User Guide
Contents
1. 2 pD xij ry where Ai j in eff Aijj in Qce ij in Qce ij all 34 The USLE with L via Kinnell 2004 option calculates erosion using Eqs 33 and 34 3 The USLE M lite Eqs 33 and 34 are also fundamental to applying the USLE M lite and the USLE M to the prediction of erosion in grid cells When runoff is generated uniformly over an area Qce ij all Aijin DA Qceij all Aij in Lume ij 35 Qreiijcen D xij 22 13 where Qreij cet is the runoff ratio for the cell Qre ij cen is the ratio of the volume of water discharged from the cell to the volume of rain water falling onto the cell Because the volume of water discharged from the cell includes runon from upslope it can have values greater than 1 0 Qreij ce occurs in Eq 35 because the erosivity index for a cell is given by the product of Qre ij ce and Elzo Runoff coefficents are ratios of rain and runoff volumes on the same area and consequently have values of 1 0 or less with Hortonian overland flow When considered in the context of Ajj in err determined by Eq 34 Eq 35 becomes Amt 1 Qce ij atett Aij in ett D Qceij atett Aij ineft Lume ij 7 36 Qreij ce Do xij 243 In the case of the USLE L lite Qce ij ai eff iS the runoff coefficient for the area including the cell when the whole area is considered to be bare fallow with cultivation up and down the slope Qceij atlett Qetei j cen D Qcteij in Aij in Aij2. 05 1 0 0 00 00 0 050 0 01 60 dummy native pasture outside 0 003 1 0 0 00 00 0 050 0 01 60 undisturbed forest 0 05 1 0 0 00 00 0 050 0 01 60 native pasture 0 10 05 2 60 60 0 060 0 15 60 improved pasture 0 15 05 2 65 65 0 250 0 05 80 crop soils Kum ident 0 50 dummy clay 0 47 Alluvial 0 40 Black Earth 0 37 Krasnozem 0 60 Lithosols 0 88 Solodics luses Cum Pum CNadj_ ident 0 111 1 0 0 80 dummy native pasture outside NOONWAUCINDOIRBWAONDNIWAUINOORWOA 0 03 1 0 0 70 undisturbed forest 0 111 1 0 0 80 native pasture 0 2 0 5 0 85 improved pasture 0 28 05 0 87 crop USDA Curve Numbers CN are used for runoff prediction in AGNPS and CN values for bare soil and cultivation up and down the slope are also entered into the USLE USLE M attribute data file together with CN conversion coefficients which are used to convert CNs for bare soil to CNs for the vegetated areas Runoff prediction from vegetated areas is based on the assumption that the ratio of CN vegetated to CN bare cultivation up and down the slope does not vary between soils Event Elso and rainfall amount are also entered via the USLE USLE M attribute data file In addition to the USLE USLE M attribute data inputs AGNPS UM User s Guide 9 the USLE USLE M attribute data file contains data normally associated with AGNPS such as Manning s n data on fertilizer use and release etc Users should consult the AGNPS User Manual which can be obtained via http www sedlab olemiss
3. Information about running example 1 Set up GIS and attribute files is the first step When this step is invoked the user specifies the GIS files to be used by the program The user needs to be aware of the format of these files and ensure that they meet the format that the software will accept GRASS ARCH INFO MAPINFO Elevation data can be stored with various levels of precision The user will be asked to provide a factor that will convert the GIS elevation data to metres The software will respond with data on maximum and minimum elevations in metres and the user can return to enter a new conversion factor if necessary The user should note the maximum and minimum elevations for use later All three GIS files elevation landuse soils must be nominated in response to selecting Set up GIS and attribute files unless they have been dealt with previously If a new set of landuses is the only change then its GIS file is the only one that needs to be processed AGNPS UM User s Guide 10 The facility to generate the agnps dat file is included in Set up GIS and attribute files As noted earlier the better option is to edit and store an existing one so that this facility may be seldom used If a new agnps dat file is generated via Set up GIS and attribute files and needs to be stored in case it gets overwritten then the user will need to use Windows facilities to do this 2 Generate grid cells and flow network causes TOPAZ to generate grid ce
