________
The Reservoir Assessment Tool (RAT) is an integrated satellite remote sensing-modeling framework with an interactive state-of-the-art web-based visualization interface to facilitate the visualization of past and current reservoir states. The tool was originally developed at the University of Washington SASWE Research Group with support from NASA Applied Sciences Program. University of Houston and SERVIR-Mekong provided additional collaborative support. RAT-Mekong has been designed to monitor the existing reservoirs over the Mekong region. The tool facilitates the visualization of reservoir states (such as storage change, inflow, and outflow), which is useful in understanding downstream impact.
The founding principle of RAT is to use satellite data of NASA and other agencies to address limitations faced by stakeholders in regions of limited, absent, or inaccessible reservoir operation data. RAT development has been based on five years of research at the University of Washington with extensive validation in South Asia (80 dams), Southeast Asia (20 dams), and North America (2 dams) (Bonnema et al., 2016; Bonnema and Hossain, 2017 and Biswas et al., 2021). The development of RAT-Mekong is always ongoing. Therefore, the developers of RAT-Mekong do not accept any responsibility for the wrongful application or faulty decision-making based on RAT-Mekong outputs. Users should read the key documentation on RAT (see ‘User Guide’) and refer to the main RAT paper by Biswas et al. (2021).
The RAT frameworks have three key modules –Storage Change Module, Data Download Module, and Model Simulation and Post-Processing Module to estimate the reservoir inflow, outflow, and storage changes. The three modules are run in the backend server, and a task scheduler is used to automate the estimation by setting up task duration.
The reservoir storage change module, written in a python programming language, is used to produce reservoir surface area time series. First, it selects a single reservoir from the reservoir database of the RAT framework. Then it creates a buffer polygon around the reservoir based on the distance provided. In the current version of RAT-Mekong, only visible (optical) satellite imageries are used with adjustments for cloud cover and cloud shadow using a bit-masking algorithm. After removing the scenes with higher than threshold cloud cover, the reservoir storage change module mosaics all the scenes and applies the threshold-based method to create the water area of the reservoir. Finally, using the area-elevation relationship for a reservoir and the water area time series calculated from satellite imageries, the module calculates the storage change for the selected reservoir. The module iterates all the reservoirs in the same way and generates the reservoir storage change time series for every reservoir. It should be noted that in an effort to continually improve accuracy and sampling, a future version of RAT-Mekong is likely to incorporate the use of data from multiple sensors in the optical (Landsat, Sentinel-2, MODIS) and microwave (Sentinel-1, Jason-3, Sentinel-6) wavelengths.
In the current version of RAT-Mekong, the hydrologic model used for simulating reservoir inflow is the Variable Infiltration Capacity (VIC) model. VIC requires different types of meteorological parameters like precipitation, minimum and maximum temperature, wind speed, etc. to run VIC and Routing model to estimate reservoir inflow. The data download module is developed to download the required data from the relevant portal and reformat & resample into a specific format to run the VIC simulation model.
This module is used to run the VIC and Route model to get the latest available reservoir inflow data. After completing data processing data and calculation, the output will save into text (.txt) file format and it will be accessed by Django and finally, visualize the data in frontend GUI.
Know More About RAT Tool GitHub Repository Training and Education ResourcesReferences
Das, P., F. Hossain, S. Khan, N. K. Biswas, H. Lee, T. Piman, C. Meechaiya, U. Ghimire, K. Hosen (2022) Reservoir Assessment Tool 2.0: Stakeholder driven Improvements to Satellite Remote Sensing based Reservoir Monitoring, Environmental Modeling and Software, vol. 157. https://doi.org/10.1016/j.envsoft.2022.105533, Download paper
Biswas, N., F. Hossain, M. Bonnema, H. Lee, F. Chishtie (2021). Towards a Global Reservoir Assessment Tool for Predicting Hydrologic Impacts and Operating Patterns of Existing and Planned Reservoirs, Environmental Modeling & Software, vol. 140 doi:10.1016/j.envsoft.2021.105043, (available at: http://saswe.net/publications/RATPaper.pdf)
Bonnema, M., F. Hossain, B. Nijssen & G. Holt (2020) Hydropower’s Hidden Transformation of Rivers in the Mekong, Environmental Research Letters, vol. 15(4), https://doi.org/10.1088/1748-9326/ab763d
Bonnema, M. and F. Hossain. (2017). Inferring reservoir operating patterns across the Mekong Basin using only space observations, Water Resources Research, vol. 53, pp. 3791–3810, (http://dx.doi.org/10.1002/2016WR019978).
Bonnema, M., S. Sikder, Y. Miao, X. Chen and F. Hossain, I.A. Pervin, S.M. M. Rahman & H. Lee. (2016). Understanding Satellite-based Monthly-to-Seasonal Reservoir Outflow Estimation as a function of Hydrologic controls, Water Resources Res., vol. 52, 4095– 4115, (doi: 10.1002/2015WR017830).”
___
