Understanding the Upper Ayeyarwady River Basin

MK 29: Working together for a better Kachin landscape – A landscape approach to the Upper Ayeyarwady River Basin:

Building inclusive governance processes to address resource conflicts

Lead: The Water Security Research Centre, School of International Development, University of East Anglia, Norwich, UK

Oliver Springate-Baginski (Oliver.springate@uea.ac.uk)

Partners: Shalom Foundation, Yangon/ Myitkyina; Friends of Wildlife, Yangon / Myitkyina; IUCN Myanmar, Yangon; London School of Economics

and Political Science

Prepared by Ben Roberts-Pierel and Oliver Springate-BaginskiSubmitted April 2018

This MK 20 project was carried out under the CGIAR Research Program on Water, Land and Ecosystems (WLE) in the Greater Mekong with support from the Australian Department of Foreign Affairs and Trade (DFAT) and the CGIAR Fund Donors http://www.cgiar.org/about-us/ourfunders/

ContentsSummary.....................................................................................................21 The UARB landscape and resources......................................................32 Using remote sensing to indicate hydrological impacts........................5

2.1 Method and sources.........................................................................52.2 Precipitation.....................................................................................62.3 Evapotranspiration...........................................................................62.4 Precipitation surplus processing......................................................72.5 Land use..........................................................................................7

3 Results...................................................................................................93.1 Precipitation surplus........................................................................93.2 Precipitation surplus and land use/land cover...............................11

4 Implications for basin management....................................................124.1 Volumetric distribution..................................................................124.2 Transboundary implications...........................................................13

5 River flow.............................................................................................146 Conclusions.........................................................................................16

6.1 Next steps......................................................................................16

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1. SummaryThe Ayeyarwady River is Myanmar’s most significant river system, running north to south and occupying an area of approximately or about 61% of the country. Three zones of the basin may be discerned, the upper basin, the central dry zone and the lower basin and delta. The relationship between the Upper Ayeyarwady River Basin (or UARB) and the central dry zone is particularly significant, as the dry zone suffers low rainfall and high aridity, and therefore the UARB is the source of the majority of river flows and nutrient transport for the dry zone. However, the specific nature of the relationships are poorly understood. The actual boundaries of the three zones are not defined, and Kachin State boundaries are used here as a proxy for the boundaries of the UARB.This paper presents the findings from a study, conducted in 2016, which sought to clarify the characteristics of the UARB and its importance for the basin, primarily by analyzing time series remote sensing data. The study used remote sensing data to indicate flow patterns, water sources, and land use change, in the entire Ayeyarwady Basin to indicate links between the upper and lower parts of the basin. The study period was limited to the years 2003-2014 by the available Et data.We found water produced in the UARB (Kachin State) accounts for approximately 27% of the basin as a whole, and almost 50% of water produced by all the forest class in the basin comes from Kachin. We confirmed that the majority of flows reaching the dry zone originate in Kachin.These numbers should be seen as significant because they clearly illustrate that Kachin, and especially the forests of Kachin, play a significant role in producing water for the entire basin, and particularly the dry zone.This information provides context for the research on land use and livelihood changes in the UARB, and the significance of the UARB for water flow in the rest of Myanmar. It also helps inform the search for inclusive forms of multi-level governance

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1 The UARB landscape and resourcesThe Ayeyarwady River is Myanmar’s most significant river system, running north to south and occupying an area of approximately 61% of the country (see Figures 1-3 below). The river provides multiple functions both locally and downstream: it is a significant source of domestic water, irrigation, nutrient transport, fishery, transportation, drainage and waste sink and so on.1 The river has a seasonal ‘flood pulse’ an important characteristic in the monsoonal climate.The river basin comprises three distinct agro-ecological areas:

1. the hilly, relatively sparsely populated and high rainfall upper basin (UARB)

2. the relatively arid central dry zone3. the densely populated lower basin and southern delta

The relationships between the zones are complex, particularly the relationship between the UARD and the central dry zone. The UARB is the source of river flows and nutrient transport for the central dry zone, and well as flows for transportation across the basin.The specific nature of the relationships is poorly understood. This paper presents the findings from a study, conducted in 2016, which sought to clarify the characteristics of the UARB and its importance for the basin, primarily by analyzing time series remote sensing data.

