A Well Head Protection Area (WHPA) is both an area modeled around an unconfined Public Community Water Supply (PCWS) well in New Jersey that delineates the horizontal extent of groundwater captured by a well pumping at a specific rate over two-, five-, and twelve-year periods of time for unconfined wells and a fifty foot radius delineated around each confined PCWS well (This corresponds to the water purveyor controlled wellhead area as defined in the Safe Drinking Water Regulations (see NJAC 7:10-11.7(b)1)). WHPA delineations are created in compliance to the Safe Drinking Water Act Amendments of 1986 and 1996 as part of the Source Water Area Protection Program (SWAP). The delineations are the first step in defining the sources of water to a public supply well. Within these areas, potential contamination will be assessed and appropriate monitoring will be undertaken as subsequent phases of the NJDEP SWAP. The WHPAs were previously defined using line and polygon coordinate files and the Arc/INFO Generate command. Individual WHPAs were then combined into county and statewide coverages with the Arc/INFO Union command. The WHPAs are currently created using the same coordinate files using a Python-based feature creation script producing an ArcGIS geodatabase feature. The individual features are then combined using ArcGIS merge command. Previous WHPA coverages were updated to features also and combined into a statewide WHPA feature. The WHPA feature is distributed as either an ArcGIS shapefile or a layerpack file. The 2011 and earlier WHPA delineation methods are described in "Guideline for Delineation of Well Head Protection Areas in New Jersey" available as a download at http://www.state.nj.us/dep/njgs/whpaguide.pdf. Due to security consideration, the PCWS well features are not available for direct download but may be requested from the NJDEP.

DESCRIPTION

The SUDOANG project aims at providing common tools to managers to support eel conservation in the SUDOE area (Spain, France and Portugal). VISUANG is the SUDOANG Interactive Web Application that host all these tools . The application consists of an eel distribution atlas (GT1), assessments of mortalities caused by turbines and an atlas showing obstacles to migration
(GT2), estimates of recruitment and exploitation rate (GT3) and escapement (chosen as a target by the EC for the Eel Management Plans) (GT4). In addition, it includes an interactive map showing sampling results from the pilot basin network produced by GT6.

The eel abundance for the eel atlas and escapement has been obtained using the Eel Density Analysis model (EDA, GT4's product). EDA extrapolates the abundance of eel in sampled river segments to other segments taking into account how the abundance, sex and size of the eels change depending on different parameters. Thus, EDA requires two main data sources: those related to the river
characteristics and those related to eel abundance and characteristics.

However, in both cases, data availability was uneven in the SUDOE area. In addition, this information was dispersed among several managers and in different formats due to different sampling sources: Water Framework Directive (WFD), Community Framework for the Collection, Management and Use of Data in the Fisheries Sector (EUMAP), Eel Management Plans, research groups, scientific
papers and technical reports. Therefore, the first step towards having eel abundance estimations including the whole SUDOE area, was to have a joint river and eel database. In this report we will describe the database corresponding to the river’s characteristics in the SUDOE area and the eel abundances and their characteristics.

In the case of rivers, two types of information has been collected:

• River topology (RN table): a compilation of data on rivers and their topological and hydrographic characteristics in the three countries.
• River attributes (RNA table): contains physical attributes that have fed the SUDOANG models.

The estimation of eel abundance and characteristic (size, biomass, sex-ratio and silver) distribution at different scales (river segment, basin, Eel Management Unit (EMU), and country) in the SUDOE area obtained with the implementation of the EDA2.3 model has been compiled in the RNE table (eel predictions).

TECHNICAL DESCRIPTION TO BUILD THE POSTGRES DATABASE

1. Build the database in postgres.

All tables are in ESPG:3035 (European LAEA). The format is postgreSQL database. You can download other formats (shapefiles, csv), here SUDOANG gt1 database.

Initial command

# open a shell with command CMD
# Move to the place where you have downloaded the file using the following command
 cd c:/path/to/my/folder
# note psql must be accessible, in windows you can add the path to the postgres 
#bin folder, otherwise you need to add the full path to the postgres bin folder see link to instructions below   
createdb -U postgres eda2.3 
psql -U postgres eda2.3 
# this will open a command with # where you can launch the commands in the next box   

Within the psql command

 create extension "postgis";
 create extension "dblink"; 
 create extension "ltree"; 
 create extension "tablefunc";
 create schema dbeel_rivers;
 create schema france;
 create schema spain;
 create schema portugal;
 -- type \q to quit the psql shell

Now the database is ready to receive the differents dumps. The dump file are large. You might not need the part including unit basins or waterbodies. All the tables except waterbodies and unit basins are described in the Atlas. You might need to understand what is inheritance in a database. https://www.postgresql.org/docs/12/tutorial-inheritance.html

2. RN (riversegments)

These layers contain the topology (see Atlas for detail)

• dbeel_rivers.rn
• france.rn
• spain.rn
• portugal.rn

Columns (see Atlas)

gid

idsegment

source

target

lengthm

nextdownidsegment

path

isfrontier

issource

seaidsegment

issea

geom

isendoreic

isinternational

country

# dbeel_rivers.rn ! mandatory => table at the international level from which 
# the other table inherit 
# even if you don't want to use other countries 
# (In many cases you should ... there are transboundary catchments) download this first.
# the rn network must be restored firt ! 
#table rne and rna refer to it by foreign keys.
pg_restore -U postgres -d eda2.3 "dbeel_rivers.rn.backup"
#france
pg_restore -U postgres -d eda2.3 "france.rn.backup"
# spain
pg_restore -U postgres -d eda2.3 "spain.rn.backup"   
# portugal
pg_restore -U postgres -d eda2.3 "portugal.rn.backup" 
# with the schema you will probably want to be able to use the functions
psql -U postgres -d eda2.3 -f "function_dbeel_rivers.sql"

3. RNA (Attributes)

This corresponds to tables

• dbeel_rivers.rna
• france.rna
• spain.rna
• portugal.rna

Columns (See Atlas)

idsegment

altitudem

distanceseam

distancesourcem

cumnbdam

medianflowm3ps

surfaceunitbvm2

surfacebvm2

strahler

shreeve

codesea

name

pfafriver

pfafsegment

basin

riverwidthm

temperature

temperaturejan

temperaturejul

wettedsurfacem2

wettedsurfaceotherm2

lengthriverm

emu

cumheightdam

riverwidthmsource

slope

dis_m3_pyr_riveratlas

dis_m3_pmn_riveratlas

dis_m3_pmx_riveratlas

drought

drought_type_calc

Code :

pg_restore -U postgres -d eda2.3 "dbeel_rivers.rna.backup"
pg_restore -U postgres -d eda2.3 "france.rna.backup"
pg_restore -U postgres -d eda2.3 "spain.rna.backup"   
pg_restore -U postgres -d eda2.3 "portugal.rna.backup"  

4. RNE (eel predictions)

These layers contain eel data (see Atlas for detail)

• dbeel_rivers.rne
• france.rne
• spain.rne
• portugal.rne

Columns (see