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Agricultural Water Management

Agricultural Water Management
Agricultural Water Management

Application of the Soil and Water Assessment Tool (SWAT)to predict the impact of alternative management practices on water quality and quantity

Antje Ullrich *,Martin Volk

Helmholtz Centre for Environmental Research –UFZ,Department of Computational Landscape Ecology,Permoserstr.15,D-04318Leipzig,Germany

1.Introduction

Due to insuf?cient water quality of European streams environmental programs such as the European Water Framework Directive (WFD)were implemented to achieve good ecological and chemical conditions of water quality of groundwater and surface water bodies (EC,2000;Rekolainen et al.,2003).Main nutrient input comes from nonpoint source pollution,mainly forced by intensive agricultural activities (Behrendt et al.,1999).Therefore,alternative land management practices are increasingly used to reduce nonpoint source pollution.Reduction of soil tillage intensity positively affects numerous soil properties,such as aggregate stability,macroporosity,and saturated hydraulic con-ductivity and consequently increases in?ltration rates and reduces surface runoff,nutrient loss,and soil erosion (Jones et al.,1969;

Pitka

¨nen and Nuutinen,1998;Schmidt et al.,2001;Kirsch et al.,2002;Pandey et al.,2005;Tripathi et al.,2005).Alternative land management practices may include reduced tillage such as

conservation tillage (e.g.without deep ploughing,?eld preparation just before planting)or no-tillage (direct drilling)(Sullivan,2003;LfULG,2006).In Germany the implementation of alternative tillage systems is increasingly supported by agro-environmental pro-grams.In the German State of Saxony,for instance,conservation tillage and mulch seeding on arable land has increased from <1%to about 27%during 1994–2004with support from the Saxonian Program for Environmental Agriculture (LfL,2006).

A number of ?eld studies have illustrated the positive effects of conservation tillage and no-tillage practices on water and material ?uxes at the ?eld local level (e.g.Sloot et al.,1994;King et al.,1996;Schmidt et al.,2001),but this effect needs to be assessed on the watershed level to guide river basin management programs as WFD claimed (Kirsch et al.,2002;Chaplot et al.,2004;Pandey et al.,2005;Behera and Panda,2006;Bracmort et al.,2006).Therefore,watershed models are useful tools and have been used for decades to evaluate nonpoint source pollution and the short-and long-term impacts of alternative management practices.

In order to ful?ll the objectives of the WFD,we have chosen the semi-distributed river basin model,Soil and Water Assessment Tool (SWAT)2005(Neitsch et al.,2002;Arnold and Fohrer,2005)to examine the impact of alternative management practices on water

Agricultural Water Management 96(2009)1207–1217

A R T I C L E I N F O Article history:

Received 3September 2008Accepted 6March 2009Keywords:SWAT

Tillage management practice Conservation tillage Water balance Nutrient Modelling

A B S T R A C T

Alternative land management practices such as conservation or no-tillage,contour farming,terraces,and buffer strips are increasingly used to reduce nonpoint source and water pollution resulting from agricultural activities.Models are useful tools to investigate effects of such management practice alternatives on the watershed level.However,there is a lack of knowledge about the sensitivity of such models to parameters used to represent these conservation practices.Knowledge about the sensitivity to these parameters would help models better simulate the effects of land management.Hence,this paper presents in the ?rst step a sensitivity analysis for conservation management parameters (speci?cally tillage depth,mechanical soil mixing ef?ciency,biological soil mixing ef?ciency,curve number,Manning’s roughness coef?cient for overland ?ow,USLE support practice factor,and ?lter strip width)in the Soil and Water Assessment Tool (SWAT).With this analysis we aimed to improve model parameterisation and calibration ef?ciency.In contrast to less sensitive parameters such as tillage depth and mixing ef?ciency we parameterised sensitive parameters such as curve number values in detail.In the second step the analysis consisted of varying management practices (conventional tillage,conservation tillage,and no-tillage)for different crops (spring barley,winter barley,and sugar beet)and varying operation dates.Results showed that the model is very sensitive to applied crop rotations and in some cases even to small variations of management practices.But the different settings do not have the same sensitivity.Duration of vegetation period and soil cover over time was most sensitive followed by soil cover characteristics of applied crops.

?2009Elsevier B.V.All rights reserved.

*Corresponding author.Tel.:+493454722602;fax:+493412351939.E-mail address:antje.ullrich@https://www.sodocs.net/doc/4f17258734.html, (A.Ullrich).Contents lists available at ScienceDirect

Agricultural Water Management

j o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /a g w a t

0378-3774/$–see front matter ?2009Elsevier B.V.All rights reserved.doi:10.1016/j.agwat.2009.03.010

quantity and quality.Gassman et al.(2007)point out that‘‘a key strength of SWAT is a?exible framework that allows the simulation of a wide variety of conservation practices and other BMP,such as fertiliser and manure application rate and timing, cover crops(perennial grasses),?lter strips,conservation tillage, irrigation management,?ood prevention structures,grassed waterways,and wetlands.The majority of conservation practices can be simulated in SWAT with straightforward parameter changes’’.Many studies have used SWAT(Saleh et al.,2000; Shanti et al.,2001;Vache et al.,2002;Shanti et al.,2003;Chaplot et al.,2004;Pandey et al.,2005;Tripathi et al.,2005;Arabi et al., 2006;Behera and Panda,2006;Rode et al.,2008;Volk et al.,2009) and EPIC(Sloot et al.,1994;King et al.,1996)to evaluate the effects of land use scenarios and management practices.Several studies have analysed the long-term effects of structural Best Management Practices(BMP)on water quality(e.g.Kirsch et al.,2002;Chaplot et al.,2004;Tripathi et al.,2005;Pandey et al.,2005or Behera and Panda,2006;Bracmort et al.,2006).Arabi et al.(2007)investigated the impact of modelling uncertainty on evaluation of management practices using a Monte Carlo-based probabilistic approach.