1 Burma Rivers Network. ‘Irrawaddy River’ [Online]. Accessed on 18 April 2016. Available at: http://www.burmariversnetwork.org/index.php?option=com_content&view=article&id=51&Itemid=48

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Figure 1: Myanmar state and regional boundaries and selected major cities. Source: Roberts-Pierel for this study, based on IWMI basin outline dataset.

Figure 2: Myanmar and neighbouring countries. The basin outline (red) overlaps into China and India in the northern end of the basin.Source: Roberts-Pierel, based on IWMI basin outline dataset.

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The three areas of the basin are not discreetly defined, as they gradually merge into one another in terms of gradation of rainfall and climate. Therefore, boundaries must be approximated and the most practical approach seems to be by using rainfall variations and subcatchments. The political boundaries of Kachin State offer a rough approximation to the UARB, in terms of higher rainfall and cooler temperatures, and so this was adopted for the purposes of the study. Kachin State has an area of 89,041 km2, is entirely within the Ayeyarwady Basin, and represents 21% of Ayeyarwady Basin area (which is 413, 674 km2).However, there are at least three anomalies to be aware of when using Kachin as a proxy for the UARB:1. two small parts of the UARB are

not included in Kachin State, as they are within China. These include most of the Shweli tributary to the east, and the uppermost area of the Nmai Hka subcatchment to the north.

2. the Chindwin is an anomaly, partly because as most of the tributaries’ subcatchment is in Sagaing State, yet it has its source in Tanai District of Kachin. Also, the subcatchment continues from high rainfall areas in the north to much more arid areas in the south before joining with the main Ayeyarwady River south of Monywa and so transcends boundaries between the UARB and the dry zone.

3. Lastly, associated with the Chindwin omission, the Mainpur subcatchment which joins the Chindwin is partly in India, and otherwise in Chin and Sagaing, so also excluded from the Kachin approximation.

Despite these omissions, Kachin State nevertheless seems a reasonably approximate working basis from which to assess the distinctive characteristics of the UARB.

2 Using remote sensing to indicate hydrological impacts2.1 Method and sourcesThe study used remote sensing data to indicate flow patterns, water sources, and land use change, in the entire Ayeyarwady Basin in order to indicate links between the upper and lower parts of the basin.

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Figure 3: The Ayeyarwady and sub-basins. Source: Roberts-Pierel for this study, based on IWMI basin outline dataset.

Having suffered very restricted engagement with international scientific community under the socialist and dictatorship eras, Myanmar lacks significant in situ data, so satellite-derived products have provided an invaluable secondary source for this research. The majority of data used was produced by the National Aeronautics and Space Administration (NASA), the European Space Agency and the Japanese Aerospace Exploration Agency and is freely available in the public domain for research purposes.The focus of the study was on utilizing three available data types to establish a basic understanding of water fluxes in the basin. These data include:

1. Precipitation, (P)2. Actual Evapotranspiration (Et) and3. Land use/land cover (LULC).

The study period was limited to the years 2003-2014 by the available Et data.

2.2 PrecipitationAn attempt was made to calibrate available precipitation data to in situ data by obtaining rain gauge data from the Global Surface Summary of the Day, as there are numerous precipitation datasets available publically. There are a number of gauging stations available in Myanmar, however, upon downloading the data and preparing it for analysis, it was determined that there was insufficient quantity and quality of data to use for calibration. Unfortunately, due to the short time frame of the study, data was not available from other sources. Therefore, a decision was made to utilize precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset which is produced by the Climate Hazards Group at the University of California, Santa Barbara. The data are available in a 5km spatial resolution, which is higher than other available datasets, and in a variety of time steps. The monthly product was used for this research. The CHIRPS dataset is based on the NASA TRMM and GPM missions, as well as a number of other satellites, and is calibrated against thousands of gauging stations worldwide.5

2.3 EvapotranspirationPublically available Actual Evapotranspiration (Et) data is very limited. The MODIS Global Terrestrial Evapotranspiration Product (MOD16) dataset from researchers at the University of Montana (USA)6 was originally 55 Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p. http://pubs.usgs.gov/ds/832/66 Mu, Q., Maosheng, Z., Running, S., 2011. ‘Improvements to a MODIS global terrestrial evapotranspiration algorithm.’ Remote Sensing of Environment: 115 pp. 1781-1800.

