ffr documentation

FFR DocumentationRelease 1.0.1

Guenther Grill

Feb 09, 2019

Contents:

1 1 Brief introduction to FRAs 31.1 1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 1.2 Data sources - Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 2 Getting started 92.1 2.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2 2.2 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3 3 Conducting the FRA 133.1 3.1 Input data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2 3.2 Configuration File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.3 3.3 Running the FRA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.4 3.4 Output and interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.5 ** . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4 4 Tailoring the FRA 454.1 4.1 Adding new dams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.2 4.2 Adding Benchmark rivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.3 4.3 Using alternative data layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.4 4.4 Creating new scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5 5 Contributing to code development 495.1 5.1 Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495.2 5.2 Git basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6 6 ANNEX 536.1 6.1 Installing Pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536.2 6.2 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 546.3 6.3 Data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 546.4 6.4 Frequently Asked Question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

i

FFR Documentation, Release 1.0.1

This guide will provide information about conducting a regional free-flowing river analysis, or FRA.

|graduation-cap| The guide starts with a brief description of the free-flowing river (FFR) methodology given in section1 Brief introduction to FRAs

|book| The section 2 Getting started will examine the requirements to conduct a FRA and will guide through theinstallation of data and the model.

|database| The section 3 Conducting the FRA, will provide details about the input data, the configuration and executionof the model, and will explain the different outputs.

|edit| Section 4 Tailoring the FRA, provides guidance on how to modify the setup to accommodate local data in theanalysis.

|code| Section 5 Contributing to code development, provides information about the source code and about how tomodify and contribute to it.

|paperclip| This documentation is hosted at: http://hydrolab.io/ffr/docs/

Contents: 1

http://hydrolab.io/ffr/docs/


2 Contents:

CHAPTER 1

1 Brief introduction to FRAs

A Free-flowing Rivers Assessment (FRA) is an assessment to determine the connectivity status of rivers by taking intoconsideration both natural connectivity as well as fragmentation from infrastructure, such as dams, roads, urban areas,water use.

The main result of a FRA is a connectivity index representing how well river stretches are still connected in the lateral,and in the upstream and downstream direction given existing infrastructure. The index is termed Connectivity StatusIndex (CSI). As such the FRA provides a layer of information that is strictly focused on connectivity, and is thereforenot a complete assessment of river health. However the results are meant to be combined and supplemented withother layers, such as species information, water quality or fluvio-geomorphological information to e.g., further assessand identify high-value conservation areas. Furthermore, the methodology can be used to assess the effect of futurechanges of infrastructure (such as dams) on the connectivity status.

A second important results is the classification of rivers into either “Free-flowing”, having a “Good Connectivity”status, or as being “Impacted”. The flowchart in the next section will make this more clear.

Important: A river is only free-flowing if the entire river is in Good connectivity status (below the threshold). Ifsome portions are not free-flowing, the river cannot be classified as free-flowing

1.1 1.1 Overview

A free-flowing river assessment (FRA) follows a set of distinct stepsoutlined in Figure 1.1. In the global assessment, we first developedan integrated definition of free-flowing rivers (FFR) (step 1) accordingto multiple aspects of connectivity (see supplementary text for the fulldefinition). Next, we identified five major pressure factors (step 2) thatinfluence river connectivity according to an extensive literature review,and collated data for each factor. These pressure factors include: (a)river fragmentation; (b) flow regulation; (b) water consumption; (d) roadconstruction; and (e) urbanization.

3


We calculated proxy indicators (see Figure 1.2) for each factor usingdata from available global remote sensing products, other data compi-lations, or numerical model outputs such as discharge simulations. Wespecifically chose indicators that we expect to have substantial influenceon connectivity and can be generated using robust global data sets ofsufficient quality and consistency between countries and regions. All in-dicators were calculated for every river reach of the global river network(step 3).

Guided by literature reviews and expert judgement, we iteratively ad-justed the weighting of each pressure indicator in a set of scenarios andtested different thresholds to yield a best match between the resultingFFRs and a benchmarking dataset of reported FFRs compiled from liter-ature resources and expert input.

Fig. 2: Figure 1.2: Pressure indicators used in this study and their data sources.

The final selection of weights was applied to a multi-criteria average cal-culation (step 4) to derive the Connectivity Status Index (CSI) for everyriver reach (step 5). The CSI ranges from 0% to 100%, the latter indi-cating full connectivity. Only river reaches with a CSI of >95% wereconsidered as having ‘good connectivity status’ while river reaches be-low 95% were classified as impacted (step 6). Finally, river reaches wereaggregated into rivers, i.e., contiguous flow paths from the source to theriver outlet. If a river is above the CSI threshold of 95% over its entirelength it is declared to be a FFR. Otherwise, the river as a whole is de-clared not free-flowing, yet it can maintain a mix of stretches with ‘goodconnectivity status’ and stretches that are impacted.

A regular assessment would include running steps 4-6 to calculate theCSI, and then continuing to run the subsequent assessments to grouprivers by status.

Steps 1 to 3 could in theory be altered as well. For example, you mightconsider to include new pressure factors into the assessment. However,this type of adjustment is not discussed in this documentation. Please getin touch directly with us, if you are planning to attempt this.

4 Chapter 1. 1 Brief introduction to FRAs


For more information on the FRA methodology, please also refer to:Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli,F., Babu, S., Cheng, L., Crochetiere, H., Filgueiras, R., Goichot, M.,Higgins, J., Hogan, Z., Lip, B., McClain, M., Meng, J.-H., Mulligan,M., Nilsson, C., Olden, J.D., Opperman, J., Petry, P., Reidy Liermann,C., Saenz, L., Salinas-Rodriguez, S., Schelle, P., Snider, J., Tockner, K.,Valdujo, P.H., van Soesbergen, A., Zarfl, C. (2018). A global map offree-flowing rivers. in review.

1.2 1.2 Data sources - Overview

We integrated all pressure indicator datasets into our modeling frame-work using the spatial units of the HydroSHEDS database. Hy-droSHEDS is a hydrographic mapping product that provides river andcatchment information for regional and global-scale applica¬tions in aconsistent format (Lehner et al., 2008), including catchment areas anddischarge estimates.

For the global study, we extracted a global river network from the pro-vided drainage direction grid at 500 m pixel resolution by definingstreams as all pixels that exceed a long-term average natural dischargeof 100 liters per second or an upstream catchment area of 10 km2. Werefrained from including streams below these thresholds as they are in-creasingly unreliable in their representation through global datasets.

These selection criteria resulted in 8,477,883 individual river reaches(i.e., line segments between confluences) with an average length of4.2 km (SD = 4.8 km), totaling 35.9 Mio km of river network. Eachriver reach is linked to a polygon of its contributing hydrological sub-catchment, with an average area of ~12 km2.

In this context, we define a river reach as a line segment between twoconfluences; a river stretch as two or more contiguous reaches but nota full river; and a river as an aggregation of river reaches that form asingle-threaded, contiguous flowpath from headwater source to river out-let. The river outlet can represent either the river mouth at the ocean; aterminal inland depression; or the confluence with a larger river (see Fig-ure 1.3).

It should be noted that while we used the full river network for con-ducting the initial calculations, we removed all rivers from the statisticalanalyses and reported results that were shorter than 10 km, showed an av-erage annual river flow of less than 1 m3/s, or were in hot or cold desertsaccording to existing physiographic maps to exclude increasingly uncer-tain results of smaller rivers (see discussion in main text). These selectioncriteria resulted in 308,015 distinct rivers with a total length of 11.7 Miokm globally.

