The TWAP Lakes and Reservoirs Component provides lake rankings based on the magnitude of their threats as considered on the basis of the previously-noted 23 basin drivers and associated driver weights. The rankings are expressed in terms of the magnitudes of the Incident Human Water Security (HWS), Adjusted Human Water Security (Ad-HWS), and Biodiversity (BD) threats. Other weighting criteria, such as Human Development Index (HDI), may give a threat perspective not adequately reflected by any of the above three threats (i.e., the threat ranks on the basis of the potential threats stemming from inadequate socio-economic and human resource fulfillment).
The HWS, BD, and Adj-HWS threats to the transboundary lakes are summarized below in terms of:
Fig. 3-1 TWAP Transboundary Lakes Ranked on Basis of HWS Threats,
(b) Adj-HWS Threats, and (c) BD Threats
(Cont., continent; Eur, Europe; N.Am, North America; Afr., Africa; S.Am, South America;
Estimated threat risks: Red – highest; Orange – moderately high; Yellow – medium; Green – moderately low; Blue – low)
(A)Lakes Ranked on Basis of Incident Human Water Security (HWS) Threats | (B) Lakes Ranked on Basis of Adjusted Human Water Security (Adj-HWS) Threats | (C) Lakes Ranked on Basis of Incident Biodiversity (BD) Threats |
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Rank | Lake | Cont. | Surface Area (km²) | HWS Threat | Rank | Lake | Cont. | Surface Area (km²) | Adj-HWS Threat | Rank | Lake | Cont. | Surface Area (km²) | BD Threat | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Cahul | Eur | 89.0 | 0.61 | 1 | Sistan | Asia | 488.2 | 0.98 | 1 | Falcon | N.Am | 120.6 | 0.62 | ||
2 | Falcon | N.Am | 120.6 | 0.61 | 2 | Ihema | Afr. | 93.2 | 0.97 | 2 | Mangla | Asia | 85.4 | 0.62 | ||
3 | Mangla | Asia | 85.4 | 0.59 | 3 | Azuei | S.Am | 117.3 | 0.96 | 3 | Cahul | Eur | 89 | 0.61 | ||
4 | Galilee | Eur | 162 | 0.59 | 4 | Rweru/Moero | Afr. | 125.6 | 0.96 | 3 | Neusiedler/Ferto | Eur | 141.9 | 0.61 | ||
5 | Aras Su Qovsaginin Su Anbari | Asia | 52.1 | 0.57 | 5 | Cohoha | Afr. | 64.8 | 0.96 | 5 | Erie | N.Am | 26560.8 | 0.57 | ||
6 | Dead Sea | Eur | 642.7 | 0.57 | 6 | Edward | Afr. | 2232 | 0.94 | 6 | Michigan | N.Am | 58535.5 | 0.56 | ||
7 | Darbandikhan | Asia | 114.3 | 0.56 | 7 | Natron/Magad | Afr. | 560.4 | 0.93 | 7 | Galilee | Eur | 162 | 0.55 | ||
8 | Neusiedler/Ferto | Eur | 141.9 | 0.54 | 8 | Abbe/Abhe | Afr. | 310.6 | 0.93 | 8 | Darbandikhan | Asia | 114.3 | 0.54 | ||
9 | Szczecin Lagoon | Eur | 822.4 | 0.54 | 9 | Victoria | Afr. | 66841.5 | 0.91 | 9 | Aras Su Qovsaginin Su Anbari | Asia | 52.1 | 0.53 | ||
10 | Josini/Pongola-poort Dam | Afr. | 128.6 | 0.52 | 10 | Albert | Afr. | 5502.3 | 0.91 | 10 | Ontario | N.Am | 19062.2 | 0.53 | ||
11 | Shardara/Kara-Kul | Asia | 746.1 | 0.52 | 11 | Kivu | Afr. | 2371.1 | 0.91 | 11 | Szczecin Lagoon | Eur | 822.4 | 0.51 | ||
12 | Erie | N.Am | 26560.8 | 0.51 | 12 | Malawi/Nyasa | Afr. | 29429.2 | 0.91 | 12 | Maggiore | Eur | 211.4 | 0.51 | ||
13 | Macro Prespa (Large Prespa) | Eur | 263 | 0.50 | 13 | Dead Sea | Eur | 642.7 | 0.90 | 13 | Dead Sea | Eur | 642.7 | 0.49 | ||