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acquired but it is only available at 1km resolution and has been determined to have mixed results for use in this type of analysis.7 In lieu of this product, the new Ensemble ET product, which is under development, was acquired from the UNESCO-IHE group at Delft University of Technology. It is available in 250m spatial resolution going back to 2003. This data is derived from the CMRSET, SEBS, SSEBop, ALEXI, MOD16, ETmonitor, and GLEAM Et datasets.8 These datasets are mostly not publically available except by request.

2.4 Precipitation surplus processingThe key output obtained from the precipitation and Et data was precipitation surplus. Precipitation surplus (P-Et-∆S), represents the quantity of water available for important hydrologic processes like runoff, aquifer recharge and soil moisture.9 On the multi-annual timescale examined here, ∆S, change in storage, is disregarded. Most processing of the CHIRPS and Ensemble ET data as well as the precipitation surplus calculation was conducted in a Python script (see Figure 9). The general work flow for this script is depicted below:

Figure 4: Simplified depiction of the processes carried out by the Python script.Note: Datasets are represented in blue, individual processes in green and joint processes in orange.

CHIRPS data are obtainable as a global file and these were batch clipped to a .shp file (a ‘shape’ file) of the basin while the Ensemble ET product was available in two tiles covering the study area; these were mosaicked and then clipped in the same manner as the CHIRPS data. When making the precipitation surplus calculation, inputs must have a resolution (pixel size) which is the same. Because there is greater local variation in Et, the 77 Hu, G., Jia L., Menenti, M., 2015. ‘Comparison of MOD16 and LSA-SAF MSG evapotranspiration products over Europe for 2011.’ Remote Sensing of Environment: 156 pp. 510-526.88 Salvadore, E., Forthcoming. ‘Global open-access ensemble of seven actual evapotranspiration products’ (working title).99 Simons, G.W.H., Bastiaanssen, W.G.M., Ngo, L.A., 2016. ‘Integrating open-access satellite-derived global data products as a pre-analysis for hydrological modelling studies: a case study for the Red River Basin.’ Remote Sensing: 8(x) Forthcoming.

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CHIRPS data was resampled to the resolution of the Ensemble ET data before the script performed the P-Et calculation. The final step was to sum the months to produce annual products. These files were then analyzed in ArcGIS.

2.5 Land useOne area of interest has been to explore what effects this deforestation might have on the Ayeyarwady River Basin. Various past research has suggested that there may potentially be a causal link between deforestation and hydrologic regimes, specifically precipitation, runoff and flooding.4 There are limited choices for high resolution or even moderate resolution LULC products available for Myanmar in the public domain. The frequently used product is the GlobCover dataset,1010 which is produced by the European Space Agency and its partners. However, because this is only available for 2005 and 2009 and does not explicitly address forest change, the choice was made to use a product produced recently by EcoDev-ALARM which focuses specifically on forest cover change from 2002 to 2014.1111 Besides the greater time span it covers, this product is based on NASA’s Landsat imagery, which has a 30m resolution.

1010 Universié catholique de Louvain, European Space Agency, 2011. ‘Globcover 2009: Products Description and Validation Report.’1111 Bhagwat, T., A. Hess, N. Horning, T. Khaing, Z.M. Thein, K.M. Aung., K. H. Aung, P. Phyo, Y. L. Tun, A. H. Oo, A. Neil, W. M. Thu, M. Songer, K. LaJeunesse Connette, A. Bernd, G. Connette, P. Leimgruber, 2015. Myanmar 2002-2014 forest cover change dataset. Retrieved from [http://geonode.themimu.info/layers/geonode%3Amyanmar_forestcoverchange/].