For each river reach, estimates of long-term (1971–2000) discharge av-erages have been derived through a geospatial downscaling procedure(Lehner and Grill, 2013) from the 0.5º resolution runoff and dischargelayers of the global WaterGAP model (Alcamo et al., 2003; Döll etal., 2003; v2.2 as of 2014). WaterGAP is a well-documented and val-idated integrated water balance model that simulates both natural dis-charge (i.e., without human modifications) and anthropogenic discharge;

1.2. 1.2 Data sources - Overview 5


Fig. 3: Figure 1.3: Schematic overview of river-related concepts. The baseline river network consists of individualriver reaches (1-31 in panel a), defined as line segments separated by confluences (black dots). River reaches canbe aggregated into rivers based on a ‘backbone’ ordering system which classifies river reaches as the mainstem or atributary of various higher orders (b). Following this system, the river network can be distinguished into distinct rivers(1-15 in panel c), defined as a contiguous stretch of river reaches from source to outlet on the mainstem, or from sourceto confluence with the next order river.

for the latter, consumptive water use, i.e., total water abstractions minusreturn flows are calculated for agricultural (mostly irrigation), industrialand municipal sectors.

For all network calculations, we applied the global river routing modelHydroROUT (Grill et al., 2015) which is built upon the HydroSHEDSdatabase and features a nested, multi-scale model approach; advancedimplementation of connectivity; and uses a novel object-oriented vectordata structure in a graph-theoretical framework. HydroROUT was im-plemented in this study to calculate river reach level indicators, such asthe Degree of Fragmentation (DOF), the Degree of Regulation (DOR),and the Connectivity Status Index (CSI) as described below.

References

Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T.,Siebert, S. (2003) Development and testing of the WaterGAP 2 globalmodel of water use and availability. Hydrological Sciences Journal 48,317-337.

Döll, P., Kaspar, F., Lehner, B. (2003) A global hydrological model forderiving water availability indicators: model tuning and validation. Jour-nal of Hydrology 270, 105-134.

Grill, G., Lehner, B., Lumsdon, A.E., MacDonald, G.K., Zarfl, C., Lier-mann, C.R. (2015) An index-based framework for assessing patterns andtrends in river fragmentation and flow regulation by global dams at mul-tiple scales. Environmental Research Letters 10, 015001.

Lehner, B., Grill, G. (2013) Global river hydrography and network rout-



ing: baseline data and new approaches to study the world’s large riversystems. Hydrological Processes 27, 2171-2186.

Lehner, B., Verdin, K., Jarvis, A. (2008) New global hydrography de-rived from spaceborne elevation data. EOS, Transactions of the Ameri-can Geophysical Union 89, 93.

1.2. 1.2 Data sources - Overview 7

CHAPTER 2

2 Getting started

This part explains how to quickly set up the data and model code. The goal is to configure the model, so that it cancreate output that replicates the global assessment. Then the goal is to gradually modify the analysis to tailor it to thelocal requirements.

2.1 2.1 Requirements

The components to set up the analysis are as follows:

• the data and code, which was provided as a ZIP file through a dropbox link

• the software ArcGIS 10.2.2 or higher, preferably ArcGIS 10.5.1

• The ArcGIS 64-bit geoprocessing module. This module is part of the ArcGIS installation package and containsthe 64-bit python geoprocessing modules. However, it is not installed by default, so we need to install it, if it’smissing.

• The python package ‘Pandas’ which is used for data analysis. It is included in ArcGIS version 10.4.1 and10.5.1, but earlier versions might not have it installed yet (in this case please see the Annex for details oninstalling Pandas.)

• A code editor to examine and run the tools. A common, free option is PyCharm Community edition.

• A program that can read EXCEL files, for example Microsoft Excel, or LibreOffice.

• Optional: a github.com account if you want to download the latest code, or if you want to contribute to the code(see section 5 Contributing to code development)

• Optional: if you want to contribute to the documentation, you need to set up Sphinx, a documentation moduleand install the html theme sphinx_rtd_theme.

2.2 2.2 Installation

This short video explains some of the issues related to installation: |

9


2.2.1 2.2.1 Extracting data and code

Fig. 1: Figure 2.1: Copy the data into thec:\ drive as shown above.

Use the link provided to you to download and unzip the data to a tem-porary location of your choice. Then copy the data folder ‘FRA’ into the‘C:’ folder so you’ll end up with a folder C:\FRA.

Below the folder FRA you should see Code and Assessment. UnderAssessment, you should see “Global”, or other folders which representsseparate case studies, for example Amazon etc.

Under the folder “Assessment” we find the folders Input and Output,like in Figure 2.1.

The folder Input holds the input data for the FRA. All data is locatedwithin geodatabases, which are organized by themes. More info on thedata sources are given in section :ref: Input data and in a table that de-scribes the data sources (see :ref: Data sets).

The folder Output is the location where the model is saving its results.

Under the folder Code a number of folders contain thepython code of the model

2.2.2 2.2.2 ArcGIS Desktop

If you have not done so yet, please install ArcGIS 10.2.2 or higher,preferably using default paths. After installation, license the product.

2.2.3 2.2.3 ArcGIS 64bit geoprocessing module

Next, install the Python 64bit geoprocessing module, if not already avail-able. If you are unfamiliar with this module, read more about it at this link. The module can be downloaded from theESRI site. You need to have access to the Downloads page for your organization on my.esri.com. Most likely, some-one in your organization can either download it for you or they can grant you the ability to download it for yourself.

2.2.4 2.2.4 PyCharm community edition

1. Download PyCharm (free community edition) from the JetBrainswebsite, and install it using the default path. Open the applicationand familiarize yourself with the application.

2. Open the FRA project in PyCharm, by pointing the application tothe path C:\FRA\CODE

3. Assign a Python interpreter. See the screenshot in Figure 2.3 andthis link for more information

(a) go to the project interpreter page (File | Settings |Project Interpreter)

(b) In the Project Interpreter page, expand the drop-down list of in-terpreters, and then choose Show all.

10 Chapter 2. 2 Getting started

http://desktop.arcgis.com/en/arcmap/latest/analyze/executing-tools/64bit-background.htm

https://www.jetbrains.com/pycharm/download/#section=windows

https://www.jetbrains.com/pycharm/download/#section=windows

https://www.jetbrains.com/help/pycharm/configuring-python-interpreter.html#available-sdks


(c) Select the python installer related to your ArcGIS instal-lation. For example, if you are working with ArcGIS10.5, the interpreter is typically called Python 2.7.13(C:\Python27\ArcGISx6410.5\python.exe). Pleasemake sure you use the 64-bit version. The path to the python.exefile must include ArcGISx6410.X, with the ‘x64’ not Ar-cGIS10.X, without it.

Fig. 3: Figure 2.3: Viewing and selecting available python interpreters.

2.2. 2.2 Installation 11


12 Chapter 2. 2 Getting started

CHAPTER 3

3 Conducting the FRA

This section describes a FRA for the Amazon region:

1. the input data and their main characteristics

2. the configuration file and its function and purpose

3. how to run a CSI analysis

4. where the results are located and how to interpret the results

3.1 3.1 Input data

Note: The paths to the data sources has slightly changed. The videos do not reflect this change yet. Sorry for theinconvenience

The different datasets are in a folder under /FRA/Assessment/Amazon/ and under /FRA/Code/

In this documentation, the paths and files for an Amazon-wide FRA is described.

Fig. 1: Figure 3.1: Contents of the inputfolder

A FRA must have four essential files:

1. The file barriers in barriers.gdb, a point feature class.

2. The file bm_rivers in benchmark.gdb. This is a polylinefeature class, which includes the benchmark rivers

13


3. The file streams in indicators.gdb, which contains theriver network, with the essential attributes to conduct the FRA

4. The file streams_join in indicators.gdb, which con-tains the river network, without the attributes. The feature classis used for joining the attribute table results back to the geometryof the river network.