4. concentration influence erosion For example the USLE RUSLE uses period fortnightly C values and it is logical to suggest that C values during a fortnightly period are equal to the period C value However this would produce Ceum values that vary between events with the ratio of Qric to Qre where as the effect of the crop on sediment concentration should remain relatively constant during that period Appropriate rules for determining Ceum values from crop morphology and management have yet to be developed The USLE M lite Procedures exist for determining short term values of C and P in the RUSLE but similar procedures have yet to be developed to determine Ceum and Peum However despite Eq 4 an approach does exist that enables short term values of C and P to be used directly when the QrEI39 is used as the event erosivity index The USLE RUSLE model is based on the prediction of erosion for the unit plot condition 22 1m long slope 9 gradient bare fallow with cultivation up and down the slope and the L S C and P factors are ratios with respect to the unit plot Thus the approach is in effect a two staged one the prediction of erosion for the unit plot condition where A RK 28 where A is the annual average erosion on the unit plot R is the annual average erosivity factor and K is the average annual soil erodibility followed by A A LSCP 29 where A is the average annual erosion on an area that differs from the unit plot is so
5. edu agnps archives html in relation to the values for these data The software package contains set of example data files bkckelev dat elevation bkckluse dat landuse bkcksoil soil and bkckcat dat USLE USLE M attribute data Table 2 shows the contents of bkckcat dat and agnps dat agnps dat is the file that is used by the software when generating the data input files that the AGNPS UM software uses during the calculation phase The contents of agnps dat can replaced by the contents of bkckcat dat or any other appropriate USLE USLE M attribute data file when necessary agnps dat can contain global data that is not relevant to catchment or watershed being modelled The software will crash if relevant data is missing A facility exists in the software package to generate new agnps dat files However it is easier to edit existing ones These files are space deliminated Installing and running the software package The software package is contained in a ZIP file which will set up the appropriate directories folders when the software is extracted using directory folder names option is flagged The program execution file is AGUMxxx exe where xxx is the version number The operations of the program are controlled through a menu 1 Set up GIS and attribute files 2 Generate grid cells and flow network 3 Generate AGNPS input and output data files 4 View catchment graphic 5 Stitch GIS outputs into larger area 6 END 7
6. AGNPS UM User s Guide 5 In most cases soil erodibility is considered to relatively constant in comparison to variations in C and P so that in most cases Kume Kum 22 can be used The gross runoff ratio for runoff producing events GRRyope is given N X Qe e 1 23 GRRoope where B is event rainfall and data obtained from the USLE runoff and soil loss plot data base shows that kxum can be estimated by kx um GRRyope Te 24 Given an ability to predict event runoff from event rainfall it is possible to determine GRR op and convert K to Kum Cum values can be calculated from C values through Cum C ke um kx um 25 where N x Ebo e e 1 kcum 26 N QrElso e e 1 As with kx um kc um GRRrope 27 when GRR is known for the vegetated condition A similar approach can be used to determine Pym from P Theortically it follows from Eqs 20 25 and 26 that event values of Cum and Pym can be determined from event values of C and P by multiplying the relevant USLE factor values by ratio of Qrie to Qre when Qre gt 0 However that assumes that the runoff effect on erosion under non unit plot conditions is considered correctly within the determination of C and P values That assumption may not be correct given that procedures for determining short term values of C and P may subjective and not give proper consideration as to how AGNPS UM User s Guide 6 runoff and sediment
