scholarly journals Spatial pattern and temporal dynamics of limnological variables in Liuxihe Reservoir, Guangdong

2009 ◽  
Vol 21 (3) ◽  
pp. 387-394 ◽  
Author(s):  
LIN Guoen ◽  
◽  
WANG Tian ◽  
LIN Qiuqi ◽  
HAN Boping
Keyword(s):  
Spatial Pattern ◽  
Temporal Dynamics ◽  
Liuxihe Reservoir

scholarly journals Effects of Initial Epidemic Conditions, Sporulation Rate, and Spore Dispersal Gradient on the Spatio-Temporal Dynamics of Plant Disease Epidemics

1998 ◽  
Vol 88 (10) ◽  
pp. 1000-1012 ◽  
Author(s):  
X.-M. Xu ◽  
M. S. Ridout
Keyword(s):  
Spatial Pattern ◽  
Power Law ◽  
Temporal Dynamics ◽  
Initial Conditions ◽  
Disease Incidence ◽  
Disease Status ◽  
Spatio Temporal ◽  
Disease Epidemics ◽  
Sporulation Rate ◽  
The Relationship

A stochastic model that simulates the spread of disease over space and time was developed to study the effects of initial epidemic conditions (number of initial inocula and their spatial pattern), sporulation rate, and spore dispersal gradient on the spatio-temporal dynamics of plant disease epidemics. The spatial spread of disease was simulated using a half-Cauchy distribution with median dispersal distance μ (units of distance). The rate of temporal increase in disease incidence (βI, per day) was influenced jointly by μ and by the sporulation rate λ (spores per lesion per day). The relationship between βI and μ was nonlinear: the increase in βI with increasing μ was greatest when μ was small (i.e., when the dispersal gradient was steep). The rate of temporal increase in disease severity of diseased plants (βS) was affected mainly by λ: βS increased directly with increasing λ. Intraclass correlation (κt), the correlation of disease status of plants within quadrats, increased initially with disease incidence, reached a peak, and then declined as disease incidence approached 1.0. This relationship was well described by a power-law model that is consistent with the binary form of the variance power law. The amplitude of the model relating κt to disease incidence was affected mainly by μ: κt decreased with increasing μ. The shape of the curve was affected mainly by initial conditions, especially the spatial pattern of the initial inocula. Generally, the relationship of spatial autocorrelation (ρt,k), the correlation of disease status of plants at various distances apart, to disease incidence and distance was well described by a four-parameter power-law model. ρt,k increased with disease incidence to a maximum and then declined at higher values of disease incidence, in agreement with a power-law relationship. The amplitude of ρt,k was determined mainly by initial conditions and by μ: ρt,k decreased with increasing μ and was lower for regular patterns of initial inocula. The shape of the ρt,k curve was affected mainly by initial conditions, especially the spatial pattern of the initial inocula. At any level of disease incidence, autocorrelation declined exponentially with spatial lag; the degree of this decline was determined mainly by μ: it was steeper with decreasing μ.

scholarly journals Spatial Pattern and Temporal Dynamics of Bryophyte Assemblages in Saline Grassland

2012 ◽  
Vol 48 (2) ◽  
pp. 189-207 ◽  
Author(s):  
Péter Ódor ◽  
Erzsébet Szurdoki ◽  
Zoltán Botta-Dukát ◽  
Beáta Papp
Keyword(s):  
Spatial Pattern ◽  
Temporal Dynamics

scholarly journals Learning from satellite observations: increased understanding of catchment processes through stepwise model improvement

2020 ◽  
Author(s):  
Petra Hulsman ◽  
Hubert H. G. Savenije ◽  
Markus Hrachowitz
Keyword(s):  
Time Series ◽  
Spatial Pattern ◽  
Temporal Dynamics ◽  
Water Storage ◽  
Remotely Sensed ◽  
Satellite Observations ◽  
Model Structure ◽  
Total Water ◽  
Benchmark Model ◽  
Groundwater Reservoir

