Introduction to Menyanthes Software for Hydrogeologic Time Series Analysis with Physical Insight

فصلنامه آب و توسعه پایدار

Article 12, Volume 3, Issue 2 - Serial Number 7, March 2017, Pages 93-104   

Document Type: review paper

DOI: 10.22067/jwsd.v3i2.53715

Author

Babak Khaiatrostami^*

Islamic Azad University

Abstract

The program Menyanthes combines a variety of functions for managing, editing, visualizing, analyzing, and modeling hydrogeologic time series. The primary aim of the software is the integration of data-driven and physics-based methods for modeling time series of groundwater heads. Within Menyanthes, time series can be modeled using both the ARMA and PIRFICT methods. The PIRFICT method is a new method of time series analysis that has practical advantages as well as facilitating the physical interpretation and implementation of knowledge on the physical behavior. The analytical solutions for specific hydrogeologic problems may be used as the response function, along with their physics-based parameters. The PIRFICT model may be fitted with a large number of time series in batch. Spatial patterns that emerge from the results provide useful information which adds a new dimension to the time series analysis. The Menyathes software provides spatial visualization and analysis tools for their interpretation. The PIRFICT method also facilitates the integration of time series and spatially-distributed models. The PIRFICT method may prove to be of use for other types of time series as well, both within and outside the realm of environmental sciences.

Keywords

PIRFICT; Time series analysis; Groundwater head; Impact assessment; Menyanthes

References

ترجمه از :

Von Asmuth J.R., Maasa K., Knotters M., Bierkens M.F.P., Bakker M., Olsthoorn T.N., Cirkel D.G., Leunk I., Schaars F. and Von Asmuth D.C. 2012. Software for hydrogeologic time series analysis, interfacing data with physical insight, Environmental Modelling & Software, 38: 178–190.

Ackoff R. 1989. From data to wisdom. Journal of Applied Systems Analysis, 16 (1): 3-9.

Bakker M., Maas K. and Von Asmuth J.R. 2008. Calibration of transient groundwater models using time series analysis and moment matching. Water Resources Research, 44: 1-19.

Box G.E.P. and Jenkins G.M. 1970. Time Series Analysis: Forecasting and Control. Holden-Day, San Fransisco.

Beven K. 2002. Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system. Hydrological Processes, 16: 189-206.

Berendrecht W.L., Heemink A.W., Van Geer F.C. and Gehrels J.C. 2004. State-space modeling of water table fluctuations in switching regimes. Journal of Hydrology, 292: 249-261.

Bruggeman G.A. 1999. Analytical Solutions of Geohydrological Problems. Elsevier, Amsterdam, P.956.

Daliakopoulos I.N., Coulibaly P. and Tsanis I.K. 2005. Groundwater level forecasting using artificial neural networks. Journal of Hydrology, 309: 229-240.

De Gooijer J.G., Abraham B., Gould A. and Robinson L. 1985. Methods for determining the order of an autoregressive-moving average process: a survey, International Statistical Review, 53(3): 321-329.

Dirac P.A.M. 1947. The Principles of Quantum Mechanics. Clarendon Press, Oxford. International Statistical Review, 53(3): 301-329.

Hantush M.S. 1956. Analysis of data from pumping tests in leaky aquifers. Transactions, American Geophysical Union, 37 (6):.702-714.

Kelleher C. and Wagener T. 2011. Ten guidelines for effective data visualization in scientific publications. Environmental Modelling & Software, 26(6): 822-827.

Knotters M., and Bierkens M.F.P. 2000. Physical basis of time series models for water table depths. Water Resources Research, 36(1): 181-188.

Knotters M. and De Gooijer J.G. 1999. Tarso modelling of water table depths. Water Resources Research, 35 (3): 695-705.

Lehsten D., Von Asmuth J.R., Kleyer M. 2011. Simulation of water level fluctuations in Kettle Holes using a time series model. Wetlands, 31: 511-520.

Ljung L. 2011. System Identification Toolbox, User’s Guide. The MathWorks, Inc., Natick, MA.

Maier H.R., Jain A., Dandy G.C. and Sudheer K.P. 2010. Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environmental Modelling &Software, 25: 891-909.

