Comparison of daily water table depth prediction by four simulation models

被引:0
|
作者
Desmond, E.D. [1 ]
Ward, A.D. [1 ]
Fausey, N.R. [1 ]
Workman, S.R. [1 ]
机构
[1] The Ohio State Univ, Columbus, United States
关键词
Agricultural engineering - Computer simulation - Drainage - Environmental impact - Finite difference method - Pesticides - Water pollution - Water quality;
D O I
暂无
中图分类号
学科分类号
摘要
The Agricultural Drainage And Pesticide Transport (ADAPT) model was compared to the water management simulation models DRAINMOD, SWATREN, and PREFLO. SWATREN and PREFLO are one-dimensional finite-difference models while ADAPT and DRAINMOD are one-dimensional mass balance models. ADAPT, an extension of the computer model GLEAMS, also provides chemical transport information. All four models were tested against field data from Aurora, North Carolina. Observed water table depth data were collected during 1973 through 1977 from a water table management field experiment with three subsurface drain spacing treatments of 7.5, 15, and 30 m. Both the standard error of estimate and the average absolute deviation were computed between measured and predicted midpoint water table depth. For the five-year period ADAPT, DRAINMOD, SWATREN, and PREFLO had standard errors of estimated water table depth of 0.18, 0.19, 0.19, and 0.18 m and absolute deviations of 0.14, 0.14, 0.14, and 0.14 m, respectively. The results show good agreement between the models for this experimental site and encourage the further adoption of ADAPT to predict chemical transport.
引用
收藏
页码:111 / 118
相关论文
共 50 条
  • [21] Performance Comparison of Artificial Neural Network Models for Daily Rainfall Prediction
    S.Renuga Devi
    P.Arulmozhivarman
    C.Venkatesh
    Pranay Agarwal
    Machine Intelligence Research, 2016, 13 (05) : 417 - 427
  • [22] COMPARISON OF 2 DAILY STREAMFLOW SIMULATION-MODELS OF AN ALPINE WATERSHED
    BRENDECKE, CM
    LAIHO, DR
    HOLDEN, DC
    JOURNAL OF HYDROLOGY, 1985, 77 (1-4) : 171 - 186
  • [23] A hybrid-wavelet artificial neural network model for monthly water table depth prediction
    Anandakumar
    Kumar, A. R. Senthil
    Kale, Ravindra
    Babu, B. Maheshwara
    Sathishkumar, U.
    Reddy, G. V. Srinivasa
    Kulkarni, Prasad S.
    CURRENT SCIENCE, 2019, 117 (09): : 1475 - 1481
  • [24] Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks
    Ghose, Dillip K.
    Panda, Sudhansu S.
    Swain, Prakash C.
    JOURNAL OF HYDROLOGY, 2010, 394 (3-4) : 296 - 304
  • [25] Mapping water table depth by electromagnetic induction
    Schumann, AW
    Zaman, QU
    APPLIED ENGINEERING IN AGRICULTURE, 2003, 19 (06) : 675 - 688
  • [26] Corn response to tillage and water table depth
    Kemper, W. Doral
    Bongert, Charles E.
    Marohn, Daniel M.
    JOURNAL OF SOIL AND WATER CONSERVATION, 2012, 67 (02) : 31A - 36A
  • [27] Simulation of groundwater evaporation and groundwater depth using SWAT in the irrigation district with shallow water table
    Tiegang Liu
    Lei Liu
    Yi Luo
    Jianbin Lai
    Environmental Earth Sciences, 2015, 74 : 315 - 324
  • [28] Simulation of groundwater evaporation and groundwater depth using SWAT in the irrigation district with shallow water table
    Liu, Tiegang
    Liu, Lei
    Luo, Yi
    Lai, Jianbin
    ENVIRONMENTAL EARTH SCIENCES, 2015, 74 (01) : 315 - 324
  • [29] The Comparison of Four Different Groundwater Level Prediction Models in Baoding City
    Wang, Qi
    Tian, Tingshan
    Li, Changqing
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCES IN ENERGY, ENVIRONMENT AND CHEMICAL ENGINEERING, 2015, 23 : 501 - 507
  • [30] Validation and Comparison of Four Mortality Prediction Models in a Geriatric Ward in China
    Li, Yuanyuan
    Liu, Xiaohong
    Kang, Lin
    Li, Jiaojiao
    CLINICAL INTERVENTIONS IN AGING, 2023, 18 : 2009 - 2019