The SWAT model was developed for application to large complex watersheds over long periods of time(Neitsch et al., 2002).Working on the watershed-scale means that required input data are often aggregated in terms of temporal scale(e.g.daily climate data).In contrast,land management parameters(tillage, fertilisation,crop rotation,etc.)can be included in high resolution and detail,due to its modular structure and its historical development based on the EPIC(Erosion Productivity Impact Calculator)model(Benson et al.,1988;Neitsch et al.,2002;Arnold and Fohrer,2005;Gassman et al.,2007).Furthermore,modelling evaluations of conservation management effects at the watershed-scale are limited by the lack of management operation data.Thus, knowledge is needed of the sensitivity of such models to conservation management parameters and practice to improve the ef?ciency of model parameterisation and the quality of model calibration.Furthermore,potential simulation uncertainties based on ranges of realistic parameter values and on in?uences of scale need to be understood because simulated effects often drive ?nancial and political decisions(Onatski and Williams,2003).

As a result,the main objective of this study is to analyse the sensitivity of the SWAT model to selected conservation manage-ment parameters and practices to improve model parameterisa-tion and calibration.To the best of our knowledge,a sensitivity analysis of the model to conservation management parameters and practices has not yet been conducted.But in our opinion this is essential for a more ef?cient use of models for the implementation of land management practices as tools to improve water quantity and quality.

We used a so-called semi-virtual watershed for which we combined topography and climate information of an existing watershed(Parthe watershed,Saxony,Germany)but with homo-geneous land use and soil information.Homogeneous land use and soil information were utilised because the resulting data sets are concise and manageable and calculation time is reduced. Recommendations are given for the parameterisation of tillage operations under certain conditions.

2.Materials and methods

2.1.Model description

The SWAT model is considered as one of the most suitable models for predicting long-term impacts of land management measures on water,sediment,and agricultural chemical yield (nutrient loss)in large complex watersheds with varying soils,land use,and management conditions(Arnold and Fohrer,2005;Behera and Panda,2006;Gassman et al.,2007).The model has been gained international acceptance as a robust interdisciplinary watershed modelling tool(Gassman et al.,2007).SWAT is a physically based, conceptual,continuous-time river basin model with spatial distributed parameters operating on a daily time step.It is not designed to simulate detailed,single-event?ood routing(Neitsch et al.,2002).The relationship between input and output variables is described by regression equations.The SWAT model integrates all relevant eco-hydrological processes including water?ow, nutrient transport and turn-over,vegetation growth,and land use and water management at the subbasin scale.Consequently, the watershed is subdivided into subbasins based on the number of tributaries.Size and number of subbasins is variable,depending on stream network and size of the entire watershed.Subbasins are further disaggregated into classes of Hydrological Response Units (HRU),whereby each unique combination of the underlying geographical maps(soils,land use,etc.)forms one class.HRU are the spatial unit where the vertical?ows of water and nutrients are calculated,which are then aggregated and summed for each subbasin.Water and material from HRU in sub-watersheds are routed to the sub-watershed outlet.The HRU in SWAT are spatially implicit,their exact position in the landscape is unknown,and it might be that the same HRU covers different locations in a subbasin(Neitsch et al.,2002;Di Luzio et al.,2005).The water balance for each HRU is represented by the four storages snow,soil pro?le,shallow aquifer and deep aquifer.The soil pro?le can be subdivided in up to ten soil layers.Soil water processes include evaporation,surface runoff,in?ltration,plant uptake,lateral?ow and percolation to lower layers(Arnold and Allen,1996;Neitsch et al.,2002).The surface runoff from daily rainfall is estimated with a modi?cation of the SCS curve number method from United States Department of Agriculture-Soil Conservation Service(USDA SCS) (Arnold and Allen,1996;Neitsch et al.,2002).

Nitrogen movement and transformation are simulated as a function of the nitrogen cycle(Neitsch et al.,2002;Jha et al.,2004). The SWAT model monitors?ve different pools of nitrogen in the soils:two inorganic(ammonium(NH4+)and nitrate(NO3à))and three organic(fresh organic nitrogen(associated with crop residue and microbial biomass)and active and stable organic nitrogen (associated with the soil humus)).Nitrogen is added to the soil by fertiliser,manure or residue application,?xation by bacteria,and rain(Neitsch et al.,2002).Nitrogen losses occur by plant uptake, surface runoff in the solution and the eroded sediment(Neitsch et al.,2002;Jha et al.,2004).

Background for the crop growth and the management practices is the EPIC crop growth model,which is a comprehensive?eld scale model.EPIC was originally developed to simulate the impact of erosion on crop productivity and has now evolved into a comprehensive agricultural management,?eld scale,nonpoint source loading model(Benson et al.,1988;King et al.,1996; Neitsch et al.,2002).The management practices are de?ned by speci?c management operations(e.g.the beginning and end of growing season,timing of tillage operations as well as timing and amount of fertiliser,pesticide,and irrigation application).These management operations take place in every HRU.The operations in turn are de?ned by speci?c management parameters(e.g. tillage depth,biological soil mixing ef?ciency,etc.)(Neitsch et al., 2002).

2.2.Input data

We used a semi-virtual watershed.Therefore we combined topography and climate information of an existing watershed with virtual land use and soil information.The Parthe watershed was chosen as study area.It is located in the State of Saxony in Central Germany and drains an area of about315km2(Fig.1).It is a

A.Ullrich,M.Volk/Agricultural Water Management96(2009)1207–1217 1208

subbasin of the Wei?e Elster catchment in the Elbe River system. The topography of the area is?at with altitudes between106and 230m above sea level.The mean annual precipitation is about 570mm.The model input data are shown in Table1.For the sensitivity analysis,we assumed‘‘arable land’’to be homogeneous land use without any further differentiation.A typical soil pro?le was used from a soil map(1:25,000)of the Parthe watershed.The use of homogeneous land use and soil(semi-virtual catchment)is advantageous because the resulting data sets are concise and manageable and calculation time is reduced.Daily precipitation data and other climate data are from one weather station in the watershed.This station is part of the environmental monitoring network of the Environmental Operation Agency of the Saxon State Agency for the Environment,Agriculture and Geology.