7. DRAFT October 2005 USER S GUIDE AGNPS UM Version 4 02 Agricultural Non Point Source Pollution Model using the USLE M not to be confused with the MUSLE P I A Kinnell University of Canberra Canberra AUSTRALIA AGNPS UM User s Guide Disclaimer The software to which this User s Guide applies is used entirely at the user s own risk While every effort has been made to ensure that the software is error free the software is used by the recipient upon the express understanding that the developer makes no warranties expressed or implied concerning the accuracy completeness reliability or suitability for any one purpose and that the developer shall be under no liability to any person by reason of any use made thereof Contents page USLE M Theory USLE M erosivity factor 1 USLE M soil erodibility 3 USLE M Crop and crop management factor 4 Predicting erosion via the USLE M 5 The USLE M lite uses USLE C and P factors directly 7 Caution USLE M lite NOT direct replacement for USLE M 8 AGNPS UM software 9 Installing and running the software 10 Erosion model options 11 Graphic output 13 Output data files 13 Example data 14 Literature 14 Erosion on a dry catchment watershed AGNPS UM takes account of moisture status AGNPS L via D amp G AGNPS UM Erosion t ha 0 5 M 5 25 E 25 50 E gt 50 AGNPS UM User s Guide II AGNPS UM AGNPS the Agricultural Non Poi
8. a bare fallow area Ce Ceum 1 with cultivation up and down the slope Pe Peum 1 event erosion is give by Ad Ceum Peum 1 b Qr E bole 8 where b Keum L S Figure provides a comparison between Eq 8 and the USLE equivalent A Ce Pe 1 b E bole 9 where b K LS 100 100 10 4 10 4 g g 14 a 3 2 2 3 o a 0 1 5 0 1 5 Effin 0 084 ee Effin 0 738 0 01 r 1 r 0 01 7 T 1 0 01 0 1 1 10 100 0 01 0 1 1 10 100 observed soil loss t ha observed soil loss t ha Figure 1 Relationships between observed and predicted event soil loss for plot 10 bare fallow in experiment 1 at Morris MN when predicted bR where R is E139 and QrRE Izo Effin is the Nash Sutcliffe 1970 efficiency factor for the In transforms of the data and reflects the amount of variation from the 1 1 lines shown in these figures NB This analysis takes no account of short term variations in K or Kum AGNPS UM User s Guide 2 The runoff and soil loss plot used in this comparison is part of the USLE data base The total loss from the plot was 374 t ha from 80 events over 10 years The top 5 events produced 177 t ha The USLE Eq 9 predicted 123 t ha 31 error while the USLE M Eq 8 predicted 164 t ha 7 error The 10 events producing the lowest soil loss contributed 0 83 t ha The USLE predicted 25 t ha for these events the USLE M 1 12 t ha The Nash Sutcliffe 1970 efficiency
9. and there may be occasions where erosion occurs on a vegetated area but not on a bare fallow area Figure 2 shows the result predicting event soil losses by multiplying observed event soil losses from an adjacent bare fallow plot by period C values for conventional corn at Clarinda Iowa over a 7 year period 100 000 lt E 10 000 n 2 3 n 1 000 9v gt v O 0 100 e a 0 010 0 0001 0 0010 0 0100 0 1000 1 0000 10 0000 100 0000 observered event soil loss 0 0001 T A Figure 2 Relationship between event soil losses predicted by multiplying event soil losses from a nearby bare fallow plot by RUSLE period Soil Loss Ratios fortnightly C factor values and event soil losses observed for conventional corn at Clarinda Iowa plus 0 0001 tons acre 1 to enable predicted losses to be displayed when observed losses are zero The total observed and predicted amounts over the 7 years were in close agreement 130 tons acre observed 131 tons acre predicted but 12 of the predicted amount was contributed by events that produced zero erosion on the cropped plot Because the USLE M event erosivity index is based on runoff from areas where C 1 and P 1 the USLE M is more appropriately applied to modelling event erosion than the USLE M lite AGNPS UM User s Guide 8 AGNPS UM software The AGNPS UM software predicts erosion in customary US units tons acre in grid cells within a catchment or watersh