Abstract. Satellite observations can provide valuable information for a better understanding of hydrological processes and thus serve as valuable tools for model structure development and improvement. While model calibration and evaluation has in recent years started to make increasing use of spatial, mostly remotely-sensed information, model structural development largely remains to rely on discharge observations at basin outlets only. Due to the ill-posed inverse nature and the related equifinality issues in the modelling process, this frequently results in poor representations of the spatiotemporal heterogeneity of system-internal processes, in particular for large river basins. The objective of this study is thus to explore the value of remotely-sensed, gridded data to improve our understanding of the processes underlying this heterogeneity and, as a consequence, their quantitative representation in models through a stepwise adaptation of model structures and parameters. For this purpose, a distributed, process-based hydrological model was developed for the study region, the poorly gauged Luangwa river basin. As a first step, this benchmark model was calibrated to discharge data only and, in a post-calibration evaluation procedure, tested for its ability to simultaneously reproduce (1) the basin-average temporal dynamics of remotely-sensed evaporation and total water storage anomalies, and (2) their temporally-averaged spatial pattern. This allowed the diagnosis of model structural deficiencies in reproducing these temporal dynamics and spatial patterns. Subsequently, the model structure was adapted in a step-wise procedure, testing five additional alternative process hypotheses that could potentially better describe the observed dynamics and pattern. These included, on the one hand, the addition and testing of alternative formulations of groundwater upwelling into wetlands as function of the water storage and, on the other hand, alternative spatial discretizations of the groundwater reservoir. Similar to the benchmark, each alternative model hypothesis was, in a next step, calibrated to discharge only and tested against its ability to reproduce the observed spatiotemporal pattern in evaporation and water storage anomalies. In a final step, all models were re-calibrated to discharge, evaporation and water storage anomalies simultaneously. The results indicated that (1) the benchmark model (Model A) could reasonably well reproduce the time series of observed discharge, basin-average evaporation and total water storage. In contrast, it poorly represented time series of evaporation in wetland dominated areas as well as the spatial pattern of evaporation and total water storage. (2) Step-wise adjusting the model structure (Models B–F) suggested that Model F, allowing for upwelling groundwater from a distributed representation of the groundwater reservoir and (3) simultaneously calibrating the model with respect to multiple variables, i.e. discharge, evaporation and total water storage anomalies, provided the best representation of all these variables with respect to their temporal dynamics and spatial pattern, except for the basin-average temporal dynamics in the total water storage anomalies. It was shown that satellite-based evaporation and total water storage anomaly data are not only valuable for multi-criteria calibration, but can play an important role in improving our understanding of hydrological processes through diagnosing model deficiencies and step-wise model structural improvement.

scholarly journals Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach

2017 ◽  
Vol 21 (12) ◽  
pp. 5987-6005 ◽  
Author(s):  
Gorka Mendiguren ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Hydrological Model ◽  
Temporal Dynamics ◽  
Remote Sensing Data ◽  
Spatiotemporal Variability ◽  
Eof Analysis ◽  
Area Index ◽  
Sensing Data

Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land–atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds spatial features found in the spatial pattern of remote-sensing-based ET.

Submesoscale Spatiotemporal Variability of North American Monsoon Rainfall over Complex Terrain

2007 ◽  
Vol 20 (9) ◽  
pp. 1751-1773 ◽  
Author(s):  
Mekonnen Gebremichael ◽  
Enrique R. Vivoni ◽  
Christopher J. Watts ◽  
Julio C. Rodríguez
Keyword(s):  
Spatial Pattern ◽  
Diurnal Cycle ◽  
North American ◽  
Rainfall Intensity ◽  
Temporal Dynamics ◽  
Spatiotemporal Variability ◽  
North American Monsoon ◽  
Spatial And Temporal Variability ◽  
Study Region ◽  
Diurnal Cycles