2.3.Sensitivity analysis

Model sensitivity analysis regarding selected management practices was done?rstly by varying the most important tillage and management parameters:tillage depth,mechanical mixing ef?ciency,biological mixing ef?ciency,curve number,and Man-ning’s roughness coef?cient for overland?ow,USLE support practice factor,and?lter strip width.Secondly,management practices were parameterised and varied for different crops and dates of operation. Subsequently,the in?uence of varying these practices on water balance components and nutrients was evaluated.

2.4.Management parameters

The applied tillage operation(plough,stubble cultivation, harrow,etc.)is de?ned by the parameters tillage depth(DEPTIL) and mechanical soil mixing ef?ciency(EFFMIX).These parameters also de?ne the fraction of crop residue,nutrients,pesticides,and bacteria for each soil horizon,which are redistributed within the mixed soil depth(Neitsch et al.,2002).The biological soil mixing ef?ciency(BIOMIX)de?nes the activity of soil organisms,such as earthworms as representatives of macrofauna,which in?uence soil porosity and water?uxes by their grubbing activity(Kladivoka, 2001;Neitsch et al.,2002).The biological soil mixing ef?ciency can be de?ned for each HRU.The SCS curve number(CN)de?nes soil permeability based on soil characteristics and land cover(land use).This parameter routes the process of in?ltration and generation of surface runoff.The parameter CN can generally be de?ned on the HRU level and more detailed based on tillage operations data(Neitsch et al.,2002).The Manning’s roughness coef?cient for overland?ow(OV_N)is a parameter to

estimate

Fig.1.Location of the study area in Germany.

Table1

Input data.

Topography Land use Soil Weather

DEM Homogeneous Homogeneous Daily values

-Area:315km2-Arable land-Cambisol-Precipitation[mm]

-Grid cell size:30m-Mean wind speed(recorded in2.5m height)[m/s]

-Max.and min.air temperature[8C]

-total solar radiation(calculated using global radiation)[MJ/m2]

-Mean relative humidity(recorded in0.5and2m height)[%]

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overland?ow velocity,which depends on characteristics of the land surface.This parameter can be de?ned for each HRU(Neitsch et al.,2002).The management parameter USLE support practice factor(USLE_P)de?nes the ratio of soil loss with a speci?c support practice(such as contour tillage,strip cropping,and terraces)to the corresponding loss with up-and-down the cultivation.The parameter USLE_P can also be de?ned on the HRU level(Neitsch et al.,2002).The width of edge of?eld?lter strip(FILTERW)de?nes ?lter strips with a speci?c width in meters.The?lter strips are not differentiated any further.Sediment and nutrient loads in surface runoff and subsurface?ow are reduced as it passes through the strip.Filter strips can be de?ned for each HRU(Neitsch et al.,2002).

For each simulation only one parameter was varied within a realistic range(see Table2).The value ranges de?ned for each model parameter are based on literature studies(Neitsch et al., 2002).SWAT also supplies the user with default parameters values. For the basic settings we used these default values,as we are not working with real conditions where these values would have to be adjusted to.The advantage of this method is that the effect on model output is related to the variability of only the selected parameter,but it does not consider the dependency on settings chosen for the other parameters(Arabi et al.,2007).

For sensitivity analysis a basic management scenario was used. This is a generalised Agricultural Land Close Grown(AGRC) scenario,which uses values for winter wheat.One fertiliser application(70kg N/ha)just after seeding,and one tillage operation(deep ploughing)just after harvesting,was implemen-ted.This basic scenario was not changed during sensitivity analysis of management parameters.The model output parameters investigated are surface runoff,base?ow,total water yield,total sediment loading,organic nitrogen,organic phosphorus,nitrate in surface runoff,nitrate,and phosphorus leached.

The results(see Fig.2)indicate SCS curve number as a very sensitive parameter for water balance components and nutrient and sediment load.This observation is con?rmed by Neitsch et al. (2002)as well as by other studies,such as Sloot et al.(1994), Heuvelmans et al.(2004),Bracmort et al.(2006),and Arabi et al. (2007).The CN is most sensitive for values above65.For example surface runoff increased from almost zero up to more than100mm between CN65to95.At the same time,corresponding surface runoff organic nitrogen,organic phosphate,nitrate in surface runoff and total sediment loading increased.In contrast,based on the decrease of base?ow,nitrate and phosphate leached decreased.

Biological soil mixing ef?ciency and Manning’s roughness coef?cient for overland?ow do only moderately affect water balance components.The values vary less than5mm for applied parameter ranges.For the value range of biological soil mixing ef?ciency(0–1)the results for organic nitrogen(simulated range between0.225kg/ha and0.55kg/ha),organic phosphate(0.034–0.069kg/ha),and total sediment loading(0.057–0.318t/ha)increased while phosphate leached(0.318–0.145kg/ha)decreased. For the watershed area(315km2)this represents load values for organic nitrogen between7.1t and17.3t,organic phosphate between1.1t and2.2t,phosphate leached between10t and4.6t, and total sediment loading between1795t and10,017t.Based on these results we assume biological mixing ef?ciency to be a sensitive parameter regarding the above described nutrient components.

Manning’s roughness coef?cient for overland?ow affected organic nitrogen(simulated range between0.248kg/ha and 0.135kg/ha;7.8–4.3t for watershed area)followed by sediment loading(0.088–0.048t/ha; 2.8–1.5t for watershed area)and organic phosphorus(0.038–0.02kg/ha; 1.2–0.6t for watershed area).Therefore,we assume this parameter to be sensitive with respect to the described nutrients and sediment load.

