OR/14/011 CLiDE Pre-processing - Revision history

Ajhil: /* Grain data */

2022-04-22T11:57:51Z

Grain data

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	Grain size data may be entered using either the grain data file method, contained in the ''Setup'' tab, or manually through the ''Sediment'' tab ([[OR/14/011 CLiDE Pre-processing#Active layers\|Active layers]]). The latter method initialises the model with a laterally and vertically homogeneous sediment. By inputting a ''grain data'' file, the model may be initialised with a distributed (both vertical and horizontal) sediment grain size dataset. The grain data file contains the percentages of each grain size, specified in the Sediment tab, for the active layer and each sub-layer, for every node within the model domain. Each line of the grain data file contains the x and y cell location, the index number within the model, the active layer grain size proportions and ten sub-layer grain size proportions. To use the grain data file, the average grainsize proportion information must also entered into the appropriate place under the Sediment tab.		Grain size data may be entered using either the grain data file method, contained in the ''Setup'' tab, or manually through the ''Sediment'' tab ([[OR/14/011 CLiDE Pre-processing#Active layers\|Active layers]]). The latter method initialises the model with a laterally and vertically homogeneous sediment. By inputting a ''grain data'' file, the model may be initialised with a distributed (both vertical and horizontal) sediment grain size dataset. The grain data file contains the percentages of each grain size, specified in the Sediment tab, for the active layer and each sub-layer, for every node within the model domain. Each line of the grain data file contains the x and y cell location, the index number within the model, the active layer grain size proportions and ten sub-layer grain size proportions. To use the grain data file, the average grainsize proportion information must also entered into the appropriate place under the Sediment tab.

	There are two options for creating the grain data file. Firstly, a simulation could be started with the tab-only data and allowed to run to a steady state (equilibrium) condition; here the model will produce a ''grain size output'' file, which may be used for a subsequent transient model runs. This method can be time consuming, and any sub-layers that remain non-active will still have the homogeneous initial grain size proportions. The alternative is to use a downloadable windows based program to generate an initial grain size distribution (Coulthard, 2013<ref name="Coulthard 2013">Coulthard, T J. 2013. https://code.google.com/p/caesar-lisflood/downloads/detail?name=grainfi lemaker1. zip&can=2&q= (accessed 15th February 2014)</ref>). To generate the file using this method, the DEM file ([[OR/14/011 CLiDE Pre-processing#DEM\|DEM]]) and an ascii map (same format as DEM) of up to five sediment types is required.		There are two options for creating the grain data file. Firstly, a simulation could be started with the tab-only data and allowed to run to a steady state (equilibrium) condition; here the model will produce a ''grain size output'' file, which may be used for a subsequent transient model runs. This method can be time consuming, and any sub-layers that remain non-active will still have the homogeneous initial grain size proportions. The alternative is to use a downloadable windows based program to generate an initial grain size distribution (Coulthard, 2013<ref name="Coulthard 2013">Coulthard, T J. 2013. https://code.google.com/p/caesar-lisflood/downloads/detail?name=grainfi lemaker1. zip&can=2&q= (accessed 15th February 2014).</ref>). To generate the file using this method, the DEM file ([[OR/14/011 CLiDE Pre-processing#DEM\|DEM]]) and an ascii map (same format as DEM) of up to five sediment types is required.

	==Meteorology==		==Meteorology==

Ajhil: /* Climate factors */

2022-04-22T11:57:30Z

Climate factors

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	==Climate factors==		==Climate factors==
	The Climate factors allow the input rainfall (PPTN) and potential evapotranspiration (PE) files to be perturbed in order to simulate the impacts of climate change. Both inputs may be perturbed on a monthly basis and the value entered each month represents a multiplication factor. For example, a value of 2 entered for January PPTN will double all input rainfall and a value of 0.5 will reduce the input rainfall by half. The perturbation is applied evenly throughout the simulation unless the linear CF application box is checked. If this box is checked, the perturbation is applied linearly over the duration of the simulation, starting with no change and ending with the full perturbation being applied.		The Climate factors allow the input rainfall (PPTN) and potential evapotranspiration (PE) files to be perturbed in order to simulate the impacts of climate change. Both inputs may be perturbed on a monthly basis and the value entered each month represents a multiplication factor. For example, a value of 2 entered for January PPTN will double all input rainfall and a value of 0.5 will reduce the input rainfall by half. The perturbation is applied evenly throughout the simulation unless the linear CF application box is checked. If this box is checked, the perturbation is applied linearly over the duration of the simulation, starting with no change and ending with the full perturbation being applied.
			==References==
			<References/>
	[[Category:OR/14/011 CLiDE version 1.0 user guide \| 03]]		[[Category:OR/14/011 CLiDE version 1.0 user guide \| 03]]