10. d Soil Loss then use the Esc key to go back up the sequence Then follow File Variable File Load Variables EROS VAR Other variables such as slope gradient K C and sediment can be displayed using GRAFIX GRAFIX enables the user to set up graphical displays of whatever variable the user may wish to examine 4 View catchment graphic also enables the user to examine GIS type data via the VBFLONET program that is part of the AnnAGNPS suite AGNPS UM User s Guide 13 Output data files The AGNPS software produces an output file which can be loaded into Microsoft EXCEL The file has the extension gis and xx before the dot where xx the code for the model option xx DG for Desment and Gover s L PK for L via Kinnell 2004 ML for the USLE M lite and UM for the USLE M When loaded into EXCEL using the space delimitated option column K contains the cell erosion data tons acre and column M the sediment delivery tons The gis files are stored in the agnpsdat directory AGNPS also generates an ascii nps file which contains sediment and nutrient data The format for this file can be found in the original AGNPS v5 0 archive AGNPS generates two binary files dep and sre Example data 5 data files are included in the software package which can be used for test purposes 3 ascii data files in ARCH INFO format contain data for elevation bkckelev asc landuse bkckluse asc and soils bkcksoil asc for a 2343 ha catchme
11. dison S Dakota Morris Minnesota meadow corn oats Presque Isle Maine potatoes AGNPS UM User s Guide 4 These values were determined using N 2 Ae c e 1 ge 17 N LS K Els e 1 and Cum 18 LS Kum QrEI30 e 1 where K and Kum were determined from data from bare fallow plots at the respective sites Predicting erosion via the USLE M While the USLE is an empirical model whose factor values were originally determined from erosion experiments procedures for determining factor values from soil crop and management data have been developed to facilitate the prediction of erosion using the USLE For example an equation was developed to calculate soil erodibility in the USLE for soils that contain less than 70 silt in the USA K 2 1 104 12 OM M 3 2 s 2 2 5 p 3 100 19 where K is in customary US units OM is percent organic matter M depends on the soil texture s is soil structure class and p is permeability Other equations exist for soils in other geographic locations such as Hawaii The USLE M soil erodibility factor is greater than the USLE soil erodibility factor because of the inclusion of Qr the runoff ratio in the event erosivity index Kum is related to K via the inverse of the runoff ratio for bare fallow and cultivation up and down the slope Qari through a coefficient kxum N Elo e 1 kum 20 N QriEls0 e 1 so that Kum a kk um K 21
12. dth of the boundary over which the runoff flows which is the width of the cell Consequently for a cell with coordinates i j CA TDI Ajjin Lij 32 2 D KG A1 where Aj j in is the area m upslope of the cell D is cell size m m is the coefficient used in the calculation of L x is a factor that depends on direction of flow with respect to the orientation of the cell and A is the length of slope for the unit plot 22 13 m The USLE with L via Desmet and Govers 1996 uses Eq 32 in the calculation of cell erosion 2 The USLE with L via Kinnell 2004 With Eq 32 if runoff does not enter from upslope then Ajj in 0 and Eq 32 gives the same value as Eq 11 Thus cells adjacent to the boundary of the catchment or watershed AGNPS UM User s Guide 11 act essentially as isolated areas However it is possible that a cell somewhere in the catchment or watershed may not produce any runoff across its downstream boundary because it has a high infiltration capacity Logically Ajj in 0 for the cell downslope of that cell and that condition can be set whenever the runoff coefficient for the uppslope area Qci j in is found to be zero However it is also logical to suggest that the effective upslope area Aj j in err is less than the physical area Ai j in whenever Qcij in is less than the runoff coefficent of the area including the cell Qcij an so that Kinnell 2005 Aij in eff Dy Aijine Lij 33
