The global dimension of water governance: Nine reasons for global arrangements in order to cope with local water problems

Where water problems extend beyond the borders of local communities, the catchment area or river basin is generally seen as the most appropriate unit for analysis, planning and institutional arrangements. In this paper it\ud is argued that addressing water problems at the river basin level is not always sufficient. It is shown that a substantial part of today’s water issues carries a (sub)continental or even global dimension, which urges for a governance approach that comprises coordination and institutional arrangements at a level above that of the river basin. This paper distinguishes and reviews nine developments that support this argument: • Local issues of water scarcity and flooding will be enhanced or weakened by human-induced global climate\ud change.\ud • Local problems of water pollution are often intrinsic to the structure of the global economy.\ud • There is a growing presence of multinationals in the drinking water sector.\ud • Several national governments are developing plans for large-scale inter-basin water transfers.\ud • An increasing number of water-short countries seek to preserve their domestic water resources through the\ud import of water in virtual form.\ud • Global trade in water-intensive commodities offers the opportunity of global water saving if this trade is\ud from countries with high to countries with low water productivity.\ud • The water footprints of individual people are increasingly externalised to other parts of the world, so that\ud many local water problems are strongly related to consumption elsewhere.\ud • Some people around the world have comparatively high water footprints, which raises the question of\ud whether this is fair and sustainable.\ud • Due to its increasing scarcity and uneven distribution across the globe, water is gradually becoming a geopolitical resource, influencing the power of nations.\ud The described developments raise the question of what kind of institutional arrangements could be developed to cope with the global dimension of water issues. A few possible directions are identified in an explorative analysis: an international protocol on full-cost water pricing, a water label for water-intensive products, a disposal tax on goods that will cause water pollution in their waste stage (to be used for pollution prevention and\ud control), international nutrient housekeeping, minimum water rights, maximum allowable water footprints, and tradable water footprint permits

The green, blue and grey water footprint of crops and derived crop products

This study quantifies the green, blue and grey water footprint of global crop production in a spatially-explicit way for the period 1996–2005. The assessment improves upon earlier research by taking a high-resolution approach, estimating the water footprint of 126 crops at a 5 by 5 arc minute grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid cell. In addition, the water pollution associated with the use of nitrogen fertilizer in crop production is estimated for each grid cell. The crop evapotranspiration of additional 20 minor crops is calculated with the CROPWAT model. In addition, we have calculated the water footprint of more than two hundred derived crop products, including various flours, beverages, fibres and biofuels. We have used the water footprint assessment framework as in the guideline of the Water Footprint Network. \ud \ud Considering the water footprints of primary crops, we see that the global average water footprint per ton of crop increases from sugar crops (roughly 200 m3 ton−1), vegetables (300 m3 ton−1), roots and tubers (400 m3 ton−1), fruits (1000 m3 ton−1), cereals (1600 m3 ton−1), oil crops (2400 m3 ton−1) to pulses (4000 m3 ton−1). The water footprint varies, however, across different crops per crop category and per production region as well. Besides, if one considers the water footprint per kcal, the picture changes as well. When considered per ton of product, commodities with relatively large water footprints are: coffee, tea, cocoa, tobacco, spices, nuts, rubber and fibres. The analysis of water footprints of different biofuels shows that bio-ethanol has a lower water footprint (in m3 GJ−1) than biodiesel, which supports earlier analyses. The crop used matters significantly as well: the global average water footprint of bio-ethanol based on sugar beet amounts to 51 m3 GJ−1, while this is 121 m3 GJ−1 for maize. \ud \ud The global water footprint related to crop production in the period 1996–2005 was 7404 billion cubic meters per year (78 % green, 12 % blue, 10 % grey). A large total water footprint was calculated for wheat (1087 Gm3 yr−1), rice (992 Gm3 yr−1) and maize (770 Gm3 yr−1). Wheat and rice have the largest blue water footprints, together accounting for 45 % of the global blue water footprint. At country level, the total water footprint was largest for India (1047 Gm3 yr−1), China (967 Gm3 yr−1) and the USA (826 Gm3 yr−1). A relatively large total blue water footprint as a result of crop production is observed in the Indus river basin (117 Gm3 yr−1) and the Ganges river basin (108 Gm3 yr−1). The two basins together account for 25 % of the blue water footprint related to global crop production. Globally, rain-fed agriculture has a water footprint of 5173 Gm3 yr−1 (91 % green, 9 % grey); irrigated agriculture has a water footprint of 2230 Gm3 yr−1 (48 % green, 40 % blue, 12 % grey)

Water footprints of nations

The water footprint concept has been developed in order to have an indicator of water use in relation to consumption of people. The water footprint of a country is defined as the volume of water needed for the production of the goods and services consumed by the inhabitants of the country. Closely linked to the water footprint concept is the virtual water concept. Virtual water is defined as the volume of water required to produce a commodity or service. International trade of commodities implies flows of virtual water over large distances. The water footprint of a nation can be assessed by taking the use of domestic water resources, subtract the virtual water flow that leaves the country and add the virtual water flow that enters the country. The internal water footprint of a nation is the volume of water used from domestic water resources to produce the goods and services consumed by the inhabitants of the country. The external water footprint of a country is the volume of water used in other countries to produce goods and services imported and consumed by the inhabitants of the country. The study aims to calculate the water footprint for each nation of the world for the period 1997-2001