14 | Azuei | S.Am | 117.3 | 0.50 | 14 | Turkana | Afr. | 7439.2 | 0.90 | 14 | Macro Prespa (Large Prespa) | Eur | 263 | 0.49 | ||
15 | Ohrid | Eur | 354.3 | 0.49 | 15 | Aras Su Qovsaginin Su Anbari | Asia | 52.1 | 0.89 | 15 | Ohrid | Eur | 354.3 | 0.49 | ||
16 | Michigan | N.Am | 58535.5 | 0.48 | 16 | Mangla | Asia | 85.4 | 0.87 | 16 | Champlain | N.Am | 1098.9 | 0.49 | ||
17 | Ontario | N.Am | 19062.2 | 0.46 | 17 | Galilee | Eur | 162 | 0.87 | 17 | Josini/Pongola-poort Dam | Afr. | 128.6 | 0.48 | ||
18 | Caspian Sea | Asia | 377543.2 | 0.45 | 18 | Darbandikhan | Asia | 114.3 | 0.87 | 18 | Huron | N.Am | 60565.2 | 0.47 | ||
19 | Amistad | N.Am | 131.3 | 0.42 | 19 | Selingue | Afr. | 334.4 | 0.87 | 12 | Shardara/Kara-Kul | Asia | 746.1 | 0.46 | ||
20 | Victoria | Afr. | 66841.5 | 0.42 | 20 | Shardara/Kara-Kul | Asia | 746.1 | 0.86 | 20 | Scutari/Skadar | Eur | 381.5 | 0.45 | ||
21 | Ihema | Afr. | 93.2 | 0.41 | 21 | Nasser/Aswan | Afr. | 5362.7 | 0.86 | 21 | Victoria | Afr. | 66841.5 | 0.44 | ||
22 | Sistan | Asia | 488.2 | 0.41 | 22 | Chilwa | Afr. | 1084.2 | 0.86 | 22 | Ihema | Afr. | 93.2 | 0.44 | ||
23 | Scutari/Skadar | Eur | 381.5 | 0.40 | 23 | Josini/Pongola-poort Dam | Afr. | 128.6 | 0.85 | 23 | Azuei | S.Am | 117.3 | 0.43 | ||
24 | Maggiore | Eur | 211.4 | 0.40 | 24 | Chiuta | Afr. | 143.3 | 0.85 | 24 | Rweru/Moero | Afr. | 125.6 | 0.42 | ||
25 | Huron | N.Am | 60565.2 | 0.40 | 25 | Chad | Afr. | 1294.6 | 0.84 | 25 | Itaipu | S.Am | 1154.1 | 0.42 | ||
26 | Rweru/Moero | Afr. | 125.6 | 0.40 | 26 | Aral Sea | Asia | 23919.3 | 0.84 | 26 | Cohoha | Afr. | 64.8 | 0.41 | ||
27 | Champlain | N.Am | 1098.9 | 0.39 | 27 | Tanganyika | Afr. | 32685.5 | 0.84 | 27 | Caspian Sea | Asia | 377543.2 | 0.4 | ||
28 | Cohoha | Afr. | 64.8 | 0.39 | 28 | Aby | Afr. | 438.8 | 0.83 | 28 | Amistad | N.Am | 131.3 | 0.39 | ||
29 | Chad | Afr. | 1294.6 | 0.38 | 29 | Cahul | Eur | 89 | 0.82 | 29 | Sistan | Asia | 488.2 | 0.38 | ||
30 | Itaipu | S.Am | 1154.1 | 0.36 | 30 | Chungarkkota | S.Am | 52.6 | 0.82 | 30 | Albert | Afr. | 5502.3 | 0.37 | ||
31 | Chungarkkota | S.Am | 52.6 | 0.36 | 31 | Titicaca | S.Am | 7480 | 0.82 | 31 | Chad | Afr. | 1294.6 | 0.36 | ||
32 | Natron/Magad | Afr. | 560.4 | 0.36 | 32 | Sarygamysh | Asia | 3777.7 | 0.82 | 32 | Aby | Afr. | 438.8 | 0.35 | ||
33 | Albert | Afr. | 5502.3 | 0.35 | 33 | Mweru | Afr. | 5021.5 | 0.81 | 33 | Edward | Afr. | 2232 | 0.35 | ||
34 | Aby | Afr. | 438.8 | 0.34 | 34 | Cahora Bassa | Afr. | 4347.4 | 0.78 | 34 | Kariba | Afr. | 5258.6 | 0.34 | ||
35 | Edward | Afr. | 2232 | 0.34 | 35 | Itaipu | S.Am | 1154.1 | 0.75 | 35 | Lago de Yacyreta | S.Am | 1109.4 | 0.34 | ||
36 | Kariba | Afr. | 5258.6 | 0.33 | 36 | Kariba | Afr. | 5258.6 | 0.75 | 36 | Natron/Magad | Afr. | 560.4 | 0.33 | ||
37 | Turkana | Afr. | 7439.2 | 0.33 | 37 | Lago de Yacyreta | S.Am | 1109.4 | 0.75 | 37 | Kivu | Afr. | 2371.1 | 0.33 | ||
38 | Titicaca | S.Am | 7480 | 0.33 | 38 | Lake Congo River | Afr. | 306 | 0.75 | 38 | Selingue | Afr. | 334.4 | 0.32 | ||