Both the USLE support practice factor and the width of edge of ?eld?lter strip do not in?uence water balance components.But USLE support practice factor is very sensitive to total sediment loading(simulated range between0.007t/ha and0.067t/ha;220–2110t for watershed area),organic nitrogen(0.019–0.187kg/ha, 0.6–5.9t for watershed area),and organic phosphorus(0.003–0.027kg/ha;0.09–0.9t for watershed area)while the width of edge of?eld?lter strip affected organic nitrogen and decreased from 0.187kg/ha to0.076kg/ha(see Fig.3).This means that with an extension of the?lter strip width from0.5m to5m the organic nitrogen loss related to the watershed area decreased about50% (from5.9t to2.4t).Furthermore,with increasing width of edge of ?eld?lter strip organic phosphate(simulated values:0.027kg/ha and0.011kg/ha;0.85t and0.35t),nitrate load in surface runoff (0.047–0.019kg/ha;1.5–0.6t)and total sediment loading0.067–0.027t/ha;2110–850t)decreased.In this study the variation of tillage depth and mechanical soil mixing ef?ciency did not in?uence neither water cycle components nor nutrient and sediment cycle components.

2.5.Management practices

With respect to the management parameters’sensitivity,the tillage operations subject to management practices(conventional (CVT),conservation(CST)and no-tillage(NOT))were parameterised exemplary(see Table3).Here,conventional tillage primarily is distinguished dependent on tillage practices applied after harvest-ing including deep ploughing,previous stubble cultivation and following harrow operation before seeding/planting.For conserva-tion management a multiplicity of measures can be taken.For tillage practice we chose altogether three variations:(a)deep ploughing operation is replaced by a less intensive operation(CST_A),(b)deep ploughing operation is left out and not replaced(CST_B)and(c) harrow operation is applied only(CST_C).

Parameters were set as follows.Differentiated by applied tillage operation,we parameterised curve number(CN)values in detail. The curve number adjustment is strongly linked to the soil dependent basic curve number identi?ed within calibration process,planted crop(grain and root crop),applied tillage operation,and residue coverage(de?ned by applied management practice).The allocation of the SCS curve number is based on the parameterisations suggested by Neitsch et al.(2002)and continuative on the comments of Rawls and Richardson(1983). Rawls and Richardson(1983)recommend lowering the SCS curve number by2%for soils with poor hydrological conditions when applying conservation tillage(compared to conventional tillage). For?elds with good hydrological conditions,the SCS curve number should be lowered by4%compared to conventional tillage.King et al.(1996)applied EPIC using curve number values of87and82 for conventional tillage and for no-tillage practices respectively representing the soil hydrological group D(clay soil).Sloot et al. (1994)used the initial curve numbers:A value of84for

Table2

Basic parameter settings and variation ranges.

Parameter Basic setting Parameter range

SCS curve number(CN)7535–95 Biological soil mixing

ef?ciency(BIOMIX)

0.20–1.0

Manning’s roughness

coef?cient for overland

?ow(OV_N)

0.140.01–0.5

Tillage depth(DEPTIL)[cm]300–95 Mechanical soil mixing

ef?ciency(EFFMIX)

0.50–1.0

USLE support practice

factor(USLE_P)

1.00.1–1.0

Width of edge of?eld

?lter strip(FILTERW)[m]0.00–5.0

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conventional tillage,83for minimum tillage(conservation tillage) and82for no-tillage.Here,the initial CN representing the soil hydrological group A(for soils with good hydrological conditions) was used.

Biological soil mixing ef?ciency is a sensitive parameter and was parameterised in detail depending on the intensity of the applied management practice.As a result,the biological mixing activity decreases with increasing tillage intensity.The

parameter Fig.2.Sensitivity of SWAT model to tillage parameters:CN,BIOMIX and

OV_N.

Fig.3.Sensitivity of SWAT model to management practice parameters USLE_P and FILTERW.

A.Ullrich,M.Volk/Agricultural Water Management96(2009)1207–12171211

values for biological soil mixing ef?ciency used in this study were discussed with local farmers.

Manning’s roughness coef?cient for overland ?ow was de?ned subject to management practise and changing residue cover (Sloot et al.,1994;Neitsch et al.,2002).The parameter increases with increasing soil coverage according to decreasing tillage intensity.We de?ned only one tillage depth and mixing ef?ciencies for one main tillage operation (as applied crop-dependent on the ?eld in reality;Abraham et al.,2004).

The timing of tillage operations affects soil coverage (residue decomposition).For example,fall tillage operation reduces residue over the winter and spring period.The timing of tillage and the

Table 3

Parameterisation of tillage operations within management practices.Scenario

Tillage operation

DEPTIL a (cm)

EFFMIX b

BIOMIX c

OV_N d

CN*e Grains

Row crops

Conventional tillage (CVT)CVT Cultivation stubble

120.450.10.09

76Plough (bare soil)250.8577

Harrow 70.3

Plant 63

67

Harvest 74

Conservation tillage (CST)CST_A Cultivation stubble

120.450.20.13

76

Harrow 70.3

Plant 62

66Harvest

74

CST_B 0.30.19CST_C Harrow

70.3

0.40.3

74

Plant 61

65Harvest 73

No-tillage (NOT)

Plant 0.40.360

64

Harvest

73

a Abraham et al.(2004)—based on practical experience.

b Neitsch et al.(2002).

c Discusse

d with local farmers.d Neitsch et al.(2002).

e

Neitsch et al.(2002)–CN is exemplarily used representatively for soil hydrological group A –soil with good hydrological conditions.

Table 4

Tillage scenarios based on tillage practices;(P)plough,(St)stubble cultivation,(H)harrow,(S)seed (dates by Abraham et al.,2004).

