Uncertainty in the model parameters due to spatial variability of rainfall

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

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Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model

10.20944/preprints201808.0209.v1 ◽

2018 ◽

Author(s):

Mehmet Cüneyd Demirel ◽

Julian Koch ◽

Gorka Mendiguren ◽

Simon Stisen

Keyword(s):

Sensitivity Analysis ◽

Spatial Variability ◽

Spatial Pattern ◽

Spatial Patterns ◽

Performance Measures ◽

Sampling Strategy ◽

Behavioral Model ◽

Hydrologic Model ◽

Actual Evapotranspiration ◽

Model Parameters

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

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Stochastic finite element analysis of rockfill dam considering spatial variability of dam material porosity

Engineering Computations ◽

10.1108/ec-06-2018-0266 ◽

2019 ◽

Vol 36 (9) ◽

pp. 2929-2959

Author(s):

Hui Chen ◽

Donghai Liu

Keyword(s):

Finite Element ◽

Constitutive Model ◽

Spatial Variability ◽

Random Field ◽

Model Parameters ◽

Mechanical Parameters ◽

Stochastic Finite Element ◽

Content Type ◽

Rockfill Dam ◽

Stochastic Fem

Purpose The purpose of this study is to develop a stochastic finite element method (FEM) to solve the calculation precision deficiency caused by spatial variability of dam compaction quality. Design/methodology/approach The Choleski decomposition method was applied to generate constraint random field of porosity. Large-scale laboratory triaxial tests were conducted to determine the quantitative relationship between the dam compaction quality and Duncan–Chang constitutive model parameters. Based on this developed relationship, the constraint random fields of the mechanical parameters were generated. The stochastic FEM could be conducted. Findings When the fully random field was simulated without the restriction effect of experimental data on test pits, the spatial variabilities of both displacement and stress results were all overestimated; however, when the stochastic FEM was performed disregarding the correlation between mechanical parameters, the variabilities of vertical displacement and stress results were underestimated and variation pattern for horizontal displacement also changed. In addition, the method could produce results that are closer to the actual situation. Practical implications Although only concrete-faced rockfill dam was tested in the numerical examples, the proposed method is applicable for arbitrary types of rockfill dams. Originality/value The value of this study is that the proposed method allowed for the spatial variability of constitutive model parameters and that the applicability was confirmed by the actual project.

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Spatial Variability of Nitrogen and Phosphorus in Wetland Soils of the Yellow River Delta, China

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.610-613.1028 ◽

2012 ◽

Vol 610-613 ◽

pp. 1028-1032

Author(s):

Chen Huang ◽

Jun Hong Bai ◽

Jun Jing Wang ◽

Qiong Qiong Lu ◽

Qing Qing Zhao ◽

...

Keyword(s):

Spatial Variability ◽

Soil Nitrogen ◽

Yellow River ◽

Yellow River Delta ◽

River Delta ◽

The Yellow River ◽

Model Parameters ◽

Wetland Soils ◽

Nitrogen And Phosphorus ◽

The Yellow River Delta

Spatial variability of soil nitrogen and phosphorus in the Yellow River Delta was investigated using geostatistical method. Our results showed moderate variation in TN and TP and high variations in NH4+-N and AP. The best semi-variogram model for each nutrient was identified. The model parameters suggested that the structure variance dominated the total variance of TN, TP and NH4+-N, while the spatial variability of AP was relatively random. The spatial variation scales of soil nitrogen and phosphorus are similar.