Ajhil: /* Sand pile */

2022-04-22T11:56:16Z

Sand pile

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	===Sand pile===		===Sand pile===
	A simple sand pile function (Figure 12) can be used to generate shallow translational landslide events, which are triggered when the slope angle between two or more cells is greater than the stated threshold. The sand pile algorithm is similar to that described by Metha and Barker (1994) and can be triggered either by erosion at a slope base or by the addition of sediment to the slope top. Both of these processes allow the ''slope failure threshold'' angle to be exceeded and sediment to be passed between cells. The passing of sediment between two cells often leads to a cascade effect that can influence many cells. Non-fluvial movement of sediment as shallow landsliding is considered instantaneous within the model framework.		A simple sand pile function (Figure 12) can be used to generate shallow translational landslide events, which are triggered when the slope angle between two or more cells is greater than the stated threshold. The sand pile algorithm is similar to that described by Metha and Barker (1994)<ref name="Metha 1994">Metha, A, and Barker, G C. 1994. The dynamics of sand. Rep. Prog. Phys., 57(4), 385–416.</ref> and can be triggered either by erosion at a slope base or by the addition of sediment to the slope top. Both of these processes allow the ''slope failure threshold'' angle to be exceeded and sediment to be passed between cells. The passing of sediment between two cells often leads to a cascade effect that can influence many cells. Non-fluvial movement of sediment as shallow landsliding is considered instantaneous within the model framework.

	===SCIDDICA===		===SCIDDICA===

Ajhil: /* General lateral erosion and rate */

2022-04-22T11:55:07Z

General lateral erosion and rate

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	The ''lateral erosion'' check box (Figure 11) determines whether bank erosion can occur within the platform. The ''lateral erosion rate'' is calculated by the radius of curvature, which is done according to the edge counting method described in Coulthard and Van de Wiel (2007)<ref name="Coulthard 2007">Coulthard, T J, and Van De Wiel, M J. 2007. Quantifying fluvial non-linearity and finding self organized criticality? Insights from simulations of river basin evolution. Geomorphology, 91, 216–235.</ref>:		The ''lateral erosion'' check box (Figure 11) determines whether bank erosion can occur within the platform. The ''lateral erosion rate'' is calculated by the radius of curvature, which is done according to the edge counting method described in Coulthard and Van de Wiel (2007)<ref name="Coulthard 2007">Coulthard, T J, and Van De Wiel, M J. 2007. Quantifying fluvial non-linearity and finding self organized criticality? Insights from simulations of river basin evolution. Geomorphology, 91, 216–235.</ref>:

	''rate of lowering of bank cell = (1/Ca) x Tau x LateralErosionRate x Time''		''rate of lowering of bank cell = (1/Ca) x Tau x LateralErosionRate x Time''

	Where ''Ca'' is radius of bend curvature (m), ''Tau'' is the shear stress of the cell adjacent to the bank, lateral erosion is the specified rate and time is in seconds. Material removed from the bank cell is added to the cell adjacent to the bank. Lateral erosion only occurs for 4 orthogonal neighbouring cells. Values for this parameter need to be derived through calibration as each catchment is unique. Following experimentation a ''lat erosion rate'' of between 0.01 and 0.001, seems adequate for braided rivers, and ''a lat erosion rate'' around 0.0001 for meandering channels or channels with little lateral erosion. This value is grid cell independent, so rates of erosion for the same site on a 20 m DEM should be the same for a 50 m DEM.		Where ''Ca'' is radius of bend curvature (m), ''Tau'' is the shear stress of the cell adjacent to the bank, lateral erosion is the specified rate and time is in seconds. Material removed from the bank cell is added to the cell adjacent to the bank. Lateral erosion only occurs for 4 orthogonal neighbouring cells. Values for this parameter need to be derived through calibration as each catchment is unique. Following experimentation a ''lat erosion rate'' of between 0.01 and 0.001, seems adequate for braided rivers, and ''a lat erosion rate'' around 0.0001 for meandering channels or channels with little lateral erosion. This value is grid cell independent, so rates of erosion for the same site on a 20 m DEM should be the same for a 50 m DEM.

