3 Input data

Four feature classes are provided with the toolbox as examples of the required data for running the tool. These files are stored in the example_data.gdb of the data folder. Each file is described in further detail below and additional tables are provided in the annex to describe the fields contained in the different feature classes.

3.1 Stream network

The streams feature class is a stream network. Each row of the database represents a river reach. In this context, we define a river reach as a line segment between two confluences; a river stretch as two or more contiguous reaches but not a full river; and a river as an aggregation of river reaches that form a single-threaded, contiguous flowpath from headwater source to river outlet. The river outlet can represent either the river mouth at the ocean; a terminal inland depression; or the confluence with a larger river.

To create the streams feature class, a stream network delineation has been extracted from World Wildlife Fund’s HydroSHEDS database (Lehner et al. 2008) at a grid resolution of 15 arc-seconds (approx. 500 m at the equator); for more information please refer to the Technical Documentation at http://www.hydrosheds.org. In HydroSHEDS, rivers were defined to start at all pixels where the accumulated upstream catchment area exceeds 10 km2, or where the long-term average natural discharge exceeds 100 liters per second, resulting in a total global river length of 35.9 million kilometers (excluding Antarctica). Rivers are broken into reaches at all confluences, creating 8,477,883 million individual river reaches with an average reach length of 4.2 km. Each river reach is linked to a polygon of its contributing hydrological sub-catchment, with an average area of ~12 km2.

There are many fields in the streams feature class, but not all of them are actually required for the analysis. The required fields in the streams feature class are necessary to preform a number of critical aspects in conducting the FRA. A series of fields are used to conduct the routing of the stream network. Examples of these fields include the NOID, NUOID, and NDOID, which correspond to the Network Object ID, the Upstream Network ID, and Downstream Network ID, respectively. Additionally, some fields in the streams feature class are required for conducting calculations in the FRA. For instance, the streams feature class contains fields to store indicator values for the degree of regulation (DOR), degree of fragmentation (DOF), sediment trapping index (SED), urban areas (URB), road density (RDD), and water consumption (USE). Lastly, the output values of theses calculations are stored in user specified fields of the streams feature class, which are used to display some of the output spatial data results discussed in the Results & Interpretation section. The table in the annex here lists fields in the streams feature class and provides a description of the field values.

3.2 Barriers

The barriers feature class is a point feature class representing the locations of barriers to water flow along the stream network. The FFR barrier database (Grill et al.2019) was used as a blueprint for this analysis, as it contains the necessary fields required to run the FRA. The global data contains only larger projects that existed approximately at or before 2018. Updating the barrier database was necessary, and the main sources of dam and reservoir data in this project were the following:

• Stimson Tracker Database: Very good and complete database including reservoirs.

• Mekong River Commission. Technical reference database with detailed technical information

• GeoDAR: Georeferenced global dam and reservoir dataset for bridging attributes and geolocations

Other sources used for validation purposes:

1. CSIS Reconnecting Asia Project Database. Reconnecting Asia’s database tracks seven types of infrastructure projects–power plants, roads, rails, ports, intermodal, transmission, and pipelines–active across the Eurasian supercontinent since 2006. See https://reconasia.csis.org/reconnecting-asia-map/

2. WRI Power Database: Download here. Citation: Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. 2018. Global Power Plant Database.

3. Water, Land and Ecosystems (WLE) 2016: Greater Mekong Dam observatory. https://wle-mekong.cgiar.org/changes/our-research/greater-mekong-dams-observatory/ (CGIAR)

4. Open Street Maps (OSM) and Google Maps: Both provide a way to verify existence and location of dams. Tip: use this extension to easily switch from ArcMAP to Google Maps or Bing maps.

The barrier database has many fields, but not all of them are actually required for the analysis. The table in the annex here lists fields in the barriers feature class. A few of the important fields are BAS_ID, GOID, STOR_MCM, PCAP_MW and INC. Each of these are described below.

BAS_ID: This field identifies which hydroBASIN the barrier is in.

