SUPPLEMENTARY INFORMATION

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1 SUPPLEMENTARY INFORMATION DOI: /NGEO Model Estimates of Sea Level Change due to Anthropogenic Impacts on Terrestrial Water Storage Yadu N. Pokhrel 1,2, Naota Hanasaki 3, Pat J.-F. Yeh 1, Tomohito J. Yamada 2, Shinjiro Kanae 4 and Taikan Oki Institute of Industrial Science, The University of Tokyo, Tokyo, Japan 2 Faculty of Engineering, Hokkaido University, Sapporo, Japan 3 National Institute for Environmental Studies, Tsukuba, Japan 4 Department of Mechanical and Environmental Informatics, Tokyo Institute of Technology, Tokyo, Japan Supplementary Methods 14 The Model The model used in this study is an integrated water resources assessment modeling framework 1, which has been developed by incorporating human impact schemes (reservoir operation, irrigation, withdrawal, and environmental flow requirements) into a land surface model (LSM) the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO 2 ). MATSIRO simulates the majority of land hydrologic processes on a physical basis. The soil column consists of five layers with a total thickness of 4 m. A simplified version of TOPMODEL is used to represent surface and subsurface runoff processes. River routing is represented by using the river 1 NATURE GEOSCIENCE 1

2 routing model the Total Runoff Integrating Pathways (TRIP; ref. 3). Since MATSIRO was developed to simulate the natural water cycle without accounting for human interventions on the terrestrial water cycle, the human impact schemes (reservoir operation 4, irrigation 1, and water withdrawal 1,5 ) were recently incorporated into the MATSIRO TRIP framework 1. The human impact schemes have been incorporated within the LSM in a consistent manner so that the integrated model simulates the natural flow of water globally while taking into account the human interventions. Subgrid variability of vegetation is represented by partitioning each grid cell into two tiles: natural vegetation and irrigated cropland, the latter of which is used to estimate irrigation water requirement which accounts for ~90% of consumptive water use globally In its fully integrated mode, the model routes the simulated runoff through the digital river networks of TRIP. The operation module of the reservoirs, which are located on the river networks, is activated if reservoirs are found in a grid cell. The reservoir operation module 4 targets the maximum storage to be 85% of the storage capacity for the large reservoirs. However, the medium-sized reservoirs, which act as a buffer to store water in wet season and supply later when necessary, can be 100% filled with the excess runoff flowing directly to the rivers (see refs 1 and 4 for details). The withdrawal module obtains domestic, industrial, and agricultural water requirements from river channels considering the lower threshold of river discharge prescribed as the environmental flow requirement. However, there is an option to withdraw water from the predefined local sources such as natural streams and medium-sized reservoirs; or unlimitedly from an imaginary source if the local sources run out. Thus, unsustainable water use is estimated as the deficit in supply from the near-surface sources to meet the total demand (see ref. 1 for further elaboration). The unsustainable water use is obtained from the imaginary source which is 2

3 identical to the nonrenewable and nonlocal blue water (NNBW) suggested by Hanasaki et al. 5 and Rost et al. 6 to implicitly account for unsustainable water withdrawal from nonrenewable fossil groundwater (groundwater depletion). However, the present integrated model does not fully simulate the interactions between surface water and groundwater, which might have led to the overestimation of unsustainable water use 1. Finally, water remaining in the river channel flows downstream to the ocean or inland sinks (see ref. 1 for detailed model descriptions). 51 Data Data for the historical land use change are prepared by combining the information of global land cover 7, historical potential vegetation 8, and cropland area 9. The global land cover map 7 representing the year 1992 is based on the analysis of satellite images obtained from the Advanced Very High Resolution Radiometer (AVHRR). All data are first aggregated to 1 1 global grids from the original resolution of 5 arc minutes, and the number of grids assigned to the cropland in the global land cover map around the year 1992 (ref. 7) are progressively reduced backward in time using the HYDE 3.1 cropland distributions 9 following Hirabayashi et al. 10. The historical potential vegetation classifications 8 are used to determine the appropriate land cover type in the pre-crop condition. Thus, the time series of land use data is obtained based on the historical evolution of croplands which accounts for the most significant changes of land cover during the 20 th century 8, Time-varying data for irrigated areas are based on the global map of irrigated areas 11 and the country level historical extent of irrigated areas 12. Large 1,4 (maximum capacity >1 km 3 ) and medium-sized 5 (maximum capacity ranging from 10 6 m 3 to 1 km 3 ) reservoir capacity data are based on the data from the International Commission on Large Dams (ICOLD, ref. 13). For large 3