39 | Kivu | Afr. | 2371.1 | 0.31 | 39 | Caspian Sea | Asia | 377543.2 | 0.73 | 39 | Nasser/Aswan | Afr. | 5362.7 | 0.32 | ||
40 | Lago de Yacyreta | S.Am | 1109.4 | 0.31 | 40 | Salto Grande | S.Am | 532.9 | 0.67 | 40 | Malawi/Nyasa | Afr. | 29429.2 | 0.32 | ||
41 | Abbe/Abhe | Afr. | 310.6 | 0.31 | 41 | Scutari/Skadar | Eur | 381.5 | 0.62 | 41 | Chungarkkota | S.Am | 52.6 | 0.31 | ||
42 | Selingue | Afr. | 334.4 | 0.30 | 42 | Neusiedler/Ferto | Eur | 141.9 | 0.58 | 42 | Cahora Bassa | Afr. | 4347.4 | 0.31 | ||
43 | Aral Sea | Asia | 23919.3 | 0.30 | 43 | Szczecin Lagoon | Eur | 822.4 | 0.53 | 43 | Turkana | Afr. | 7439.2 | 0.30 | ||
44 | Salto Grande | S.Am | 532.9 | 0.29 | 44 | Erie | N.Am | 26560.8 | 0.51 | 44 | Salto Grande | S.Am | 532.9 | 0.30 | ||
45 | Nasser/Aswan | Afr. | 5362.7 | 0.29 | 45 | Macro Prespa (Large Prespa) | Eur | 263 | 0.51 | 45 | Chilwa | Afr. | 1084.2 | 0.30 | ||
46 | Malawi/Nyasa | Afr. | 29429.2 | 0.29 | 46 | Falcon | N.Am | 120.6 | 0.50 | 48 | Titicaca | S.Am | 7480 | 0.29 | ||
47 | Cahora Bassa | Afr. | 4347.4 | 0.29 | 47 | Amistad | N.Am | 131.3 | 0.49 | 47 | Abbe/Abhe | Afr. | 310.6 | 0.29 | ||
48 | Chilwa | Afr. | 1084.2 | 0.28 | 48 | Ontario | N.Am | 19062.2 | 0.48 | 48 | Tanganyika | Afr. | 32685.5 | 0.29 | ||
49 | Sarygamysh | Asia | 3777.7 | 0.26 | 49 | Ohrid | Eur | 354.3 | 0.47 | 43 | Aral Sea | Asia | 23919.3 | 0.28 | ||
50 | Chiuta | Afr. | 143.3 | 0.25 | 50 | Michigan | N.Am | 58535.5 | 0.44 | 50 | Mweru | Afr. | 5021.5 | 0.28 | ||
51 | Tanganyika | Afr. | 32685.5 | 0.25 | 51 | Huron | N.Am | 60565.2 | 0.42 | 51 | Chiuta | Afr. | 143.3 | 0.26 | ||
52 | Mweru | Afr. | 5021.5 | 0.24 | 52 | Maggiore | Eur | 211.4 | 0.33 | 52 | Sarygamysh | Asia | 3777.7 | 0.25 | ||
53 | Lake Congo River | Afr. | 306 | 0.20 | 53 | Champlain | N.Am | 1098.9 | 0.29 | 53 | Lake Congo River | Afr. | 306 | 0.20 |
The top dozen transboundary lakes exhibiting the greatest HWS threats were largely located in developed countries, including five European, four Asian, two North American and one African lake. Comparison of the HWS and Adj-HWS scores highlighted the positive impacts attributable to investments in measures to address the threatened transboundary lakes, resulting in a reduced number of threatened lakes in developed countries, while markedly increasing those in developing nations. The top dozen lakes exhibiting the greatest Adj-HWS threats included ten African, one Asian and one South American lake, highlighting their greater need for catalytic funding for transboundary lake management interventions. In regard to biodiversity, the top dozen lakes exhibiting the greatest Incident Biodiversity (BD) threats were also largely in developed countries, including five European, four North American and three Asian lakes. In contrast to their Adj-HWS threat rankings, the African transboundary lakes collectively exhibited lower BD threats than those in the developed countries, suggesting their biodiversity exhibited a more robust condition in spite of their greater human water security threats.