GOID: GOID stands for “Global Object Identifier”. Each dam has a value in the field called GOID. The value corresponds to the GOID of the stream nearby. For the model to recognize on which river the dam is located, each dam must have the correct GOID value (i.e., the one that is corresponding to the GOID of the river reach). Note: The position of the dam feature doesn't need to be close to the river reach. The value in this field is used to identify the position of the dam in the stream network.

STOR_MCM: This field contains the storage volume, if available. 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 the fragmentation will be calculated.

PCAP_MW: This field contains the power capacity of the barrier, if available. The units are “megawatts”.

INC: This field is a binary field (values of 0 or 1) indicating whether to include the barrier (value = 1) or exclude the barrier (value = 0) from the analysis. Variations of this field are included in the annex table here, which can be used to run different scenarios. For instance, variations of this field might indicate to include only existing dams, dams under construction, or planned dams as well.

3.3 Benchmark Rivers

The benchmark rivers feature class is composed of line features and represents rivers that were positively identified as free-flowing. The benchmarking dataset of reported FFRs was compiled from literature resources and expert input.

In the global study, we created 100 scenarios where we manipulated the individual weights within the plausible ranges and compared the results of each scenario to the set of benchmark rivers reported to be free-flowing. For the final CSI application, we selected the weights of the scenario that best reproduced the FFR status of the benchmark rivers. The same procedure was applied in this case study.

The Benchmark Rivers feature class contains 5 fields described below.

• GOID: GOID stands for “Global Object Identifier”. The value in this field is derived from the streams feature class to ensure the GOID of the benchmark river matches the corresponding GOID of the river reach in the streams feature class. For the model to recognize which river is the benchmark river, each benchmark river must have the correct GOID (i.e., the GOID that corresponds to the GOID of the river reach).

• BB_ID: BB_ID stands for "backbone identifier". The value in this field is a integer identifying the backbone river the river reach corresponds to. The BB_ID is derived from the streams feature class and the values for the same river reach must match between the two datasets.

• FFRID: FFRID stands for "free-flowing river identifier". This field contains a integer identifier for the benchmark river. Each benchmark river must have a unique FFRID value.

• Name_Expert: Provides the name of the benchmark river. The name must be consistent across the same FFRID.

• BENCH_SRC: Provides the source used to designate the river as a benchmark river. This can be the name of a researcher or a publication, a category such as "Expert" or "Publication" etc. The field is used to analyze the results of the benchmarking separately by source name or category.

A benchmark river can be a full river, i.e. all reaches with the same BB_ID are included or part of a river. Only the segments that are benchmark rivers are included in this feature class, the rest are deleted.

3.4 Lakes

The lakes feature class is a point feature class representing the location of lakes through out the study area. The fields contained in the lakes feature class are mainly used for establishing positional relationships of the lake features within the stream network and calculating the Sediment Trapping Index (SED).

The lakes feature class is based off of HydroLAKES, which contains many of the fields required in the lakes feature class for running the FRA. A few new fields have been added to the HydroLAKES point features for use in the FRA analysis. These fields include:

• GOOD: This is a binary field indicating if the lake is a reservoir identified in the global georeferenced database of dams (GOODD) database.

• GOID: GOID stands for “Global Object Identifier”. Each lake feature has a value in the field called GOID. The value corresponds to the GOID of the stream nearby. For the model to recognize on which river the lake is located, each lake must have the correct GOID value, the one that is corresponding to the GOID of the river reach. Note: You don't have to move the lake close to the river reach. You only need to set the value in this field correctly.

• SED_ACC: This field provides the sediment accumulation, measured in tons per year. The values in this field come from Borrelli et al., (2017).

• IN_STREAM: This is a binary field that indicates if the lake is connected to the stream network.

• IN_CATCH: This a binary field indicating if the lake is in a catchment of the stream network.

A brief description of the fields in the lakes feature class is provided in an annex table here.

Key References

GeoDAR: Wang, J., Walter, B.A., Yao, F., Song, C., Ding, M., Maroof, A.S., Zhu, J., Fan, C., Xin, A., McAlister, J.M., Sikder, S., Sheng, Y., Allen, G.H., Crétaux, J.F., Wada, Y. (2021) GeoDAR: Georeferenced global dam and reservoir dataset for bridging attributes and geolocations. Earth Syst. Sci. Data Discuss. 2021, 1-52.