Data preparation of demo watershed

Table of Contents

In simple terms, watershed modeling is to simulate the behavior of watershed such as runoff, soil erosion, and nutrient transfer using the empirical or physical formulas based on the geographic information data (e.g., digital elevation model [DEM], landuse map, and soil map), meteorological data, and management data, etc. Thus, data collection and preparation are the first key step for watershed modeling. As a demo, data from the Youwuzhen watershed, Changting County, Fujian province, China, is selected for long-term (i.e., daily time-step) watershed modeling and application.

Basic conventions of file formats

Basically, the input data of SEIMS-based watershed model includes two categories such as spatial data and plain text.

Spatial data

Spatial data includes raster data and vector data. Theoretically, all formats of raster (https://www.gdal.org/formats_list.html) and vector (https://www.gdal.org/ogr_formats.html) data supported by GDAL are acceptable for SEIMS. Even though, the most commonly used GeoTIFF and ESRI Shapefile are highly recommended for raster and vector data, respectively.

  • Please make sure all spatial data have the same projected coordinate system, NOT geographic coordinate system.
  • The spatial extents of different raster data are not necessarily the same. However, the gridded cells at the same location are preferably coincident. Otherwise, the raster data will be interpolated based on the DEM data which may cause undesired distortion.

Plain text

Except for the spatial data, almost all the other data can be provided as plain text. The basic conventions of plain text are designed as:

  • The line starts with number sign (#) will be regarded as a comment line and ignored by SEIMS. However, there is one exception when the first line of one plain text file is for recording the time-system and timezone, e.g., #LOCALTIME 8 means date time recorded in the current file is east 8th district time and #UTCTIME means Coordinated Universal Time which is also known as Greenwich Mean Time (GMT).
  • The first valid line is headers if needed.
  • Comma (,) is the delimiter for values within each data line.
  • En dash (-) is the primary delimiter within each value while colon (:) is the secondary delimiter.

For example, the following plain text

# This line is a comment.
SUBSCENARIO,NAME,LANDUSE,PARAMETERS
1, Closing measures,7-16,Interc_max:Maximum Interception Capacity:AC:1-Conductivity:Soil hydraulic conductivity:RC:3.5

can be parsed as a Python data structure of dictionary like:

demo_dict = {
'SUBSCENARIO': 1,
'NAME':'Closing measures'
'LANDUSE': [7, 16],
'PARAMETERS': [
['Interc_max', 'Maximum Interception Capacity', 'AC', 1],
['Conductivity', 'Soil hydraulic conductivity', 'RC', 3.5]
]
}

Spatial data

The demo data named Youwuzhen watershed (~5.39 km2) is located in Changting County of Fujian province, China (Fig. Youwuzhen Map). It belongs to the typical red-soil hilly region in southeastern China and suffers from severe soil erosion. The study area has hills with steep slopes (up to 52.9° and with an average slope of 16.8°) and broad alluvial valleys. The elevation ranges from 295.0 m to 556.5 m. The landuse types are mainly forest (59.8%), paddy field (20.6%), and orchard (12.8%) (Fig. Youwuzhen Landuse). Soil types in the study area are red soil (78.4%) and paddy soil (21.6%) which can be classified as Ultisols and Inceptisols in US Soil Taxonomy, respectively (Fig. Youwuzhen Soil Type).

To improve the computational efficiency for demonstration in this manual, the DEM (ywzdem30m.tif), landuse (ywzlanduse30m.tif), and soil (ywzsoil30m.tif) map are all unified to be of 30 m resolution.

ywzloc ywzloc

Fig. Location of the demo watershed named Youwuzhen watershed

ywzlucc ywzlucc

Fig. Landuse map of the Youwuzhen watershed

ywzsoil ywzsoil

Fig. Soil map of the Youwuzhen watershed

The outlet location (i.e., as vector point) of the Youwuzhen watershed is prepared as ESRI Shapefile. If the outlet data cannot be predetermined, the location (i.e., center of the gridded cell) with largest flow accumulation will be marked as the outlet of the current study area.

