Environmental Modelling and Software 123 (2020) 104552

Available online 17 October 20191364-8152/© 2019 Published by Elsevier Ltd.

A publicly available GIS-based web platform for reservoir inundation mapping in the lower Mekong region

Aekkapol Aekakkararungroj a,b, Farrukh Chishtie b,c,*, Ate Poortinga b,c, Hamid Mehmood e, Eric Anderson f,g, Thailynn Munroe h, Peter Cutter b,c, Nuntarut Loketkawee a,b, Githika Tondapu g, Peeranan Towashiraporn a,b, David Saah b,c,d

a Asian Disaster Preparedness Center, SM Tower, 24th Floor, 979/69 Paholyothin Road, Samsen Nai Phayathai, Bangkok, 10400, Thailand b SERVIR-Mekong, SM Tower, 24th Floor, 979/69 Paholyothin Road, Samsen Nai Phayathai, Bangkok, 10400, Thailand c Spatial Informatics Group, LLC, 2529 Yolanda Ct., Pleasanton, CA, 94566, USA d Geospatial Analysis Lab, University of San Francisco, 2130 Fulton St., San Francisco, CA, 94117, USA e United Nations University Institute for Water, Environment and Health, 204-175, Longwood Rd S, Hamilton, ON, L8P 0A1, Canada f Earth System Science Center, The University of Alabama in Huntsville, 320 Sparkman Dr., Huntsville, AL, 35805, USA g SERVIR Science Coordination Office, NASA Marshall Space Flight Center, 320 Sparkman Dr., Huntsville, AL, 35805, USA h World Resources Institute, 10 G Street, NE Suite 800, Washington, DC, 20002, USA

A R T I C L E I N F O

Keywords: Water resource management Reservoir monitoring Lower Mekong region GIS

A B S T R A C T

Dam construction in mainland Southeast Asia has increased substantially in recent years. Most dams have the potential to generate value, however, potential and existing impacts include alterations in water regimes, loss and degradation of natural forests and bio-diversity. For mapping impacts, we have developed the Reservoir Mapping Tool for the greater Mekong region designed in ArcGIS Desktop 10.5 ModelBuilder, with dam point location and digital elevation model SRTM-30 m on a publicly available web interface which provides inundated area and dam volume based on user inputs. We validate our results and find excellent agreement with ground data from the Lanh Ra dam located in Vietnam. Further validation and error quantification are done comparing results of three different DEMS’s and compared with reported values. We also illustrate various application areas using this information in combination with other geospatial layers, which could provide key inputs towards assessing overall social impacts of dams.

1. Introduction

Natural resources are under great pressure in Southeast Asia due to population dynamics and rapid economic development (Pech and Sunada, 2008; Smith et al., 2016). Demand for housing, infrastructure development, mining and energy are key drivers for rapid land cover changes as many people move to the lower-middle- and upper-middle-income brackets. For example, forest resources are being removed for wood production or replaced by plantations because of their high productivity and profitability (Poortinga et al., 2019). There is growing demand for energy, water, and flood mitigation driving the development of more dams. Hydropower projects are often, though controversial, considered as a clean and renewable source of energy. While hydropower production is usually considered as the primary goal of hydropower dams, other benefits such as water storage, reducing

flood risk and upstream environmental protection projects to reduce sedimentation risks should also not be ignored (Ziv et al., 2012; Grum-bine and Xu, 2011).

Densely populated countries in Southeast Asia such as Vietnam and Thailand have moved from water abundant to water scarce countries in the last decades. Pressure on forest and water resources, combined with climate variability and increasing downstream water demands are main causes of water shortages with insufficient water to meet water supply to all sectors and sustain freshwater and estuarine ecosystems. However, the wet season is plagued with excessive and non-utilizable water re-sources, often in the form of floods causing large scale economic damage and loss of human lives (Poortinga et al., 2017; Simons et al., 2017). Potential climate change impacts might exacerbate this in the future (Arnell and Gosling, 2016; Tolentino et al., 2016; Lawrence and Van-decar, 2015). Dams are a popular mitigation measure to balance water

* Corresponding author. SERVIR-Mekong, SM Tower, 24th Floor, 979/69 Paholyothin Road, Samsen Nai Phayathai, Bangkok, 10400, Thailand. E-mail address: farrukh.chishtie@adpc.net (F. Chishtie).

