## 2 Material and Methods  
### Data and Key Drivers  
This study first collected a vast amount of data about domestic water resources from 17 typical countries from 1950 to 2020 according to the publicly available data. The water data in this research are mainly from three parts. Firstly, the global water-related database with free access was established in AQUASTAT by the Food and Agriculture Organization of the United Nations (FAO) and the water databases in the World Bank Open Data [5, 14]. It should be noted that national water-use records were conducted every 5 years or longer, and most of the water records could only be traced to 1960 or later. Secondly, a few of the detailed water-use categories were collected from national statistical offices, such as the U.S. Geological Survey (USGS) and the Eurostat and German Association of Energy and Water Industries [24, 25, 26]. Thirdly, a series of published literature reviews and statical surveys about water use were consulted. [PERSON] [27] and [PERSON] [28] tried to conduct an adequate data survey for the World's Water Report and USA water data. [PERSON] et al. used the WaterGAP 3 model for back-calculating water-use data on a global scale [23]. However, many historical records on domestic water use were incomplete or discontinuous, as shown Figure 1, for the primary selection of 17 countries. The relationship between water usage and economic development is shown as the domestic water per capita (DWPC) and GDP per capita to offset the regional disparity. Figure 1 shows the collected domestic water data from 17 countries, including Japan (JP), China (CN), the United States (USA), Spain (ES), France (FR), the United Kingdom (UK), Poland (PL), India (IND), Indonesia (ID), South Africa (ZA), South Korea (KR), Greece (GR), Romania (RO), Germany (GER), Brazil (BR), Mexico (MX), and Canada (CA). However, several countries showed poor data availability. South Africa and Brazil, as shown in Figure 1a, showed a C-type curve, meaning that the water usage was reduced during economic recession and increased during economic recovery. Spain, Poland, Greece, Romania, and Mexico showed an entangled type, showing that the water usage drastically changed during economic transition. These 7 countries with cluttered data were excluded and the remaining 10 countries were collected as our research objects. To get consistent and long-term DWPC data, the relationship between urbanization rate and DWPC was derived first, and changes in water-usage intensity were expressed as urbanization rate change due to the observation that as urbanization rate increases, water users in a more urban population trend toward a more water-intensive lifestyle. After the maximum level was reached, DWPC was either stable or declined with the increasing urbanization rate. The relationship between urbanization rate and DWPC is shown in Figure 2. Instead of using solely regional curves to estimate past DWPC, the current data model version was derived for 9 countries. Germany was an exception due to the lower DWPC, so the DWPC with urbanization between 20% and 60% in Germany was derived from its original data. Where data availability was missing with an urbanization of between 20% and 60%, the available information was combined in order to allow for the fitting of a simulating curve to the historical data, as shown in Figure 2.  
Figure 1: Relationship between GDP per capita and DWPC for (**a**) South Africa and Brazil; (**b**) Spain, Poland, Greece, Romania, and Mexico; and (**c**) all 17 countries. The urbanization data could be traced back to 1900 and the relationship between urbanization and DWPC was simulated as three different models in Table 1, with the R-squared above 0.85. In addition, the corresponding models for applicable countries were listed according to the trends of the curves after urbanization above 60%, as shown in Table 1. When the urbanization was acquired, the DWPC could be derived with corresponding models.