Real-Time Management of Groundwater Resources Based on Wireless Sensors Networks

Groundwater plays a vital role in the arid inland river basins, in which the groundwater management is critical to the sustainable development of area economy and ecology. Traditional sustainable management approaches are to analyze different scenarios subject to assumptions or to construct simulation–optimization models to obtain optimal strategy. However, groundwater system is time-varying due to exogenous inputs. In this sense, the groundwater management based on static data is relatively outdated. As part of the Heihe River Basin (HRB), which is a typical arid river basin in Northwestern China, the Daman irrigation district was selected as the study area in this paper. First, a simulation–optimization model was constructed to optimize the pumping rates of the study area according to the groundwater level constraints. Three different groundwater level constraints were assigned to explore sustainable strategies for groundwater resources. The results indicated that the simulation–optimization model was capable of identifying the optimal pumping yields and satisfy the given constraints. Second, the simulation–optimization model was integrated with wireless sensors network (WSN) technology to provide real-time features for the management. The results showed time-varying feature for the groundwater management, which was capable of updating observations, constraints, and decision variables in real time. Furthermore, a web-based platform was developed to facilitate the decision-making process. This study combined simulation and optimization model with WSN techniques and meanwhile attempted to real-time monitor and manage the scarce groundwater resource, which could be used to support the decision-making related to sustainable management.


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sustainable management approaches are to analyze different scenarios subject to assumptions or 17 to construct simulation-optimization models to obtain optimal strategy. However, groundwater 18 system is time-varying due to exogenous inputs. In this sense, the groundwater management 19 based on static data is relatively outdated. As part of the Heihe River Basin (HRB), which is a 20 typical arid river basin in northwestern China, the Daman irrigation district was selected as the 21 study area in this paper. First, a simulation-optimization model was constructed to optimize the 22 pumping rates of the study area according to the groundwater level constraints. Three different 23 groundwater level constraints were assigned to explore sustainable strategy for groundwater 24 resource. The results indicated that the simulation-optimization model was capable of identifying 25 the optimal pumping yields while satisfying the given constraints. Second, the 26 simulation-optimization model was integrated with Wireless Sensors Network technology to 27 provide real-time features for the management. The results showed time-varying feature for the 28 groundwater management which was capable of updating observations, constraints and decision 29 variables in real-time. Furthermore, a web-based platform was developed to facilitate the 30 decision-making process. This study combined simulation and optimization model with WSN 31 techniques and meanwhile attempted to real-time monitor and manage the scarce groundwater 32 resource which could be used to support decision making related to sustainable management.

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Freshwater is one of the precious, unique resources on the planet. It is meanwhile essential for 37 agriculture, domestic usage, industry and environment. The rapid economic growth, population 38 growth, urbanization and continuous expansion of human development, have aggravated water 39 scarcity in many basins. Owing to several unique features (e.g. widespread and continuous 40 availability, low development cost, drought reliability), groundwater has become one of the 41 important sources of water supplies among the available water resources throughout the world in 42 the last decades. The importance of groundwater resources increases in pace with the continuous 43 growth of world population, which is expected to reach 11.2 billion in 2100 [1]. Therefore, it is very 44 important to sustainably manage the groundwater resources in order to satisfy the increasing demand. However, because of the lack of policy making and supervision measures for the utilization, the over-exploitation of groundwater in many areas was serious, which may alter the flow regimes and become a threat to socio-economic development and ecological health [2]. The 48 middle reaches of Heihe River Basin (HRB), which is located in the arid regions of northwestern 49 China, have faced serious water problems [3]. In the last 30 years, the groundwater level declines 50 along with the dramatically increase of agricultural pumping wells (from 3199 in 1985 to 6275 in 51 2005). Especially, in Daman irrigation district, the groundwater level has dropped 20m due to the 52 unconstrained groundwater exploitation. A number of researches have tried to tackle the 53 groundwater resources management problems by using scenario analysis [4,5]. However, to select 54 the optimal operational procedure or policy could be extremely difficult because of the complexity 55 of groundwater systems and relatively limited onsite studies. To address this difficulty, the 56 groundwater simulation models are suggested to be linked with optimization techniques to obtain 57 the best (or optimal) management strategy from many possible strategies [6]. These approaches are 58 all based on static data which cannot reflect the real-time situations. Therefore, decisions, which are 59 made based on these approaches, are always obsolete to some extents. On the other hand, among 60 traditional sampling techniques related to groundwater monitoring, the most common method is 61 grab sampling which can be conducted on-site using hand held instruments. Grab Sampling is 62 subject to several disadvantages. First, process is labor intensive and costly. Second, the sampling 63 interval is quite large which leads to sparse results datasets.

