DATASET, CALIBRATION RULES, AND TRUTH TABLES

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1 Supplementary Material (Online Resource) Click here to view linked References Click here to download attachment to manuscript Supplementary_Material.pdf Supplementary Material (Online Resource) DATASET, CALIBRATION RULES, AND TRUTH TABLES A Comparative Analysis of Water Governance, Water Management, and Environmental Performance in River Basins Journal: Water Resources Management Authors: Christian Knieper*, Claudia Pahl-Wostl * Corresponding author, Osnabrück University, Institute of Environmental Systems Research Contents Table A1 Uncalibrated values of all indicators from the study... 2 Table A2 Calibration rules for indicators taken from the Twin2Go dataset... 3 Table A3 Calibration rules for context indicators... 5 Table A4 Calibrated fsqca values of all conditions from the study... 6 Table A5 Calibrated dataset used in the fsqca assessment... 7 Table A6 Truth table for the model MNGT = f(gni, CORR, POLY, USE)... 8 Table A7 Truth table for the model MNGT = f(gni, CORR, POLY)... 8 Table A8 Truth table for the model ENV = f(hydro, USE, MNGT)... 9 Table A9 Truth table for the model ENV = f(use)... 9 References

2 see GNI value Income (GNI/capita purchase-powercorrected US-$) Corruption (Corruption Perceptions Index) Degree of aridity/humidity (Climate Moisture Index) Inter-annual flow variability (coefficient of variation of annual flow) Intensive agriculture Percentage of Cultivated and managed land ) Population (persons/km 2 ) Economic development Flow regime modification ( Environmental Stress induced by Flow Regime Alterations ) Twin2Go indicator 5 Twin2Go indicator 6 Twin2Go indicator 34 Twin2Go indicator 35 Twin2Go indicator 36 Twin2Go indicator 40 Twin2Go indicator 41 Twin2Go indicator 87 Twin2Go indicator 89 Twin2Go indicator 93 Twin2Go indicator 94 Twin2Go indicator 95 Twin2Go indicator 96 Table A1 Uncalibrated values of all indicators from the study (see the manuscript for data sources) GNI CORR HYDRO USE POLY ENV MNGT Case Biobio (Chile) 17, C B A B C C B B B A B B C Catamayo/Chira (Peru) 9, A A C C A B B B C C B B B Catamayo/Chira (Ecuador) 8, C B E E C C A B B C A B B Upper Cauca (Colombia) 10, B A C C B B A B B B A A A Quaraí/Cuareim (Brazil) 13, C A C D A A B B B B B A B Lake Cocibolca (Nicaragua) 3, A B B B B B B B C A C C B Baker (Chile) 17, C B C C C B B B B A B C C Quaraí/Cuareim (Uruguay) 15, B B B B C B B A B C B A B Guayas (Ecuador) 8, C B E E C C A B B B B C C Norrström (Sweden) 41, A A B A B B A A C A A A A Nura (Kazakhstan) 17, B A D D B C B B C B B A B Okavango (Namibia) 8, A A A C B B B A A A C B B Thames (United Kingdom) 34, A A A A A B A B B B A A A Lake Kyoga (Uganda) 1, A A C C C B A B C C B C B Inner Niger Delta (Mali) 1, A A B B B B A A B B B B C Bang Pakong (Thailand) 12, C C A C C B B C C A A B B Volga (Russian Federation) 21, A A C C B C B C B A A A A Raidak (Bhutan) 6, A A A C A B B B B C B B C Brahmaputra (India) 4, C A D E C A B A B C B B B Kosi (Nepal) 2, A A A C C C B B B B B C C Tisza (Hungary) 15, A A A B A A A B B A A A B Upper Guadiana (Spain) 31, A A A B B C A D C B A A A Elbe (Germany) 40, A A B C B B B B B A A A A Rhine (Netherlands) 42, A A A B A B A B B A A A A Amu Darya (Uzbekistan) 4, A B D D C C B C C - B C B Orange (South Africa) 11, A A D C B B A A D C B B B Red River (Viet Nam) 4, B B B B B B B C C C B B C Olifants (South Africa) 11, A A D C A B A A C B B A B Rio Grande (USA) 49, A A A B A A A D C A A A B 2