Contents lists available at ScienceDirect

Environmental Modelling and Software

journal homepage: http://www.elsevier.com/locate/envsoft

https://doi.org/10.1016/j.envsoft.2019.104552 Received 18 April 2019; Received in revised form 8 October 2019; Accepted 9 October 2019

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between the wet and dry season, but bring unique challenges and trade-offs to the local environment (Ziv et al., 2012; Hurford et al., 2014; Wang et al., 2013). As such, there is a growing awareness that hydro-power production should be embedded in a broader framework of ecosystem services to align and balance the multiple, often competing, interests and goals of all stakeholders (Zarfl et al., 2015).

Dam construction has traditionally been a top-down decision making process with large social, economic and ecological ramification for local communities and other downstream stakeholders (Hughes, 2017; Kirchherr et al., 2017; Hirsch, 2016). Direct impacts of dam construction include the involuntarily displacement of large numbers of people but more recent work also focus on the longer term environmental impacts (Tilt et al., 2009; Wu et al., 2004; Nhung and Thang, 2017). Central governments have the capacity and resources to investigate the wide range of potential impacts of dam construction. However, these analyses are usually focused around technical details and often do not include the vast local institutional and ecological complexity (�Egr�e and Sen�ecal, 2003). Hence, dam construction projects have been the subject of con-troversy and debate because of their social and environmental impacts along with unequal distribution of costs and benefits (Siciliano et al., 2018). Local stakeholders, NGOs and other stakeholders oftentimes have a good understanding of the institutional and social context, but lack technical information to understand the full impact of a dam. As such, it is evident that there is a need for a simple to use tool to study potential social and ecological impacts of potential dam construction for better planning and management.

Here, we present state-of-the-art Geographic Information Science (GIS) technology to model dam construction impacts in the context of the SERVIR-Mekong program. SERVIR-Mekong is a joint USAID and NASA collaborative project aimed to provide support to dedicated development and sustainable landscape projects in the Mekong region. SERVIR-Mekong developed various user-friendly tools for example to monitor vegetation development (Poortinga et al., 2018), surface water quality (Markert et al., 2018) and land cover change (Poortinga et al., 2019) using state-of-the-art cloud computing technologies.

In this work, and to address the environmental issues related to dams, we present the newly developed online Reservoir Mapping tool for the greater Mekong region. It is an open source tool which is

available online and provides users estimates of the area and volume of inundation of existing and proposed dams. We also provide details on an innovative aspect of this tool which is the utilization of ESRI’s ready-to- use watershed service tool.

This tool covers the area of greater Mekong region. It is primarily developed for the majority of regional water resource management or-ganizations, NGOs, river basin planning organizations, regional disaster management organizations, and conservation societies in the Mekong region which lack required GIS skills and resources to carry out the modeling of these dams. This tool is presented with use cases from the greater Mekong region, but can be extended for areas across the globe. It is expected to be used for evaluating cumulative impacts of dams on land cover and carbon emissions, preliminary alternative scenario analyses for planned dams, and application in the risk assessment contexts mentioned above.

2. Methodology

The main focus of the reservoir mapping tool is to map the inundated area and volume of a potential dam. In the following sections, we first introduce the computational model for the reservoir which is used for area and volume calculations. In the following sub-section we delineate the software workflow of the tool and next provide details on its system architecture.

2.1. Computational model

Both the inundated area and volume are dependent on the full supply level height of the dam and the topography of the upstream area. We incorporate these considerations for the reservoir area and volume as follows.

2.1.1. Reservoir area A schematic overview of the dam area calculation is shown in Fig. 1.

We used a pixel based approach in the application where the potential dam is constructed on a specific base elevation measured in meters above mean sea level. The inundated area was then calculated from the dam full supply level height and surface elevation. The topography and

Fig. 1. Schematic overview of inundated area of a reservoir behind a potential dam.