Dates of operation Fall tillage

Spring tillage Spring barley 03.August 10.October 01.March 05.March 10.March

15.March

Winter barley 12.August 20.August 01.September 05.September Sugar beet

08.October 11.October 26.March 01.April 05.April 10.April Conventional tillage CVT

St P ––H S CVT_1St P –––S CVT_2–P ––H S CVT_3–P –––S CVT_4St ––P H S CVT_5St ––P –S CVT_6––St P H S CVT_7––St P –S Conservation tillage CST_A St St ––H S CST_A1St St –––S CST_A2St ––St H S CST_A3St ––St –S CST_A4––St St H S CST_A5––St St –S CST_B St –––H S CST_B1St ––––S CST_B2–St ––H S CST_B3–St –––S CST_B4–––St H S CST_B5–––St –S CST_B6––St –H S CST_B7––St ––S CST_C ––––H S No-tillage NOT

S

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choice of tillage operation depend on the crop being planted and the chosen management practice (Kirsch et al.,2002).Therefore differences between spring and winter crops as well as between grains and root crops are expected.Hence we applied commonly planted crops:spring barley (Hordeum vulgare )—representative for grains planted in early spring;winter barley (Hordeum vulgare var.a genuinum )—representative for grains planted in early fall and sugar beet (Beta vulgaris )—representative for root crops planted in early spring.For each crop depending on management practice basic scenarios were de?ned (see Table 4).Conventional and conservation tillage are primarily distinguished by tillage practices applied directly after harvesting (deep ploughing,previous stubble cultivation).The harrow operation is applied for all scenarios just before seeding/planting.Following sub-scenarios for conventional and conservation tillage (CVT_1,CVT_2,etc.)we de?ned where,e.g.tillage operations,were applied at different dates (for spring

planted crops spring tillage instead of autumn tillage)to identify the sensitivity of SWAT model to the timing of the tillage operations.

Management practices related tillage operations are chronolo-gical (e.g.conventional tillage:stubble cultivation/deep ploughing/harrowing).Thus,the harrow operation is least intensive operation.In the following,varying tillage operation combinations were applied to ?nd out if it is necessary to parameterise a less intensive operation which follows a more intensive operation (e.g.harrowing after ploughing).If the effect on model output parameters is only marginal the less intensive operation could be left out.

Additionally,the conservation management practice contour-ing and implementation of ?lter strips was applied to the base tillage scenarios of conservation tillage CST_A and CST_B (e.g.CST_Aa)and no-tillage practise (see Table 5).Finally,a catch crop (red clover)scenario was implemented (CST_CC)for green manuring (only applied for conservation tillage basic scenario with sugar beet).

3.Results and discussion

Generally,our results con?rm the outcome of studies under-taken by Kirsch et al.(2002),Chaplot et al.(2004),Pandey et al.(2005),Tripathi et al.(2005),and Behera and Panda (2006)that conventional tillage practices need to be replaced by less intensive

Table 5

Parameter settings for contouring and ?lter stripes.Scenario

Parameter USLE_P

FILTERW (m)_a 0.60_b 1.02_c

0.6

2

Fig.4.Percentage deviation of modelling results regarding to application of basic management scenarios with a)sugar beet,b)spring barley and c)winter barley on water balance components,nutrients and sediment loading.

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tillage practices in order to minimise the sediment yield and nutrient losses.Decreasing tillage intensity resulted in an increase of base?ow while surface runoff and total water yield decreased;organic nitrogen,nitrate in surface runoff,and phosphorus leached decreased while nitrate leached increased regarding to the increase of groundwater recharge.Sloot et al.(1994)came to similar results concerning runoff and soil loss under spring wheat.For total sediment load no obvious trend was identi?ed (decrease for sugar beet,increase for spring barley,no change for winter barley).

First we compared different tillage practice base scenarios.Regarding the in?uence on hydrology,nutrient,and sediment output with varying tillage operations for each crop we observed the largest differences between conventional tillage scenarios and no-tillage scenarios (see Fig.4a–c).Differences are also visible between base scenarios of conventional tillage and conservation tillage practice.But results of conventional scenarios CST_A and CST_B vary less than 1%,except for organic nitrogen (10%with sugar beet,6%with spring barley),phosphate leached (around 16%with sugar beet and spring barley,7%with winter barley).Therefore,we suppose that the in?uence of a directly following second tillage operation (which is not more intensive than the one before)is only marginal.

Similarly,results of base scenarios CST_C and NOT vary less than 1%,except for nitrate yield in surface runoff (10%)with sugar beet and total sediment yield (3%)and organic phosphate (5%)with spring barley.Both scenarios only differ in harrow operation application just before seeding.Therefore,we assume that these operations do not necessarily need to be implemented.

We also found the results of water cycle output parameters strongly differing with respect to applied crops (see Fig.5).The differences between spring planted crops and winter barley were larger than between sugar beet and grains.Generally we found the highest total water yield with spring barley (69mm with CVT)followed by sugar beet (46mm with CVT)and winter barley (28mm with CVT).For winter barley total water yield is only almost half the amount of spring barley.Therefore,based on the longer period of soil cover for winter barley,we found proportional highest base?ow values related to surface runoff (85%with CVT and 88%with NOT)compared to spring barley (54%with CVT and 66%with NOT)and sugar beet (45%with CVT and 59%with NOT).In contrast for winter barley,we found the proportional lowest surface ?ow values (14%with CVT and 11%with NOT)compared to spring barley (46%with CVT and 34%with NOT)and sugar beet (55%with CVT and 41%with NOT).Sediment loading is highest for sugar beet (0.173t/ha with CVT;5449t for watershed area)followed by spring barley (0.037t/ha with CVT;1165t for watershed area)and winter barley (0.001t/ha with CVT;31t for watershed area).Nutrient output is not comparable between the different crops because we used crop speci?c fertiliser amounts.

