Assessing impacts of water regulations on alleviating regional water stress with a system dynamics model

Many areas around the world are faced with water scarcity and virtual water can provide ways to resolve the problem. This paper presents a comprehensive water system based on a system dynamics model to assess how water regulations from the viewpoint of virtual water affect the regional water stress index in the Haihe River Basin, China. The results show that green water absorption, blue water consumption, virtual water flow, and water use efficiency play important roles in the water resources system. Water stress can be relieved by improving the infiltration coefficient, irrigation efficiency, industrial water use efficiency, and virtual water import. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). doi: 10.2166/ws.2018.112 s://iwaponline.com/ws/article-pdf/19/2/635/592395/ws019020635.pdf Huiping Huang Jiangfeng Wang Yuping Han (corresponding author) Lei Wang Xinsheng Li North China University of Water Resources and Electric Power, Zhengzhou, Henan 450045, China E-mail: han0118@163.com Huiping Huang Collaborative Innovation Center of Water Resources Efficient Utilization and Support Engineering, Zhengzhou, Henan Province 450046, China Yuping Han Henan Key Laboratory of Water Environment Simulation and Treatment, Zhengzhou, Henan Province 450046, China


INTRODUCTION
The World Water Assessment Programme's 2014 report indicates that one-third of the world's population is faced with water scarcity (WWAP ). Many areas in China, especially western and northern China, are confronted with moderate to severe water shortages, which can impede regional development (Grace et al. ). It is predicted that in 2030, the total national water shortage will reach nearly 200 billion m 3 around the world. Anticipated population, rapid urbanization, economic growth, as well as climate change, are expected to further stress water resources, which may prolong extreme droughts, aggravate climate anomalies, impact food security, and so on (Haines et al. ; Aizebeokhai ).
Before the concept of virtual water content was established, common management for alleviating water stress included: cross-region water diversion projects (e.g. the South-to-North Water Transfer Project in China), protection of natural springs and ponds, rainwater collection, and use of less water-intensive sanitation techniques (Ashton ); however, these strategies focused only on physical water. Allan () proposed the virtual water concept to describe the total volume of water used for agricultural products.
Since the introduction of virtual water, many scholars had quantified and analyzed its impacts on regional water resources systems (Dietzenbacher & Velázquez ; Zeitoun et al. ). There are also many studies that discuss the effects of various regulations on regional water resources' systems (Meng et al. ; Julius et al. ). However, few studies explore how water regulations alleviate regional water stress from the viewpoint of virtual water with a system dynamics (SD) model. This paper presents an integrated SD simulation model in the form a decisionsupport system of water resources system for the Haihe River Basin (HRB) in China. The specific objectives of this study are: (1) to analyze the specific composition of regional water supply and demand; (2) to quantitatively determine the key and controllable elements in regional water resources systems; and (3) to quantify changes in water stress caused by different regulations from the viewpoint of virtual water and provide plausible measures to relieve regional water stress.

Data
The meteorological data used to calculate agricultural virtual water were extracted from China's meteorological data sharing service system (http://cdc.cma.gov.cn/home. do). Crop yield, industrial gross domestic product (GDP), population, input-output table and other economic data were acquired from the statistical yearbooks and regional Bureau of Statistics.

Blue water footprint (WF) and green water footprint
The green WF is defined as rainwater that is stored in the soil and evaporated or consumed during production, mainly during crop growth. The blue WF refers to surface and groundwater that are consumed or evaporated during irrigation and crop growth. The green and blue WFs of primary crops can be calculated according to the following equations proposed by Hoekstra et al. (): where WF green is a crop's green WF (m 3 /t), WF blue is the blue WF; CWU green and CWU blue are the green and blue water consumption (m 3 /ha); the number 10 is the conversion coefficient to convert water depth (mm) to water volume (m 3 /ha); Y is crop yield (t/ha); and ET green and ET blue are defined as the evaporative demand (mm/d) satisfied by green and blue water, respectively.

Method of calculating virtual water flow (VWF) among regions
Based on input-output tables, a direct consumption coefficient matrix A and direct water consumption coefficient are calculated as follows: where a ij is the direct input from sector i needed to increase per monetary unit output in sector j; x ij is the investment in sector i during production of department j; x j is the total output of department j; K j is the direct water consumption coefficient; and w j is the water consumption of sector j.
The total water consumption coefficient is then calculated as: where q ij is the gross water input from sector i necessary per monetary unit of final demand in sector j, and I is the identity matrix.
The net import of virtual water in a region is: where W net is the net virtual water import; E and M are output and input values in the column vectors of the input-output table.
Water stress index (WSI) In this paper WSI refers to blue water stress and is calculated as follows: where WA is agricultural irrigation water demand; WT is the total of industrial, domestic, and ecological water demand; WE blue is blue water imported from outside regions through virtual water trade; and WS is the freshwater available in the region.

SD model development
The SD model in HRB includes six subsystems and the study period is 2000-2015.