4 reservoirs, the historical time series of reservoir capacity is generated based on the completion year of dams provided by ICOLD 13. For medium-sized reservoirs, since no completion year has been compiled, we assume that the rate of increase in total capacity is similar to that of large reservoirs. The global total reservoir capacity of the data used in this study, including large and medium-sized reservoirs, is ~8000 km 3 (~95% of the documented global capacity; refs 13 15). 72 Simulation design A series of global simulations at the 1 1 resolution is performed using two different settings; namely, the NAT (natural) and HI (human impacts), and for different periods using different forcing datasets. In the NAT simulation only the natural water cycle is simulated, while in the HI simulation all anthropogenic water regulation schemes are also activated. The terrestrial water storage (TWS) simulated in the NAT consists of soil water, river water, and snow and ice, while the HI case also considers reservoir storage. Therefore, the difference in the TWS of these simulations is the water storage variation reflecting reservoir operations. Moreover, in regions with significant irrigated areas, the soil water in the HI case may also be affected by the increased soil water content through irrigation. However, such effects are relatively small when averaged over a large basin. All simulations have a 15-year spin-up period by repeating the forcing of the first year, but this is discarded in the analysis of the simulations. For the GPCC HI (the simulation using GPCC precipitation data) and the other three simulations conducted for uncertainty analysis, the year 1980 is also discarded as an additional spin-up because this simulation was initialized from the GPCC-NAT spin-up simulation

5 89 Validation of Simulated TWSA with Observations from GRACE The simulated terrestrial water storage anomaly (TWSA) is validated against the measurements from the Gravity Recovery and Climate Experiment (GRACE) satellite mission 16 over the selected highly regulated river basins, based on which the role of artificial reservoir storage in the total TWSA is evaluated. The global monthly 1 1 GRACE data (RL4.0) are provided by the Center for Space Research (CSR), University of Texas at Austin. The river basins selected for the validation are shown in Fig. S1, which are the basins where river discharge is largely regulated, and that incorporation of human impacts considerably improves discharge simulations 1. Only the basins with the area >250,000 km 2 are selected considering the lower limit in the hydrologic application of GRACE data 17,18. Further, some river basins with significant amount of reservoir storages are added (e.g., Nile, Parana), resulting in total 24 basins (Fig. S1). We note that the Angara, Snake, and Vilyuy basins are the tributaries of the Yenisey, Columbia, and Lena basins, respectively. These tributaries are selected instead of the whole basins because most reservoirs are located in these headwater catchments, and therefore the reservoir storage impacts are not apparent when averaged over the entire large basin areas. We also note that the Parana basin considered here is a sub-basin of the Parana/La Plata basin, with the most downstream point considered being the Posadas gauging station (55.5 W, 25.5 S). For the Murray basin, the upper reach of the gauging station Lock 9 upstream (141.5 E, 34.5 S) is considered. 5

6 Figure S1: River basins selected for the TWSA comparison. Locations of large reservoirs (storage >1 km 3 ) are also shown (black stars). Reservoirs with a maximum capacity between km 3 and >=100 km 3 are shown by green and red circles, respectively. Note that the mediumsized reservoirs with a maximum storage capacity ranging from 10 6 m 3 to 1 km 3 are not shown. TWSA comparison for the basins 1~12 is presented in Fig. S2, whereas the comparison for the basins 13~24 is shown in Fig. S Figure S2 compares the total TWSA from the GPCC HI and GPCC NAT simulations (see Methods) with the GRACE observations over the basins which are highly regulated (basins 1~12 in Fig. S1) among the selected 24 basins. A notable improvement can be seen in the GPCC HI over the GPCC NAT in several river basins. In particular, the seasonal fluctuation of TWSA is reduced after including reservoir storage. For example, in the Angara, Churchill, Nelson, and Vilyuy river basins where TWSA is largely affected by river storage, considerable differences are evident between two simulations. In fact, most of these basins are heavily 6