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The significance of spatial variability of rainfall on simulated runoff: an evaluation based on the Upper Lee catchment, UK

Hydrology Research ◽

10.2166/nh.2016.038 ◽

2016 ◽

Vol 48 (4) ◽

pp. 1118-1130 ◽

Cited By ~ 11

Author(s):

I. G. Pechlivanidis ◽

N. McIntyre ◽

H. S. Wheater

Keyword(s):

Spatial Variability ◽

Distributed Model ◽

Model Parameters ◽

Rainfall Time Series ◽

Catchment Scale ◽

Rainfall Events ◽

Spatial Properties ◽

Antecedent Conditions ◽

Parameter Uncertainty Analysis ◽

The Impact

The significance of spatial variability of rainfall on runoff is explored as a function of catchment scale and type, and antecedent conditions via the continuous time, semi-distributed probability distributed model (PDM) hydrological model applied to the Upper Lee catchment, UK. The impact of catchment scale and type is assessed using 11 nested catchments, and further assessed by artificially changing the catchment characteristics and translating these to model parameters (MPs) with uncertainty using model regionalisation. Dry and wet antecedent conditions are represented by ‘warming up’ the model under different rainfall time series. Synthetic rainfall events are introduced to directly relate the change in simulated runoff to the spatial variability of rainfall. Results show that runoff volume and peak are more sensitive to the spatial rainfall for more impermeable catchments; however, this sensitivity is significantly undermined under wet antecedent conditions. Although there is indication that the impact of spatial rainfall on runoff varies as a function of catchment scale, the variability of antecedent conditions between the synthetic catchments seems to mask this significance. Parameter uncertainty analysis highlights the importance of accurately representing the spatial variability of the catchment properties and their translation to MPs when investigating the effects of spatial properties of rainfall on runoff.

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Spatial Probabilistic Modeling of Corrosion in Ship Structures

ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg ◽

10.1115/1.4035399 ◽

2017 ◽

Vol 3 (3) ◽

Cited By ~ 3

Author(s):

Jesus Luque ◽

Rainer Hamann ◽

Daniel Straub

Keyword(s):

Spatial Variability ◽

Spatial Dependence ◽

Measurement Data ◽

Corrosion Process ◽

Model Parameters ◽

Plate Element ◽

Structural Element ◽

Ship Structures ◽

Operational Conditions ◽

Corrosion Model

Corrosion in ship structures is influenced by a variety of factors that are varying in time and space. Existing corrosion models used in practice only partially address the spatial variability of the corrosion process. Typical estimations of corrosion model parameters are based on averaging measurements for one ship type over structural elements from different ships and operational conditions. Most models do not explicitly predict the variability and correlation of the corrosion process among multiple locations in the structure. This correlation is of relevance when determining the necessary inspection coverage, and it can influence the reliability of the ship structure. In this paper, we develop a probabilistic spatiotemporal corrosion model based on a hierarchical approach, which represents the spatial variability and correlation of the corrosion process. The model includes as hierarchical levels vessel–compartment–frame–structural element–plate element. At all levels, variables representing common influencing factors (e.g., coating life) are introduced. Moreover, at the lowest level, which is the one of the plate element, the corrosion process can be modeled as a spatial random field. For illustrative purposes, the model is trained through Bayesian analysis with measurement data from a group of tankers. In this application, the spatial dependence among corrosion processes in different parts of the ships is identified and quantified using the proposed hierarchical model. Finally, how this spatial dependence can be exploited when making inference on the future condition of the ships is demonstrated.

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Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model

Water ◽

10.3390/w10091188 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1188 ◽

Cited By ~ 3

Author(s):

Mehmet Demirel ◽

Julian Koch ◽

Gorka Mendiguren ◽

Simon Stisen

Keyword(s):

Sensitivity Analysis ◽

Spatial Variability ◽

Spatial Pattern ◽

Spatial Patterns ◽

Performance Measures ◽

Sampling Strategy ◽

Behavioral Model ◽

Hydrologic Model ◽

Actual Evapotranspiration ◽

Model Parameters

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represent an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity typically do not reflect other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). The Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definitions based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

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A Bayesian framework for updating model parameters while considering spatial variability

Georisk Assessment and Management of Risk for Engineered Systems and Geohazards ◽

10.1080/17499518.2016.1255760 ◽

2016 ◽

Vol 11 (4) ◽

pp. 285-298 ◽

Cited By ~ 1

Author(s):