Agricultural irrigation water demand subsystem
Agricultural water use amounts to about 70% of total regional water consumption. Cultivated crops include winter wheat, summer maize, rice, soybean, oil crops, cotton, vegetables, potato, watermelon, muskmelon, tobacco, and so on. From 1991 to 2015, the average annual WF of crops was 49.54 billion m 3 , of which 25.58 billion m 3 are blue water. The virtual water of winter wheat, summer maize, rice, oil crops, cotton, and vegetables accounts for more than 90% of the regional agricultural virtual water. Therefore, the agricultural water requirements of these six crops can be substituted for the regional total agricultural water demand. During agricultural irrigation, the ratio of water absorbed by crops to the total volume of irrigation water is irrigation efficiency, and the ratio of blue water requirements to irrigation efficiency is the theoretical irrigation water demand. If the volume of green water increases, the blue water demand will decrease.
Thus, the infiltration coefficient of precipitation during crop growth also plays important role in regional water resources systems.

Industrial water demand subsystem
In HRB, industrial virtual water consumption is 1. Household water demand is related to both water consumption per capita and total population.

Ecological water demand subsystem
Ecological water demand mainly includes water consumption of green space. The calculation method is just the same as that of agricultural water demand.

Blue water import subsystem
The input-output analysis shows that in 2012 the net import virtual water in HRB was 5.32 billion m 3 , of which agricultural virtual water accounted for ∼72% and industrial virtual water accounted for ∼29%. For the SD model in this paper, water scarcity indicates a shortage of physical water, and the volume of net imported virtual water should be transferred into blue water. For agricultural products in this region, blue water accounts for 60% of the virtual water content; for industrial products, blue water is almost 83.3% of the virtual water content.

Water supply sub model subsystem
Water supply in HRB consists of surface water, groundwater and waste water after treatment, and desalinated seawater.
Of these, surface water and groundwater are the main components. In 2015, groundwater, surface water and other water resources were 56.5%, 38.3% and 5.2% of the total water supply, respectively.

Model simulation
Stock-flow figure

Model test and sensitivity analysis
The software program Vensim PLE6.3E is used to formulate and simulate the water resources system in HRB. Mean absolute percent error (MAPE) is a measure of prediction accuracy of a simulation method and usually expressed as a percentage. It is calculated as follows: where n is the number of years involved in the calculation.
In this paper, real data in 2005, 2010, and 2015 are compared with the simulation data in the same period to test the predictive accuracy and stability of the SD model.

Irrigation efficiency
Data show that irrigation water in HRB is almost equal to the blue water demand; that is, if there is no loss in irrigation water, the crop water demand can be satisfied by irrigation. However, irrigation efficiency is 0.5 in HRB; meaning that crops absorb only 50% of irrigation water, and the remaining is lost as runoff. Improvement in irrigation efficiency can reduce the irrigation water demand. An increase in irrigation efficiency from 0.5 to 0.55 decreases WSI from 1.74-2.14 to 1.57-1.98.

Water consumption of industrial unit output value
Parameters of the industrial water demand sub-model include industrial output value, industrial output growth speed, and industrial unit output value. At present, industrial economic growth in HRB is improving, so water scarcity can be relieved by decreasing water consumption of unit industrial output value. A 10% reduction in this variable results in WSI of 1.74-2.13.

Virtual water import
Virtual water trade can alleviate water scarcity. If HRB can import more products rich in virtual water, that is, if imported water increases by 10%, WSI can be reduced to 1.52-2.13.

Comprehensive regulations
The WSI will be decreased if the four regulations described above are implemented individually, thereby alleviating water scarcity. If the four measures are implemented simultaneously, WSI can be lowered to 1.27-1.82, which reflects a moderate to significant water scarcity status.

Policy implications
This paper describes the development of an SD model to explore the characteristics of water resources in HRB.
Controllable variables such as infiltration coefficient, irrigation efficiency, water consumption per unit industrial output value, and virtual water import were identified as key parameters. Without any regulations, water resources for HRB reflected significant or severe water scarcity during 2000-2015. The impact of comprehensive regulations can convert the water resources status to moderate or significant scarcity. Infiltration coefficient and irrigation efficiency are the most important variables for HRB's water resources system, and a 10% increase in both will reduce WSI by 6.3-15.7%. Improving the efficiency of green water use and blue water use can mitigate water scarcity.
Increasing the precipitation infiltration coefficient can help relieve water scarcity. Methods for improving the infiltration coefficient are described as follows. First, plastic film mulching is a common and effective practice that has been adopted worldwide for many years, especially in arid and semi-arid areas (Sharma et al. ). It can reduce soil evaporation and water consumption, change precipitation infiltration patterns, and increase rainfall runoff (Rice et al. ). The air permeability of the plastic film can hinder the evaporation of soil moisture to the outside Third, crop structure should be adjusted and arranged rationally according to the rainy season and the water requirement of crops so as to make full use of precipitation and cut down.

CONCLUSIONS
In this paper, the impact of various regulations from the viewpoint of virtual water on regional water stress is simulated by applying an SD model to HRB, China. Analyses documented that that this region is experiencing significant scarcity of water resources under current development.
Sensitivity analysis indicated that elements such as infiltration coefficient, irrigation efficiency, water consumption of industrial unit output value and virtual water import play important roles in the regional water resources system.
Among the regulations, improving infiltration coefficient has the most positive impact on alleviating water stress.
In general, this model shows the important role of regulations in view of virtual water in a regional water resources system and the thought can provide a way to mitigate water stress for other water scarcity areas. However, it should be noted first that only the main elements of the water resources system are comprised in the modeling process and factors such as grey water, water quality, groundwater change and market-related factors are missed owing to data availability. Second, the factors in the model are generalized and it cannot completely reflect the complex water resources system. In the future, an SD model can be incorporated with a grey model and genetic algorithm to improve its utility.