Global water scarcity: the monthly blue water footprint compared to blue water availability for the world's major river basins

Conventional blue water scarcity indicators suffer from four weaknesses: they measure water withdrawal instead of consumptive water use, they compare water use with actual runoff rather than natural (undepleted) runoff, they ignore environmental flow requirements and they evaluate scarcity on an annual rather than a monthly time scale. In the current study, these shortcomings are solved by defining blue water scarcity as the ratio of blue water footprint to blue water availability – where the latter is taken as natural runoff minus environmental flow requirement – and by estimating all underlying variables on a monthly basis. In this study we make for the first time a global estimate of the blue water footprint of humanity at a high spatial resolution level (a five by five arc minute grid) on a monthly basis. In order to estimate blue water scarcity at river basin level, we aggregated the computed monthly blue water footprints at grid cell level to monthly blue water footprints at river basin level. By comparing the estimates of the monthly blue water footprint with\ud estimates of the monthly blue water availability at river basin level, we assess the intra-annual variability of blue water scarcity for the world’s major river basins. Monthly blue water footprints were estimated based on\ud Mekonnen and Hoekstra (2011a). Natural runoff per river basin was estimated by adding estimates of actual runoff from Fekete et al. (2002) and estimates of water volumes already consumed. Environmental flow requirements were estimated based on the presumptive standard for environmental flow protection as proposed\ud by Richter et al. (2011), which can be regarded as a precautionary estimate of environmental flow requirements. Within the study period 1996-2005, in 223 river basins (55% of the basins studied) with in total 2.72 billion\ud inhabitants (69% of the total population living in the basins included in this study), the blue water scarcity level exceeded one hundred per cent during at least one month of the year, which means that environmental flow requirements were violated during at least one month of the year. In 201 river basins with in total 2.67 billion people there was severe water scarcity, which means that the blue water footprint was more than twice the blue water availability, during at least one month per year. Global average blue water scarcity – estimated by averaging the annual average monthly blue water scarcity\ud values per river basin weighted by basin area – is 85%. This is the average blue water scarcity over the year within the total land area considered in this study. When we weight the annual average monthly blue water scarcity values per river basin according to population number per basin, global average blue water scarcity is 133%. This is the average scarcity as experienced by the people in the world. This population-weighted average scarcity is higher than the area-weighted scarcity because the water scarcity values in densely populated areas –\ud which are often higher than in sparsely populated areas – get more weight. Yet another way of expressing water scarcity is to take the perspective of the average water consumer. The global water consumption pattern is different from the population density pattern, because intensive water consumption in agriculture is not specifically related to where most people live. If we estimate global blue water scarcity by averaging monthly blue water scarcity values per river basin weighted based on the blue water footprint in the respective month and basin, we calculate a global blue water scarcity at 244%. This means that the average blue water consumer in the world experiences a water scarcity of 244%, i.e. operates in a month in a basin in which the blue water footprint is 2.44 times the blue water availability and in which presumptive environmental flow requirements are thus strongly violated.\ud The data presented in this report should be taken with care. The quality of the presented blue water scarcity data depends on the quality of the underlying data. The estimates of both monthly blue water footprint and monthly blue water availability per river basin can easily contain an error of ± 20 per cent, but a solid basis for making a precise error statement is lacking. This obviously needs additional research. Furthermore, improvements in the estimates can be made by including the effect of dams on the blue water availability over time, by accounting for inter-basin water transfers, by distinguishing between surface water, renewable groundwater and fossil groundwater, by improving estimates of environmental flow requirements, by looking at water scarcity at the level of sub-basins, and by considering inter-annual variability as well. Despite this great room for improvement and bringing in more detail, the current study is a milestone in global water scarcity studies by mapping water\ud scarcity for the first time on a monthly basi

Mitigating the water footprint of export cut flowers from the Lake Naivasha Basin, Kenya

Kenya’s cut-flower industry has been praised as an economic success as it contributed an annual average of US$141 million foreign exchange (7$ 352 million in 2005 alone. The industry also provides employment, income and infrastructure such as schools and hospitals for a large population around Lake Naivasha. On the other hand, the commercial farms have been blamed for causing a drop in the lake level and for putting the lake’s biodiversity at risk. The objective of this study is to quantify the water footprint within the Lake Naivasha Basin related to cut flowers and assess the potential for mitigating this footprint by involving cut-flower traders, retailers and consumers overseas. The water footprint of one rose flower is estimated to be 7-13 litres. The total virtual water export related to export of cut flowers from the Lake Naivasha Basin was 16 Mm3/yr during the period 1996-2005 (22% green water; 45% blue water; 33% grey water). Our findings show that, although the commercial farms around the lake have contributed to the decline in the lake level through water abstractions, both the commercial farms and the smallholder farms in the upper catchment are responsible for the lake pollution due to nutrient load. The\ud observed decline in the lake level and deterioration of the lake’s biodiversity calls for sustainable management of the basin through pricing water at its full cost and other regulatory measures