Ajhil: /* General lateral erosion and rate */

2022-04-22T11:54:55Z

General lateral erosion and rate

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	===General lateral erosion and rate===		===General lateral erosion and rate===
	The ''lateral erosion'' check box (Figure 11) determines whether bank erosion can occur within the platform. The ''lateral erosion rate'' is calculated by the radius of curvature, which is done according to the edge counting method described in Coulthard and Van de Wiel (2007):		The ''lateral erosion'' check box (Figure 11) determines whether bank erosion can occur within the platform. The ''lateral erosion rate'' is calculated by the radius of curvature, which is done according to the edge counting method described in Coulthard and Van de Wiel (2007)<ref name="Coulthard 2007">Coulthard, T J, and Van De Wiel, M J. 2007. Quantifying fluvial non-linearity and finding self organized criticality? Insights from simulations of river basin evolution. Geomorphology, 91, 216–235.</ref>:

	rate of lowering of bank cell = (1/Ca) x Tau x LateralErosionRate x Time		''rate of lowering of bank cell = (1/Ca) x Tau x LateralErosionRate x Time''

	Where ''Ca'' is radius of bend curvature (m), ''Tau'' is the shear stress of the cell adjacent to the bank, lateral erosion is the specified rate and time is in seconds. Material removed from the bank cell is added to the cell adjacent to the bank. Lateral erosion only occurs for 4 orthogonal neighbouring cells. Values for this parameter need to be derived through calibration as each catchment is unique. Following experimentation a ''lat erosion rate'' of between 0.01 and 0.001, seems adequate for braided rivers, and ''a lat erosion rate'' around 0.0001 for meandering channels or channels with little lateral erosion. This value is grid cell independent, so rates of erosion for the same site on a 20 m DEM should be the same for a 50 m DEM.		Where ''Ca'' is radius of bend curvature (m), ''Tau'' is the shear stress of the cell adjacent to the bank, lateral erosion is the specified rate and time is in seconds. Material removed from the bank cell is added to the cell adjacent to the bank. Lateral erosion only occurs for 4 orthogonal neighbouring cells. Values for this parameter need to be derived through calibration as each catchment is unique. Following experimentation a ''lat erosion rate'' of between 0.01 and 0.001, seems adequate for braided rivers, and ''a lat erosion rate'' around 0.0001 for meandering channels or channels with little lateral erosion. This value is grid cell independent, so rates of erosion for the same site on a 20 m DEM should be the same for a 50 m DEM.

Ajhil: /* Specific yield */

2022-04-22T11:53:43Z

Specific yield

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	===Specific yield===		===Specific yield===
	The aquifer specific yield (-) is set using the same method as hydraulic conductivity and must be specified as either uniform or distributed. To set a uniform value across the domain, enter the required value (0–1) in the ''Specific Yield Multiplier'' box and leave the ''Specific Yield'' file name as ‘null’. For a spatially distributed specific yield, a gridded ascii file with header information is required in the same format as the ''Hydrology boundary'' file. If a distributed specific file is used, the ''Specific Yield Multiplier'' box allows every value to be scaled by the number in the box. For example, ‘2’ would double the specific yield as specified in the file and ‘0.25’ would quarter the values. This value must be positive and if no scaling is required, then it must be set to ‘1’. The scaling factor may be useful in determining specific yield during the calibration process (~~see Section 5~~). Specific yield is limited to unity and therefore any values greater than this will be reduced to one without producing any warnings.		The aquifer specific yield (-) is set using the same method as hydraulic conductivity and must be specified as either uniform or distributed. To set a uniform value across the domain, enter the required value (0–1) in the ''Specific Yield Multiplier'' box and leave the ''Specific Yield'' file name as ‘null’. For a spatially distributed specific yield, a gridded ascii file with header information is required in the same format as the ''Hydrology boundary'' file. If a distributed specific file is used, the ''Specific Yield Multiplier'' box allows every value to be scaled by the number in the box. For example, ‘2’ would double the specific yield as specified in the file and ‘0.25’ would quarter the values. This value must be positive and if no scaling is required, then it must be set to ‘1’. The scaling factor may be useful in determining specific yield during the calibration process ([[OR/14/011 Calibration\|''See'' Calibration]]). Specific yield is limited to unity and therefore any values greater than this will be reduced to one without producing any warnings.