A¼fXN

ipxi ei � me þ dh

0 ei > me þ dh

(1)

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upstream area was derived from the global Shuttle Radar Topography Mission (SRTM) digital elevation model (Farr et al., 2007; Jarvis et al., 2008). While we have chosen a specific base elevation value at mean sea level, generally, the reservoir inundation area can be calculated from the dam full supply level height above the ground in meters and lowest elevation value, pixel elevation value and pixel resolution as shown in eq. (1) below.where:

A ¼ Reservoir inundation area (m2) ei ¼ a pixel elevation value of clipped watershed dataset (m) pxi ¼ pixel resolution (m2) me ¼ lowest elevation value (m) dh ¼ dam full supply level height value (meters above ground) N ¼ Number of pixels

2.1.2. Reservoir volume The reservoir volume was calculated from the average height value

of the reservoir, as shown in eq. (2). The calculation can be done for each pixel in the upstream area, but to speed up the process we multiplied the total area of the reservoir with the average depth.

V ¼fXN

iHmeanpxi ei � me þ dh

0 ei > me þ dh

(2)

where:

Hmean¼1N

XN

iHi (3)

and.

V ¼ Reservoir inundation volume (m3) Hmean ¼Mean elevation value of clipped watershed dataset (m) pxi ¼ pixel resolution (m2) Hi ¼ dh þ me - ei (m)

2.2. Workflow

The system was built with ArcGIS ModelBuilder, using the Esri Ready-to-use watershed service tool. The Ready-To-Use suite is an on-line set of geoprocessing services which use ArcGIS Online data and analysis capabilities and does not require local data and computational

Fig. 2. Reservoir mapping tool workflow.

Fig. 3. ESRI ready-to-use Watershed service delineates a watershed.

Fig. 4. Clipped SRTM 30m watershed raster dataset.

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resources. The workflow is graphically depicted in Fig. 2. The required inputs

are the location of dam, and dam full supply level height which are used in the ready-to-use watershed service tool to calculate the upstream area, as illustrated in Fig. 3. The upstream area is then used to clip the digital elevation model as illustrated below in Fig. 4. Zonal statistics tool is then used to calculate the minimum elevation of the clipped area, which should be equal to the pixel where the potential dam is con-structed. The full supply level height of the dam is then used to calculate the inundated upstream area on from the gridded data. The raster domain tool is then used to construct a polygon of the reservoir area from which the total area is calculated. This reservoir area map is then combined with the DEM and dam full supply level height information to calculate the volume. In some specific case, a reservoir polygon includes small non-contiguous inundated areas. In order to eliminate these small polygons, all feature area value will be sorted in descending order, and then only the feature with the largest area will be kept as a reservoir

area.

2.3. System architecture

The Reservoir Mapping Tool requires user to input (1) dam point location and (2) estimated dam full supply level height (meters above ground) as the primary inputs. The output of the model is an estimated reservoir polygon, area, and total storage for a particular dam.

An innovative part in this study is the use of the Esri ready-to-use watershed service tool, which is hosted on ArcGIS Online and can be accessed through an ArcGIS Server connection. The watershed service tool is imported to a custom toolbox to identify watershed area based on a particular location. The model was published as Geoprocessing Service in ArcGIS Server 10.5. The ArcGIS Web AppBuilder 2.11 is used to consume the service and also to create and customize the application interface and functionalities (Fig. 5) (see Table 1).

2.4. Error analysis

We compiled a list of dams that are planned, commissioned and under construction. Table 2 provides an overview including the status, Commercial Operation Date (COD), reservoir area and total storage. Reliable numbers on the full supply level were difficult to obtain. Most data were derived from a report that was prepared for the worldbank (M. L. in association with Lahmeyer Gm, 2004). The numbers for Nam Ngiep 1, Nam Ngum 2, Nam Ngum 5 and Theun-Hinboun exp were obtained from technical documents of the respective companies operating the dams.