3 Table A2 Calibration rules for indicators taken from the Twin2Go dataset Topic Water governance: Polycentricity (Pahl-Wostl and Knieper 2014) Indicator Integration of domestic water legislation Distribution of functions, responsibilities and authority Vertical coordination between governance authorities Horizontal coordination between governance authorities Involvement of local governments Calibration Rules Original score [Some answer options have been simplified here. See Pahl-Wostl and Lebel (2010, updated 2011) for the original answer options.] A: One single piece of legislation exists to coordinate/integrate the water-related framework 1 B: A single piece of legislation does not exist, but is under formation 0 C: A single piece of legislation does not exist 0 A: Functions, responsibilities and authority are allocated to various administrative levels 1 B: Functions and responsibilities are distributed, but no authority 0.3 C: Legislation only at one level, no distribution at all 0 A: Cooperation and clear allocation of tasks 1 B: Clear allocation of tasks, and coordination 0.8 C: Task overlap, but coordination 0.4 D: Clear allocation of tasks, but no coordination 0.2 E: No coordination, much overlap 0 A: Cooperation and clear allocation of tasks 1 B: Clear allocation of tasks, and coordination 0.8 C: Task overlap, but coordination 0.4 D: Clear allocation of tasks, but no coordination 0.2 E: No coordination, much overlap 0 A: Local governments are involved in the creation of institutions at higher levels and participate in decision-making if they will be 1 affected by these institutions B: Local governments are consulted in the creation of institutions at higher levels if they will be affected by these institutions 0.7 C: The role of local governments is basically restricted to the implementation of institutions from higher levels 0 Degree of centralization A: Not centralized system 1 B: Policy development is centralized, but implementation is decentralized 0.6 C: Both policy development and implementation are controlled by one central actor 0 Technical capacity and A: The existing level of decentralization is in accordance with available technical capacity, economies of scale are taken into consideration. 1 economies of scale B: The existing level of decentralization is not in accordance with available technical capacity / economies of scale are not taken into consideration. 0 fsqca value 3

4 Water Management Environmental performance Response to water pollution incidents Comprehensiveness of water quality monitoring Presence of sound hydro-meteorological monitoring Understanding of groundwater resources A: Pollution incidents are always or usually dealt with in a timely and appropriate fashion 1 B: Pollution incidents are in some places/sometimes dealt with in a timely and appropriate fashion 0.4 C: Pollution incidents are rarely or never dealt with in a timely and appropriate fashion 0 A: Comprehensive monitoring of chemical, physical and biological parameters 1 B: Some parameters monitored in some places and times 0.4 C: No systematic monitoring 0 A: Adequate hydro-meteorological monitoring has been operating for ca. 20 years or longer 1 B: Adequate hydro-meteorological monitoring has been established more recently 0.8 C: Hydro-meteorological monitoring is not adequate 0.2 D: No monitoring takes place 0 A: Good (adequate knowledge) 1 B: Intermediate (important gaps and uncertainties) 0.4 C: Poor (few observations or analysis) 0 Aquatic biodiversity A: Most (> 95%) native fish species are still present in the basin 1 B: A majority (50-95%) of the native fish is species still present in the basin 0.7 C: A minority (20-50%) of the native fish species is still present in the basin 0.2 D: Few (< 20%) native fish species are still present in the basin 0 Surface and groundwater quality A: No, very rare, or hardly any impacts on ecosystems through adverse changes in water quality 1 B: Low impact; a few places affected by negative impacts, limited locally 0.7 C: Intermediate impact; bad water quality causes ecosystem degradation at several places 0.2 D: High impact; bad water quality causes ecosystem degradation in large parts of the basin 0 4

5 Table A3 Calibration rules for context indicators Topic Factor Indicator Hydroclimatic context Degree of Aridity / humidity Interannual flow variability Income Implementation capacity Use pressure Corruption Intensive agriculture Population Economic development Flow regime modification Average value of Climate Moisture Index Average value of coefficient of variation of annual flow GNI per capita (purchasepower-corrected US-$) Corruption Perceptions Index Proportion of Cultivated and managed land (%) Population density (persons/km 2 ) GNI per capita (purchasepower-corrected US-$) Average value of Environmental Stress induced by Flow Regime Alterations Spatial Calibration Rules Explanation Scale case study f(x) = (x + 1)/2 Linear normalization of the entire original data range. case study f(x) = 1 x for x < 1 f(x) = 0 for x 1 country f(x) = 1 for x > f(x) = (x 12,276)/(36,455-12,276)/2 for 12,276 < x f(x) = (x 1.006)/(12, )/2 for 1,006 x 12,276 f(x) = 0 for x < 1,006 Linear normalization of values below 1. Values from 1 are set to 0. The function generates fsqca values below 0.5 for original values higher than 0.5, which we regard as high variation of annual flow. Thresholds with regard to income were chosen according to a classification by the World Bank (2011), which classified countries with a GNI per capita below as low-income economies (represented by an fsqca value of 0) and countries below $ as middle-income economies (represented by fsqca values below 0.5). Countries in both groups are regarded as developing countries according to this World Bank classification. Countries with $ or more GNI per capita are classified as high-income economies (represented by fsqca values of 0.5 or higher). According to the GNI dataset, the average GNI per capita across all high-income countries amounts to $ (represented by an fsqca value of 1). country f(x) = x/10 Linear normalization of the entire original data range. case study f(x) = 1 x/70 for x < 70 f(x) = 0 for x 70 case study f(x) = 1 x/200 for x < 200 f(x) = 0 for x 200 Linear normalization of values below 70. Values from 70 are set to 0. The calibration function reflects that the state of the environment tends to decline when agriculture exceeds 30-50% of the area (Allan 2004: 272). For shares above 35%, this function generates values below 0.5, indicating rather unfavorable context. Linear normalization of values below 200. Values from 200 are set to 0. We regard areas with more than 100 inhabitants/km 2 as relatively populous. For such population densities, the function generates values below 0.5, indicating rather unfavorable context. country f(x) = 1 x, with x = Income Negated fsqca value from the income row. case study f(x) = 1 x/12 for x < 12 f(x) = 0 for x 12 Linear normalization of values below 12. Values from 12 are set to 0. The function generates values below 0.5 for values higher than 6. Such a value can, for example, result from a change in monthly flow magnitude or its inter-annual variability of >20-40% in six months of the year. 5