3.1.1 3.1.1 Stream network

The stream network is stored as an ESRI feature class within a Geo-database. Each row of the database represents a river reach.

River reaches can be connected to 2 or more river reaches upstream(but never to only one). If there are no reaches upstream, the reachis a headwater stream, and if there is no downstream reach, the reachrepresents a terminal reach, e.g. an inland sink or a discharge pointinto the ocean.

The python class var in the module config.py defines and explains the field and field names used in barriers,bn_rivers and streams throughout the assessment, as well as some additional parameters unrelated to fieldnames.

Note: The following table lists fields in the river reaches feature class

Table 1: 1Field Alias DescriptionGOID **G**lobal **O**bject

**ID**entifierID is used to identify the river reach across different FFR assess-ments. Also used to link the dam to the river reach

NOID **N**etwork **O**bject**ID**entifier

Same purpose as GOID, used internally if the analysis is regional

NDOID **N**etwork **D**ownstream**O**bject **ID**entifier

Identifies the NOID of the next downstream river reach. If there isno value, the river reach represents a terminal reach (ocean, inlandsink)

NUOID **N**etwork **U**pstream**O**bject **ID**entifier

Identifies the NOIDs of the next upstream river reach. If there is novalue, the reach is a headwater reach. Otherwise, the field holds 2 ormore NOIDs. In case of multiple NOIDs, the NOIDs are separatedby an underscore

CON_ID Identifier for the continent towhich the reach belongs

Continent boundaries are delineated based on HydroBASINS.

1 = North America2 = South America3 = Europe4 = Africa5 = Asia6 = Australia

BAS_ID **BAS**in **ID**entifier Identifies the hydrological river basin according to HydroSHEDS.Note, that the Basis are hydrological, and can be different from Hy-droBASIN delineations

BAS_NAMEHydrological Basin name Based on HydroSHEDS original basinsContinued on next page

14 Chapter 3. 3 Conducting the FRA


Table 1 – continued from previous pageField Alias DescriptionRIV_ORD River Order based on field

DIS_AV_CMS (Discharge /Flow)

River order is based on the long-term average discharge using loga-rithmic progression:

1 = > 1000002 = 10000 - 1000003 = 1000 - 100004 = 100 - 10005 = 10 - 1006 = 1 - 10

RIV_CLASSRiver Class based on fieldDIS_AV_CMS (Discharge /Flow)

River order is based on the long-term average discharge using sen-sible cut-off points:

1 = 3 - 302 = 30 - 3003 = 300 - 10004 = 1000 - 30005 = 3000 - 10,0006 = 10,000 - 50,0007 = >50,000

LENGTH_KMLength of the river reach Length of the river reach in kilometersVOLUME_TCMVolume of the reach channel Calculated using width, length and depth of river channelBB_ID Backbone River IdentifierBB_NAMEBackbone River Name Name of the backbone river. From multiple sources.BB_OCEANOcean connectivity river reach is part of backbone river that is directly connected to

ocean sinkBB_LEN_KMBackbone river length Sum of kilometers (LENGTH_KM) of the river reaches of the back-

bone riverBB_VOL_TCMBackbone river volume Sum of volume (VOLUME_TCM) of the river reaches of the back-

bone riverBB_DIS_ORDBackbone discharge order River Order (RIV_ORD) of the most downstream reach of the back-

bone riverINC Filter field Only rivers that are longer than 10 km (BB_LEN_KM), their

discharge is larger than 1 cms at the most downstream reach(BB_DIS_ORD), and not a cold or hot desert river (BB_DRY <50).

DOF Degree of Fragmentation Index from 0 to 100%DOR Degree of Regulation Index from 0 to 100%SED Sediment Trapping Index from 0 to 100%USE Water consumption Index from 0 to 100%RDD Road construction Index from 0 to 100%URB Urban areas Index from 0 to 100%FLD Floodplain extent in river reach

catchment (%)Based on Fluet et al.’s floodplain map

CSI Connectivity Status Index Index from 0 to 100%; see figure 1a; 100% = full connectivity; 0%= no connectivity

CSI_D Dominant pressure factor see figure 1b; possible field values are: DOF; DOR; SED; USE;RDD; and URB

Continued on next page

3.1. 3.1 Input data 15


Table 1 – continued from previous pageField Alias DescriptionCSI_FF CSI Threshold Indicates if river reach CSI value is below the threshold used for the

scenarioCSI_FF12 Free-flowing status Indicates Free-flowing status (1) or non-free-flowing status (3)CSI_FF123Free-flowing status (2) Indicates Free-flowing status (1), “good connectivity” (2) or im-

pacted status (3)CSI_FFID River stretch Identifier Additional identifier for each backbone river stretch

3.1.2 3.1.2 Barriers

The barriers feature class is a point feature class holding the dam information.

There are a number of field in this database, that are not necessarily critical for running the model. The importantfields include:



















1 = > 1000002 = 10000 - 1000003 = 1000 - 100004 = 100 - 10005 = 10 - 1006 = 1 - 10



1 = 3 - 302 = 30 - 3003 = 300 - 10004 = 1000 - 30005 = 3000 - 10,0006 = 10,000 - 50,0007 = >50,000












3.1. 3.1 Input data 17





Field Alias DescriptionTBL 1CONTI-NENT

Continent name Based on our hydrological database

BAS_ID Hydrological Basin identifierBAS_NAMEHydrological Basin name If availableBAR_ID Barrier identifierBAR_NAMEBarrier Name If availableBAR_TYPEPrimary use of barrier (HYDRO

IRRIGATION etc)Not updated

GOID Stream identifier This is the Global Object Identifier. The number in this field cor-respond to the GOID of the river reach. Provides a link Link withfeature class (river_reaches)

STOR_MCMStorage capacity of the reservoirin million cubic meters (mcm)

The name of the field holding the storage capacity of the dam inmillion cubic meters (MCM). The name of the field can be differentbut must be set in the configuration file.

LongitudeLatitudeINC1 Dams to include The name of the field determining which dams to select.

If the value = 1 the dam is included in the analysis if the value is 0 it is excluded.

The name of the field can be different as long as it is referenced in the configuration file.

3.1.3 3.1.3 Waterfalls

3.1.4 3.1.4 Roads

3.1.5 3.1.5 Urban Areas

3.1.6 3.1.6 Nightlight intensity

3.1.7 3.1.7 Water abstractions

3.1.8 3.1.8 Benchmark rivers

Benchmark rivers are rivers that were positively identified as free-flowing. The benchmarking dataset of reportedFFRs was compiled from literature resources and expert input.

In the global study, we created 10 scenarios where we manipulated the individual weights within the plausible rangesand compared the results of each scenario to the set of benchmark rivers reported to be free-flowing. For the final CSIapplication, we selected the weights of the scenario that best reproduced the FFR status of the benchmark rivers.



No matter where you go, there you are.

—Buckaroo Banzai

becomes this document tree fr You can add your own benchmark rivers following the geodatabase template provided.

Sidebar Title

Optional Sidebar Subtitle

Subsequent indented lines comprise the body of the sidebar, and are interpreted as body elements.

Action ask local experts which rivers can be considered as free-flowing. Provide information about theFFR analysis to the experts so they can make an informed decision

The benchmarking procedure occurs during the CSI calculations.

3.2 3.2 Configuration File

The configuration file config.xls, located in the folder Code, provides the main parameters for the analysis.

