PMID- 32405952
OWN - NLM
STAT- MEDLINE
DCOM- 20200710
LR  - 20211216
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 27
IP  - 22
DP  - 2020 Aug
TI  - Uncertainty analysis for precipitation and sea-level rise of a variable-density 
      groundwater simulation model based on surrogate models.
PG  - 28077-28090
LID - 10.1007/s11356-020-09177-2 [doi]
AB  - Effective coastal aquifer management typically relies on numerical models to 
      analyze the seawater intrusion (SI) process. Before using groundwater simulation 
      models to predict the extent of SI in the future, preparing input data is an 
      extremely necessary and important step. For precipitation and sea-level rise 
      (SLR), which are two of the most influential factors for SI, it is difficult to 
      precisely forecast their variations. Current studies of using numerical models to 
      predict future SI often overlook the uncertainty of these two factors. This can 
      result in compromised predictions of SI. In this study, a three-dimensional 
      variable-density groundwater simulation model was established for a coastal area 
      in Longkou, China. Then, the Monte Carlo method was applied to perform 
      uncertainty analysis for the input data of precipitation and SLR of the SI model. 
      In order to reduce the huge computational load brought by repeated invocation of 
      the SI model during the process of Monte Carlo simulation, a surrogate model 
      based on a multi-gene genetic programming (MGGP) method was developed to replace 
      the SI simulation model for calculation. A comparison between the MGGP surrogate 
      model and the Kriging surrogate model was carried out, and the results show that 
      the MGGP surrogate model has a distinct advantage over the Kriging surrogate 
      model in approximating the excitation-response relationship of the 
      variable-density groundwater simulation model. Through statistical analysis of 
      Monte Carlo simulation results, an object and reasonable risk assessment of SI 
      for the study area was obtained. This study suggests that it is essential to take 
      the uncertainty of precipitation and SLR into account when modeling and 
      predicting the extent of SI.
FAU - Han, Zheng
AU  - Han Z
AD  - College of New Energy and Environment, Jilin University, Changchun, 130021, 
      China.
AD  - Key Laboratory of Groundwater Resources and Environment (Jilin University), 
      Ministry of Education, Changchun, China.
AD  - Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin 
      University, Changchun, China.
FAU - Lu, Wenxi
AU  - Lu W
AD  - College of New Energy and Environment, Jilin University, Changchun, 130021, 
      China. luwx999@163.com.
AD  - Key Laboratory of Groundwater Resources and Environment (Jilin University), 
      Ministry of Education, Changchun, China. luwx999@163.com.
AD  - Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin 
      University, Changchun, China. luwx999@163.com.
FAU - Lin, Jin
AU  - Lin J
AD  - Nanjing Hydraulic Research Institute, Nanjing, China.
LA  - eng
PT  - Journal Article
DEP - 20200514
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
SB  - IM
MH  - China
MH  - *Groundwater
MH  - Models, Theoretical
MH  - *Sea Level Rise
MH  - Uncertainty
OTO - NOTNLM
OT  - MGGP surrogate model
OT  - Monte Carlo method
OT  - Precipitation
OT  - SLR
OT  - Seawater intrusion
OT  - Uncertainty analysis
OT  - Variable-density groundwater model
EDAT- 2020/05/15 06:00
MHDA- 2020/07/11 06:00
CRDT- 2020/05/15 06:00
PHST- 2020/03/03 00:00 [received]
PHST- 2020/05/04 00:00 [accepted]
PHST- 2020/05/15 06:00 [pubmed]
PHST- 2020/07/11 06:00 [medline]
PHST- 2020/05/15 06:00 [entrez]
AID - 10.1007/s11356-020-09177-2 [pii]
AID - 10.1007/s11356-020-09177-2 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2020 Aug;27(22):28077-28090. doi: 
      10.1007/s11356-020-09177-2. Epub 2020 May 14.