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Figure 5: Forest cover change 2002-2014 for the Ayeyarwady Basin.Source: EcoDev-ALARM.

Figure 6: Forest cover change detail around Myitkyina. Note the heavy concentration of ‘degraded forest’ in this valley.

Unfortunately, because the EcoDev-ALARM map only covers the country of Myanmar and the Ayeyarwady Basin extends into neighboring countries, either this portion is lost from analysis or a small area of the GlobCover dataset can be mosaicked with the EcoDev product for full basin coverage. The figures presented in the results section are based on this mosaicked product.To determine precipitation surplus for specific LULC classes, masks of each relevant class were extracted from the mosaicked forest cover map. These masks were then used to extract the corresponding pixels from the yearly P-Et totals and the full-time span (2002-2014) was averaged to determine a net average P-Et volume for each class.

3 Results3.1 Precipitation surplusAs noted above, the study utilized primarily precipitation and evapotranspiration data to understand water fluxes in the Ayeyarwady Basin. Precipitation is shown in Figure 10, evapotranspiration is shown in Figure 11. Figure 12 displays the precipitation surplus over the basin.

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These figures show that the Ayeyarwady follows what one might expect for a ‘normal’ basin, high concentrations of water coming from the northern, forested highlands (max of ~4000mm/yr) as well as the southern delta, which is influenced by its proximity to the Bay of Bengal (max of ~3500-4000mm/yr).

Figure 7: Mean total annual precipitation for the Ayeyarwady Basin, 2003-2014.Source: Roberts-Pierel for this study, based on CHIRPS.

Figure 8: Total annual Ensemble Evapo Transpiration (mm/yr), mean 2003-2014.Source: Roberts-Pierel, based on basin outline dataset.

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Figure 9: 2003-2014 Average Total Annual precipitation surplus (P-Et).Source: Roberts-Pierel for this study.

Figure 10: Annual variation in P-Et across the basin for period 2002-2014.Source: Roberts-Pierel for this study.

This finding also correlates well with the known area of aridity in the middle of the country (maximum of ~400-1600mm/yr) throughout the zone. It is also interesting to note for the purposes and aims of MK29 that Kachin State, as well as northern neighboring Sagaing State, experience a very high concentration of precipitation (Figure 10), and thus contribute a large volume of water to the basin as a whole. Although Sagaing is not the focus of this project, it is home to the Chindwin River, one of the major tributaries to the Ayeyarwady.As expected from the precipitation distribution, P-Et is highest in the northern reaches of the basin as well as the southern delta area. Similarly, the arid zone in the middle of the basin tends to have lower P-Et values with few or no values going below zero. This suggests that even in the driest part of the country, over the twelve-year annual time scale, there are few areas that are net consumers of water. However, this does not mean that within a year time frame there are not deficits in a localized area as these could be cancelled out over the year by high monsoonal rainfall or be ‘lost’ in a basin-wide analysis. One example of this phenomenon will be discussed in the next section. This is an important distinction to better understand and will be investigated in ensuing research. Figure 10 displays the annual variation in P-Et values. It suggests that there is no meaningful trend over the time span investigated. There is a

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very large shift in the years 2013 and 2014, which could indicate increasing uncertainty and variability in yearly values. However, this is impossible to show conclusively given that it is based on only two years of data.