The configuration file serves two purposes:

1. It provides parameter settings, for example settings to control the Degree of Fragmentation (DOF) analysis, aswell as settings that define the directory paths of the input data and output folder paths (see sheet PV_AMA inconfig.xlsx).

2. It provides the settings for a set of scenarios for the CSI analysis and for the benchmarking. The workbook isalso the location where new scenarios can be defined and weights allocated (see different sheet SCE in config.xlsx).

3.2.1 3.2.1 Parameter settings

In sheet PV_AMA in config.xlsx there are several different columns (see Figure 3.2):

Script Explains which parameter setting applies to which script. If the value here is all, it applies to all scripts.

Explanation A description of what the parameter does

Key Used by the python module (do not change)

Value The parameter value. This can be adjusted if needed. I suggest to leave the default paths as they are now, untilyou have gained a better understanding how all the puzzle pieces fit together.

The CSI analysis derives its parameters from the scenario sheet (SCE) in the same workbook, but for the DOR/DOFanalysis, there are a number of relevant parameter setting in this worksheet.

3.2. 3.2 Configuration File 19


Key barrier_inc_field The field describes the field that controls which dams should be included. The fieldmust be present in the barrier feature class. For example, the barrier feature class for the Amazon basin hasthe field INC1. All dams that are currently existing have a value of 1 in there. There is another field calledINC2, where the “under construction” dams are also included. The field INC3 includes all dams, including theones planned. Using this field it is possible to produce results for different sets of dams. These results are storedin the output table in the fields provided in key dor_field and key dof_field.

Key dor_field and dof_field Provide the names for the field in the output table (where the results are stored).

Key svol_field The field in the barrier feature class holding the storage volume for each dam (Note: unitsmust be in million cubic meters).

Key dis_range_factor Can be used to limit or increase the strength of the DOF effect. The higher the value,the stronger the effect. The minimum value is 1 (this corresponds to no effect at all), so the value must be largerthan one to produce an effect. There is no upper limit, but a value larger than ten is not recommended. Thestandard value is 10, which means that the DOF effect occurs in river reaches with up to ten times larger orsmaller from the location of the dam. Please review the methodology section of the research article (Grill et al.,2017) for further details.

Key use_dam_level_df This key can be set to “True” if you would like to set the discharge range factor(Key dis_range_factor) for each dam individually. This will then ignore the “global” setting of keydis_range_factor and derive the discharge range factors from the barrier feature class. You must cre-ate two new fields, DFU (Discharge Factor Upstream), and DFD (Discharge Factor Downstream), and assignvalues for each dam.

Key update_stream_mode This value can be set to “yes”, which means that the results of the DOF and DORanalysis are automatically written into the stream feature class (key stream_fc). For this to work, the fieldsdefined in key dor_field and in dof_field must also be present in the stream feature class before thescript is started.

Key dof_mode The DOF mode describes how the fragmentation effect decays from the point of the barrier. Wediscussed a linear decay, but finally settled on a logarithmic decay, since a logarithmic decay is in line withproperties of river networks themselves. I would suggest to leave this setting as is (logarithmic).

3.2.2 3.2.2Sce-nar-ios

Thein-di-vid-ualsce-nar-iosarede-finedinasep-a-rate



work-sheet called SCE in config.xlsx. Under the header, each row corresponds to adifferent scenario. You can modify the indicators to use, the weights for each in-dicator, the CSI threshold, and some other settings. These are explained below:

Scenario_nameNameoftheSce-nario.

Note:Thisnamewillbewrit-tenintotheout-putfea-tureclassastheCSIfield. So please keep this name very simple, i.e. under 10 characters, no blanks, no special characters, do not start thename with a number.

Indicator_1 to 5Thesearethepres-surein-di-ca-torstobeusedinthesce-nario.You typically use all five of them, in other word, it is best to not delete any indicator.

Note:You

3.2. 3.2 Configuration File 21


cansettheweightofanin-di-ca-tortozero,ifyoudon’twant to use it.

Fig. 3: Figure 3.3: Scenario configuration (see sheet SCE in config.xlsx).

Thenamewrit-tenheremustre-flectthefieldnamesinthein-putstreams feature class in the geodatabase indicators.gdb. For example, the name in cell B2 is DOF_EXI. Inthe streams feature class, a field with that name must be also present.

Weight_1 to 5 These are the weights assigned to each Indicator. The sum of the weights should equal to 100%,otherwise there is a distortion.

You can get some guidance setting the weight in the Excel Sheet “Weighting”, located in the Excel file config.xlsxin the folder Code..

CSI_threshold The CSI threshold to use, for example 95%. Above 95%, the river reach is considered “affected”,but not “impacted”. Below the threshold, the river or river reach is considered “impacted” and can no longer be“free-flowing”.

Fld_dampen This parameter determines the strength of the floodplain weighting. It can range from 0 to 100%.

To_filter Determines if the filtering procedure should be conducted. Leave as 1 for now.

Filter_threshold Provides a parameter for filtering. Leave as is for now.

3.3 3.3 Running the FRA

Start the script using the PyCharm user interface. In the PyCharm user interface, right-click on the script you wantto run, and select run. For example, to run the CSI analysis, right-click on the script f4_csi.py and then choose



run f4_csi.py. In the “run” window, you will see some messages starting to appear.

3.4 3.4 Output and interpretation

3.4.1 3.4.1 Folder structure

Fig. 4: Figure 3.4: Overview of results

An example of the contents of the folder are listed inFigure 3.4. The output path of the results is defined inthe configuration file, and is set to C:\FRA\OUTPUT\plus the region, e.g. AMAZON by default. E.g.:C:\FRA\OUTPUT\AMAZON (see red bullet b1)

Within the folder a folder called Results (b2) isgenerated, supplemented by a time stamp, for exam-ple Results_171115_115824_500000. The times-tamp indicates year, month, day, hour, minute, and second.The last number (here 500000 indicates milliseconds andcan be ignored). Every new model run generates a newfolder with a new timestamp having identical structure in-side.

The model generates two types of results:

1) Tabular data in the form of an Excel workbook withseveral worksheets (Figure 3.5). After the model hasbeen run, you should see a folder called STAT (b3), whichholds a workbook called results.xls (b4). The work-book is generated and opened automatically by the code,therefore it should be visible after the model has been run.

2) Geodata as feature classes in a geodatabase. You will find a Geodatabase called CSI.gdb(b5) which holds a feature class of the streams (b6) with all the scenario results (each CSI re-sult will have its own field). The same results are provided as a table (b7). Furthermore,there will be one river feature class for each scenario, which holds the status of the river (b8).

Fig. 5: Figure 3.5: Output Excel Workbook as tabular result of the FRA.

3.4. 3.4 Output and interpretation 23


3.4.2 3.4.2Tab-u-larre-sults

Intheresults.xlswork-book(Fig-ure3.5),thereareanum-berofwork-sheets,asde-scribed below:

Bench_rivers This sheet simply lists the benchmark rivers used for benchmarking as defined in the feature classbm_rivers. The field BENCH_SRC refers to the source of the information, for example NLS = “Nilssonstudy” or to EXP = “Expert” opinion. The field FFRID holds the unique identifier for the benchmark river.

Bench_results This sheet holds the results of the benchmarking. The field MATCH indicates of there is a matchbetween the “known” status, and the “predicted” status as calculated by the CSI analysis. The field SCE_NAMErefers to the scenario name. lists the benchmark rivers used for benchmarking as defined in the feature classbm_rivers. The field BENCH_SRC refers to the source of the information, for example NLS = “Nilssonstudy” or to EXP = “Expert” opinion. The field FFRID holds the unique identifier for the benchmark river. Thefield BENCH_CAT is equivalent to the field BENCH_SRC. The next Sheet provides a pivot table using data fromthis sheet.