In the current version of SEIMS, the Thiessen polygon of meteorological stations and precipitation stations that covers the entire watershed should also be provided as vector polygon data, e.g., thiessen_meteo.shp and thiessen_pcp.shp, respectively. The attributes of each polygon should include the unique ID (ID) which is coincident with station ID introduced in the following section, station name (Name), X and Y coordinates under the projected coordinate system (LocalX and LocalY), latitude and longitude under the WGS84 coordinate system (Lat and Lon), and altitude (Elevation).

TODO: The requirements of the Thiessen polygon of meteorological stations and precipitation stations should be removed since the spatial information of these stations presented as plain text (see here) can provide similar information.

All these spatial data are in SEIMS\data\youwuzhen\data_prepare\spatial.

Precipitation data

The annual average precipitation of the Youwuzhen watershed is 1697.0 mm and intense short-duration thunderstorm events contribute about three-quarters of annual precipitation from March to August.

Spatial information of precipitation station

The fields of spatial information of precipitation station are shown in Tab. Precipitation Station.

Tab. Fields of precipitation station

Field name Datatype Description
StationID Integer Unique station ID
Name String Station name
LocalX Float X coordinates (unit: m) under the projected coordinate system
LocalY Float Y coordinates (unit: m)
Lat Float Latitude (unit: degree) under the WGS84 coordinate system
Lon Float Longitude (unit: degree)
Elevation Float Altitude (unit: m)

For example, SEIMS\data\youwuzhen\data_prepare\climate\Sites_P.csv:

StationID,Name,LocalX,LocalY,Lon,Lat,Elevation
81502750,HeTianZhan,39444759.232,2840563.152,116.4,25.683333,298

Records of precipitation data

The first line is to state the time-system and time zone (see here). If not provided, #UTCTIME will be regarded as the default. The fields and formats of precipitation data are as follows.

Tab. Fields and formats of precipitation data item

Field name Datatype Description
DATETIME Datetime string Date time with the format of YYYY-MM-DD HH:MM:SS
StationIDs Float Precipitation value for each Station IDs. Multiple stations can be StationID1,StationID2, … StationIDN

Thus, the records of precipitation data are something like (SEIMS\data\youwuzhen\data_prepare\climate\pcp_daily.csv):

#UTCTIME
DATETIME,81502750
2012-01-01 00:00:00,0
2012-01-02 00:00:00,0
2012-01-03 00:00:00,9.00
2012-01-04 00:00:00,16.50
2012-01-05 00:00:00,16.00
2012-01-06 00:00:00,1.50

Meteorological data

The Youwuzhen watershed is characterized by a mid-subtropical monsoon moist climate and has an annual average temperature of 18.3 °C. The format of meteorological station is the same as that of precipitation station (see here), e.g., SEIMS\data\youwuzhen\data_prepare\climate\Sites_M.csv.

Same to precipitation data, the first line of meteorological data text is to state the time-system and timezone (see here). If not provided, #UTCTIME will be regarded as default. The fields and formats of meteorological data are as follows. Note that there is no fixed order of these fields.

Tab. Fields and formats of meteorological data item

Field name Datatype Description
StationID Integer Station ID
DATETIME Datetime string Date time with the format of YYYY-MM-DD HH:MM:SS
TMAX Float Maximum temperature (unit: degC)
TMIN Float Minimum temperature (unit: degC)
TMEAN Float (Optional) Mean temperature (unit: degC)
RM Float Relative moisture (unit: %)
WS Float Wind speed (unit: m/s)
SR or SSD Float Solar radiation (units: MJ/m2/day) or sunshine duration hour (unit: hr)
PET Float Potential evapotranspiration (mm)

Thus, the records of meteorological data are something like (SEIMS\data\youwuzhen\data_prepare\climate\meteo_daily.csv):