13. ed given 3 ascii GIS files and a USLE USLE M attribute data file The GIS files are grid cell files for elevation soils and land use and the user needs to know the grid cell coordinates of the catchment or watershed outlet and the cell size metres The cell size should be of the order of 100 m or less The acsii files can have a GRASS ARCH INFO or MAPINFO format The ascii files are used in conjunction with TOPAZ http duke usask ca martzl topaz to identify the catchment or watershed boundary generate an artificial stream network as well as determine grid cell slope gradient and flow direction using the D8 method The software can handle grids of up to 1000 by 1000 1 million cells The restriction of 32 000 cells that applies to the original AGNPS executables does not apply to the AGNPS UM software The USLE USLE M attribute data file is generated by the user to contain Ke Ce Pe values for the USLE and K um Ceum Peum values for the USLE M for the relevant soils and landuses The units for these data are customary US units Table 2 provides an example of the USLE USLE M attribute data file Table 2 Example of the AGNPS USLE M data file Back Ck Part of Chaffey Dam catchment 2 80 rain inch 62 0 E130 100ft ton inch A h 6 soils K texture CN bare ident 0 38 3 80 dummy clay 0 30 2 75 Alluvial 0 30 3 75 Black Earth 0 28 3 70 Krasnozem 0 38 2 85 Lithosols 0 40 1 80 Solodics luses C P fert avN avP man s n s cond COD ident 0
14. f Hydrology 10 282 290 Renard K G Foster G R Weesies G A McCool D K and Yoder D C 1997 Predicting soil erosion by water A guide to conservation planning with the Revised Universal Soil Loss Equation RUSLE U S Dept Agric Agric Hbk No 703 Wischmeier W C and Smith D D 1978 Predicting rainfall erosion losses a guide to conservation planning Agric Hbk No 537 US Dept Agric Washington DC AGNPS UM User s Guide 15
15. factor Effin provides a measure of a model s performance A value of 1 0 is achieved by the perfect model Effi for the QREI30 index is 0 734 while the E39 index gives 0 084 A value of zero means that the model predicts no better than if the mean of the independent variable Elbo or QrEI30 is used USLE M Soil Erodibility Kym A noted above the soil erodibility factor for the USLE M Kum differs from that of the USLE because the soil erodibility factor in both models has units of soil loss per unit of the erosivity factor Because Ce Ceum Pe Peum 1 0 when the area being eroded is bare fallow with cultivation up and down the slope the annual average value of the USLE M soil erodibility factor can be determined from data such as shown in Figure 1 The general equation for determining average annual soil erodibility for any given event erosivity index Xo is N DAel e 1 KX 10 where Ag is event soil loss on what is called the unit plot bare fallow with cultivation up and down the slope on an 22 13 m long slope with a gradient of 9 and N is the number of events used to determine K X Since the USLE M uses the USLE L and S factor values event soil losses obtained on areas of bare fallow with cultivation up and down the slope that are not 22 13 m long on a 9 slope can be converted to unit plot values using the USLE or the RUSLE L and S factors L O 22213 11 where A is the slope length and
16. he area being considered instead of the Elzo for the event erosivity factor Because the USLE is an empirical model changing the event erosivity index from Elso has consequences The soil erodibility factor must be changed because it has units of soil loss per unit of the erosivity factor The crop and support practice factors must also be changed to account for the movement of the runoff effect which they normally deal with to the erosivity factor Consequently the USLE M is given by Ae Qr E bole Keum L S Ceum Peum 4 where the subscript UM indicates factors that differ in value from the factors used in the USLE While the USLE M is an empirical model the QrEIs index has a physical basis The sediment discharged with runoff is given by the product of runoff and sediment AGNPS UM User s Guide 1 predicted soil loss t ha concentration and the QrEI3o index results from considering that the sediment concentration for an event is dependent the energy per unit quantity of rain and a measure of the maximum rainfall intensity since a large proportion of the runoff is generated during time when the rainfall intensity is high The energy per unit quantity of rain is given by E divided by rainfall amount and lso is a measure of the maximum rainfall intensity Thus Reum Qe E event rainfall amount Ibo 5 where Qe is the amount of runoff for the event However Qr Qe event rainfall amount 6 so that Reum Qr E bo 7 On