Bench_results_piv The results are based on the previous and transformed as a pivot table. For example, the Figure 3.6below indicates that in scenario CSI_EXI, all 6 benchmarking rivers were “matched”, whereas in the scenarioCSI_PLA (the scenario with planned dams in Amazon), only 2 matched.

´The table indicates for each scenario and benchmark river, the type and number of dominant pressures. In otherwords, it answers the question as to why a river did not match the benchmarking. In the example provided, the Juruenariver would be impacted by DOF and DOR in the scenario CSI_PLA in 367 and 6 river reaches respectively. Theresults are based on the previous table and transformed as a pivot table. This pivot table aggregates the dominantpressure by scenario (no longer by river). In the current example, for scenario CSI_PLA, the rivers in the Amazonwould be affected by DOF and DOR at 740 and 20 river reaches respectively.



Fig. 6: Figure 3.6: Example bench-mark results

´The table shows for each scenario, the number of river reaches affected by a certain pressure indicator.In this example (Figure 3.7), DOF was the leading cause of impact in 2464 river reaches in the CSI_EXIscenario, etc. The table shows similar results than in the previous table, now as a pivot table This tableprovides some “global” results (Figure 3.8), applicable to the entire data set. The field count_reachesshows the number of river reaches included in the analysis. Note that only river reaches that belong torivers that fulfilled certain criteria were included in the analysis. Those criteria were:

Bench_dominanceBench_dominance_pivGlobal_dominanceGlobal_dominance_pivGlobal_stats 1. River must be 10 km orlonger

2. River must discharge at least 1 cms on average at its mouth

3. River must be more than 50% perennial

This type of selection can be reproduced in the GIS using the following fields:

BB_DIS_ORD < 7 and BB_LEN_KM >= 10 and BB_DRY_PCT < 50

The field count_reaches_affected shows the number of river reaches affected in any form by a loss of con-nectivity (`CSI < 100`). The field percent_reaches_affected presents the same statistic as a percentageof count_reaches.

The field mean_csi indicates the average CSI values across all river reaches.

The field count_nff indicates the number of river reaches below the CSI threshold (e.g., CSI < 95%).

The field perc_nff indicates the percent of non-free-flowing river reaches based on the toal river reaches.

Fig. 7: Figure 3.7: Global bench-mark results

3.4.3 3.4.3Geo-datare-sults

Note: Please also see the project file AMA_Draft1.mxd, where you can see which fields are used to create the mapsshown in the reports and the paper. It is located in the folder C:\FRA\Output\Amazon.



3.4.3.1 Connectivity Status Index (CSI)

There-sultsoftheCSIanalysis results are saved in the feature class csi_fc in the geodatabase CSI.gdb(See also Figure 3.10) and contain several fields relevant to the CSI (Figure 3.9).

1.theCSIval-uesthem-selves. The values range from 0-100%, with 100% indicating full connectivity. The field that holds the CSI valuesis names after the Scenario name given in the scenarios in config.xls. For example if the scenario was namesCSI_EXI, then that is going to be the field name of the field holding the CSI value.

2.afieldin-di-cat-ing if the CSI is above (value=1) or below the CSI threshold (value=0). The name of the field is composed ofthe scenario name + “_FF”, for example CSI_EXI_FF.

Fig. 9: Figure 3.9: Example output showing fields in the feature class csi_fc in the geodatabase CSI.gdb that arerelevant for CSI and DOM indicators.

Fig. 10: Figure 3.10: Example output showing the CSI indicator in the GIS user interface (seeC:\FRA\Assessment\Amazon\Output\AMA_Draft1.mxd).

3.4.3.2 Dominant Pressure Indicator (DOM)

There-sultsfortheDOM



anal-y-sis(Dom-i-nantPres-sureIn-di-ca-tor;Fig-ure3.11)arealsosavedinthefea-tureclass csi_fc in the geodatabase CSI.gdb. The relevant field name is composed of the scenario name plus theletters “_D”, for example CSI_EXI_D.

Thefieldholdthenamesofthepres-surein-di-ca-torsasde-finedinthesce-nariolist(inconfig.xls).Pos-si-bleval-ues include DOF, DOR, CON, URB, ROA, or it may be empty, if there is no pressure present.



Fig. 11: Figure 3.11: Example output showing the DOM indicator in the GIS user interface (seeC:\FRA\Assessment\Amazon\Output\AMA_Draft1.mxd)

3.4.3.3 Rivers Status (FFR)

There-sultval-uesfortheFFRin-di-ca-tor(Fig-ure3.12),ortheriversta-tus,arenotstoredintheinthefea-tureclass csi_fc, but they are stored in separate feature classes for each scenario. These results are based on dissolvingthe stream network into stretches based on the backbone river concept (see again Figure 1.3).

Thenameoftheirfea-tureclassarecom-posedofthepre-fixffr_bb_+“sce-narioname”,for



ex-am-pleffr_bb_csi_exi.

Thereareanum-berofstatis-tics,thatcouldberel-e-vantforfur-theranal-y-sis,butthemostim-por-tantfieldisCAT_FFR.

CAT_FFRholdtheFFRsta-tusval-ues.Ariverstretchcanbeei-therbe“Free-flowing”(value=1),with



“Goodcon-nec-tiv-itysta-tus”(value=2),or“impacted” (value=3).

Note: The following table lists fields in the rivers (ffr_bb_XY) feature class (not in order)

3.5 **



















1 = > 1000002 = 10000 - 1000003 = 1000 - 100004 = 100 - 10005 = 10 - 1006 = 1 - 10



1 = 3 - 302 = 30 - 3003 = 300 - 10004 = 1000 - 30005 = 3000 - 10,0006 = 10,000 - 50,0007 = >50,000












3.5. ** 31













Ifthevalue=1thedamisin-cludedintheanal-y-sisifthevalueis0itisex-



cluded.

Thenameofthefieldcanbedif-fer-entaslongasitisref-er-encedinthecon-fig-u-ra-tionfile.**..list-table:

:widths:→˓10,→˓

→˓30,→˓

→˓60:header-→˓rows:→˓1

*→˓

→˓-→˓

→˓Field

→˓

→˓

→˓-→˓

→˓Alias

→˓

→˓

→˓-→˓

→˓Description

(continues on next page)

3.5. ** 33


(continued from previous page)

*→˓

→˓-→˓

→˓CONTINENT

→˓

→˓

→˓-→˓

→˓Continent→˓name

→˓

→˓

→˓-→˓

→˓Delineated→˓based→˓on→˓HydroBASINS

*→˓

→˓-→˓

→˓ISO_→˓NAME

→˓

→˓

→˓-→˓

→˓Country→˓name

→˓

→˓

→˓-→˓

→˓ISO→˓country→˓name

*→˓

→˓-→˓

→˓BAS_→˓ID

→˓

→˓

→˓-→˓

→˓Hydrological→˓Basin→˓identifier

→˓

→˓

→˓-→˓

→˓Based→˓on→˓HydroSHEDS→˓original→˓basins





*→˓

→˓-→˓

→˓BAS_→˓NAME

→˓

→˓

→˓-→˓

→˓Hydrological→˓Basin→˓name

→˓

→˓

→˓-→˓

→˓Based→˓on→˓HydroSHEDS→˓original→˓basins

*→˓

→˓-→˓

→˓BB_→˓ID

→˓

→˓

→˓-→˓

→˓River→˓Identifier

→˓

→˓

→˓-→˓

→˓Attribute→˓from→˓rivers→˓feature→˓class

*→˓

→˓-→˓

→˓BB_→˓NAME

→˓

→˓

→˓-→˓

→˓River→˓Name


3.5. ** 35



→˓

→˓

→˓-→˓

→˓Attribute→˓from→˓

→˓"rivers→˓"→˓feature→˓class

*→˓

→˓-→˓

→˓BB_→˓OCEAN

→˓

→˓

→˓-→˓

→˓Stream→˓reach→˓is→˓part→˓of→˓river→˓that→˓is→˓directly→˓connected→˓to→˓ocean

→˓

→˓

→˓-→˓

→˓Attribute→˓from→˓

→˓"rivers→˓"→˓feature→˓class

*→˓

→˓-→˓

→˓BB_→˓DRY_→˓PCT

→˓

→˓

→˓-→˓

→˓Percentage→˓of→˓river→˓length→˓classified→˓as→˓

→˓"dry→˓"