#LOCALTIME 8
StationID,DATETIME,TMEAN,TMAX,TMIN,RM,WS,SSD
58911,2012-01-01 20:00:00,10.0 ,13.6 ,7.8 ,76 ,1.2 ,1.2
58911,2012-01-02 20:00:00,10.6 ,15.7 ,7.0 ,73 ,0.7 ,1.5
58911,2012-01-03 20:00:00,7.1 ,12.0 ,4.6 ,89 ,1.6 ,0.0
58911,2012-01-04 20:00:00,3.9 ,6.6 ,2.5 ,78 ,1.7 ,0.0

Besides, the units of each type of data should also be provided, e.g., SEIMS\data\youwuzhen\data_prepare\climate\Variables.csv.

TODO: In the current version of SEIMS, the units should be the same as Table 3. However, some unit convert functions should be added in the future to make SEIMS more compatible with commonly used units.

Observed data

The periodic site-monitoring streamflow, sediment, or nutrient data collected within the watershed are regarded as observed data. The observed data is organized as one site information file and several data files corresponding to the number of monitoring sites and monitoring variables.

Spatial information of monitoring sites

The fields of spatial information of monitoring sites are as follows.

Tab. Fields of monitoring site data

Field name Datatype Description
StationID Integer Unique station ID
Name String Station name
Type String Monitoring variables, use En dash as separator for multiple variables. Avoid En dash in each single Type.
Unit String Units of monitoring variables, use En dash as separator for multiple units. Avoid En dash in each single Unit.
LocalX Float X coordinates (unit: m) under the projected coordinate system
LocalY Float Y coordinates (unit: m)
Lat Float Latitude (unit: degree) under the WGS84 coordinate system
Lon Float Longitude (unit: degree)
Elevation Float Altitude (unit: m)
isOutlet Integer Types of monitoring site:
1: The outlet of watershed
2: The outlet of one subbasin
3: The junction of multiple subbasins
0: Other spatial location

For example, SEIMS\data\youwuzhen\data_prepare\observed\SiteInfo.csv:

StationID,Name,Type,Lat,Lon,LocalX,LocalY,Unit,Elevation,isOutlet
1,hetianzhan,SED,25.680207,116.406401,440409.511725,2841541.17804,g/L,280,1
1,hetianzhan,Q,25.680207,116.406401,440409.511725,2841541.17804,m3/s,280,1

Note that the observed data is primarily used for postprocessing such as matching to the corresponding simulated values. Thus, the Type should be accord with the output of SEIMS-based watershed model. All the currently available outputs can be found in SEIMS\seims\preprocess\database\AvailableOutputs.csv. For example, if the total nitrogen data is monitored, the type should be CH_TN according to the value of the FILENAME field (obviously, not include the suffix, e.g., .txt) in the output item of total nitrogen amount in reach:

MODULE_CLASS,OUTPUTID,DESCRIPTION,UNIT,TYPE,STARTTIME,ENDTIME,INTERVAL,INTERVAL_UNIT,SUBBASIN,FILENAME,USE
NutrientTransport,CH_TN,total nitrogen amount in reach,kg,NONE,1970-01-01 00:00:00,1970-01-01 00:00:00,-9999,-9999,ALL,CH_TN.txt,0

Records of observed data

Like the format of precipitation data, the first line is optionally to state the time-system and timezone (see here). If not provided, #UTCTIME will be regarded as the default. The fields and formats of observed data are as follows.