3.2 Precipitation surplus and land use/land coverBeyond the general understanding of the spatial distribution of precipitation surplus in the basin, it is important to break apart the different LULC classes and understand in more detail how different classes are contributing to the overall flows within the basin. It should be noted that this is somewhat limited by the currently available LULC maps and by the designation utilized in the EcoDev-ALARM product, which was used for this analysis. This will serve as an important early step in illuminating the general phenomena but further analysis and differentiation is required.There is, unfortunately, no way to understand from these maps when forest resources changed over the study period from 2002 to 2014. Furthermore, because the threshold for forest is set at >80% or >60% for the more arid regions, it leaves a high degree of uncertainty about the actual quality of those forests or the cause of their degradation.However, there are very limited resources for a longer timescale understanding of LULC change in Myanmar so the ‘degraded forest’ class is helpful for highlighting those areas where forest degradation and deforestation are occurring. In Figures 5 and 14 it is apparent that there is a high concentration of degraded forest around the town of Myitkyina, an area known to suffer from intense forest harvesting. In addition to the areas of degraded forest, this study investigated the actual average quantities of water produced by some of the important LULC classes in order to understand how changes may be affecting water fluxes in the basin.

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Figure 11: Precipitation Surplus (mm/yr) for the intact forest (left) and degraded forest (right) LULC classes. Forest is defined as greater than 80% canopy or greater than 60% in dry deciduous forests. Degraded forest is defined as 10-80% canopy or 10-60% in dry deciduous forests.

Like the distribution of precipitation, precipitation surplus distribution shows the highest values in the northern forested, mountains region and in the southern delta. Figure 10 illustrates the high concentration of forested land in the northern end of the basin, specifically in Kachin and Sagaing states.Land use change, specifically in the form of forest degradation through overlogging, is a major concern here. Between 1990 and 2015, in the country as a whole, approximately 15 million hectares of forests and woodland were lost to legal and illegal logging, much of which takes place in the northern most Kachin State.2 Not only do forest resources play a major role in the lives of many people in Myanmar, the country is home to significant biodiversity and some of Southeast Asia’s most intact remaining forests.3 (See Figures 4 and 5).

22 Food and Agriculture Organization of the United Nations (FAO), 2016. ‘UN and Government partner for the protection of Myanmar’s forests, critical for reducing climate change.’ [Online] Accessed on 17 April 2016. Available at: http://www.fao.org/myanmar/news/detail-events/en/c/408987/33 Woods, K., 2015. ‘Commercial Agriculture Expansion in Myanmar: Links to Deforestation, Converstion Timber and Land Conflicts.’ [Online] Accessed on 18 April 2016. Available at: http://forest-trends.org/releases/uploads/Conversion_Timber_in_Myanmar.pdf

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4 Implications for basin management4.1 Volumetric distributionAlthough this study will not assign specific values for the destination of this water (e.g. surface runoff vs aquifer recharge), spatially, these results suggest the importance of Kachin as a major source of water for the basin. In the following figures a series of approximate values are calculated to quantify the actual volumes these regions are producing.

Figure 12: Total volumes from the basin and from Kachin.Note: Average Total P-Et is all of the precipitation surplus produced by the basin, Kachin and the area of the basin outside Myanmar while the other columns are volumes for those LULC classes.

According to these calculations, total precipitation surplus for the basin, averaged over the 2003-2014 study period was 413.39km3. This roughly checks out with various sources but requires further ground-truth data to verify and to understand how much of that is runoff and how much is available for other purposes. Non-forest classes produced 166.33km3 over this period, which is unsurprising given the broadly encompassing nature of this category and the fact that it accounts for a large percentage of the overall area of the basin. Interestingly, ‘forest’ and ‘degraded’ forest still accounted for 118.31 and 109.20 km3, respectively, further showing that they are very important contributors to water production in the basin. It is also interesting to note that these categories are nearly equal in quantity produced. This is significant because it means roughly a quarter of the water produced within the basin comes from forest classified as ‘degraded’ (<80% cover or <60% cover in the dry zone).

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Figure 13: Kachin volumetric flows as a percentage of the basin total.

The water produced in Kachin State accounts for approximately 27% of the basin as a whole (Figure 15). Almost 50% of water produced by all the forest class in the basin comes from Kachin (Figure 13). Likewise, almost a quarter of the water produced by degraded forest comes from Kachin. Although not the focus of this study, it is assumed that a significant portion also comes from Sagaing and flows into the Chindwin River system. As expected, the percentage of water produced by the non-forest class in Kachin is relatively low (~6%). These numbers should be seen as significant because they clearly illustrate that Kachin, and especially the forests of Kachin, play a significant role in producing water for the basin.