→˓

→˓

→˓-→˓

→˓Based→˓on→˓HydroSHEDS→˓discharge→˓layer→˓and→˓new→˓intermittency→˓classification.→˓

*→˓

→˓-→˓

→˓BB_→˓LEN_→˓KM,→˓

→˓

→˓

→˓-→˓

→˓Length→˓of→˓the→˓Backbone→˓River→˓in→˓kilometer

→˓

→˓

→˓-→˓

*→˓

→˓-→˓

→˓BB_→˓VOL_→˓TCM

→˓

→˓

→˓-→˓

→˓Volume→˓of→˓the→˓river→˓channel


3.5. ** 37



→˓

→˓

→˓-→˓

→˓Width→˓X→˓Depth→˓X→˓Length→˓of→˓the→˓river→˓channel.→˓

→˓Calculation→˓of→˓width→˓and→˓depth→˓based→˓on→˓hydrometric→˓geometry→˓rules.→˓

*→˓

→˓-→˓

→˓BB_→˓DIS_→˓ORD

→˓

→˓

→˓-→˓

→˓Discharge→˓Order→˓of→˓the→˓river

→˓

→˓

→˓-→˓

→˓Calculated→˓as→˓the→˓log10→˓of→˓the→˓discharge→˓at→˓the→˓mouth→˓of→˓the→˓river→˓(see→˓``RIV_→˓ORD``)





*→˓

→˓-→˓

→˓RIV_→˓ORD

→˓

→˓

→˓-→˓

→˓River→˓Order→˓based→˓on→˓field→˓DIS_→˓AV_→˓CMS

→˓

→˓

→˓-→˓

→˓1→˓=→˓>→˓

→˓100000→˓2→˓=→˓10000→˓-→˓

→˓100000→˓3→˓=→˓1000→˓-→˓

→˓10000→˓4→˓=→˓100→˓-→˓

→˓1000→˓5→˓= 10 - 100 6 = 1 - 10

*→˓

→˓-→˓

→˓LENGTH_→˓KM

→˓

→˓

→˓-→˓

→˓Length→˓of→˓the→˓river→˓reach→˓in→˓kilometers


3.5. ** 39



→˓

→˓

→˓-→˓

→˓HydroSHEDS

*→˓

→˓-→˓

→˓DIS_→˓AV_→˓CMS

→˓

→˓

→˓-→˓

→˓Long-→˓term→˓average→˓discharge→˓in→˓cubic→˓metres→˓per→˓second→˓(cms)

→˓

→˓

→˓-→˓

→˓Modelled→˓discharge→˓based→˓on→˓watergap→˓Global→˓Hydrological→˓Model

*→˓

→˓-→˓

→˓BB_→˓SUM_→˓KM

→˓

→˓

→˓-→˓

→˓Length→˓of→˓the→˓section→˓of→˓the→˓river→˓in→˓kilometer





→˓

→˓

→˓-→˓

→˓In→˓this→˓feature→˓class→˓the→˓river→˓can→˓be→˓broken→˓into→˓sections→˓if→˓the→˓river→˓is→˓not→˓free-→˓flowing.→˓

→˓BB_→˓SUM_→˓KM→˓is→˓the→˓length of that section. BB_SUM_KM is equal to BB_LEN_KM if the river is free-→˓flowing. The sum of all sections of each river (sum of BB_SUM_KM) is equal to BB_→˓LEN_KM

*→˓

→˓-→˓

→˓BB_→˓SUM_→˓VOL

→˓

→˓

→˓-→˓

→˓Volume→˓of→˓the→˓section→˓of→˓the→˓river→˓in→˓million→˓cubic→˓meters→˓(mcm)

→˓

→˓

→˓-→˓

→˓BB_→˓SUM_→˓VOL→˓is→˓the→˓volume→˓of→˓the→˓river→˓section.→˓

→˓BB_→˓SUM_→˓VOL→˓is→˓equal→˓to→˓BB_→˓VOL_→˓TCM→˓if→˓the→˓river→˓is→˓free-flowing. The sum of all sections of each river (sum of BB_SUM_VOL) is equal to→˓BB_VOL_TCM


3.5. ** 41



*→˓

→˓-→˓

→˓RIV_→˓CLASS

→˓

→˓

→˓-→˓

→˓River→˓size→˓class

→˓

→˓

→˓-→˓

→˓Similar→˓to→˓RIV_→˓ORD→˓as→˓it→˓is→˓based→˓on→˓discharge.→˓

→˓Use→˓primarily→˓for→˓cartography.→˓

→˓

→˓1→˓=→˓>→˓

→˓100000→˓2→˓=→˓10000 - 100000 3 = 1000 - 10000 4 = 100 - 1000 5 = 10 - 100 6 = 1 - 10

Fig. 12: Figure 3.12: Example output showing the FFR status in the GIS user interface (seeC:\FRA\Output\Amazon\AMA_Draft1.mxd).

ReferencesGrill,G.,Lehner,B.,Thieme,M.,Gee-nen,B.,Tick-



ner,D.,An-tonelli,F.,Babu,S.,Cheng,L.,Cro-chetiere,H.,Filgueiras,R.,Goi-chot,M.,Hig-gins, J., Hogan, Z., Lip, B., McClain, M., Meng, J.-H., Mulligan, M., Nilsson, C., Olden, J.D., Opperman, J., Petry,P., Reidy Liermann, C., Saenz, L., Salinas-Rodríguez, S., Schelle, P., Snider, J., Tockner, K., Valdujo, P.H., vanSoesbergen, A., Zarfl, C. (2017) Assessing global river connectivity to map the world’s remaining free-flowing rivers.in review.

3.5. ** 43

CHAPTER 4

4 Tailoring the FRA

4.1 4.1 Adding new dams

When you introduce new dams, remove dams, or the dams’ attributes change, the DOR and DOF indicators need tobe recalculated before you can run the CSI analysis again.

4.1.1 4.1.1 Preparing dam point

Start with the existing database and build upon it. It is best to follow the barrier template provided. First, pleasehave a look at the barrier file in ArcMap together with the stream feature class.

Removing dams from dataset Removing dams from the database is straightforward - simply delete them from thedataset, or assign the value 0 to the INC field to exclude the dam from the assessment altogether, without losing itsgeometry (preferred approach).

Adding dams to dataset To add dams to the database you could either manual digitize the dam into the feature class,or merge another dataset into the existing dataset using your preferred GIS.

Fields Make sure the required fields are located in the feature class. The barrier database has many fields, but notall of them are actually required for the analysis.

The important fields are BAS_ID, GOID, STOR_MCM, PCAP_MW and INC.