In addition to single ranking criteria, the transboundary lake threats were also ranked on the basis of simultaneous consideration of multiple filtering criteria, including the Adj-HWS threat and Human Development Index (HDI). It was not possible to develop an ‘adjusted biodiversity’ threat parameter similar to the Adj-HWS threat. Accordingly, a surrogate ‘Reverse Biodiversity’ (RvBD metric was developed to reflect the magnitude of the pristine nature of the biodiversity, with the lowest RvBD score indicating the greatest biodiversity threat. The computed ranks from these various parameters were also summed to obtain an overall threat ranking encompassing all of them.
Fig. 3-2 Transboundary Lake Threat Ranks by Multiple Ranking Criteria
(Cont., continent; Eur, Europe; N.Am, North America; Afr, Africa; S.Am, South America;
Adj-HWS, Adjusted Human Water Security threat; HWS, Incident Human Water Security threat; BD, Incident Biodiversity threat;
HDI, Human Development Index, RvBD, surrogate for ‘Adjusted’ Biodiversity threat;
Estimated risks: Red – highest; Orange – moderately high; Yellow – medium; Green – moderately low; Blue – low)
Cont. | Lake Name | Adj-HWS Threat | RvBD Threat | HDI | Adj-HWS Rank | HDI Rank | RvBD Rank | Sum Adj- HWS + RvBD | Relative Rank | Sum Adj-HWS + HDI | Relative Rank | Sum Adj- HWS + RvBD + HDI | Overall Rank | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Afr | Abbe/Abhe | 0.93 | 0.71 | 0.4 | 7 | 7 | 7 | 14 | 1 | 14 | 3 | 21 | 1 | ||||
Afr | Turkana | 0.9 | 0.7 | 0.41 | 13 | 10 | 9 | 22 | 2 | 23 | 10 | 32 | 2 | ||||
Afr | Selingue | 0.87 | 0.68 | 0.36 | 16 | 2 | 15 | 31 | 11 | 18 | 5 | 33 | 3 | ||||
Afr | Malawi/Nyasa | 0.91 | 0.68 | 0.42 | 9 | 12 | 14 | 23 | 3 | 21 | 9 | 35 | 4 | ||||
Afr | Chiuta | 0.85 | 0.74 | 0.41 | 23 | 9 | 3 | 26 | 5 | 32 | 15 | 35 | 4 | ||||
Afr | Cohoha | 0.96 | 0.59 | 0.38 | 3 | 4 | 28 | 31 | 2 | 7 | 1 | 35 | 4 | ||||
Afr | Kivu | 0.91 | 0.67 | 0.38 | 12 | 6 | 18 | 30 | 8 | 18 | 4 | 36 | 7 | ||||
Afr | Rweru/Moero | 0.96 | 0.58 | 0.36 | 4 | 3 | 30 | 34 | 16 | 7 | 2 | 37 | 8 | ||||
Afr | Lake Congo River | 0.75 | 0.78 | 0.34 | 35 | 1 | 1 | 36 | 18 | 36 | 19 | 37 | 8 | ||||
Afr | Tanganyika | 0.84 | 0.71 | 0.4 | 26 | 8 | 6 | 32 | 14 | 34 | 17 | 40 | 10 | ||||
Afr | Edward | 0.94 | 0.65 | 0.43 | 6 | 13 | 22 | 28 | 7 | 19 | 6 | 41 | 11 | ||||
Afr | Chilwa | 0.86 | 0.7 | 0.41 | 21 | 11 | 10 | 31 | 10 | 32 | 14 | 42 | 12 | ||||