Tab. Fields and formats of observed data item

Field name Datatype Description
StationID Integer Station ID
DATETIME Datetime string Date time with the format of YYYY-MM-DD HH:MM:SS
Type String Monitoring variable
VALUE Float Monitoring value of current variable at the current date time

Thus, the records of observed data are something like (SEIMS\data\youwuzhen\data_prepare\observed\observed_Q.csv):

#UTCTIME
StationID,DATETIME,Type,VALUE
1,2012-01-14 00:00:00,Q,0.1615
1,2012-01-15 00:00:00,Q,0.578
1,2012-01-16 00:00:00,Q,0.4317

Lookup tables

Lookup tables, including crop, fertilizer, tillage, lanuse, soil, and urban, are adopted from SWAT and predefined in SEIMS\seims\preprocess\database. Parameters specific to study areas can be appended to these lookup tables or prepared in separate files in SEIMS\data\youwuzhen\data_prepare\lookup. The details of the most common used lookup tables are as follows.

Soil properties

Soil properties include physical properties and chemical properties. The fields and descriptions are shown in Tab. Soil Lookup. The optional parameters can be omitted. Note that the SEQN and NAME may not be consistent with soil types (SOILCODE), so that to represent heterogeneity of the same soil type according to different landcover or topographic positions. However, the SEQN MUST be consistent with the values in soil map, i.e., ywzsoil30m.tif. The soil properties of multiple layers are concatenated with En dash (-) as described in here.

Tab. Fields and description in lookup table of soil properties

Field name Datatype Unit Description
SEQN or SOILCODE Integer - Unique identifier of soil map
NAME String - Soil name
SOILLAYERS Integer - Number of soil layers
SOL_Z Float array mm Depth from soil surface to bottom of layer
SOL_OM Float array % Organic matter content
SOL_CLAY Float array % Clay content, diameter D < 0.002 mm
SOL_SILT Float array % Silt content, 0.002 mm < D < 0.05 mm
SOL_SAND Float array % Sand content, 0.05 mm < D < 2 mm
SOL_ROCK Float array % Rock fragment content, D > 2 mm
SOL_BD Float array Mg/m3 Moist bulk density, value ranges 1.1 ~ 1.9
SOL_AWC Float array mm Available water capacity
SOL_ZMX Float mm (Optional) Maximum rooting depth of soil profile
ANION_EXCL Float - (optional) Fraction of porosity (void space) from which anions are excluded, default is 0.5
SOL_CRK Float - (optional) Potential or maximum crack volume of the soil profile expressed as a fraction of the total soil volume.
SOL_K Float array mm/hr (optional) Saturated hydraulic conductivity
SOL_WP Float array mm (optional) Wilting point
SOL_FC Float array mm (optional) Field capacity
SOL_POROSITY Float array - (optional) Porosity
SOL_USLE_K Float array - (optional) USLE K factor
SOL_ALB Float - (optional) Albedo when soil is moist
ESCO Float - (optional) Soil evaporation compensation factor, the default is 0.95
SOL_NO3 Float array g/kg (optional) concentration of nitrate
SOL_NH4 Float array g/kg (optional) concentration of ammonium-N in soil
SOL_ORGN Float array g/kg (optional) concentration of organic nitrogen
SOL_ORGP Float array g/kg (optional) concentration of organic phosphorus
SOL_SOLP Float array g/kg (optional) concentration of soluble phosphorus

Thus, the lookup table of soil properties of specific study area is something like (SEIMS\data\youwuzhen\data_prepare\lookup\soil_properties_lookup.csv):

SEQN,SNAM,SOILLAYERS,SOL_ZMX,SOL_Z,SOL_BD,SOL_OM,SOL_CLAY,SOL_SILT,SOL_SAND,SOL_ROCK,SOL_WP,SOL_FC,SOL_POROSITY,SOL_K,SOL_AWC,SOL_NO3,SOL_NH4,SOL_ORGN,SOL_SOLP,SOL_ORGP
201,WNT,3,600,200-400-600,1.5-1.57-1.45,2.31-0.84-0.84,15.66-17.36-20.94,13.8-17.31-22.23,52.25-44.6-35.9,18.29-20.73-20.93,0.12-0.14-0.18,0.21-0.24-0.31,0.44-0.41-0.45,26.16-11.43-7.87,0.1-0.1-0.13,0.004-0.002-0.002,0-0-0,0.164-0.079-0.077,0.005-0.002-0.001,0.047-0.018-0.012

Initial landcover parameters

Some parameters of landcover at the beginning of simulation should be defined. The fields and descriptions are shown in Tab. LUCC Lookup. Please refers to the predefined database seims/preprocess/database/CropLookup.csv and LanduseLookup.csv.