Please see the field description for more info

GOID GOID stands for “Global Object Identifier”. Notice how each dams has a value in the field called GOID. Thevalue corresponds to the GOID of the stream nearby. For the model to recognize on which river the dam is

45


located, each dam must have the correct GOID value, the one that is corresponding to the GOID of the riverreach.

Note: You don’t have to move the dam close to the river reach. You only need to set the value in this fieldcorrectly.

STOR_MCM Assign the storage volume if available, (Field: STOR_MCM), the units are “million cubic meters”

Note: If you don’t have storage capacity, you could use the power production capacity to derive a rough estimate(Get in touch for details). If the storage capacity is zero, the DOR will not be calculated for the dam, and only thefragmentation will be calculated.

PCAP_MW Assign the power capacity in megawatt, if available (Field: PCAP_MW), the units are “megawatts”.

BAS_ID Make sure you assign the correct Basin ID (“Field: BAS_ID) to each dam. See the basin1 feature classin the geodatabase indicators.gdb to extract the correct BAS_ID and copy it into the field.

INC Purpose and function will be described below

4.1.2 4.1.2 Selecting dams

Create a field in the barrier feature class with the name INC or something similar. Use a value of 1 in this field,to include the barrier in the simulations, otherwise choose a value of 0 to exclude it. The name of this field can laterbe used as the parameter setting in the ‘‘ barrier_inc_field‘‘ field. This will limit the DOF / DOR analysis to the damswith a value of 1 in the given field.

4.1.3 4.1.3 Running the DOR / DOF

Before running the script, please examine the configuration file again (config.xlsx) and set the parameters forthe DOF / DOR analysis accordingly (see above and section :ref: 3.2.1 Parameter settings for a description of theparameters).

This DOF and DOR calculations are done together in the script f3_dorf.py. Run this script using PyCharm asdescribed in :ref: 3.3 Running the FRA. The script generates a table with the new DOF and DOR values.

4.1.4 4.1.4 Results of the DOR / DOF analysis

The results of this analysis will be stored as a table in the geodatabase dorf.gdb in the results folder.

Open the table in ArcMap and create a table join to the streams feature class using the field GOID. Copy the resultsinto a new field or update an existing field with the new values.

Note: Only river reaches of river basins that have dams will have values in the resulting table. Therefore you mightencounter som NULL values after the table join was done.

4.2 4.2 Adding Benchmark rivers

1. First use the streams feature class to select the benchmark river that should be add to the database.

46 Chapter 4. 4 Tailoring the FRA


2. Export only the selection and merge it with bm_rivers database. Using this technique, the required fieldsBB_ID, BB_NAME, BAS_ID, BAS_NAME, LENGTH_KM, VOLUME_TCM, and RIV_ORD are derived from thestream feature class, and will be present in the bm_rivers database

3. Update the fields FFRID, BENCH_SRC, and Name_Expert

4.3 4.3 Using alternative data layers

4.3.1 4.3.1 Roads and urban areas

Geographical selection We only consider the effect of roads and urban areas in a distance of up to one kilometer fromthe river (i.e. a buffer of one kilometer each side of the river.

Please refer to the auxiliary data in the The reason why the two are mentioned together is the fact that we do notconsider roads in urban areas, because it would be “double-counting”.

Urban areas are an indicator for dense infrastructure development, which includes roads, parking lots, various typesof buildings, weirs, locks channels etc, which may all impact adjacent rivers and streams.

Using OpenStreetMap to extract data for roads could be a feasible option.

The goal is to calculate the percent of road coverage

4.3.2 4.3.2 Water Consumption

Information about water consumption data should be added

4.3.3 4.3.3 Waterfalls

Waterfalls naturally reduce connectivity in river networks by reducing or blocking species movement in river net-works. Waterfalls should therefore be considered in the fragmentation analysis. They are blocking species movementupstream, while the downstream connectivity remains intact.

4.4 4.4 Creating new scenarios

TODO

4.3. 4.3 Using alternative data layers 47


48 Chapter 4. 4 Tailoring the FRA

CHAPTER 5

5 Contributing to code development

5.1 5.1 Preparation

If you do decide to make contributions and change the source code, there are many tools that make this processeasier. Even if you don’t plan to contribute to the development of the tool now, you may find a version control systembeneficial when working with the code.

5.1.1 5.1.1 Create a Github account

The code for the FRA is hosted on Github as a code repository. To download the latest code, you need to sign upfor a user account and then let me know your Github user name. I will add you as a contributor to the project andthen you can then download the latest source code. You’ll also be able to see different issues, make suggestions onimprovements, report bugs, or even contribute and improve the source code

5.1.2 5.1.2 Download and install SourceTree

There is an application called ‘SourceTree’, which acts as an intermediary between your local code and the coderepository. The software is also free. SourceTree can be found at http://www.sourcetreeapp.com/. It is preferred toGithub for Windows or Visual Studio’s git integration because only SourceTree gives you UI support to follow thegit-flow development process.

During installation:

• Enter your full name and email address

• Choose the option to install the self-contained Git version to be used by when prompted

• Skip the Mercurial option

• Choose Putty as SSH option

• Enter your github.com username and password

49

http://www.sourcetreeapp.com/


Figure 5.1: Image with caption.

5.2 5.2 Git basics

If you’re unfamiliar with Git, please take the time to review the basics on a tutorial site such as:

http://gitimmersion.com/

https://www.atlassian.com/git

http://www-cs-students.stanford.edu/~blynn/gitmagic/

http://stackoverflow.com/questions/315911/git-for-beginners-the-definitive-practical-guide

Interactive web-based 15 minute tutorial: http://try.github.io/

A look at git internals: http://ftp.newartisans.com/pub/git.from.bottom.up.pdf

50 Chapter 5. 5 Contributing to code development

http://gitimmersion.com/

https://www.atlassian.com/git

http://www-cs-students.stanford.edu/~blynn/gitmagic/

http://stackoverflow.com/questions/315911/git-for-beginners-the-definitive-practical-guide

http://try.github.io/

http://ftp.newartisans.com/pub/git.from.bottom.up.pdf


References 2

5.2. 5.2 Git basics 51


52 Chapter 5. 5 Contributing to code development

CHAPTER 6

6 ANNEX

6.1 6.1 Installing Pandas

For older versions of ArcGIS only (10.2 or 10.3): Please verify that the package Pandas is installed. 1) Once youassigned the Python interpreter in PyCharm, you might need to install the ‘Python packaging tools’ as suggested bythe app. The packaging tool allow you to review existing packages, or to add new packages to the installation.

2. After you installed the ‘Python Packaging Tools’, verify if the Pandas package is installed. Pandas is apackage that helps with statistics analysis. It should be listed as an available package.

Figure 6.1

Fig. 1: Figure 6.2: Checking for installed package Pandas in PyCharm

If you have ArcGIS 10.4 or higher, this package should be installed by default. However, if Pandas is not in-stalled, please follow these instructions: First, open a command prompt with administrator rights. Then change

53

https://pandas.pydata.org/


directory into the script path by typing ‘cd’ (without the quotation marks) then a blank and then the path to theArcGIS python scripts folder, which is C:\Python27\ArcGISx6410.2\Scripts (See Figure 6.3 below).

Fig. 2: Figure 6.3: Installing pandas from the command prompt

The latter depends on theArcGIS version you haveinstalled. It could alsobe ArcGISx6410.3, orArcGISx6410.4 etc. Onceyou are in the right directory,type ‘pip install pandas’ likein the image below.

Note: Tip: You can copy the pathnames outside the command prompt using Control+C and paste text into theterminal to avoid typos. (just right-click in terminal window, or use Control+V) .

