Key words: World Water Distribution, System Dynamics, Continental Scope, Economic Activities, Water Utilization, Global Worming

Transcription

1 J. Japan Soc. Hydrol. & Water Resour. Vol. 20, No.1, Jan pp This research addresses the problem of growing shortage of water resources from the world-wide viewpoints by modeling of the socio-environmental changes. There are two main objects; namely (i) to simulate and predict trends in the development of human civilization in different continents taking into account the effects of limited water resources through feedback links between water availability and the human actions, and (ii) to identify the important roles and effects of water resources that have on the growth of the world so as to aid in the planning of scenarios and policies for sustainable development. A model exploring water needs in the various continents is developed using System Dynamics. Identification of continents with acute water shortage and development limitations will also be possible with this model. Key words: World Water Distribution, System Dynamics, Continental Scope, Economic Activities, Water Utilization, Global Worming Freshwater withdrawals by human activities have increased dramatically over the years. Already, at the beginning of the 21st century, onesixth of the worlds population was without access to improved water supply while two-fifths lacked access to improved sanitation. Problems of water resources have also become much discussed issues in international conferences and multi-national organizations. Currently, many countries experience water resources related problems such as insufficient supply of fresh water and deteriorating water quality. The situation is aggravated by rapid population growth, massive industrial development and urbanization which lead to a growing thirst for fresh water supply. There is an urgent need to understand and project the development of the human society under the influences of limited water resources, so as to plan and initiate policies to alleviate the worsening situation. This research models the development trends of the various regions of the world from 1960 to 2100, paying special emphasis on the effects and conditions of the world water resources. Unlike many projections of water usage which rely heavily on exogenous data inputs, the model is designed to take into account the dynamical feedback

2 interactions between socio-economical activities and water resources in a closed system. The model provides a detailed picture of the world that can be used as a basis for scenario and policy planning to attain a more sustainable development. Some attempts to model the socio-economic activities of the world using System Dynamics (SD) have been done by past researchers. The World3 model developed by Meadows et al. (Meadows et al., 1974), (Meadows et al., 1992) in 1974 and improved in 1991 was a SD model of the world. The aim of the model was to better understand the long-term causes and consequences of growth in the world population and material production. It did not take into account the restrictions from renewable resources such as water resources. Simonovic paid special attention to the importance of water resources on global development by introducing two new sectors (water quality and water quantity) to World3 and constructed the WorldWater model (Simonovic, 2002). Both models treated the world as one gigantic unit whereby interactions among internal spatial units were not considered. Our World Continental Water Model will go a step further to producing a more detailed world by fragmenting the world into regions interacting with one another, each having its own characteristics and conditions. defines the regional classification of the world. This research takes into account the effects of possible future climate changes on available water resources through the introduction of simulation results from the Global Circulation Model (GCM) in water budget calculation. Monthly temperature and precipitation results from the HadCM2 climate change model developed at the Hadley Centre, Bracknell UK are used. Besides the reference scenario, two other scenarios in which CO2 concentration is increased 1 % annually, and both CO2 and aerosols concentrations are increased 1 % annually are tested. The Hamon method (Hamon, 1963) is chosen for the calculation of potential evapotranspiration. The Water Balance Model (WBM) is used to generate spatially distributed monthly runoffs which are then used for prediction of the future water resources availability. Observed runoff data from the International Hydrological Programme (IHP) is used for calibration and validation of the WBM. In this model, the world is treated as an aggregation of six continents, each having its own social and economical characteristics. Six sectors deemed most significant in representing human development are modeled in each continent. They are population (birth, maturation and death), agriculture (land development, land fertility, food production); capital (industrial and service outputs), nonrenewable resources (resources use and extraction), persistent pollution (pollution emission and assimilation) and water (water quality and water quantity). Exchanges of information and material among the continents are also considered. The interactions modeled are migration, trade of food, nonrenewable resources

