Potential Disruption of Flood Dynamics in the Lower Mekong River Basin Due to Upstream Flow Regulation

The Mekong River Basin (MRB) is undergoing unprecedented changes due to the recent acceleration in large-scale dam construction. While the hydrology of the MRB is well understood and the effects of some of the existing dams have been studied, the potential effects of the planned dams on flood pulse dynamics over the entire Lower Mekong remains unexamined. Here, using hydrodynamic model simulations, we show that the effects of flow regulation on downstream river-floodplain dynamics are relatively predictable along the mainstream Mekong, but flow regulations could potentially disrupt the flood dynamics in the Tonle Sap River (TSR) and small distributaries in the Mekong Delta. Results suggest that TSR flow reversal could cease if the Mekong flood pulse is dampened by 50% and delayed by one-month. While flood occurrence in the vicinity of the Tonle Sap Lake and middle reach of the delta could increase due to enhanced low flow, it could decrease by up to five months in other areas due to dampened high flow, particularly during dry years. Further, areas flooded for less than five months and over six months are likely to be impacted significantly by flow regulations, but those flooded for 5–6 months could be impacted the least.


Terrestrial Water Storage (TWS) and its Estimation
TWS is composed of water stored over and underneath the land surface; thus, it is estimated by vertically integrating snow water, canopy water, river and floodplain water, soil water, and groundwater storages over a given spatial domain, typically a river basin. Mathematically, this can be expressed as 1 : TWS = Surface water + Subsurface water Surface water = FW + RW + SW + CW (1) Subsurface water = VW + GW Where, FW = water on the floodplains RW = water in the river channels SW = snow water CW = water stored in canopy surfaces VW = soil water in the vadose zone (unsaturated store) GW = groundwater (below the water table, saturated store) The TWS derived from the measurements made by the Gravity Recovery and Climate Experiment (GRACE) satellite mission 2 provides the vertically-integrated TWS and thus includes all components listed in Equation (1). In hydrological models such as HiGW-MAT 3 , however, each of the components is typically simulated on an individual basis. Thus, vertically integrated TWS for comparison with GRACE-based TWS is estimated by adding all components using Equation (1).
As briefly described in the "Materials and Methods" section in the main text, we use two different GRACE products. The first is a set of three Spherical Harmonics (SH) products 4 available from three different processing centers: (i) the Center for Space Research (CSR), (ii) the Jet Propulsion Laboratory (JPL), and (iii) the German Research Center for Geoscience (GFZ). These products are available for download at: https://grace.jpl.nasa.gov/data/getdata/monthly-mass-grids-land/. The second is the mascon product 5 available from two different processing centers: JPL (https://grace.jpl.nasa.gov/data/get-data/jpl_global_mascons/) and CSR (http://www2.csr.utexas.edu/grace/RL05_mascons.html). Both the SH and mascon products are readily available in terms of equivalent water height, thus no additional processing (i.e., filtering and smoothing) is applied in this study. We generate the spatially-averaged TWS time series for the entire MRB for each of the products as described below.
Because GRACE measures the TWS variations over large regions, the GRACE data and model results are typically compared as basin averages 1,6,7 over river basins having an area larger than the GRACE footprint 8 of ~200,000 km 2 . In this study, we estimate the basin-averaged TWS from both GRACE and HiGW-MAT model by taking an area-weighted average following the approach used in our recent study 6 : where s is the LSM or GRACE estimate, a i is the cell area, S i is the weighted estimate for each cell inside the basin, n is the number of cells in a basin, A is the total area of the basin, and H(x,t) represents the estimate of water storage for basin at time t.
As briefly described in the "Methods" section in the main text, two sets of basin-averaged TWS are derived from the model results. In the first set, the flood water (FW) component in Equation (1) doesn't exist because river-floodplain storage is lumped in the river water (RW) component of the TRIP 9 routing model used in HiGW-MAT 3 . In the second set, FW based on the explicit simulation by the CaMa-Flood 10 model is included. For uniformity, CaMa-Flood results at 10km grids are first upscaled to the grid resolution of GRACE data and HiGW-MAT model (i.e., 1º×1º). The basin boundary for the entire Mekong is used as shown in Fig. S2. Figure S1. Simulated annual mean flood depth downscaled to 500m spatial resolution using high resolution SRTM 11 topography data for (a) average year (mean of 1981-2010), (b) dry year (1998), and wet year (2000). The region enclosed by magenta lines shows the areas of major flood around Tonle Sap Lake (TSL) and Lower Mekong within Cambodia (source: https://data.humdata.org/), and the thick black outline marks the flooded areas around TSL used in previous studies 12 . The domain displayed is same as that shown in Figure 1 in the main text.   Note that GRACE data are not included here because of the reduced reliability of the data when averaged over small regions.  Figure S6. Same as in Fig. 4 in the main text but for simulated water level (i.e., water surface elevation).     Study   Table S1. Comparison of flooded areas with Arias et al. 12 for the major flood regions around Tonle Sap Lake indicated by thick black line in Figure 1a in the main text.

Dry Season
Flooded  Table S2. Changes in major flood characteristics (e.g., onset, magnitude, duration, and amount) compared to the baseline simulation at different stations analyzed in Fig. 4 in the main text (station names provided in Fig. 4 caption and locations shown by red circles in Fig. 5a). The Lake Outlet (LO) and Prek Kdam (PK) stations are indicated by grey shading. Numbers enclosed in boxes are those noted in the main text. For 10, 30, and 50% peak flow alteration scenarios, results for the scenarios with one-month early and delayed peak timing are also provided.   Table S3. A summary of the results of potential changes in flooded area around Tonle Sap Lake under different flow regulation scenarios from this study and those from Arias et al. 13 . Flooded days with the smallest change are marked by grey shading. As noted in the main text, the results are not directly comparable due to differences in simulation settings between the two studies.

Changes in Flood Characteristics under Different Flow Regulation Scenarios
a Spatial domain of Tonle Sap Lake region is exactly same as in Arias et al. 13