The reported area and storage estimates were compared with esti-mate from our tool using different Digital Elevation models. We used the SRTM Digital Elevation Data Version 4, ALOS World 3D (AW3D30) global digital surface model (DSM) (Takaku and Tadono, 2017; Tadono et al., 2016) and the ASTER Global Digital Elevation Model Version 3 (GDEM v3). These three products were developed using different methodologies and sensors. SRTM was created using radar technology whereas the others used stereo photography as the main technology. All three datasets have a spatial resolution of approximate 30 m. However, The data-sets have differences in reported vertical accuracy. A study of (Farr et al., 2007) reported a vertical absolute height error of less than 16 m for SRTM, whereas a vertical accuracy of 5 m was reported for ALOS (Tadono et al., 2014). The ASTER GDEM v3 was recently released, therefore we used the 17 m vertical accuracy that was reported for the previous version of the data product (Elkhrachy, 2018; Meyer et al., 2012).

3. Results

3.1. Web platform interface and functionality

The web platform interface is shown in Fig. 6 and can be accessed from: (https://damtool-servir.adpc.net/). The interface shows a base-map with the main rivers in the lower Mekong region. Furthermore, the river basin and administrative boundaries are shown. Information on dams was extracted from the Water Land Ecosystems (WLE) database (CGIAR, 2019). This database contains information on the dams that are commissioned, planned, under construction, cancelled, proposed and suspended. Metadata of the dam is included and displayed upon click-ing. This metadata includes information on commission date, capacity (MW), irrigation area, height, length, costs etc. (Fig. 7). The same panel can be used to scroll through the country, basin and river information. The UNEP-WCMC layer from the UN Environment World Conservation Monitoring Centre was also included in the interface, this layer contains information on areas with special importance such as protected areas and locations of other historical and ecological significance.

The quick start guide provides users an easy-to-follow step by step how to operate the tool. It contains the five steps of: (1) zooming to a

Fig. 5. Reservoir mapping tool architecture.

Table 1 User input and output of the reservoir mapping tool.

User inputs Output

Dam location Estimated reservoir polygon Full supply level (meter above ground) Area (square kilometer)

Volume (million cubic meter)

Table 2 Dam projects with there status, Commercial Operation Date (COD), reservoir area and total storage.

Project Status COD Area (km2)

Total storage (Mm3)

Nam Kong 1 Planned 2014 12.1 297 Nam Ngiep 1 Under

construction 2019 70 2250

Nam Ngum 2 Commissioned 2011 122 6774 Nam Ngum 3 Under

construction 2020 25.6 1320

Nam Ngum 5 Commissioned 2012 14.74 314 Nam Pha (Nam

Fa) Planned 2019 71.6 2330

Nam San 3A Commissioned 2016 5.8 90.3 Theun-Hinboun

exp Commissioned 2013 105 4960

Xe Kaman 1 Commissioned 2015 222 17400 Xe Kaman 3 Commissioned 2014 12 467 Xekong 4 Proposed 160 9350 Xekong 5 Proposed 2020 70 4780 Xepian-

Xenamnoy Under construction

2018 43.5 979

Xe Pon 3 Planned 2018 29.5 406

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location, (2) adding a dam (3) specifying the dam full supply level height, (4) executing the tool and (5) inspecting the results. The results include a shapefile of the inundated area. For example, the inundated area and volume of the proposed Xekong 4 dam is shown in Fig. 8. Users can interactively pan and zoom to a specific dam location or enter the dam name in the search box. The tool enables users to analyze the impact of multiple dams.

3.2. Comparison of area and volume calculation with ground survey record

We used the tool to validate estimations on reservoir area and vol-ume with measured ones. The Lanh Ra reservoir in Ninh Thuan prov-ince, Vietnam (Fig. 9) was selected for the analysis. We compared the model outputs with the ground survey record provided by Institute for Water and Environment (IWE) in Vietnam (unpublished report) (IWE, 2017). The results for the area and volume estimations are shown in

Fig. 6. The Reservoir Mapping Tool user interface contains a basemap overlayed with administrative and dam information (https://damtool-servir.adpc.net/).