	==Lisflood surface flow==		==Lisflood surface flow==

Ajhil: /* Specific yield */

2022-04-22T11:53:22Z

Specific yield

← Older revision		Revision as of 12:53, 22 April 2022
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	===Specific yield===		===Specific yield===
	The aquifer specific yield (-) is set using the same method as hydraulic conductivity and must be specified as either uniform or distributed. To set a uniform value across the domain, enter the required value (~~0 - 1~~) in the ''Specific Yield Multiplier'' box and leave the ''Specific Yield'' file name as ‘null’. For a spatially distributed specific yield, a gridded ascii file with header information is required in the same format as the ''Hydrology boundary'' file. If a distributed specific file is used, the ''Specific Yield Multiplier'' box allows every value to be scaled by the number in the box. For example, ‘2’ would double the specific yield as specified in the file and ‘0.25’ would quarter the values. This value must be positive and if no scaling is required, then it must be set to ‘1’. The scaling factor may be useful in determining specific yield during the calibration process (see Section 5). Specific yield is limited to unity and therefore any values greater than this will be reduced to one without producing any warnings.		The aquifer specific yield (-) is set using the same method as hydraulic conductivity and must be specified as either uniform or distributed. To set a uniform value across the domain, enter the required value (0–1) in the ''Specific Yield Multiplier'' box and leave the ''Specific Yield'' file name as ‘null’. For a spatially distributed specific yield, a gridded ascii file with header information is required in the same format as the ''Hydrology boundary'' file. If a distributed specific file is used, the ''Specific Yield Multiplier'' box allows every value to be scaled by the number in the box. For example, ‘2’ would double the specific yield as specified in the file and ‘0.25’ would quarter the values. This value must be positive and if no scaling is required, then it must be set to ‘1’. The scaling factor may be useful in determining specific yield during the calibration process (see Section 5). Specific yield is limited to unity and therefore any values greater than this will be reduced to one without producing any warnings.

	==Lisflood surface flow==		==Lisflood surface flow==
	Following the daily partitioning of surface water by the SLiM module, and calculation of baseflow return to rivers by the groundwater module, Lisflood controls the surface routing of water around the domain using a variable timestep based on domain maximum water depth. The calculated daily addition or removal of water by SLiM and the groundwater module are discretised at the timestep used by Lisflood. Lisflood takes slope, water depth, and surface frictional characteristics into account whilst distributing water around the catchment.		Following the daily partitioning of surface water by the SLiM module, and calculation of baseflow return to rivers by the groundwater module, Lisflood controls the surface routing of water around the domain using a variable timestep based on domain maximum water depth. The calculated daily addition or removal of water by SLiM and the groundwater module are discretised at the timestep used by Lisflood. Lisflood takes slope, water depth, and surface frictional characteristics into account whilst distributing water around the catchment.

Ajhil: /* Hydrology Of Soil Type (HOST) */

2022-04-22T11:52:53Z

Hydrology Of Soil Type (HOST)

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	===Hydrology Of Soil Type (HOST)===		===Hydrology Of Soil Type (HOST)===
	The ''HOST'' file allows further vegetation and soil hydrological properties to be defined, and is composed of 31 different classes (Boorman et al., 1995) as defined in the input file. Each class has a known field capacity (m), wilting point (m) and baseflow index (-). Field capacity describes the maximum amount of water that the soil can hold and wilting point the minimum soil moisture a particular vegetation class requires not to wilt. The soil baseflow index (between 0 and 1) determines the partitioning ratio between groundwater recharge and surface runoff under conditions of excess water for each HOST soil class. Under high BFI values greater proportion of excess surface water is passed on as recharge. As BFI approaches zero, a greater proportion of excess surface water is partitioned as runoff. The HOST datasets work in conjunction with the Landuse dataset described in [[OR/14/011 CLiDE Pre-processing#Landuse\|Landuse]].		The ''HOST'' file allows further vegetation and soil hydrological properties to be defined, and is composed of 31 different classes (Boorman et al., 1995<ref name="Boorman 1995">Boorman, D B, Hollis, J M, and Lilly, A. 1995. Hydrology of soil types: a hydrologically-based classification of the soils of the United Kingdom. Report No.126, Institute of Hydrology, Wallingford, UK.</ref>) as defined in the input file. Each class has a known field capacity (m), wilting point (m) and baseflow index (-). Field capacity describes the maximum amount of water that the soil can hold and wilting point the minimum soil moisture a particular vegetation class requires not to wilt. The soil baseflow index (between 0 and 1) determines the partitioning ratio between groundwater recharge and surface runoff under conditions of excess water for each HOST soil class. Under high BFI values greater proportion of excess surface water is passed on as recharge. As BFI approaches zero, a greater proportion of excess surface water is partitioned as runoff. The HOST datasets work in conjunction with the Landuse dataset described in [[OR/14/011 CLiDE Pre-processing#Landuse\|Landuse]].