Afr | Mweru | 0.81 | 0.72 | 0.38 | 33 | 5 | 4 | 37 | 21 | 38 | 20 | 42 | 12 | ||||
Asia | Sistan | 0.98 | 0.62 | 0.46 | 1 | 20 | 25 | 26 | 6 | 21 | 8 | 46 | 14 | ||||
Afr | Natron/Magadi | 0.93 | 0.67 | 0.51 | 8 | 23 | 17 | 25 | 4 | 31 | 13 | 48 | 15 | ||||
Afr | Nasser/Aswan | 0.86 | 0.68 | 0.43 | 20 | 16 | 16 | 36 | 19 | 36 | 18 | 52 | 16 | ||||
Afr | Albert | 0.91 | 0.63 | 0.46 | 10 | 19 | 24 | 34 | 15 | 29 | 12 | 53 | 17 | ||||
Afr | Ihema | 0.97 | 0.56 | 0.44 | 2 | 18 | 33 | 35 | 17 | 20 | 7 | 53 | 17 | ||||
S.Am, | Azuei | 0.96 | 0.57 | 0.46 | 5 | 21 | 31 | 36 | 20 | 26 | 11 | 57 | 19 | ||||
Asia | Aral Sea | 0.84 | 0.62 | 0.6 | 27 | 26 | 5 | 32 | 13 | 53 | 31 | 58 | 20 | ||||
Asia | Sarygamysh | 0.82 | 0.75 | 0.67 | 29 | 29 | 2 | 31 | 9 | 58 | 32 | 60 | 21 | ||||
Afr | Cahora Bassa | 0.78 | 0.69 | 0.43 | 34 | 15 | 13 | 47 | 25 | 49 | 25 | 62 | 22 | ||||
Afr | Victoria | 0.91 | 0.56 | 0.47 | 11 | 22 | 32 | 43 | 24 | 33 | 16 | 65 | 23 | ||||
Afr | Chad | 0.84 | 0.64 | 0.43 | 25 | 17 | 23 | 48 | 26 | 42 | 21 | 65 | 23 | ||||
Afr | Kariba | 0.75 | 0.66 | 0.43 | 36 | 14 | 19 | 55 | 30 | 50 | 28 | 69 | 25 | ||||
S.Am | Titicaca | 0.82 | 0.71 | 0.71 | 32 | 32 | 8 | 40 | 22 | 25 | 35 | 72 | 26 | ||||
Afr | Aby | 0.83 | 0.65 | 0.52 | 28 | 24 | 21 | 49 | 27 | 52 | 30 | 73 | 27 | ||||
S.Am | Chungarkkota | 0.82 | 0.69 | 0.71 | 31 | 33 | 12 | 43 | 23 | 64 | 34 | 76 | 28 | ||||
Asia | Shardara/Kara-kul | 0.86 | 0.54 | 0.65 | 22 | 28 | 35 | 57 | 31 | 50 | 27 | 85 | 29 | ||||
Eur | Dead Sea | 0.9 | 0.51 | 0.72 | 14 | 34 | 38 | 52 | 29 | 48 | 24 | 86 | 30 | ||||
Afr | Josini/Pongola-poort Dam | 0.85 | 0.52 | 0.61 | 24 | 27 | 37 | 61 | 34 | 51 | 29 | 88 | 31 | ||||
S.Am | Salto Grande | 0.67 | 0.7 | 0.74 | 40 | 38 | 11 | 51 | 28 | 78 | 39 | 89 | 32 | ||||
Asia | Darbandikhan | 0.87 | 0.46 | 0.68 | 17 | 30 | 46 | 63 | 35 | 47 | 23 | 93 | 33 | ||||
S.Am | Lago de Yacyreta | 0.75 | 0.66 | 0.73 | 38 | 36 | 20 | 58 | 32 | 74 | 38 | 94 | 34 | ||||
Asia | Aras Su Qovsaginin Su Anbari | 0.89 | 0.47 | 0.73 | 15 | 35 | 44 | 59 | 33 | 50 | 26 | 94 | 34 | ||||
Asia | Mangla | 0.87 | 0.38 | 0.54 | 18 | 25 | 53 | 71 | 39 | 43 | 22 | 96 | 36 | ||||
S.Am | Itaipu | 0.75 | 0.58 | 0.73 | 37 | 37 | 29 | 66 | 37 | 74 | 37 | 103 | 37 | ||||
Asia | Caspian Sea | 0.73 | 0.6 | 0.77 | 39 | 41 | 27 | 66 | 36 | 80 | 40 | 107 | 38 | ||||
Eur | Galilee | 0.87 | 0.45 | 0.88 | 19 | 46 | 47 | 66 | 38 | 65 | 36 | 112 | 39 | ||||