Tab. Fields and descriptions in the lookup table of initial landcover parameters

Field name Datatype Unit Description
LANDUSE_ID Integer - Landuse ID (MUST cover IDs in the landuse map)
IGRO Integer - Land cover status: 0-none growing; 1-growing
LANDCOVER or ICNUM Integer - ICNUM, Land cover ID number (required when IGRO is 1)
LAI_INIT Float - Initial leaf area index (required when IGRO is 1)
BIO_INIT Float kg/ha Initial biomass (required when IGRO is 1)
PHU_PLT Float degC Number of heat units to bring plant to maturity (required when IGRO is 1)
EPCO Float - Plant uptake compensation factor, 0.01 ~ 1
RSDIN Float kg/ha Initial residue cover
CURYR_INIT Float year Initial age of trees
CHT Float m Initial canopy height
DORMI Float - Dormancy status code: 1 - growing and 0 - dormancy
USLE_P Float - Conservation practice management factor of USLE

Thus, the lookup table of initial landcover parameters of specific study area is something like (SEIMS\data\youwuzhen\data_prepare\lookup\landcover_initial_parameters.csv):

LANDUSE_ID,IGRO,LANDCOVER,LAI_INIT,BIO_INIT,PHU_PLT,EPCO,RSDIN,CURYR_INIT,CHT,DORMI,USLE_P
33,0,33,0,0,0,1,100,0,0,0,0.084
4,1,4,2,200,0,1,200,2,2,0,0.8
8,1,8,3,1000,0,1,300,5,5,0,0.8
6,1,6,2,600,0,1,200,3,2,0,0.8
18,0,18,0,0,0,0,0,0,0,0,1
104,0,-9999,0,0,0,0,0,0,0,0,1
106,0,-9999,0,0,0,0,0,0,0,0,1

Management practices data

There are three different types of Best Management Practices (BMPs) supported or will be supported by SEIMS.

  • Reach BMPs: BMPs that attached to specific reaches and will change the characters of the reach, such as point source, stream flow diversion, reservoir, riparian wetland, and riparian buffer, etc.
  • Areal structural BMPs: BMPs that are corresponding to a specific structure in the watershed and will change the characters of the specified locations, such as grass waterway, filter strip, pond, isolated wetland, terrace, overland flow diversion, tile drain management, and urban management, etc.
  • Areal non-structure BMPs: BMPs that are NOT corresponding to a specific structure in the watershed and will change the characters of the specified locations, such as plant management.

In this section, the organization of BMP scenario is firstly presented, then followed by the detail parameter settings of different BMPs. In the current version of this user manual, plant management and general areal structural BMP are introduced as an example. More BMPs should be updated in the future version.

BMP scenario

A BMP scenario is a collection of different BMPs which will be applied to an SEIMS-based watershed model to affect watershed behaviors. There can be many different BMP scenarios for the BMP scenarios analysis based on one watershed model. Each BMP scenario is identified using a unique integer ID. For each BMP of one scenario, the location and parameters must be specified. For reach BMPs, the location is the reach ID. For two areal BMPs, the location is the areas identified by a raster data, i.e., the so-called BMP configuration units. The BMP parameters are defined in separate plain text files, in which different combinations of parameters are allowed and distinguished by unique ID, i.e., SUBSCENARIO. The fields and descriptions of BMP scenario table are as follows.