Manzione R., Knotters M., Heuvelink G., Von Asmuth J. and Camara G. 2010. Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water-table levels in the Brazilian Cerrados. Hydrogeology Journal, 18(8): 1927-1937.

Nash J.E. 1959. Systematic determination of unit hydrograph parameters. Journal of Geophysical Research, 64(1): 111-115.

Olsthoorn T.N. 2000. Hydrologic restoration measures in the dunes, cause of millions of damage to flower bulbs? (in Dutch). H2O 33 (25/26), (p. 23-24).

Oreskes N., Schrader-Frechette K. and Belitz K. 1994. Verification, validation and confirmation of numerical models in the earth sciences. Science, 263: 641-646.

Peterson T.J. and Western A.W. 2011. Time-series modelling of groundwater head and its de-composition to historic climate periods. In: Proceedings of the 34th IAHR Congress, Brisbane, Australia.

Price L.E., Goodwill P., Young P.C. and Rowan J.S. 2000. A data-based mechanistic modelling (DBM) approach to understanding dynamic sediment transmission through Wyresdale Park Reservoir, Lancashire, UK. Hydrological Processes, 14: 63-78.

Rowley J. 2007. The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science, 33(2): 163-180.

Sorensen J.P.R. and Butcher A.S. 2011. Water level monitoring pressure transducers - a need for industry-wide standards. Ground Water Monitoring & Remediation, 31(4): 1-7.

Taylor C.J., Pedregal D.J., Young P.C. and Tych W. 2007. Environmental time series analysis and forecasting with the captain toolbox. Environmental Modelling & Software, 22: 797-814.

Van Geer F.C. 1987. Application of Kalman Filtering in the Analysis and Design of Groundwater Monitoring Networks. PhD thesis, Delft University of Technology, Delft.

Veling E.J.M. 2010. Approximations of impulse response curves based on the generalized moving Gaussian distribution function. Advances in Water Resources, 33: 546-561.

Von Asmuth J.R. and Maas K. 2001. The method of impulse response moments: a new method integrating time series-, groundwater- and eco-hydrological modelling. In: Gehrels, J.C., Peters, N.E., Hoehn, E., Jensen, K., Leibundgut, C., Griffioen, J.,Webb, B., Zaadnoordijk, W.J. (Eds.), Impact of Human Activity on Groundwater Dynamics. IAHS Press, Centre for Ecology and Hydrology, Wallingford, (p. 51-58).

Von Asmuth J.R., Bierkens M.F.P. and Maas K. 2002. Transfer function noise modeling in continuous time using predefined impulse response functions.Water Resources Research, 38(12): 1287-1299.

Von Asmuth J.R. and Knotters M. 2004. Characterising spatial differences in groundwater dynamics based on a system identification approach. Journal of Hydrology, 296 (1e4): 118-134.

Von Asmuth J.R. and Bierkens M.F.P. 2005. Modeling irregularly spaced residual series as a continuous stochastic process. Water Resources Research, 41 (12):W12404.

Von Asmuth J.R., Maas K., Bakker M. and Petersen J. 2008. Modeling time series of groundwater head fluctuations subjected to multiple stresses. Ground Water, 46(1): 30-40.

Von Asmuth J.R. 2010. On the Quality, Frequency and Validation of Pressure Sensor Series (in Dutch). Rapportnr. KWR 2010.001. KWR Watercycle Research Institute, Nieuwegein.

Yi M.J. and Lee K.K. 2004. TFN modeling of irregularly observed groundwater heads. Journal of Hydrology, 288: 272-287.

Yihdego Y. and Webb J.A. 2011. Modeling of bore hydrographs to determine the impact of climate and land-use change in a temperate subhumid region of southeastern Australia. Hydrogeology Journal, 19: 877-887.

Young P.C. 1998. Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. Environmental Modelling & Software, 13: 105-122.

Young P.C. and Beven K.J. 1994. Data-based mechanistic modelling and the rainfall flow non-linearity. Environmetrics, 5: 335-365.

Young P.C. and Garnier H. 2006. Identification and estimation of continuous-time, data-based mechanistic (DBM) models for environmental systems. Environmental Modelling & Software, 21(8): 1055-1072.