17. in D 37 where Qciec ij cett is the runoff coefficient for the cell when C P 1 and Qcieij in is the runoff coefficient for the upslope area when C P 1 Eq 36 is Eq 33 multiplied by Qce ij all eff Qreij cell AGNPS UM User s Guide 12 The USLE M lite option predicts erosion using Eqs 34 36 and 37 It uses the Kym data in agnps dat together with CN bare K C and P It ignores the data for Cum Pum CNagj 4 The USLE M In the case of the USLE M Qce ij all ett Qce ij cell D Qceij in Aij in Aij inetr D 38 Again in the case of the USLE M Eq 36 is Eq 33 multiplied by Qce ij all eff QRe ij cen but Qce ij allefr is determined using Eq 38 rather than Eq 37 The USLE M option predicts erosion using Eqs 34 36 and 38 Upon completion of the calculation of erosion within the catchment or watershed the software will move directly to 4 View catchment graphic see below 4 View catchment graphic enables the user to view the model output using the GRAFIX utility that is part of the AGNPS v5 suite The 5 letter catchment watershed identity is used in this process followed by dg for Desment and Govers 1996 L pk for Kinnell 2004 L ml for the USLE M lite and um for the USLE M Once the appropriate output is selected GRAFIX produces an outline of the catchment To view the graphic of cell erosion follow the following path Variables Add Variable AGNPS Parameters Runoff an
18. lls and the flow network from the elevation file During this process the user has to enter data on cell size valid elevations recall maximum and minimum elevations given during Set up GIS and attribute files the grid cell coordinates of the outlet cell and the critical source area CSA in hectares for channel initiation TOPAZ initiates a channel where ever the upslope area exceeds the CSA 3 Generate AGNPS input and output data files gives the user the choice of a number of models to run 1 The USLE with L via Desmet and Govers 1996 2 The USLE with L via Kinnell 2004 3 The USLE M lite 4 The USLE M 1 The USLE with L via Desmet and Govers 1996 The L factor in the USLE is designed to work over an area whose length begins where overland flow starts A grid cell often is an area some distance downslope from where runoff starts In such circumstances a grid cell receives runoff from upslope and the erosion within that cell depends on the length of the cell and the effective length of the upslope area If the upslope area is rectangular and the same width as the cell the slope effect length of the upslope area is the same as the physical slope length However this is not the case when the flow in through the upslope boundary of the cell comes from an area with some other shape According to Desmet and Govers 1996 the effective slope length of the upslope area in all cases is given by the upslope area divided by the wi
19. m is a power that is related to the ratio of rill and interrill erosion In the USLE m 0 6 slope gt 10 12a m 0 5 5 10 12b m 0 4 3 5 12c m 0 3 1 3 12d m 0 2 lt 1 12e In the RUSLE m 1 13 where for soil that is moderately susceptible to rilling AGNPS UM User s Guide 3 B sin 0 0 0896 3 0 sin 0 0 56 14 where 0 angle to horizontal The slope gradient factor S for the USLE is given by S 65 4 sin 6 4 56 sin 8 0 654 15 but was replaced by S 10 8 sin 0 0 03 slopes lt 9 16a S 16 8 sin 0 50 slopes 9 16b in the RUSLE because Eq 15 overestimated erosion on high slope gradients USLE M Crop and Crop Management Cym As indicated in Eq 4 the USLE M Crop and Crop Management factor Cum values differ from the USLE Crop and Crop Management factor C values because the runoff effect that is included in C is moved to the erosivity factor when the erosivity factor is based on runoff from the vegetated area Table 1 shows annual average values of the Crop and Crop Management factor for the USLE M and the USLE for crops determined from erosion experiments at various locations in the USA Table 1 Examples of Cum values for crops at various USA locations pam f A a TT LD Bethany Missouri alfalfa corn corn meadow wheat Clarinda Iowa corn corn oats meadow Guthrie Oklahoma cotton Bermuda grass wheat clover cotton LaCrosse Wisconsin Ma