Tab. Fields and descriptions of BMP scenario table

Field name Datatype Description
ID Integer Unique ID of BMP scenario
NAME String Scenario name
BMPID Integer Predefined BMP ID (e.g., SEIMS\data\youwuzhen\scenario\BMP_index.csv)
SUBSCENARIO Integer Sub-scenario ID of BMP defined in specific BMP parameters

| DISTRIBUTION | String | The format is <Type>|<Filename>. <Type> may be REACH or RASTER for reach BMP and areal BMP, respectively. <Filename> is the corresponding reach table name or raster filename. For example, RASTER|LANDUSE means the configuration of the current areal BMP is based on landuse types. | | COLLECTION | String | The name of the plain text file that defines the BMP parameters (e.g., SEIMS\data\youwuzhen\scenario\plant_management.csv) | | LOCATION | Integer array | Location values of DISTRIBUTION for configuring BMP, e.g., 33 means the BMP will be configured on the landuse type of 33. Multiple location values are separated by En dash. |

Thus, the BMP scenarios table of specific study area is something like (SEIMS\data\youwuzhen\data_prepare\scenario\BMP_scenarios.csv):

ID,NAME,BMPID,SUBSCENARIO,DISTRIBUTION,COLLECTION,LOCATION
0,base,12,0,RASTER|LANDUSE,plant_management,33

The scenario ID of 0 named base includes one BMP with the BMPID of 12 which is plant management according to BMP_index.csv. The configuration unit of this BMP is based on LANDUSE map and the landuse of 33 will be configured. The parameters of this BMP can be loaded from plant_management.csv and the SUBSCENARIO of 0 will be applied.

Plant management

By drawing lessons from the SWAT model, a total of 15 different types of plant management operations are considered in the BMP module of SEIMS. A combination of several plant management operations (e.g., plant, fertilize, harvest, etc.) is regarded as a SUBSCENARIO of plant management practices such as the crop rotation practices with rice and winter wheat. Each management operation of one SUBSCENARIO has the same fields such as follows.

Tab. Fields and descriptions of plant management practices

Field name Datatype Description
SUBSCENARIO Integer Unique sub-scenario ID in current BMP parameters table
NAME String Sub-Scenario name, e.g., Crop_rotation
LANDUSE_ID Integer Landuse type that the SUBSCENARIO can be applied
SEQUENCE Integer Sequence No. of the plant management related operations of the SUBSCENARIO, start from 1
YEAR Integer Year No. of the SUBSCENARIO, start from 1
MONTH Integer Month of the operation takes place
DAY Integer Day of the operation takes place
BASE_HU Boolean Use (1) the fraction of annual total heat units (HU) or the fraction of total heat units of a plant to reach maturity (PHU) (0)
HUSC Float Heat unit scheduling for operation expressed as the fraction of PHU or the fraction of HU if BASE_HU=1
MGT_OP Integer Management operation number
MGT<N> Float array Operation related parameters. <N> ranges from 1 to 10. Multiple location values are separated by Comma.

Plant management practices are scheduled according to heat units and/or operation date from local experiences. The operation can be performed if either of the conditions is met. For example, the plant operation is set at 5th, May or HUSC greater or equal to 0.2. If the BASE_HU is used and the HUSC which represents the fraction of annual total heat units (HU) has reached 0.2 at 1st, May, then the plant operation will occur since the HUSC condition is first met than MONTH/DAY.

The type of plant management operation simulated is identified by the code given for the field MGT_OP. The different codes for MGT_OP are:

