A spatially distributed model of pesticide movement in Dutch macroporous soils

2012 ◽  
Vol 470-471 ◽  
pp. 316-327 ◽  
Author(s):  
A. Tiktak ◽  
R.F.A. Hendriks ◽  
J.J.T.I. Boesten ◽  
A.M.A. van der Linden
Keyword(s):  
Distributed Model ◽  
Spatially Distributed

Models of the mechanical sensitivity and growth of otoliths in fish

2003 ◽  
Vol 13 (4-6) ◽  
pp. 189-203
Author(s):  
Alexander V. Kondrachuk
Keyword(s):  
Young’S Modulus ◽  
Young's Modulus ◽  
Oscillator Model ◽  
Distributed Model ◽  
Mechanical Sensitivity ◽  
Otolith Growth ◽  
Altered Gravity ◽  
Gel Layer ◽  
Longitudinal Size ◽  
Spatially Distributed

It has been suggested that, in the fish, the change of otolith mass during development under altered gravity conditions [1,2,3,4,5,6,24,25,36,37] and the growth of otoliths in normal conditions [22,23,26], are determined by feedback between otolith dynamics and the processes that regulate otolith growth. The hypothesis originates from an oscillator model of the otolith [30] in which otolith mass is one of the parameters. However, the validity of this hypothesis is not obvious and has not been experimentally verified. We tested this hypothesis by comparing the oscillator model with a simplified spatially distributed model of the otolith. It was shown that in the case of a spatially distributed fixation of the otolith plate (otoconial layer) to the macular surface, the mechanical sensitivity of the otolith does not depend on the total otolith mass nor on its longitudinal size. It is determined by otolith thickness, the Young's modulus and viscosity of gel layer of the growing otolith. These parameters may change in order to maintain otolith sensitivity under conditions (such as growth or altered gravity) that change the dynamics of otolith movement.

Flatness-Based Control for a Non-Linear Spatially Distributed Model of a Drilling System

2014 ◽  
pp. 205-218
Author(s):  
Torsten Knüppel ◽  
Frank Woittennek ◽  
Islam Boussaada ◽  
Hugues Mounier ◽  
Silviu-Iulian Niculescu
Keyword(s):  
Distributed Model ◽  
Non Linear ◽  
Spatially Distributed ◽  
Drilling System

scholarly journals Hydrological Modelling of Sidi Jabeur Watershed (Morocco) Using Spatially Distributed Model ATHYS

2016 ◽  
Vol 04 (01) ◽  
pp. 77-83
Author(s):  
Mourad Khattati ◽  
Mostapha Serroukh ◽  
Ismail Rafik ◽  
Hakim Mesmoudi ◽  
Brirhet Hassane ◽  
...  
Keyword(s):  
Hydrological Modelling ◽  
Distributed Model ◽  
Spatially Distributed

scholarly journals Spatially distributed tracer-aided runoff modelling and dynamics of storage and water ages in a permafrost-influenced catchment

2019 ◽  
Vol 23 (6) ◽  
pp. 2507-2523 ◽  
Author(s):  
Thea I. Piovano ◽  
Doerthe Tetzlaff ◽  
Sean K. Carey ◽  
Nadine J. Shatilla ◽  
Aaron Smith ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Temporal Dynamics ◽  
Snow Accumulation ◽  
Yukon Territory ◽  
Distributed Model ◽  
Runoff Generation ◽  
Frozen Ground ◽  
Isotope Data ◽  
Spatially Distributed ◽  
Thaw Layer

Abstract. Permafrost strongly controls hydrological processes in cold regions. Our understanding of how changes in seasonal and perennial frozen ground disposition and linked storage dynamics affect runoff generation processes remains limited. Storage dynamics and water redistribution are influenced by the seasonal variability and spatial heterogeneity of frozen ground, snow accumulation and melt. Stable isotopes are potentially useful for quantifying the dynamics of water sources, flow paths and ages, yet few studies have employed isotope data in permafrost-influenced catchments. Here, we applied the conceptual model STARR (the Spatially distributed Tracer-Aided Rainfall–Runoff model), which facilitates fully distributed simulations of hydrological storage dynamics and runoff processes, isotopic composition and water ages. We adapted this model for a subarctic catchment in Yukon Territory, Canada, with a time-variable implementation of field capacity to include the influence of thaw dynamics. A multi-criteria calibration based on stream flow, snow water equivalent and isotopes was applied to 3 years of data. The integration of isotope data in the spatially distributed model provided the basis for quantifying spatio-temporal dynamics of water storage and ages, emphasizing the importance of thaw layer dynamics in mixing and damping the melt signal. By using the model conceptualization of spatially and temporally variable storage, this study demonstrates the ability of tracer-aided modelling to capture thaw layer dynamics that cause mixing and damping of the isotopic melt signal.

scholarly journals A spatially distributed model for foreground segmentation

2009 ◽  
Vol 27 (9) ◽  
pp. 1326-1335 ◽  
Author(s):  
Patrick Dickinson ◽  
Andrew Hunter ◽  
Kofi Appiah
Keyword(s):  
Foreground Segmentation ◽  
Distributed Model ◽  
Spatially Distributed

scholarly journals Validating a spatially distributed hydrological model with soil morphology data

2014 ◽  
Vol 18 (9) ◽  
pp. 3481-3498 ◽  
Author(s):  
T. Doppler ◽  
M. Honti ◽  
U. Zihlmann ◽  
P. Weisskopf ◽  
C. Stamm
Keyword(s):  
Groundwater Level ◽  
Morphological Data ◽  
Distributed Model ◽  
Management Decisions ◽  
Data Set ◽  
Detailed Model ◽  
Groundwater Levels ◽  
Artificial Drainage ◽  
Spatial Coverage ◽  
Spatially Distributed

Abstract. Spatially distributed models are popular tools in hydrology claimed to be useful to support management decisions. Despite the high spatial resolution of the computed variables, calibration and validation is often carried out only on discharge time series at specific locations due to the lack of spatially distributed reference data. Because of this restriction, the predictive power of these models, with regard to predicted spatial patterns, can usually not be judged. An example of spatial predictions in hydrology is the prediction of saturated areas in agricultural catchments. These areas can be important source areas for inputs of agrochemicals to the stream. We set up a spatially distributed model to predict saturated areas in a 1.2 km2 catchment in Switzerland with moderate topography and artificial drainage. We translated soil morphological data available from soil maps into an estimate of the duration of soil saturation in the soil horizons. This resulted in a data set with high spatial coverage on which the model predictions were validated. In general, these saturation estimates corresponded well to the measured groundwater levels. We worked with a model that would be applicable for management decisions because of its fast calculation speed and rather low data requirements. We simultaneously calibrated the model to observed groundwater levels and discharge. The model was able to reproduce the general hydrological behavior of the catchment in terms of discharge and absolute groundwater levels. However, the the groundwater level predictions were not accurate enough to be used for the prediction of saturated areas. Groundwater level dynamics were not adequately reproduced and the predicted spatial saturation patterns did not correspond to those estimated from the soil map. Our results indicate that an accurate prediction of the groundwater level dynamics of the shallow groundwater in our catchment that is subject to artificial drainage would require a model that better represents processes at the boundary between the unsaturated and the saturated zone. However, data needed for such a more detailed model are not generally available. This severely hampers the practical use of such models despite their usefulness for scientific purposes.

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