Pinom Ering ◽

G. L. Sivakumar Babu

Keyword(s):

Spatial Variability ◽

Bayesian Framework ◽

Model Parameters

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Assessing the spatial variability in peak season CO<sub>2</sub> exchange characteristics across the Arctic tundra using a light response curve parameterization

Biogeosciences ◽

10.5194/bg-11-4897-2014 ◽

2014 ◽

Vol 11 (17) ◽

pp. 4897-4912 ◽

Cited By ~ 15

Author(s):

H. N. Mbufong ◽

M. Lund ◽

M. Aurela ◽

T. R. Christensen ◽

W. Eugster ◽

...

Keyword(s):

Spatial Variability ◽

Response Curve ◽

Dark Respiration ◽

Light Response ◽

Arctic Tundra ◽

Co2 Exchange ◽

The Arctic ◽

Model Parameters ◽

Light Saturation ◽

Light Response Curve

Abstract. This paper aims to assess the spatial variability in the response of CO2 exchange to irradiance across the Arctic tundra during peak season using light response curve (LRC) parameters. This investigation allows us to better understand the future response of Arctic tundra under climatic change. Peak season data were collected during different years (between 1998 and 2010) using the micrometeorological eddy covariance technique from 12 circumpolar Arctic tundra sites, in the range of 64–74° N. The LRCs were generated for 14 days with peak net ecosystem exchange (NEE) using an NEE–irradiance model. Parameters from LRCs represent site-specific traits and characteristics describing the following: (a) NEE at light saturation (Fcsat), (b) dark respiration (Rd), (c) light use efficiency (α), (d) NEE when light is at 1000 μmol m−2 s−1 (Fc1000), (e) potential photosynthesis at light saturation (Psat) and (f) the light compensation point (LCP). Parameterization of LRCs was successful in predicting CO2 flux dynamics across the Arctic tundra. We did not find any trends in LRC parameters across the whole Arctic tundra but there were indications for temperature and latitudinal differences within sub-regions like Russia and Greenland. Together, leaf area index (LAI) and July temperature had a high explanatory power of the variance in assimilation parameters (Fcsat, Fc1000 and Psat, thus illustrating the potential for upscaling CO2 exchange for the whole Arctic tundra. Dark respiration was more variable and less correlated to environmental drivers than were assimilation parameters. This indicates the inherent need to include other parameters such as nutrient availability, substrate quantity and quality in flux monitoring activities.

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Uncertainty in soil physical data at river basin scale

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-3-1281-2006 ◽

2006 ◽

Vol 3 (4) ◽

pp. 1281-1313 ◽

Cited By ~ 1

Author(s):

P. van der Keur ◽

B. V. Iversen

Keyword(s):

Spatial Variability ◽

River Basin ◽

Hydraulic Properties ◽

Data Uncertainty ◽

Model Parameters ◽

Length Scales ◽

River Basin Scale ◽

Physical Data ◽

Basin Scale ◽

Modelling Studies

Abstract. For hydrological modelling studies at the river basin scale, decision makers need guidance in assessing the implications of uncertain data used by modellers as an input to modelling tools. Simulated solute transport through the unsaturated zone is associated with uncertainty due to spatial variability of soil hydraulic properties and derived hydraulic model parameters. In general for modelling studies at the river basin scale spatially available data at various scales must be aggregated to an appropriate scale. Estimating soil properties at unsampled points by means of geostatistical techniques require reliable information on the spatial structure of soil data. In this paper this information is assessed by reviewing current developments in the field of soil physical data uncertainty and adopting a classification system. Then spatial variability and structure is inspected by reviewing experimental work on determining spatial length scales for soil physical (and soil chemical) data. Available literature on spatial length scales for soil physical- and chemical properties is reviewed and their use in facilitating change of spatial support discussed. Uncertainty associated to the derivation of hydraulic properties from soil physical properties in this context is also discussed.

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