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In this paper, we developed a real-time groundwater management system for Daman irrigation 79 district in the middle reaches of the HRB. The WSN techniques were used in the system in order to 80 provide the real-time data. We also optimized the proposed highly efficient and reliable method to      irrigation districts and the time period was set to year. All these well data were collected and 141 preprocessed for the structured groundwater study via the sensor network [7,25,26]. Meanwhile, the 142 feedback and executors, which are operating in the real fields, were also deployed by the remote 143 sensor network. We will detail the backend system design and development in following sections.

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The idea of the response matrix is to approximate the relations between the decision variables 189 and the constraints which are originally described in the numerical model by physical equations.

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Suppose the groundwater level is a function of a set of pumping rates.

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Where Qw represents the vector of all withdrawal and/or injection rates in Daman irrigation 193 district; H is the groundwater level; (i, j, k) represents a location in the three-dimensional aquifer 194 system; and t is time.

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A first-order Taylor series expansion can be applied to approximate the groundwater level at 198 Where H 0 and Qw 0 represent the base (initial) condition of groundwater level and pumping 199 rates; , , , are the response coefficients; n is the number of decision variables.

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The response coefficients are approximated as:

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Where ∆ is the perturbation for the n-th decision variable; Qw△n represent the pumping 203 rates after perturbation.

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The groundwater pumping data, irrigation data and cultivated areas obtained from WestDC

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[33] were divided into three periods (1986 ~ 1989, 1990 ~ 2001, and 2002 ~ 2012). Therefore, the water 235 balance for the middle reaches of the HRB was analyzed corresponding to the three periods ( Figure   236 4). Figure 4 indicated that the main groundwater recharge source of the study area was the leakage from the Heihe River which accounted for about 50% of the total recharge amount. Other important 238 sources of groundwater recharge were the irrigation backflow and the lateral inflow from the 239 mountain area which accounted for about 27% and 21%, respectively. The principal sink term of the 240 groundwater was the drainage from the groundwater to the river which accounted for about 80%, 241 67%, and 60% of the total amount in different periods. The difference between periods represented 242 the groundwater dynamics, which is mainly due to the different groundwater exploitations in 243 different periods. In addition, the figure also indicated that the groundwater system was under 1475 m, and 1476 m were applied (hereafter referred as S1, S2, and S3, respectively). By analyzing 258 the historical data, the LB and UB of the pumping yields in all scenarios were set to be 0.05× 259 10 8 m 3 /a, and 1.0×10 8 m 3 /a respectively. In most researches, offline data was used to sustainably 260 manage the groundwater resources. However, the groundwater level was observed incessantly. A

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WSN with HOBO water level Logger U20-001-01 was deployed to measure and record the

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which could be sustained by the available water resources, was calculated. Furthermore, the 317 real-time management system was integrated into a web-based platform to ease the decision 318 makers' work. In our future direction, we will deploy more wireless sensors and further expand the 319 concept of real-time management to the whole basin with the consideration of surface water to 320 regulate water resources in a basin scale.

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Acknowledgments: The authors would like to thank the associated editors and the reviewers for their precious 323 time and efforts in reviewing our paper and providing constructive comments to improve the paper. Gratitude

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Yan helped the implementation and tested the whole system. The manuscript was accomplished by Chong

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Chen and Huaming Chen with Jun Shen providing reviews and comments. All the authors were engaged in 339 the final manuscript preparation and agreed to the publication of this paper.

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Conflicts of Interest: The authors declare no conflict of interest.