20

30

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

⊿V

(km

3）

Vanern

-4-20246

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

⊿V

(km

3）

Diefenbak

-1.5

-0.5

0.5

1.5

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

⊿V

(km

3）

図 3 衛星による貯水量変化の推定結果（1992 – 2004） 点線は推定結果，実線は貯水量変化の長期傾向（移動平均）,四角で囲まれた地点名はダム貯水池，それ以外は湖沼を示す

-13-

4. RESULTS AND DISCUSSIONS The annual sedimentation in the Sengguruh reservoir was estimated around 2,148,000 ton, and Sutami reservoir was 1,707,600 ton. Compare to observed data, the percentages error in volume of sedimentation were given as 9.5% and 13.6% respectively8,9). As results, it was found that the Brantas origin sub-basin and Lesti sub-basin gave high contribution, as shown in Fig.6. The sediment distributions in the reservoirs are estimated using Area-Reduction Method that recognizes the sediment distribution in reservoirs10). Results shows that their effective storages were estimated will be full of sediment in 2.78 years for Sengguruh reservoir and 147.6 years for Sutami reservoir in case of no countermeasures done, as shown in Fig.7.

Figure.7 Estimated distribution of sediment in Sengguruh (up) and Sutami (down) reservoirs Following with qualitative analysis of sediment sources and deposited, it was found that in Sengguruh reservoir, Brantas origin sub-basin contributed higher percentage of silt-clay particle than Lesti sub-basin. It is indicated that higher rate of surface erosion was occurred, as well as quantitative results. Moreover, sediment distribution of Lesti sub-basin contained more coarse materials, Fig.8. Basically, sediment material was trapped in Sengguruh reservoir before it was released to Sutami reservoir as suspended load. Dempok is situated just downstream confluence of Brantas river and Metro river, Fig.9. Sediment in Dempok shows higher percentage of sand particle and has similar distribution with Metro sub-basin. Since there is no inflow other than Metro river, it indicates increasing contribution of sedimentation from Metro sub-basin. This is useful finding to evaluate the existing reservoir sedimentation countermeasures. The major clay mineral of Sengguruh reservoir were Feldspar, Carbonate (Calcite), Kaolinite, Mica, Chlorate, Montmorillonite, Illite and Quartz. Feldspar almost dominantly came from Lesti reach and Carbonate (Calcite) from Brantas reach.

Figure.４ Global distribution of 137Cs Fallout rate

Figure.5 Hierarchical Cluster Analysis

R

K

LS

CP

Figure.6 Quantitative prediction of sediment yield by USLE, and

estimation of sediment budget in Upper Brantas basin

-17-