Abstract The authors analyze information from rain gauges, geostationary infrared satellites, and low earth orbiting radar in order to describe and characterize the submesoscale (<75 km) spatial pattern and temporal dynamics of rainfall in a 50 km × 75 km study area located in Sonora, Mexico, in the periphery of the North American monsoon system core region. The temporal domain spans from 1 July to 31 August 2004, corresponding to one monsoon season. Results reveal that rainfall in the study region is characterized by high spatial and temporal variability, strong diurnal cycles in both frequency and intensity with maxima in the evening hours, and multiscaling behavior in both temporal and spatial fields. The scaling parameters of the spatial rainfall fields exhibit dependence on the rainfall rate at the synoptic scale. The rainfall intensity exhibits a slightly stronger diurnal cycle compared to the rainfall frequency, and the maximum lag time between the two diurnal peaks is within 2.4 h, with earlier peaks observed for rainfall intensity. The time of maximum cold cloud occurrence does not vary with the infrared threshold temperature used (215–235 K), while the amplitude of the diurnal cycle varies in such a way that deep convective cells have stronger diurnal cycles. Furthermore, the results indicate that the diurnal cycle of cold cloud occurrence can be used as a surrogate for some basic features of the diurnal cycle of rainfall. The spatial pattern and temporal dynamics of rainfall are modulated by topographic features and large-scale features (circulation and moisture fields as related to geographical location). As compared to valley areas, mountainous areas are characterized by an earlier diurnal peak, an earlier date of maximum precipitation, closely clustered rainy hours, frequent yet small rainfall events, and less dependence of precipitation accumulation on elevation. As compared to the northern section of the study area, the southern section is characterized by strong convective systems that peak late diurnally. The results of this study are important for understanding the physical processes involved, improving the representation of submesoscale variability in models, downscaling rainfall data from coarse meteorological models to smaller hydrological scales, and interpreting and validating remote sensing rainfall estimates.

scholarly journals Fractal measures of spatial pattern as a heuristic for return rate in vegetative systems

2016 ◽  
Vol 3 (3) ◽  
pp. 150519 ◽  
Author(s):  
M. A. Irvine ◽  
E. L. Jackson ◽  
E. J. Kenyon ◽  
K. J. Cook ◽  
M. J. Keeling ◽  
...  
Keyword(s):  
Time Series ◽  
Spatial Pattern ◽  
Temporal Dynamics ◽  
Single Species ◽  
Return Rate ◽  
Long Term Study ◽  
Fractal Measures ◽  
Long Time ◽  
Predictive Relationship

Measurement of population persistence is a long-standing problem in ecology; in particular, whether it is possible to gain insights into persistence without long time-series. Fractal measurements of spatial patterns, such as the Korcak exponent or boundary dimension, have been proposed as indicators of the persistence of underlying dynamics. Here we explore under what conditions a predictive relationship between fractal measures and persistence exists. We combine theoretical arguments with an aerial snapshot and time series from a long-term study of seagrass. For this form of vegetative growth, we find that the expected relationship between the Korcak exponent and persistence is evident at survey sites where the population return rate can be measured. This highlights a limitation of the use of power-law patch-size distributions and other indicators based on spatial snapshots. Moreover, our numeric simulations show that for a single species and a range of environmental conditions that the Korcak–persistence relationship provides a link between temporal dynamics and spatial pattern; however, this relationship is specific to demographic factors, so we cannot use this methodology to compare between species.

Effects of Monetary Incentives on Task Switching

2012 ◽  
Vol 59 (4) ◽  
pp. 216-226 ◽  
Author(s):  
Thomas Kleinsorge ◽  
Gerhard Rinkenauer
Keyword(s):  
Task Switching ◽  
Temporal Dynamics ◽  
Reward Processing ◽  
General Level ◽  
Monetary Incentives ◽  
Switch Costs ◽  
Expectancy Effect ◽  
Reward Expectancy ◽  
Trial Basis

In two experiments, effects of incentives on task switching were investigated. Incentives were provided as a monetary bonus. In both experiments, the availability of a bonus varied on a trial-to-trial basis. The main difference between the experiments relates to the association of incentives to individual tasks. In Experiment 1, the association of incentives to individual tasks was fixed. Under these conditions, the effect of incentives was largely due to reward expectancy. Switch costs were reduced to statistical insignificance. This was true even with the task that was not associated with a bonus. In Experiment 2, there was a variable association of incentives to individual tasks. Under these conditions, the reward expectancy effect was bound to conditions with a well-established bonus-task association. In conditions in which the bonus-task association was not established in advance, enhanced performance of the bonus task was accompanied by performance decrements with the task that was not associated with a bonus. Reward expectancy affected mainly the general level of performance. The outcome of this study may also inform recently suggested neurobiological accounts about the temporal dynamics of reward processing.

Spatial Pattern Discrimination in Rats

2003 ◽  
Author(s):  
Kelly A. Digian ◽  
Michael Brown
Keyword(s):  
Spatial Pattern ◽  
Pattern Discrimination
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