7 regulated 1 with a significant amount of water stored in large reservoirs. In the basins such as the Sao Francisco and Syr Darya, seasonal variation of TWSA is largely controlled by soil water storage rather than river storage, while reservoir storage plays only a relatively minor role. Still, for the Parana, Sao Francisco, Syr Darya, and Volga basins, representing reservoir storage improves TWSA simulations modestly. Figure S3 presents the Taylor diagram 19 illustrating the statistics of the simulated TWSA against GRACE observations for the basins selected for TWSA comparison in Fig. S2. Taylor diagram provides the ratio of standard deviation as the radial distance and the correlation with the GRACE observations as the angle in the polar plot. In Fig. S2, the squares (circles) denote the GPCC NAT (GPCC HI) simulations of TWSA, while the GRACE TWSA is plotted as a black point on the horizontal axis (zero correlation error) and at unit distance from the origin (no error in standard deviation). The linear distance between the square/circle and GRACE storage is proportional to the root mean square error (RMSE) between them. It is evident from the figure that the representation of reservoir storage improves the overall accuracy of model simulations over the majority of selected basins. Over most of these basins, the GPCC NAT simulation overestimates TWS variations and the correlation with GRACE is low. In contrast, over some basins such as the La Grande and Parana, the GPCC HI underestimates TWS variations although RMSE (see the numbers in each panel in Fig. S2) improves as compared to GPCC NAT. Overall, these results indicate that artificial reservoir water impoundments play a non-negligible role in modulating TWS seasonal variations, hence the incorporation of reservoir storage notably improves TWS simulations over most of the highly regulated basins. 7

8 Figure S2: Comparison of the seasonal cycle ( ) of terrestrial water storage anomaly (TWSA) between the GRACE observations (black dots) and model simulations (red lines). Blue lines indicate river storage which lumps the storages in river channels and in large and medium-sized reservoirs. For the simulated TWSA and river storage, solid and dashed lines represent the GPCC HI and GPCC NAT simulations, respectively. The remaining TWSA components (mainly soil water and snow storages) are the same for both simulations (not shown). The numbers in each panel indicate the root mean square error (RMSE) for the two simulations. 8

9 Figure S3: Taylor diagram illustrating the statistics of simulated TWSA over the selected river basins compared with the GRACE observations. Squares and circles indicate the GPCC NAT and GPCC HI simulations, respectively. Inscribed numbers indicate the basins in the same order as in Fig. S

10 Figure S4 compares the simulated total TWSA against the GRACE observations for the river basins with a relatively smaller degree of flow regulation (basins 13~24 in Fig. S1). Over these basins, TWSA is not significantly affected by reservoir storage, except for the (15) Koksoak, (16) La Grande, and (20) Saguenay river basins, all of which with a relatively small basin area (~150,000, 103,000, and 96,000 km 2, respectively). Therefore, the comparison over these three basins should be interpreted with caution since the GRACE data provides reliable estimates only for the basins larger than ~ km Figure S4: Similar to Fig. S2, but for the less regulated river basins (basins 13~24 in Fig. S1)

11 168 Comparison of Simulated Reservoir Storage with Observations Figure S5 compares the simulated mean annual reservoir storage with the observations for the data available period The period is chosen because the simulations using different forcing data overlap within this period making the uncertainty analysis possible. Observed reservoir storage data are obtained from Hanasaki et al. 4. Out of the total 28 reservoirs compiled in their study from various data sources, 19 reservoirs with observed storage data over are selected. The annual mean is calculated based on the available data length: Oroville ( ), Sirikit ( ), American Falls ( ), Fort Peck ( ), Fort Randall ( ), Garrison ( ), Bhumibol ( ), Grand Coulee ( ), Oahe ( ), and the others ( ). 11

12 Figure S5: Comparison of simulated mean annual reservoir storage against the observations for the selected reservoirs. The inset shows the reservoirs with observed storage <3 km 3 for clarity. Error bars indicate the uncertainty (i.e., ± two standard deviations) estimated from the model results using four different precipitation forcing data

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