However,in this study we also paid attention to the dates of application,even small variations of tillage operation combina-tions,and varying crops and how they affect water balance components,nutrient losses,and sediment load.We consider both current and previous applied management practices and their speci?c parameterisations (crop rotations and tillage operation con?guration)to be important for calibration and validation as well as to generate comparable results.

Small variations (tillage sub-scenarios)of tillage intensity affected the water balance components,nutrient losses,and sediment yield also with regard to the crops applied.Most important was the differentiation in summer (spring planted)

and

Fig.5.Average values of the sensitivity analysis:Effect of different tillage operations with sugar beet,spring barley and winter barley on water balance components,nutrients and sediment loading.

A.Ullrich,M.Volk /Agricultural Water Management 96(2009)1207–1217

1214

winter (fall planted)crops.Consequently,results for water cycle components,nutrient loss and sediment loading for spring barley and sugar beet varied with even small changes of tillage intensity.For winter barley signi?cant changes only occurred between basic scenarios.This is due to the limited temporal variation of operation application with winter crops.

First dates of application were analysed.For spring planted crops comparable scenarios (e.g.CVT and CVT_6)regarding the in?uence of spring tillage instead of fall tillage were investigated (see Fig.6).First we chose scenarios where all tillage operations were converted.For both crops we found strong differences between results of spring tillage application instead of fall tillage.For example for conventional fall tillage scenario (CVT)compared to conventional spring tillage (CVT_6)with sugar beet the results varied as follows:surface runoff (à24%),base?ow (11%),total water yield (à8%),total sediment loading (à27%),organic nitrogen (à27%),organic phosphate (à26%),nitrate yield in surface runoff (à26%),nitrate leached (14%),and phosphate leached (2%)).With spring barley we came to similar results.Furthermore,we found that the output parameters more affected with conventional tillage than those with conservation tillage.

We also investigated the effect if only one tillage operation (plough operation or second stubble cultivation operation)was applied in spring instead of fall (e.g.CVT and CVT_4)(see Fig.7).Compared to completely spring tillage the effect here is less intensive but still effective in order to reduce nutrient losses and sediment yield (e.g.for sugar beet (CVT–CVT_4):surface runoff (à13%),base?ow (6%),total water yield (à4%),total sediment loading (à13%),organic nitrogen (à13%),organic phosphate (à12%),nitrate yield in surface runoff (à23%),nitrate leached (7%),and phosphate leached (1%)).As described before we found that the output parameters more affected with conventional tillage than those with conservation tillage.

Finally,we compared scenarios where tillage operation application varies just for a few days (CST_B–CST_B2;CST_B1–CST_B3;CST_B6–CST_B4;CST_B7–CST_B5)and found only a marginal effect (<1%)on model output parameters.

Furthermore,the variations of tillage operation combinations were tested (e.g.leaving out harrowing).As shown in Fig.8on the example of sugar beet no speci?c trend could be identi?ed regarding an increase or decrease of water cycle components,nutrients,and sediment load.But we found a decreasing effect with decreasing tillage intensity (conventional management practice compared to conservation management practice).

In a nutshell a closer examination of (1)dates of tillage operation application,and (2)tillage intensity showed

that:

Fig.6.Percentage deviation of modelling results regarding to different tillage application dates with a)sugar beet and b)spring barley on water balance components,nutrients and

sediment.

Fig.7.Percentage deviation of modelling results regarding to different tillage application dates with a)sugar beet and b)spring barley on water balance components,nutrients and sediment.

A.Ullrich,M.Volk /Agricultural Water Management 96(2009)1207–12171215

(1)Most considerable effects occurred for spring planted crops if

all operations after harvesting (e.g.CVT_6/7)were ?rst applied just before sowing (complete spring tillage),followed by partly spring tillage (e.g.ploughing or second stubble cultivation operation is still ?rst applied in fall).Small variations of tillage dates did not affect model output substantial.

(2a)Leaving out an intensive tillage operation (CVT:CST_A and

CST_B)affected the water balance components,the nutrient losses,and sediment yield considerable.

(2b)For leaving out single less intensive tillage operations

followed more intensive operation (e.g.CVT:CVT_1)no trend could be identi?ed.Thereby,the in?uence is larger for nutrients and sediment than for the water balance components;in addition,the in?uence is larger for spring planted crops than for winter barley.

Results of the implementation of conservation practices contouring and setting ?lter strips did not affect water balance components but lead to a signi?cant decrease of organic nitrogen,organic phosphorus and sediment loading while nitrate in surface runoff is only affected by ?lter strips.These effects are visible for all crops but only marginal developed for the winter

grain.Fig.9shows the results for sugar beet.For all basic scenarios we found the same percentage variations for nutrients output and sediment load.With implementation of conservation practice contouring (e.g.CST_Aa)the organic nitrogen,organic phosphorus,and total sediment loading was reduced by around 40%,with implementation of ?lter strip (2m—e.g.CST_Ab)by around 45%,and with implementation of both practices (e.g.CST_Ac)by around 67%compared to the basic scenario (e.g.CST_A).Furthermore,nitrate yield in surface runoff decreased with implementation of ?lter strip about 45%.Compared to the CST_A basic scenario the application of catch crop (CST_CC)showed a decrease of organic nitrogen (80%),organic phosphorus (83%),nitrate in surface runoff (63%),and sediment loading (80%).The absolute values of this scenario are similar to the no-tillage scenario NOT_c.4.Conclusions

Based on the initial conditions (semi-virtual watershed with homogeneous arable land and soil characteristics)the analysis has shown that the SWAT model is very sensitive to applied crop rotations and in some cases even to small variations of manage-ment practices.But the different settings do not have the same sensitivity.Based on the results of our analysis the following sensitivity ranking can be concluded:

(1)Duration of vegetation period and soil cover over the time with

(1a)implementation of catch crop;

(1b)dates of planting (winter/spring crop);

(1c)date of ?rst tillage operation applied after harvesting (fall

tillage/spring tillage);

(2)Soil cover characteristics of applied crops (e.g.grains/row

crops);

(3)Conservation support practices (contouring)and ?lter strips;(4)Tillage intensity (means applied tillage practice;basic scenar-ios).We consider this ranking as a ?rst recommendation for the parameterisation of tillage operations and management practices for SWAT users and for our further studies—always with the view to the initial conditions of input data.