	[[Image:OR14011tab5.jpg\|thumb\|center\|550px\| '''Table 5'''    Definition of the 29 HOST soil class numbers (modified from Boorman et al., 1995). The remaining two classes (30 and 31) are used to describe surface water features. The integrated air capacity (IAC - is the average percentage air volume over a depth of one metre) is used to differentiate some classes. ]]		[[Image:OR14011tab5.jpg\|thumb\|center\|550px\| '''Table 5'''    Definition of the 29 HOST soil class numbers (modified from Boorman et al., 1995). The remaining two classes (30 and 31) are used to describe surface water features. The integrated air capacity (IAC - is the average percentage air volume over a depth of one metre) is used to differentiate some classes. ]]

Ajhil: /* Landuse */

2022-04-22T11:52:12Z

Landuse

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	===Landuse===		===Landuse===
	Landuse divides the model domain based on vegetation properties at each node in the data array based on 10 class types (Morton et al. (2011). The Landuse file provides the surface partitioning model SLiM with the rooting depth (m), depletion factor (-) and crop coefficient (-) for each landuse class. Rooting depth is defined as the maximum depth of root penetration for a particular vegetation type. Depletion factor and the crop coefficient take into account the influencing conditions under which evapotranspiration is determined for vegetation classes.		Landuse divides the model domain based on vegetation properties at each node in the data array based on 10 class types (Morton et al. (2011)<ref name="Morton 2011">Morton, D, Rowland, C, Wood, C, Meek, L, Marston, C, Smith, G, Wadsworth, R, and Simpson, I C. 2011. Final Report for LCM2007 — the new UK Land Cover Map. Centre for Ecology and Hydrology, Lancaster, 115pp.</ref>. The Landuse file provides the surface partitioning model SLiM with the rooting depth (m), depletion factor (-) and crop coefficient (-) for each landuse class. Rooting depth is defined as the maximum depth of root penetration for a particular vegetation type. Depletion factor and the crop coefficient take into account the influencing conditions under which evapotranspiration is determined for vegetation classes.

	<center>		<center>

Ajhil: /* Potential evapotranspiration */

2022-04-22T11:50:57Z

Potential evapotranspiration

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	===Potential evapotranspiration===		===Potential evapotranspiration===
	The three files needed to represent monthly averaged potential evapotranspiration are specified under the ''Hydrology'' tab. The file system is designed for use with the Meteorological Office Rainfall and Evapotranspiration Calculation System) MORECS (Hough and Jones, 1997), although the CLiDE system can be populated with other data.		The three files needed to represent monthly averaged potential evapotranspiration are specified under the ''Hydrology'' tab. The file system is designed for use with the Meteorological Office Rainfall and Evapotranspiration Calculation System) MORECS (Hough and Jones, 1997<ref name="Hough 1997">Hough, M N, and Jones, R J A. 1997. The United Kingdom Meteorological Office rainfall and evaporation calculation system: MORECS version 2.0 — an overview. Hydrol. Earth Sys. Sci., 1, 227–239.</ref>), although the CLiDE system can be populated with other data.

	The ''PE grid'' file consists of a single distributed ascii file, where individual nodes contain an identifier, which for the MORECS dataset is related to UK evaporation stations. The gridded file has to use the same discretisation as the CLiDE platform. The first six lines of the gridded file contains; the number of data columns and rows in the file, their geospatial referencing details, grid sizing and a no-data value. The grid file links to the ''PE List'' file, which contains a list of time stamped, monthly averaged potential evapotranspiration (kg m<sup>-2</sup>s<sup>-1</sup>) values for each of the identifiers (Figure 6). As the MORECS dataset is valid for the whole of the UK, the time stamped dataset contains unnecessary data. To reduce the computational cost of reading all the data from the time-stamped file, the third potential evapotranspiration file (''PE Numbers'' file) required by the CLiDE contains a list of the PE identifiers required from the ''PE List'' file (Figure 7) by the ''PE grid'' file.		The ''PE grid'' file consists of a single distributed ascii file, where individual nodes contain an identifier, which for the MORECS dataset is related to UK evaporation stations. The gridded file has to use the same discretisation as the CLiDE platform. The first six lines of the gridded file contains; the number of data columns and rows in the file, their geospatial referencing details, grid sizing and a no-data value. The grid file links to the ''PE List'' file, which contains a list of time stamped, monthly averaged potential evapotranspiration (kg m<sup>-2</sup>s<sup>-1</sup>) values for each of the identifiers (Figure 6). As the MORECS dataset is valid for the whole of the UK, the time stamped dataset contains unnecessary data. To reduce the computational cost of reading all the data from the time-stamped file, the third potential evapotranspiration file (''PE Numbers'' file) required by the CLiDE contains a list of the PE identifiers required from the ''PE List'' file (Figure 7) by the ''PE grid'' file.