Eur | Cahul | 0.82 | 0.39 | 0.69 | 30 | 31 | 51 | 81 | 42 | 61 | 33 | 112 | 39 | ||||
Eur | Scutari/Skadar | 0.62 | 0.55 | 0.78 | 41 | 42 | 34 | 75 | 41 | 83 | 41 | 117 | 41 | ||||
N.Am | Amistad | 0.49 | 0.61 | 0.86 | 47 | 45 | 26 | 73 | 40 | 47 | 40 | 118 | 42 | ||||
Eur | Macro Prespa (Large Prespa) | 0.51 | 0.51 | 0.75 | 44 | 40 | 40 | 84 | 43 | 84 | 42 | 124 | 43 | ||||
Eur | Ohrid | 0.47 | 0.51 | 0.74 | 49 | 39 | 39 | 88 | 46 | 88 | 44 | 127 | 44 | ||||
Eur | Szczecin Lagoon | 0.53 | 0.49 | 0.83 | 43 | 43 | 43 | 86 | 44 | 86 | 43 | 129 | 45 | ||||
N.Am | Huron | 0.42 | 0.53 | 0.93 | 51 | 50 | 36 | 87 | 45 | 101 | 51 | 137 | 46 | ||||
Eur | Neusiedler/Ferto | 0.58 | 0.39 | 0.88 | 42 | 47 | 50 | 92 | 47 | 89 | 45 | 139 | 47 | ||||
N.Am | Ontario | 0.48 | 0.47 | 0.92 | 48 | 49 | 45 | 93 | 48 | 97 | 49 | 142 | 48 | ||||
Eur | Lake Maggiore | 0.33 | 0.5 | 0.89 | 52 | 48 | 42 | 94 | 50 | 100 | 50 | 142 | 48 | ||||
N.Am | Falcon | 0.5 | 0.38 | 0.85 | 46 | 44 | 52 | 98 | 53 | 90 | 46 | 142 | 48 | ||||
N.Am | Erie | 0.51 | 0.43 | 0.93 | 45 | 51 | 49 | 94 | 51 | 96 | 48 | 145 | 51 | ||||
N.Am | Champlain | 0.29 | 0.51 | 0.94 | 53 | 52 | 41 | 94 | 49 | 105 | 53 | 146 | 52 | ||||
N.Am | Michigan | 0.44 | 0.44 | 0.94 | 50 | 53 | 48 | 98 | 52 | 103 | 52 | 151 | 53 |
Relative Risk Category |
Low |
Moderately Low |
Medium |
Moderately High |
Highest |
Based on these criteria, the African transboundary lakes are collectively the most threatened, comprising 21 of the top 25 most threatened lakes. The remaining lakes include three Asian and one South American lake. However, the relative threat ranks differ when the Adj-HWS, RvBD or HDI are considered individually, with the developed countries generally exhibiting lower threat ranks. As previously noted, because the context under which the transboundary lake threat rankings should be interpreted must be determined by the user of the ranking results, based on selected weighting criteria and relevant regional physical and socioeconomic factors.
These factors, considered alone or collectively, can readily lead to erroneous conclusions regarding the comparative transboundary lake threats. Thus, the calculated lake threats presented below represent only one approximation of the actual threat rankings. As demonstrated in the transboundary lakes assessment, different rankings, sometimes striking, can be obtained when different ranking factors are considered in the ranking calculations.