Also we reason that it is not necessary to implement tillage operation successions in detail into the model especially for winter crops.Less intensive operations in connection with more intensive operations can be left out.Important is to apply the date of

?rst

Fig.8.Percentage deviation of modelling results regarding to the variations of tillage operation combinations with sugar beet a)conventional tillage and b)conservation tillage on water balance components,nutrients and

sediment.

Fig.9.Percentage deviation of modelling results regarding to implementation conservation practice contouring and ?lter strip with sugar beet on the example of conservation management practice CVT_A.

A.Ullrich,M.Volk /Agricultural Water Management 96(2009)1207–1217

1216

intensive operation(fall/spring)and to know most important crops grown in the investigated area.Furthermore,it is important to know and to implement conservation practices like catch crops, contouring and?lter strips.

With these results and based on catchment size and rate and distribution of arable land within watershed area we reason the parameterisation of crop rotations and tillage operation con?g-urations of management practices to be important for the calibration and validation procedure as well as for the generation of comparable results.

5.Outlook

Our overall goal is to give recommendations for land manage-ment parameterisation on different catchment sizes following a nested approach:Parthe(about300km2),Wei?e Elster (5300km2),Saale River Basin(23,000km2).Therefore,our next step is the application of tillage scenarios used in this study to these different watersheds of different sizes and with differen-tiated land use and soil data input in order to evaluate the results for the virtual area.Afterwards,the application of differentiated crop rotations and tillage operations for different management practices as well as regionalisation of management input data and different management systems are planned.

Acknowledgements

The authors gratefully acknowledge Daren Harmel,Temple,TX, USA,Philip W.Gassman,Ames,IA,USA,and Daniel Doktor,Leipzig, Germany for their helpful comments to improve the quality of the paper.

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广州新版八年级英语下册unit1

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关于公司节约成本的具体方案

关于公司节约成本的具 体方案 Pleasure Group Office【T985AB-B866SYT-B182C-BS682T-STT18】

关于公司节约成本的具体方案 作为一个自主经营,自负盈亏的企业来讲,不光要潜心研究怎么挣钱如何创造效益,更要考虑如何节约成本,降低损耗。俗话说:“不省不成家”,企业就是一个大家庭,只有这个“大家”发展了,们各自的“小家”才会殷实,也就是印证了“大河有水小河满,大河无水小河干”的这句话。 最初我理解的公司节约,只考虑到了节约水电等能源方面的节约,缺忽视了还有其他很多方面。针对集团公司目前的状况,从三个方面着手精简公司成本。 一.集团公司的能源节约。主要为节电 电的使用主要集中在照明设备电灯、电脑饮水机、和空调三个方面 水的成本主要在饮用水上面。公司的饮用水分为两种,分别是饮水机上大桶升的桶装水和农夫4升桶装水。其中大桶装水升,每桶10元,农夫桶装水每桶4升,每桶7元。 尽量做到以下几方面: 1.白天上班时间除非室内采光不足,否则不开启室内照明灯。 2.人走关灯,人来开灯,外出办公超过半小时离开时需关闭电灯,电脑。否则一经 发现罚款10元 3.空调恒定开在26度,关机后须断电。对于下班没关电脑(包括显示器)的员工, 罚款10元,以示批评。 水的成本主要在饮用水上面。公司的饮用水分为两种,分别是饮水机上大桶18升的桶装水和农夫4升桶装水。其中大桶装水升,每桶10元,农夫桶装水每桶4升,每桶7元。

目前情况下,每周平均使用7桶大桶装水和40桶农夫纯净水。如果提高大桶纯净水的使用量而降低农夫桶装水,则是一笔可观的费用。 二.集团公司的办公费节约。 由财务部财务总监计算出当月办公费使用的大体金额,主要包括纸张、墨盒、一次性口杯、易损耗的其他办公用品。立足当月,参照前几月,做出下月的办公费预算,月底检查完成情况,如果超支了,则要找出超支的重点,从源头上控制浪费。 纸张双面打印提倡了很多年,但我发现很少时候能做到。我觉得问题的关键不是大家不知道双面打印节省纸张,而在于很少人能在打印的时候把用过的纸放进打印机里。没有人做这个动作,那打出来的东西就只能是单面的、一次性的。解决这个问题,可以出台规定,除了公司重要文件不允许直接使用白纸打印。另外将打印过的纸张放到打印机放的显眼地方,方便看到使用。(这个动作我已经做了)即使双面打印的纸张在不是完全看不清的情况下仍旧可以留下来当草稿纸。 在采购的时候同时采购相应的内件产品,中性笔、圆珠笔的笔芯,活页本的内页。都可以增加办公用品的使用寿命。 对于每月的办公成本实际支出低于预算的这部分钱可以拿出来发给员工作为奖金,谁节约谁受益,以便更好的调动员工积极性参与公司降低成本的活动,又可以培养员工好的工作习惯。或是将这部分钱定向用于公司年会奖品的购置。 三.损耗管理--降低损耗,修旧利废。库存是成本,减少不必要的库存对于有破损的大件办公用品,如桌椅,电脑、空调,在无法修理或修理成本太高的情况下,建议一次性处理,直接卖掉。毕竟库存也是成本,减少不必要的库存,其实就是节省了仓库空间,减少库管的工作量。