The blue water footprint of electricity from hydropower

Hydropower accounts for about 16% of the world's electricity supply. It has been debated whether hydroelectric generation is merely an in-stream water user or whether it also consumes water. In this paper we provide scientific support for the argument that hydroelectric generation is in most cases a significant water consumer. The study assesses the blue water footprint of hydroelectricity – the water evaporated from manmade reservoirs to produce electric energy – for 35 selected sites. The aggregated blue water footprint of the selected hydropower plants is 90 Gm3 yr−1, which is equivalent to 10% of the blue water footprint of global crop production in the year 2000. The total blue water footprint of hydroelectric generation in the world must be considerably larger if one considers the fact that this study covers only 8% of the global installed hydroelectric capacity. Hydroelectric generation is thus a significant water consumer. The average water footprint of the selected hydropower plants is 68 m3 GJ−1. Great differences in water footprint among hydropower plants exist, due to differences in climate in the places where the plants are situated, but more importantly as a result of large differences in the area flooded per unit of installed hydroelectric capacity. We recommend that water footprint assessment is added as a component in evaluations of newly proposed hydropower plants as well as in the evaluation of existing hydroelectric dams, so that the consequences of the water footprint of hydroelectric generation on downstream environmental flows and other water users can be evaluate

A global and high-resolution assessment of the green, blue and grey water footprint of wheat

The aim of this study is to estimate the green, blue and grey water footprint of wheat in a spatially-explicit way,\ud both from a production and consumption perspective. The assessment is global and improves upon earlier\ud research by taking a high-resolution approach, estimating the water footprint of the crop at a 5 by 5 arc minute\ud grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time\ud step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid\ud cell. In addition, the water pollution associated with the use of nitrogen fertilizer in wheat production is\ud estimated for each grid cell. We have used the water footprint and virtual water flow assessment framework as\ud in the guideline of the Water Footprint Network (Hoekstra et al., 2009).\ud The global wheat production in the period 1996-2005 required about 1088 billion cubic meters of water per\ud year. The major portion of this water (70%) comes from green water, about 19% comes from blue water, and the\ud remaining 11% is grey water. The global average water footprint of wheat per ton of crop was 1830 m3/ton.\ud About 18% of the water footprint related to the production of wheat is meant not for domestic consumption but\ud for export. About 55% of the virtual water export comes from the USA, Canada and Australia alone. For the\ud period 1996-2005, the global average water saving from international trade in wheat products was 65 Gm3/yr.\ud A relatively large total blue water footprint as a result of wheat production is observed in the Ganges and Indus\ud river basins, which are known for their water stress problems. The two basins alone account for about 47% of\ud the blue water footprint related to global wheat production. About 93% of the water footprint of wheat\ud consumption in Japan lies in other countries, particularly the USA, Australia and Canada. In Italy, with an\ud average wheat consumption of 150 kg/yr per person, more than two times the word average, about 44% of the\ud total water footprint related to this wheat consumption lies outside Italy. The major part of this external water\ud footprint of Italy lies in France and the USA

Living With Peak Discharge Uncertainty: The Self-learning Dike

Although river dikes still play a key role for flood protection in the Netherlands\ud there is a growing interest for other measures to deal with larger peak discharges, such as\ud lowering or widening the floodplains. Regardless of the strategy chosen the assessment of\ud its effect on the flood risk depends on the peak discharge statistics. A problem here is that\ud the statistical analysis of peak discharges relies on probability distributions based on the\ud limited time series of extreme discharges. The extrapolation of these distributions are\ud subject to considerable uncertainty, because there is a measuring record of only about 100\ud years and the natural variability can be expected to change as a result of climate change.\ud This raises the question whether a more direct response to the effects of climate change is\ud possible. The natural variability of the peak discharge changes, the changes in this\ud variability due to e.g. climate change and the new statistical distribution can only be\ud established after the actual change has happened. Even with regular updates of the\ud statistical distributions it is inherent that the actions taken to reduce the flood risk are not\ud anticipatory but delayed. As an alternative, this paper presents an adaptive or so-called selflearning\ud approach to deal with the uncertainty in the peak discharge statistics. The\ud difference with the probabilistic design of flood defense works, which depends on the\ud analysis and prediction of uncertain peak discharges, is that the dike is adapted in direct\ud response to peak water levels exceeding the dike height minus a certain safety margin. The\ud results indicate that, on average, adaptive flood management based on observed peak water\ud levels is at least as safe as a probabilistic approach, which necessarily relies on uncertain\ud discharge statistics. Other advantages of the adaptive strategy are also obvious: the rule of\ud response is simple and easy to communicate to the public, and peak water levels are less\ud difficult to measure. In general the example demonstrates that flood management can be\ud based on a direct response to the effects of climate change, without tedious statistical\ud analysis of peak discharge records