Pandas will be installed including packages that depend on it. Please get in touch if the installation fails.

6.2 6.2 Glossary

“Good connectivity status” Set of contiguous, aggregated river reaches forming a river stretch; one or more parts ofthe remaining river are above the CSI threshold. Field: `CAT_FFR = 2`

Connectivity Status Index (CSI) Index value on a sliding scale between 0 to 100% (100 = best connectivity) foreach river reach. Field: CSI

CSI threshold Threshold at which river reach is considered free-flowing (in a binary sense). CSI threshold is 95%, ifbelow, river reach is considered not Free-flowing

Dominant Pressure Index The pressure that had the largest effect on the cumulative CSI value. Field: DOM

FRA Free-flowing River Analysis

Free-flowing river Set of contiguous, aggregated river reaches where the CSI value of all river reaches are above thethreshold from source (i.e., the location where a river exceeds 100 l/s average discharge or 50 km2 upstreamarea) to sink (i.e., river mouth at ocean or at inland depression where flow terminates). Field: `CAT_FFR =1`

Pressure indicator Data layers used in a model to determine impact score / CSI of each river reach. The fields forthe five pressures are: DOR, DOF, URB, RDD, USE

River Set of contiguous river reaches forming one linear feature from source (i.e., the location where a river exceeds100 l/s average discharge or 50 km2 upstream area) to sink (i.e., river mouth at ocean or at inland depressionwhere flow terminates). Tributaries to larger rivers form distinct rivers. Each river is defined by its identifier(BB_ID). The river name is given in field BB_NAME

River reach Individual element of a river network defined as the segment between two or more confluences. Globally,there are n = 8,477,883 river reaches. Each river reach has a unique identifier (Field: GOID).

6.3 6.3 Data sets

Note: The following table describes data layers included in the documentation package

54 Chapter 6. 6 ANNEX














BAS_NAMEHydrological Basin name Based on HydroSHEDS original basinsRIV_ORD River Order based on field



1 = > 1000002 = 10000 - 1000003 = 1000 - 100004 = 100 - 10005 = 10 - 1006 = 1 - 10



1 = 3 - 302 = 30 - 3003 = 300 - 10004 = 1000 - 30005 = 3000 - 10,0006 = 10,000 - 50,0007 = >50,000

LENGTH_KMLength of the river reach Length of the river reach in kilometersVOLUME_TCMVolume of the reach channel Calculated using width, length and depth of river channel


6.3. 6.3 Data sets 55


Table 1 – continued from previous pageField Alias DescriptionBB_ID Backbone River IdentifierBB_NAMEBackbone River Name Name of the backbone river. From multiple sources.BB_OCEANOcean connectivity river reach is part of backbone river that is directly connected to










CSI_FF CSI Threshold Indicates if river reach CSI value is below the threshold used for thescenario

CSI_FF12 Free-flowing status Indicates Free-flowing status (1) or non-free-flowing status (3)CSI_FF123Free-flowing status (2) Indicates Free-flowing status (1), “good connectivity” (2) or im-












If the value = 1 the dam is included in the analysis if the value is 0 it is excluded.

The name of the field can be different as long as it is referenced in the configuration file. **.. list-table:

:widths: 10, 30, 60:header-rows: 1

* - Field- Alias- Description

* - CONTINENT- Continent name- Delineated based on HydroBASINS

* - ISO_NAME- Country name- ISO country name

* - BAS_ID- Hydrological Basin identifier- Based on HydroSHEDS original basins

* - BAS_NAME- Hydrological Basin name- Based on HydroSHEDS original basins

* - BB_ID- River Identifier- Attribute from rivers feature class

* - BB_NAME- River Name- Attribute from "rivers" feature class

* - BB_OCEAN- Stream reach is part of river that is directly connected to ocean- Attribute from "rivers" feature class

* - BB_DRY_PCT- Percentage of river length classified as "dry"- Based on HydroSHEDS discharge layer and new intermittency classification.

* - BB_LEN_KM,


6.3. 6.3 Data sets 57



- Length of the Backbone River in kilometer-

* - BB_VOL_TCM- Volume of the river channel- Width X Depth X Length of the river channel. Calculation of width and depth

→˓based on hydrometric geometry rules.

* - BB_DIS_ORD- Discharge Order of the river- Calculated as the log10 of the discharge at the mouth of the river (see ``RIV_

→˓ORD``)

* - RIV_ORD- River Order based on field DIS_AV_CMS- 1 = > 100000 2 = 10000 - 100000 3 = 1000 - 10000 4 = 100 - 1000 5 = 10 - 100 6 =

→˓1 - 10

* - LENGTH_KM- Length of the river reach in kilometers- HydroSHEDS

* - DIS_AV_CMS- Long-term average discharge in cubic metres per second (cms)- Modelled discharge based on watergap Global Hydrological Model

* - BB_SUM_KM- Length of the section of the river in kilometer- In this feature class the river can be broken into sections if the river is not

→˓free-flowing. BB_SUM_KM is the length of that section. BB_SUM_KM is equal to BB_LEN_→˓KM if the river is free-flowing. The sum of all sections of each river (sum of BB_→˓SUM_KM) is equal to BB_LEN_KM

* - BB_SUM_VOL- Volume of the section of the river in million cubic meters (mcm)- BB_SUM_VOL is the volume of the river section. BB_SUM_VOL is equal to BB_VOL_TCM

→˓if the river is free-flowing. The sum of all sections of each river (sum of BB_SUM_→˓VOL) is equal to BB_VOL_TCM

* - RIV_CLASS- River size class- Similar to RIV_ORD as it is based on discharge. Use primarily for cartography.

→˓1 = > 100000 2 = 10000 - 100000 3 = 1000 - 10000 4 = 100 - 1000 5 = 10 - 100 6 = 1 -→˓ 10



Datasetname

Dataset description Geodatabase Relatedtopres-surefac-tor

Source

FRA_barriers Existing and Planned damsfrom the Amazon region ex-tracted from global dataset

Barriers.gdb DOR,DOF

GRanD, GOOD, Zarfl et al

FRA_roads_grip_v3_globalISO_NAME RDD Link<https://www.europeandataportal.eu/data/en/dataset/grip-v3-2013-densities-global-roads-inventory-project>

River name BB_NAME Attribute from “rivers” fea-ture class.

Streamreach is partof river thatis directlyconnectedto ocean

BB_OCEAN Attribute from “rivers” fea-ture class

6.4 6.4 Frequently Asked Question

What is a FRA? A Free-flowing Rivers Assessment (FRA) is a methodology to determine the connectivity statusof rivers by taking into consideration both natural connectivity as well as fragmentation from infrastructure, such asdams, roads, urban areas, water use).

What is the focus of a FRA? The main result of a FRA is a connectivity index representing how well river stretchesare still connected in the lateral, and in the upstream and downstream direction given existing infrastructure. Assuch the FRA provides a layer of information that is strictly focused on connectivity, and is as such not a completeassessment of river health.

What else can you do with a FRA? However the results are meant to be combined and supplemented with otherlayers, such as species information, water quality or fluvio-geomorphological information to e.g., further assess andidentify high-value conservation areas. Furthermore, the methodology can be used to assess the effect of future changesof infrastructure (such as dams) on the connectivity status.

Indices and tables

• genindex

• modindex

• search

6.4. 6.4 Frequently Asked Question 59

Index

Symbols"Good connectivity status", 54

CConnectivity Status Index (CSI), 54CSI threshold, 54

DDominant Pressure Index, 54

FFRA, 54Free-flowing river, 54

PPressure indicator, 54

RRiver, 54River reach, 54

61

ffr documentation

Documents