安全生产演讲稿

安全生产、党员争先 煤矿生产,安全为天。当单位或部门新分来学员和新工人时,都要进行有关安全政策、法令、规章制度的学习、培训。上岗工作时,安监部门、各级领导又是人人讲安全,天天进行安全戴帽说安全。可是,每次事故的发生都事出有因,究其原因何在?答案是:绝大多违章人员都是没有管好自己。由于参加工作时间长了,工作环境熟悉了,讲安全听腻了,思想上逐渐产生了麻痹大意思想,尽管安全规章天天讲,也当成了耳旁风,心里有一搭无一搭。事故发生后,震动一阵子,过后又随着时间的流逝而淡化。这种“管不住自己”的毛病,无疑是酿成事故的病症所在。 要搞好安全,真正做到安全为了生产、生产必须安全,除了严格按照各项安全操作规程规定作业外,最重要的一点就是要“自己管住自己”。各科室、区队里有科长、队长、班长、青年安全监督岗员和群监员,但都不能时时处处跟着你、看着你、附在你身上,钻进你心里。你工作走神,思想溜号,有意违章,违章操作,事故往往就在这种情况下找上门来。有位在煤矿工作多年,从未发生过任何事故的老工人说过这样一句话:“我的安全经验就是自己管自己”,这句话言简意赅,值得品味再三。在工作时如果三心二意,就有可能发生事故。根据有关部门统计资料表明:百分之九十以上的事故,都是由于思想麻痹和“三违”造成的。 俗话说得好:“世界上什么药都可以买到,就是‘后悔药’买不到”。出了事故,单位受损失不说,自己轻则受皮肉之苦,重则缺胳膊少腿,甚至连生命都没有了,你的父母、妻子、儿女还要为你承担痛苦与不幸。生活是美好的,家庭是温暖的,何必为一时之错、一念之差,付出如此沉重的代价?

作为一名党员干部,我们应当牢固树立“本质安全,珍爱生命”的理念,去营造遵章守法,治理“三违”的安全文化氛围,把安全生产的制度,变为我们自己的自觉行动。同时还要加强学习、注重提高自身的安全技术素质和安全生产能力、切实在安全生产工作中发挥出党员的先进性和模范带头作用。 党旗在飘扬,党旗在召唤。让我们在全矿蓬勃开展的“创先争优”活动中,时刻牢记这样一个道理:党员就是责任,党员就是旗帜,党员就是榜样,党员就是先锋;让我们忠诚做好人民群众的领头雁,真情当好安全发展的排头兵,为鲜红的党旗增辉添彩,为建设庇山和谐矿区、本质安全矿井、五优矿井做出积极的贡献。

放飞梦想演讲稿500字5篇

放飞梦想演讲稿500字5篇 当我听了这个故事后吗,我有了非常深的感受。每个人都有自己的梦想,我的梦想就是当一名科学家,每天我都在搞科研。但是直 到有一天,我脑海了闪过一个念头,现在我还在小学阶段,以后还 要走更长的路,此时我应该好好读书。之后,我就换了一个梦想, 努力考上名牌大学,长大后好好孝顺父母,报答父母多年来的养育 之恩。我就像杨孟衡一样,虽然第一个梦想不能实现,但还能有第 二个梦想,第三、第四甚至更多。因为人的一生可以不止一个梦想。失败并不可怕,可怕的是没有梦想。有了梦想就是有了希望,有了 光芒! 梦想并不是一个梦。有志者,事竟成。只要你有努力过、坚持过,就一定会成为现实!放飞我们的梦想吧,一起加油!我的梦,中国梦! 每个人都不愿在牢笼中平庸的度过一生,而是要插上自由的翅膀飞翔。 孩提时,我们童稚的认为翅膀是一种毛绒绒的东西。能带我们飞上蓝蓝的天空,悠闲的坐在白白的云朵上。 长大后,我们固执的认为翅膀是一种载满希望的东西。能使我们乘着翅膀越过高山,跨过河流,飞向有梦的未来。 成年后,我们诚恳的认为翅膀是一种具有超能量的东西。能让我们驾驭着翅膀战胜更多的竞争对手,直至的飞上顶峰。 翅膀总是神奇的,无论是在儿时的眼中抑或是成年后的心中。 现在,我发现我有一双翅膀。一双隐形的翅膀。它指引我飞向明天,飞向未来。尽管途中会遇到很多困难,但一种毅力激励着我前进。很多人没有坚持,便在这一座山峰前坠落了,而我有我超越的 翅膀,助我度过绝望,飞越山峰。 其实每个人身上都有一双隐形的翅膀,取决于你是否发现它,运用它。

张开翅膀,放飞梦想,带你自由飞翔,到达一个任心灵翱翔的世界…… 会当凌绝顶,一览众山小。——杜甫 “我的未来不是梦,我认真的过着每一分钟”,每当我听到《我的未来不是梦》这首歌曲,我的理想不由得放飞了起来。当一名人 民教师,是我最大的理想。 高中 我现在是为初中二年级学生,再过一年多就要考高中。我的目标是灵宝一高。 从现在开始,我要分秒必争,积极乐观的完成初中教学任务,还要努力培练自己的独立自主能力,我坚信“阳光总在风雨后”经过 我长期的奋斗,我一定会成功。 大学 我的第二梦想是考上清华大学,清华大学人才济济,我在他它里可以学到很多的知识。大学能成就我的人生,在大学里,我可以实 现我的梦想,放飞我的梦想。 我时刻准备着,时刻努力着,时刻拼搏着。 教师 “教师是人类灵魂的工程师”,从小就树立了远大的梦想,当一名人民教师。为千千万万的学子传授知识。 在儿时,爷爷经常给我讲一些带有一定哲理的故事,虽然我那时听不懂故事中的哲理,但我完全可以把爷爷给我讲的故事叙述下来,爷爷听后两眼瞪圆,十分惊讶,就抚摸着我的头悄悄对我说:“不 愧为我的好孙儿,你真是当教师得料,你真是太棒了。”我的心不 由扑通扑通地跳了起来,不由的喊了起来:我是人民教师。

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