Fig. 7. Metadata is included for each dam and can be displayed by clicking on the dam location.

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Fig. 10a and b respectively. It can be seen that there is a good agreement between the estimated and measured values. A linear regression analysis shows a coefficient of determination (R2) > 0.99 for both area and volume.

3.3. Error analysis

We estimated the reservoir area and volume for 14 planned dams using three different digital elevation models and compared them to the number we found in reports. Fig. 11 shows reported values in blue and the estimated values from SRTM, ALOS and ASTER in green, red en yellow respectively. The error bars show the range of values including DEM specific uncertainties. It can be seen that reported values are generally higher than the estimated values from the DEM. Moreover, it was found that the SRTM has higher estimates than ALOS and ASTER for bigger dams. SRTM and ALOS are generally close to the reported values whereas ASTER underestimates the total reservoir area.

Reservoir volumes were also estimated using the different DEM’s. Result of the volume analysis are shown in Fig. 12. It was found that volume estimations are close to the reported ones for smaller reservoirs. Estimates for Xekong 4 are in line with the reported values for srtm, but show smaller volumes for ALOS and ASTER. Volume estimates for Xe Kaman 1 are lower than the reported ones for all digital elevation models. Theun-Hinboen exp also shows an underestimation. Fig. 13 shows the estimated inundation area for 14 dams. The color gradient and the inundated areas from the SRTM as displayed with different colors. For the completed dams (San 3A, Nam Ngum 2 anbd 5, Theun- Hinboun exp, Xe Kaman 1 and 3), we found good agreement with inundated areas from high resolution satellite data for the Nam dams.

4. Discussion

Dams can potentially have profound environmental and social im-pacts. While their primary purpose is oftentimes only cited as either

Fig. 8. Estimated inundated area and storage capacity of Xekong 4 proposed dam are calculated.

Fig. 9. Estimated inundated area and storage capacity of Lanh Ra dam were calculated using the reservoir mapping tool.

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Fig. 10. The area and volume of a newly constructed was estimated using the reservoir tool and compared with the measured values.

Fig. 11. Area estimations (km2) for 14 dams that are planned, commissioned or under construction.

Fig. 12. Volume estimations (million m3) for 14 dams that are planned, commissioned or under construction.

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provision of electricity production or flood control and/or irrigation, the overall negative consequences can be significant and at multiple levels of the society. Hence, the conceptualization of their existing and po-tential impacts of dam construction are inherently complex, due to the multiple dimensions involved, which includes consideration of social, physical and environmental domains. To consider these important facets in assessing the social impacts of dams (Kirchherr and Charles, 2016), have developed a matrix framework from the perspective of considering key dimensions and components. Space, time and value are considered as dimensions, while components are considered to be infrastructure, livelihood and community. Based on the examples of applications of our tool provided above, such information can provide spatio-temporal in-formation which can be integrated along with other information into this framework, for assessing potential social impacts in a reliable and consistent manner.

It should be noted that the tool only produces reliable estimates for

(potential) reservoirs of which the bathymetry is represented in the SRTM. We use the SRTM as this is the most widely used and tested digital elevation model. However, new products like the ALOS Global Digital Surface Model (Takaku and Tadono, 2017; Tadono et al., 2016) can also be used. Higher quality surface elevation estimates will improve the quality of the outputs of the product. The tool was presented in the context of the Mekong region, but could be extended to all geographical regions in the world using global elevation products. Whereas user friendliness was a main objective in creating the tool. For many stake-holders, information on area and volume are not very informative as they require information on e.g. potential electricity generations, number of involuntary displacements or cultural heritage that could be affected. Also, it should be noted that results should be interpreted with caution by qualified end-users. Future improvements might include more exhaustive testing of different scenarios and incorporating the different components and dimensions in ready-to-use formats.

Fig. 13. Estimated inundation area for 14 dams that are planned, commissioned or under construction.