  1. Plant/beginning of growing season. Initializes the growth of a specific landcover/plant type.
  2. Irrigation operation. Applies water to the location.
  3. Fertilizer application. Adds nutrients to the soil.
  4. Pesticide application. Applies a pesticide to the plant and/or soil.
  5. Harvest and kill operation. Harvest the portion of the plant designated as yield, removes the yield from the location and converts the remaining plant biomass to residue on the soil surface.
  6. Tillage operation. Mix the upper soil layers and redistributes the nutrients/chemicals/etc. within those layers.
  7. Harvest only operation. Harvest the portion of the plant designated as yield and removes the yield from the location but allows the plant to continue growing. This operation is used for hay cuttings.
  8. Kill/ending of growing season. Stop all plant growth and covert all plant biomass to residue.
  9. Grazing operation. Remove plant biomass and allow simultaneous application of manure.
  10. Auto irrigation initialization. Initialize auto irrigation within the location. This operation applies water whenever the plant experiences a user-specified level of water stress.
  11. Auto fertilization initialization. Initialize auto fertilization within the location. This operation applies nutrients whenever the plant experiences a user-specified level of nitrogen stress.
  12. Street sweeping operation. Remove sediment and nutrient build-up on impervious areas in the location. This operation can only be used when the urban build up/wash off routines are activated for the location.
  13. Release/impound. Release or impound water for rice or other plants.
  14. Continuous fertilization. Apply fertilizer/manure to the soil surface on a continuous basis.
  15. Continuous pesticide. Apply pesticides to the soil surface on a continuous basis
  16. Burning operation. Remove the user-specified portion of pant biomass from the location.

The parameters of different plant management operations are listed in Tab.. Please refers to SWAT 2012 Input/Output Documentation for more details of each parameter.

Note that, to simulate the water level changes in different growth stages of paddy rice, the parameters of the release/impound operation are expanded than SWAT, i.e., maximum ponding depth (MAX_PND), minimum fitting depth (MIN_FIT), and maximum fitting depth (MAX_FIT).

Tab. Parameters defined for plant management operations

NAME OP MGT1 MGT2 MGT3 MGT4 MGT5 MGT6 MGT7 MGT8 MGT9 MGT10
Plant 1 PLANT_ID NONE CURYR_MAT HEAT_UNITS LAI_INIT BIO_INIT HI_TARG BIO_TARG CNOP NONE
Irrigate 2 NONE IRR_SC NONE IRR_AMT IRR_SALT IRR_EFM IRR_SQ NONE NONE IRR_NO
Fertilize 3 FERT_ID NONE NONE FRT_KG FRT_SURFACE NONE NONE NONE NONE NONE
Pesticide 4 PEST_ID NONE NONE PST_KG PST_DEP NONE NONE NONE NONE NONE
Harvest & Kill 5 NONE NONE NONE CNOP HI_OVR FRAC_HARVK NONE NONE NONE NONE
Tillage 6 TILL_ID NONE NONE CNOP NONE NONE NONE NONE NONE NONE
Harvest Only 7 NONE NONE NONE HARVEFF HI_BMS HI_RSD NONE NONE NONE NONE
Kill 8 NONE NONE NONE NONE NONE NONE NONE NONE NONE NONE
Grazing 9 GRZ_DAYS MANURE_ID NONE BIO_EAT BIO_TRMP MANURE_KG NONE NONE NONE NONE
Auto Irrigation 10 WSTRS_ID IRR_SCA NONE AUTO_WSTRS IRR_EFF IRR_MX IRR_ASQ NONE NONE IRR_NOA
Auto Fertilize 11 AFERT_ID NSTRESS NONE AUTO_NSTRS AUTO_NAPP AUTO_NYR AUTO_EFF AFRT_SURFACE NONE NONE
Sweep 12 NONE NONE NONE SWEEPEFF FR_CURB NONE NONE NONE NONE NONE
Release/Impound 13 IMP_TRIG MAX_PND MIN_FIT MAX_FIT NONE NONE NONE NONE NONE NONE
Cont. Fertilize 14 FERT_DAYS CFRT_ID IFRT_FREQ CFRT_KG NONE NONE NONE NONE NONE NONE
Cont. Pesticide 15 IPST_ID PEST_DAYS IPST_FREQ CPST_KG NONE NONE NONE NONE NONE NONE
Burning 16 NONE NONE NONE BURN_FRLB NONE NONE NONE NONE NONE NONE

Note: NONE means the reserved position for further potential parameters and the default value is 0.