3 and industrial outputs, and foreign investments of services. Each sector of each continent is calibrated using data from 1960 to 1995 before integrating the sectors to form the entire model. The model is run from 1960 to 2100 at yearly time steps. Since the model is not a replica of the real world, some assumptions have to be made to justify the model structure. One assumption is the exclusion of socio-political issues such as war and religious conflict in the model due to the unpredictable psychological decisions involved. Also, although water is a renewable resource, accessible and usable water supply is assumed to be limited and its impact on development needs examination. The objective of the water quantity sector is to model the balance between water supply and water demand in each continent, and the effects of water deficiencies on the other sectors of activities. Water withdrawals are calculated from the volumes of agricultural, municipal and industrial activities. Three sources of water supply are considered: renewable water resources, water reuse and desalinization of sea water. Withdrawals of nonrenewable groundwater resources are omitted because data is only limited to certain water-scare regions, mostly Africa and Arab countries where such resources are exploited extensively. The deficit in water resources is then found by subtracting withdrawals from the supply volumes. Restrictive effects of water deficit are fed back to the respective sectors. This implies that growth will be retarded if there is a deficit in water resources, causing future demand of water to be reduced. The simplified causal loop diagram of the water quantity sector is shown in. Arrow with a sign denotes reinforcing relation between the two linked components while arrow with a sign means a restrictive relation between the linked components. Feedback loop is indicated by a curved arrow with a or a sign in it depending on whether it is a negative or a positive feedback loop. In this sector, there are three negative feedback loops as shown. Among these feedback loops, one of the most important but hard to express in quantitative way is the relation between municipal water withdrawal and the human life. Under the condition when there is insufficient water for municipal usage, the highest priority is on securing enough water for drinking, which means other water uses can be cut out momentarily for the sake of survival. On the other words, the deficit of municipal water withdrawal has the effect on the human population under only the catastrophic situation in which withdrawal is even less than the minimum volume needed to ensure survival. This volume of water is about 2 liters per day or about 0.75 cubic meters per year. Therefore, the life expectancy multiplier is assumed to take the values of 0 for no water and 1 for 0.75 cubic meters per year and to vary proportional to the

4 level of minimum available water between the two values. When there is no deficit, water quantity has no effect on the human life expectancy. Life expectancy multiplier for water quantity is equal to 1. Water quality is designed as parameters affecting human life-expectancy. A water quality variable called life-expectancy multiplier from water qualityis statistically derived from the percentage of population that has access to improved sanitation and water supply in rural and urban areas. Definitions of improved sanitation and water supply follow the guidelines set by World Health Organization (WHO, 2000). It is found that the percentage of population with access to improved sanitation and water supply has the strongest correlations with industrial output per capita and renewable water resources per capita. Populations in richer regions and regions with abundant precipitation have better access to improved sanitation and water supply. The simplified causal loop diagram of the water quality sector is shown in. In, we can find the two negative feedback loops related to industrial output per capita and to water resources quantity per capita. If the total industrial output is fixed, the industrial output per capita decreases as the population increase. Population growth may bring the chance of increasing total industrial output through the increase of working people. This relation is incorporated in the interaction between the population sector and the industrial one. So in this simplified causal diagram shown in, only the negative impact of population growth on the per capita output is considered. All sectors are individually calibrated and verified with historical data. Then, they are integrated as one whole socio-economical model of the world. The calibration is carried out in respect to all factors whose historical data exist. Other factors, whose historical data are not available, are subjectively judged and estimated using incomplete information and, as well as, common-sense. Some calibration results are shown in. Figure 4 (a) shows the comparison between the simulated population and the historical one from

