- **bayesian approach**: "no" - The text does not mention any use of Bayesian methods or prior knowledge in the model development or analysis.
- **clustering**: "no" - The text does not mention any clustering techniques or grouping of data points based on similarities.
- **copulas**: "yes" - The text explicitly mentions the use of copulas to model the joint distribution of multiple flooding indicators (maximum soil moisture, runoff, and precipitation). "The multidimensional representation of the joint distributions of relevant hydrological climate impacts is based on the concept of statistical copulas [43]."
- **forecasting**: "yes" - The text explicitly mentions the use of models to predict future scenarios of flooding hazards and damage. "Future scenarios use hazard and damage data predicted for the period 2018–2100."
- **geographical scale**: "river basin" - The text focuses on the Colorado River Basin (CRB) as the study area. "The hazard data we use to develop our adaptation model refer spatially to 395,526 U.S. Census blocks that intersect the CRB (Figure 1)."
- **historical data analysis**: "yes" - The text analyzes historical data on flooding indicators, economic activity, and damage to calibrate the adaptation model. "Our economic model is calibrated using data for the 1997–2017 (current) period. The hazard data we use for analyzing paste hazard expectations extend from 1977 to 2017."
- **historical period studied**: ["1977 to 2017", "1970–1999", "2000–2017", "1980–2000", "1997–2017"] - The text mentions these specific periods for analyzing historical data and developing future scenarios. "The hazard data we use for analyzing paste hazard expectations extend from 1977 to 2017. We consider a historical period (1970–1999) along with current (2000–2017) and multiple future scenarios for different times (2020–2039, 2040–2069, and 2070–2099). We use predictions about flooding hazards in CRB until the end of this century to study three scenarios of 𝐸[𝐻] designed above to investigate the impact on adaptation of quickly updating hazard expectations. In particular, we consider a scenario of a constant level of 𝐸[𝐻] equal to the average H experienced by each block in the 1980–2000 period. We use the dataset described above for the period 1997–2017 to calibrate the adaptation model."
- **regression models**: "yes" - The text explicitly mentions the use of regression models to analyze the relationship between flooding hazards, exposure, and damage. "Following recent empirical modeling approaches [19,20,21,49] to the adaptation to climate disasters and extremes, we calibrate the adaptation model by using count variables regression approaches and accounting for statistical over dispersion in the data." 
