# Urban Metabolic Analysis of a Food-Water-Energy System for Sustainable Resources Management

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

_{2}in Beijing and Shanghai [26]. Zhang et al. (2018) utilized statistics, equilibrium, econometric, ecological, life cycle, system dynamics, agent-based, and integrated index methods to explore the water-energy-food nexus [27].

## 2. Materials and Methods

## 3. Results and Discussion of Urban Metabolic Food-Water-Energy System

^{3}and a catchment area of 760 km

^{2}. The reservoir is confronted with a serious sediment accumulation problem. One-third of the active storage capacity has been taken over by sediments.

^{3}/s and total installed capacity of 90 MW. The average annual hydropower generation is about 230,000 MWh. The maximal rice production is estimated at 180,000 tons per season for 36,000 hectares of paddy rice land of the Taoyuan Irrigation Association and the Shihmen Irrigation Association. The average water consumption of paddy rice irrigation is approximately 350 million m

^{3}.

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Nomenclature

Indices | |

$\mathrm{i},\text{}\mathrm{j}$ | node of water network, $\mathrm{i},\text{}\mathrm{j}=1,\text{}2,\text{}3,\text{}\dots ,\text{}\mathrm{I}$ |

$\mathrm{k}$ | residential, industrial, and agricultural water demand, $\mathrm{i}=\mathrm{RES},\text{}\mathrm{IND},\text{}\mathrm{AG}$ |

$\mathrm{p}$ | crop, $\mathrm{p}=1,\text{}2,\text{}3,\text{}\dots ,\text{}\mathrm{P}$ |

$\mathrm{t}$ | time, $\mathrm{t}=1,\text{}2,\text{}3,\text{}\dots ,\text{}\mathrm{T}$ |

Parameters | |

${\mathrm{FARM}}_{\mathrm{i}}^{}$ | farm size with water supply from node $\mathrm{i}$ |

${\mathrm{IN}}_{\mathrm{i}}^{}$ | water inflow at node $\mathrm{i}$ |

${\mathrm{LOSS}}_{\mathrm{i}}^{}$ | water loss at node $\mathrm{i}$ |

$\mathrm{R}{1}_{\mathrm{p}}^{}$ | production rate of crop $\mathrm{p}$ |

$\mathrm{R}{2}_{\mathrm{p}}^{}$ | biofuel production rate of crop $\mathrm{p}$ |

$\mathrm{R}{3}_{\mathrm{j},\mathrm{i}}^{}$ | water loss rate from node $\mathrm{j}$ to node $\mathrm{i}$ |

$\mathrm{R}{4}_{\mathrm{p}}^{}$ | water demand rate of crop $\mathrm{p}$ |

$\mathrm{R}{5}_{\mathrm{i}}^{}$ | hydropower production rate of node $\mathrm{i}$ |

${\mathrm{STORAGE}}_{\mathrm{i}}^{}$ | water storage capacity at node $\mathrm{i}$ |

Variables | |

$\mathrm{biofuel}$ | biofuel production |

${\mathrm{flow}}_{\mathrm{i},\mathrm{j}}^{}$ | water flow from node $\mathrm{i}$ to node $\mathrm{j}$ |

${\mathrm{food}}_{\mathrm{p}}^{}$ | food production of crop $\mathrm{p}$ |

${\mathrm{hydropower}}_{\mathrm{i}}^{}$ | hydropower generation at node $\mathrm{i}$ |

${\mathrm{s}}_{\mathrm{i}}^{}$ | water storage at node $\mathrm{i}$ |

${\mathrm{spill}}_{\mathrm{i}}^{}$ | water spillage at node $\mathrm{i}$ |

${\mathrm{supply}}_{\mathrm{i},\mathrm{k}}^{}$ | water supply of $\mathrm{i}$ to demand $\mathrm{k}$ |

${\mathrm{waterdmd}}_{\mathrm{i},\mathrm{k}}^{}$ | water demand $\mathrm{k}$ supplied from node $\mathrm{i}$ |

${\mathrm{x}}_{\mathrm{i},\mathrm{p}}^{}$ | tillage size of crop $\mathrm{p}$ with water supply from node $\mathrm{i}$ |

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**Figure 4.**Simulation results of water storage in the Shihmen Reservoir under 10% uncertainty of water inflow.

**Figure 5.**Simulation of water storage in the Shihmen Reservoir under 20% uncertainty of water inflow.

**Figure 6.**Simulation of water storage in the Shihmen Reservoir under 30% uncertainty of water inflow.

**Table 1.**Uncertain simulation of water inflow, outflow, storage, and hydropower of the Shihmen Reservoir.

Uncertainty | Water Inflow | Water Outflow | Water Storage | Hydropower |
---|---|---|---|---|

Total (Standard Deviation) | Total (Standard Deviation) | Mean (Standard Deviation) | Mean (Standard Deviation) | |

million m^{3} | million m^{3} | million m^{3} | MWh | |

5% | 776.13 | 773.61 | 124.82 | 322,338 |

(4.92) | (15.60) | (15.57) | (6499) | |

10% | 785.45 | 789.23 | 133.36 | 328,846 |

(5.35) | (17.72) | (19.81) | (7384) | |

15% | 817.02 | 787.63 | 150.95 | 328,181 |

(7.10) | (19.17) | (15.30) | (7988) | |

20% | 764.54 | 754.18 | 135.54 | 314,241 |

(5.82) | (14.16) | (13.06) | (5900) | |

25% | 759.71 | 820.12 | 59.62 | 341,717 |

(12.27) | (22.75) | (36.85) | (9479) | |

30% | 763.26 | 776.79 | 108.59 | 323,662 |

(12.55) | (16.74) | (21.53) | (6975) | |

35% | 759.14 | 759.88 | 143.83 | 316,616 |

(16.49) | (18.18) | (19.40) | (7576) | |

40% | 834.72 | 754.82 | 121.33 | 314,510 |

(15.68) | (24.33) | (39.35) | (10,136) |

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**MDPI and ACS Style**

Hu, M.-C.; Fan, C.; Huang, T.; Wang, C.-F.; Chen, Y.-H.
Urban Metabolic Analysis of a Food-Water-Energy System for Sustainable Resources Management. *Int. J. Environ. Res. Public Health* **2019**, *16*, 90.
https://doi.org/10.3390/ijerph16010090

**AMA Style**

Hu M-C, Fan C, Huang T, Wang C-F, Chen Y-H.
Urban Metabolic Analysis of a Food-Water-Energy System for Sustainable Resources Management. *International Journal of Environmental Research and Public Health*. 2019; 16(1):90.
https://doi.org/10.3390/ijerph16010090

**Chicago/Turabian Style**

Hu, Ming-Che, Chihhao Fan, Tailin Huang, Chi-Fang Wang, and Yu-Hui Chen.
2019. "Urban Metabolic Analysis of a Food-Water-Energy System for Sustainable Resources Management" *International Journal of Environmental Research and Public Health* 16, no. 1: 90.
https://doi.org/10.3390/ijerph16010090