4.2 Transboundary implicationsAlthough the Ayeyarwady River is commonly associated with Myanmar and is almost exclusively contained within the country, it is in fact a transboundary river. In Figure 17, it is clear that a portion of the basin extends into India and into China. The combined total area outside Myanmar is about 34890km2, with 15623km2 in India and 19258km2 in China. Based on allocations of precipitation surplus in these areas, India’s share of the basin produced an average of 16.59km3 or about 4% of the basin total in the period from 2003-2014. Interestingly, the area of the basin in China represented an average net loss of about -.06km3 for the period from 2003-2014.

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2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

-5

-3

-1

1

3

5 V

olum

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m3)

Figure 14: Annual precipitation surplus for the area of the Ayeyarwady Basin in China.

To attempt to develop a better understanding of why this might be, the annual precipitation surplus for China was plotted to look for any trends over time. It is evident from Figure 17 that there is no discernable trend for the precipitation surplus contributed from China over the study period but that it fluctuates around zero, providing no net loss or gain to the system. That is further reinforced by the long-term average close to zero. At this point no conclusion has been made on what might be causing this deficit but there are a number of possibilities.These figures could certainly prove controversial as there are a number of issues at stake in a transboundary context, not least of which right now is the fate of a number of dams on the main stream of the Ayeyarwady and its tributaries. One thing that is clear based on this analysis is that Myanmar is not the only country with the ability to influence the future of water in the basin and this should serve as illustration as to the importance of better understanding and quantifying these external flows.

5 River flowA second aspect of this work has been to link river gauge data for the same period (generated by the Ministry of Transport) with the ET data derived from remote sensing above, and produced the map shown in Figure 17, in which the width of the representation of the river reflects interpolated flows between gauging stations, and the intensity of the blue shading indicates precipitation.The map clearly expresses how important Kachin and the upper Ayeyarwady Basin is for river flows through the dry zone. (Improved versions of this map would be possible with more gauging data).

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Figure 15: River flow in the Ayeyarwady Basin. Source: Oliver Springate-Baginski, with support from Ben Roberts-Pierel and Clear Mapping Co. Data from various sources.

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6 ConclusionsThe main points are that Kachin covers about 21% of the Ayeyarwady Basin, but receives around 27% of the rainfall, which comprises the vast majority of the river flows reaching the dry zone. Kachin is therefore a critically important part of the basin.Furthermore, the results clearly show that ‘degraded forest’ accounts for a significant portion of basin inputs and that forests in general account for a very significant portion of water in the basin. The results also suggest that a large portion of the forest in Kachin State is classified as degraded. This should serve as impetus to better understand, quantify and address changes in forest cover change in Kachin, and in Myanmar more broadly as it is very likely to have an impact on the system as a whole.Additional research and data will be required to expand this study and better understand how different types of land use or land cover (LULC) affect river fluxes.

6.1 Next stepsThese would be to incorporate additional ground trothing of flows and precipitation data, as well as additional precipitation datasets to qualify and calibrate the most appropriate data for this region of the world. It will also be important to perform this analysis with a LULC map, which offers greater differentiation than the EcoDev-ALARM product and would allow for a greater understanding of water produced by LULC classes such as agriculture as well as different types of forest. Additionally, in the transboundary context, it will be crucial to ensure that basin boundaries are thoroughly researched and understood as this has a strong influence on any volumetric calculations of water from neighbouring countries.Finally, if this work is ultimately going to investigate flooding patterns and change, these findings should be integrated with satellite-derived as well as ground-based flooding data. In this space, there may be high potential to incorporate user-generated or ‘citizen science’ data into analysis as the early stages have already been laid in the form of flood tracking apps. Much of the satellite-derived data, like the majority of data used for this project, is publically available and could be invaluable in progressing toward a greater understanding of the causes and factors affecting flooding regimes in the Ayeyarwady Basin.

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