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The error analysis shows the range of area estimates included the reported vertical error for different products. We found best perfor-mance for SRTM in this study, as ALOS and ASTER generally under-estimated area and volume. This could be due to the various differences in these products. SRTM is a digital terrain model wheres ASTER and ALOS are digital surface models. Area estimates of the tool were in agreement with the reported values whereas volume estimations were generally lower than the reported ones. It should be noted here that there is lot of ambiguity in reported area and volume estimates. as dif-ferences exist between data from different sources as was also reported by (R€as€anen et al., 2017). For example, the reported value for total reservoir storage for Nam Kong 1 in this study was 297 million m3. Other sources report a 505 million m3 active storage (Boungnong and Pho-nekeo, 2012) and a 683 million m3 (Wild and Loucks, 2014) storage capacity. Our estimates range between 170 and 667 million m3 using ASTER and SRTM respectively.

The output reservoir inundation polygon can be used to assess the impacts of prospective dams on various built and environmental assets.

Fig. 14 shows the impact of a potential dam on environmentally pro-tected areas. It can be seen that part of the area will be inundated. Flooding of natural habitats will lead to the loss of terrestrial wildlife but might promote other terrestrial and aquatic fauna (Ledec and Quintero, 2003). Protected areas located upstream of a dam might increase the longevity of the dam because of the important role of natural landscapes in soil and water conservation.

Another example we highlight is the impact of dams on deep pools. Deep pools are defined as confined, relatively deep areas within a river channel. Deep pools are important for a number of important fish species as they act as a refuge for the dry season (Poulsen and Valbo-Jorgensen, 2001). Fig. 15 shows the location of deep pools and the inundated area of a dam. It can be seen that the dam would severely impact deep pools in the area as the reservoir is located upstream and on top of the loca-tions. It should be noted that many riverine fish species do not thrive well in artificial lakes (Ledec and Quintero, 2003).

Future versions of the tool could also include explicit information on land cover. Fig. 16 shows the inundated area of the lower Sesan 3 dam. Overlaying this information with data on landcover will provide important information on the potential impacts on e.g. food production and carbon emissions. Artificially inundated areas can have a large ecological footprint when vast areas of natural habitat are inundated (Yu et al., 2016). Also impacts on other existing infrastructure should be evaluated. Fig. 17 shows effects of a dam on transportation as part of the national highway will be inundated. The tool could also include more outputs on e.g. reservoir yield and dam costs using the comprehensive method proposed by (Petheram et al., 2017). Fig. 10a and b shows the estimated area and volume for a range of water levels. These were created by iteratively running the tool for different water levels. The ESRI Storage Capacity tool has this functionality built in and could be added to the platform as all required inputs are already calculated.

5. Conclusion

In this work we have provided details on the computational model, software development, performance and applicability of the Reservoir Mapping Tool developed by SERVIR-Mekong. This production was spurred by needs expressed by local and regional stakeholders due to intensive construction of dams in the Lower Mekong Basin. It is available

Fig. 14. Mapping impact on environmental protected area. (data source: WWF).

Fig. 15. The impact of dam construction on deep pools. (data source: MRC).

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online and is based on open source software features provided by ESRI. We highlight the innovative features of the tool in incorporating ESRI’s ready-to-use services, in particular the watershed service tool. While the tool computes dam inundation area and reservoir volume at a high ac-curacy in comparison with ground data, we also provide instances on how other layers of information can be combined with this such outputs to assess impacts on environmentally protected areas, biodiversity, land cover and transportation.

Acknowledgements

The authors would like to thank Kim Geheb from CGIAR Research Program on Water, Land and Ecosystems for supplying the dam data used in this study. Also, the authors would like to thank Ariel Walcutt for assistance in developing the model and ESRI for their technical support. Thanks goes to the three anonymous reviewers for their comments that improved the quality of the manuscript. Support for this work was provided through the joint US Agency for International Development

(USAID) and National Aeronautics and Space Administration (NASA) initiative SERVIR, particularly through the NASA Applied Sciences Ca-pacity Building Program.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi. org/10.1016/j.envsoft.2019.104552.

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