5 1960 to The broken line denotes the simulated population for the world, Asia, Africa, Europe and North-America and the solid line denotes the historical data for corresponding continents. For Africa and North America, relatively larger errors can be observed but for Asia, Europe and the world total simulated population and historical one show pretty good matches. South America and Australia are not shown in the figure in order to avoid the difficulty to determine the lines but the comparison results are almost the same as ones shown in the figure. Figures 4 (b) and (c) show the comparison about food production and industrial output per capita. In both figures, the simulated values are denoted by the broken lines and the historical ones by solid lines. For food production, simulated results shows good matches with historical data in Asia, Europe and world total. In North America and Africa, our model tends to underestimate the values. For industrial output per capita, our model gives good values for Asia, but in North America and Europe it gives a little bit larger errors. Even though some errors are observed in the comparison between simulated and historical data, it can be concluded that on the whole the proposed model is reasonable and substantial to properly represent the world. The standard run is a simulation run of the model assuming that the current conditions remain the same. It is apparent that the results do not reflect the exact stage of the future world. The results are meant for comparisons with other scenario runs when changes to the model are introduced. Figure 5 shows five important outputs representing the world development: population, food per capita, industrial output per capita, total water demand and nonrenewable resources. Since these five outputs have different dimensions, relative dimensionless values normalized by corresponding values in Unlike the results in

6 World3 and WorldWater model whereby limits of growth and decline are sharp and prominent, a world divided into continents linked by trading activities give more attenuated development patterns. Irregular fluctuations in industrial output per capita and food per capita seen after 2015 are due to water resources shortage in certain regions of the world. Results of material movements across continents can be examined to identify the major importing, exporting; and immigrating, emigrating continents. Results of net food imports are shown in. Fluctuations in the graphs are influences of water shortages on food production which lead to irregular trade patterns. Although water shortages occur only in Asia and North America in the standard run, their impacts on trade over all regions are seen. This shows that water scarcity has widespread effects on the world even when its occurrence is local. Besides the standard run using the reference GCM data, two GCM scenario results are used to examine the effects of greenhouse gases (GHG) on water resources and the human society. One assumes 1 % per annum increment in CO 2 while the other assumes 1 % per annum increases in CO 2 and aerosol. The results of total accessible water resources of the world for the two scenarios do not show much variation from that in the standard run. However, when results for each continent are examined discretely, some variations are observed. For example, Asia shows more severe water shortages in the CO 2 and aerosol run than in the CO2 run and the standard run, see. However, in North America and Europe, the emission of aerosol appears to abate the strained water shortage problems induced by the emission of CO2. Much cannot be concluded from such observations, other than the realization that the effects of GHG emissions have to be examined in more detailed spatial models. The scenario assumes that there is going to be increasing difficulties in securing safe water supply due to pollutant emissions and salinisation in the 21st century. Usable water resources are restricted to half of that in the standard run by 2100 starting from Figure 8 shows the world population under the influence of deteriorating water quality. The world population falls under the standard run level due to shortened human lifeexpectancy and decrease in food output. Water quality is an important issue and its impact must not be ignored.

7 As in World3, the main restriction governing the macro-behavior of the system is nonrenewable resources. The scenario assumes that there will be enough resources to sustain development due to technological advancement and exploitation of renewable resources. On the whole, the world progresses beyond the standard run but is mired by more severe regional water shortages as seen from the fluctuations in output graphs, see. Water shortages are as important as any other issues concerning development. information and materials have been considered. Utilization of several GCM scenario results has allowed us to test the impacts of GHG emissions on water availability and the human society. Several conclusions can be drawn from the run results. Firstly, water resources deficits give very fast feedbacks, and the human society is very sensitive to these feedbacks. This can be seen from the erratic fluctuations in the development patterns. There will be a need to constantly check water resources availability. Secondly, localized water shortage problems can affect all parts of the world through the form of trade as have been seen from outputs of continental material movements. Multi-national co-operations should be encouraged to prevent a global water crisis. The water pollution scenario suggests that deterioration in water quality is a serious problem that cannot be overlooked. The sustainable resources scenario gives us an idea of how severe water shortage problems will become even if we managed to solve the resources problems. This research has further elucidated the future world. Although distributive rainfall patterns are treated in the model, corresponding distribution of human activities with spatial rainfall patterns is limited due to the difficulties in linking SD modeling with spatial models. Exploring the possibility of utilizing the simplicity in SD to water resources problems, and at the same time taking into account the distributive nature of such problems will be very beneficial in improving the accuracy of this model. The World Continental Water Model has been developed to provide insights of the world development at continental scope. Movements of

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