General areal structural BMP

Generally, the areal structural BMP takes effects by modifying watershed modeling related parameters on the locations that configured with BMP. Thus, a general table for areal structural BMP is designed as shown in Tab. General Areal Structual BMP, which basically includes spatial parameters (e.g., suitable LANDUSE and SLPPOS), environmental effectiveness parameters (e.g., PARAMETERS and EFFECTIVENESS), and economic benefits (e.g., CAPEX, OPEX, and INCOME).

Note that, fields of the general areal structural BMP can be extended or modified by users according to the requirements of scenario analysis. These fields will be used in the predefined scenario analysis program (e.g., SEIMS\seims\scenario_analysis\spatialunits) or other scenario analysis program developed based on the base class in SEIMS (SEIMS\seims\scenario_analysis).

Tab. Fields and descriptions of general areal structural management practices

Field name Datatype Description
SUBSCENARIO Integer Unique sub-scenario ID in current BMP parameters table
NAME String Sub-Scenario name, e.g., closing measures (CM)
DESC String Description of BMP
REFERENCE String Literature reference
LANDUSE String Suitable landuse types, default is ‘ALL’. Multiple landuse types are concatenated by En dash, e.g., ‘6-8’.
SLPPOS String Suitable slope positions, default is ‘ALL’. Multiple slope position tags are concatenated by En dash, e.g., ‘4-16’.
PARAMETERS String Spatial parameters that the BMP affects, the format MUST be: NAME1:DESC1:CHANGE1:IMPACT1-NAME2:DESC2:CHANGE2:IMPACT2-, where, NAME is the parameter's ID, which MUST be one of the GridFS file in SPATIAL collection in MongoDB, DESC is the corresponding description, CHANGE is the change method (VC, RC, or AC. VC: replace, RC: multiply, and AC: add), IMPACT is the impact value.
EFFECTIVENESS Integer Overall environmental effectiveness (e.g., reducing soil erosion) grade, range from 1 to 5, with higher-numbered grades representing better effectiveness
CAPEX Float Initial implementation cost per km2
OPEX Float Annual maintenance cost per km2
INCOME Float Annual benefit per km2

One of the general areal structural management practices of the Youwuzhen watershed prepared in SEIMS\data\youwuzhen\data_prepare\scenario\areal_struct_management.csv is as follows:

SUBSCENARIO,NAME,DESC,REFERENCE,LANDUSE,SLPPOS,PARAMETERS,EFFECTIVENESS,CAPEX,OPEX,INCOME
1,fengjin, CM (closing measures),Qin et al (2018),8-6,1-4,OM:Organic matter:RC:1.22-Density:bulk density:RC:0.98-Porosity:Total porosity:RC:1.02-USLE_K:USLE soil erodibility:RC:1.01-Conductivity:Soil hydraulic conductivity:RC:0.81-FieldCap:Soil field capacity:RC:1.02-Wiltingpoint:Wiltingpoint:RC:1.02-SOL_AWC:Soil available water:RC:1.02-SOL_UL:Soil saturated water:RC:1.02-SOL_CBN:Soil carbon content:RC:1.22-USLE_P:USLE practice factor:RC:0.9,3,15.5,1.5,2.0

which can be parsed as a Python data structure of dictionary like:

demo_dict = {'NAME': 'fengjin',
'DESC': 'CM (closing measures)',
'REFERENCE': 'Qin et al (2018)',
'LANDUSE': [8, 6],
'SLPPOS': [1, 4],
'PARAMETERS': [
{'NAME': 'OM',
'DESC': 'ORGANIC MATTER',
'CHANGE': 'RC',
'IMPACT': 1.22
},
{'NAME': 'DENSITY',
'DESC': 'BULK DENSITY',
'CHANGE': 'RC',
'IMPACT': 0.98
}
],
'EFFECTIVENESS': 3,
'CAPEX': 15.5,
'OPEX': 1.5,
'INCOME': 2,
}

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Predefined database: Predefined database