6 Income Corruption Degree of aridity/humidity Inter-annual flow variability Intensive agriculture Population Economic development Flow regime modification Twin2Go indicator 5 Twin2Go indicator 6 Twin2Go indicator 34 Twin2Go indicator 35 Twin2Go indicator 36 Twin2Go indicator 40 Twin2Go indicator 41 Twin2Go indicator 87 Twin2Go indicator 89 Twin2Go indicator 93 Twin2Go indicator 94 Twin2Go indicator 95 Twin2Go indicator 96 Table A4 Calibrated fsqca values of all conditions from the study GNI CORR HYDRO USE POLY ENV MNGT Case Biobio (Chile) Catamayo/Chira (Peru) Catamayo/Chira (Ecuador) Upper Cauca (Colombia) Quaraí/Cuareim (Brazil) Lake Cocibolca (Nicaragua) Baker (Chile) Quaraí/Cuareim (Uruguay) Guayas (Ecuador) Norrström (Sweden) Nura (Kazakhstan) Okavango (Namibia) Thames (United Kingdom) Lake Kyoga (Uganda) Inner Niger Delta (Mali) Bang Pakong (Thailand) Volga (Russian Federation) Raidak (Bhutan) Brahmaputra (India) Kosi (Nepal) Tisza (Hungary) Upper Guadiana (Spain) Elbe (Germany) Rhine (Netherlands) Amu Darya (Uzbekistan) Orange (South Africa) Red River Viet Nam) Olifants (South Africa) Rio Grande (USA)

7 Table A5 Calibrated dataset used in the fsqca assessment Case HYDRO USE GNI CORR POLY MNGT ENV Biobio (Chile) Catamayo/Chira (Peru) Catamayo/Chira (Ecuador) Upper Cauca (Colombia) Cuareim/Quarai (Brazil) Lake Cocibolca (Nicaragua) Baker (Chile) Cuareim/Quarai (Uruguay) Guayas (Ecuador) Norrström (Sweden) Nura (Kazakhstan) Okavango (Namibia) Thames (United Kingdom) Lake Kyoga (Uganda) Inner Niger Delta (Mali) Bang Pakong (Thailand) Volga (Russian Federation) Raidak (Bhutan) Brahmaputra (India) Kosi (Nepal) Tisza (Hungary) Upper Guadiana (Spain) Elbe (Germany) Rhine (Netherlands) Amu Darya (Uzbekistan) a 0.20 Orange (South Africa) Red River (Viet Nam) Olifants (South Africa) Rio Grande (USA) a As the value of indicator 93 is missing for the Amu Darya case, MNGT was calculated on the basis of the three remaining indicators for this case 7

8 Table A6 Truth table for the model MNGT = f(gni, CORR, POLY, USE). Logical remainders are indicated with dashes (-) in the outcome column GNI CORR POLY USE Number of cases Outcome MNGT PRI Table A7 Truth table for the model MNGT = f(gni, CORR, POLY). The dash (-) in the last outcome cell indicates a logical remainder GNI CORR POLY Number of cases Outcome MNGT PRI 8

9 Table A8 Truth table for the model ENV = f(hydro, USE, MNGT). Logical remainders are indicated with dashes (-) in the outcome column HYDRO USE MNGT Number of cases Outcome ENV PRI Table A9 Truth table for the model ENV = f(use) USE Number Outcome PRI of cases ENV References Allan JD (2004) Landscapes and Riverscapes: The Influence of Land Use on Stream Ecosystems. Annual Reviews of Ecology, Evolution and Systematics 35: doi: /annurev.ecolsys Pahl-Wostl C, Knieper C (2014) The capacity of water governance to deal with the climate change adaptation challenge: Using fuzzy set Qualitative Comparative Analysis to distinguish between polycentric, fragmented and centralized regimes. Global Environmental Change 29: doi: /j.gloenvcha Pahl-Wostl C, Lebel L (2010, updated 2011) Methods for Comparative Analysis. [= Twin2Go deliverable 1.3] World Bank (2011) Changes in Country Classifications. Accessed 17 October