20. me way In the context of a rainfall event these two equations become Ale Re Ke 30 and Ae Aje L S Ce Pe 31 where Re Elo As indicated above Kume Ke Cume Ce Pume Pe However Cume Ce and Pume Pe only applies when the runoff values used to determine Qr in Eq 4 are those associated with an area that is vegetated and cultivation is not up and down the slope Thus if the QrE so index is used to predict erosion for the bare fallow cultivation up and down the slope condition C P 1 then AC P 1 k Kyme QriEbole 32 where k LS and values for C and P generated by the USLE or the RUSLE can be used to give Ae QriEbole Kume LS Ce Pe 33 where Qa is the runoff ratio for the bare fallow and cultivation up and down the slope condition The procedures for determining short term values for C and P in the RUSLE AGNPS UM User s Guide 7 documentation Renard et al 1997 can thus be used as a guide to determining Ce and Pe values for use in Eq 31 The model described by Eq 33 is called the USLE M lite to distinguish it from that described by Eq 4 Caution The USLE M lite is NOT a direct replacement for the USLE M in respect to modelling event erosion The USLE M lite like the USLE is based on the assumption that erosion occurs when C 1 and P 1 whenever erosion occurs when C 1 and P 1 Normally there are many occasions where erosion occurs on a bare fallow area but not on a vegetated one
21. nt Source Pollution model was developed in the USA to predict the effect of land use on the quality of water discharged from catchments or watersheds It is an event based model and uses the Universal Soil Loss Equation USLE to predict erosion within grid cells on hillsides The USLE was not designed to predict event erosion A modification of the USLE called the USLE M can do this better AGNPS UM uses the USLE M instead of the USLE USLE M theory The USLE M is a variant of the Universal Soil Loss Equation USLE and the Revised version of it RUSLE The USLE is an empirical model that predicts average annual erosion A in terms of 6 factors A RKLSCP 1 where R is the rainfall runoff factor K is the soil erodibility factor L is the slope length factor S is the slope gradient factor C is the crop and crop management factor and P is the supporting practices factor The R factor is the annual average value of the event erosivity factor Re where R E bo 2 where E is the total kinetic energy of the rainfall event and Iso is the maximum 30 minute rainfall intensity maximum intensity using a 30 minute time frame USLE M event erosivity factor the OrEI9 index Although the USLE was not designed to predict event erosion it follows from Eqs 1 and 2 that Ae T E bole Ke LS Ce Pe 3 where the subscript e indicates factor values that vary between rainfall events The USLE M uses QrEI30 where Qr is the runoff ratio for t
22. nt watershed bkckcat dat and agnps dat contain the agnps attribute data In step 1 The answer to the factor that is required to convert the elevations to metres is 0 01 Because agnps dat already contains the agnps attribute data it does not need to be setup during step 1 In step 2 The valid elevation range can be set at 200 to 2000 grid cell size is 100 m The outlet cell is row 21 column 85 area for channel initiation about 10 to 15 ha is ok min length of channel say 300 m The 15 ha 300 m setting will cause TOPAZ to indicate that the number of cells selected for channel initiation is too small This is not correct Select 1 when TOPAZ does this and continue major catchment name the catchment is part of the Chaffey Dam catchment catchment name back creek AGNPS UM User s Guide 14 Literature Kinnell P I A 2000 AGNPS UM applying the USLE M within the agricultural non point source pollution model Environmental Modelling amp Software 15 331 341 Kinnell P I A and Risse L M 1998 USLE M Empirical modeling rainfall erosion through runoff and sediment concentration Soil Science Society America Journal 62 1667 1672 Kinnell P I A 2005 Alternative approaches for determining the USLE M slope length factor for grid cells Soil Science Society America Journal 69 674 680 Nash J E and J E Sutcliffe 1970 River flow forecasting through conceptual models Part 1 A discussion of principles Journal o
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