Abstract
Despite increasing evidence of intensification of extreme precipitation events associated with a warming climate, the magnitude of extreme river flows is decreasing in many parts of the world. To better understand the range of relationships between precipitation extremes and floods, we analyzed annual precipitation extremes and flood events over the Contiguous United States from 1980 to 2014. A low spatial correlation (less than 0.2) between changes in precipitation extremes and changes in floods was found, attributable to a weak causal relationship. The co-variation between precipitation extremes and floods is also substantially low, with a majority of catchments having a coefficient of determination of less than 0.5, even among the catchments with a relatively strong causal relationship. The findings indicate a need for more investigations into causal mechanisms driving a non-linear response of floods to intensified precipitation extremes in a warming climate.
1 Introduction
Among the most important implications of global climate change is the intensification of the hydrologic cycle [Huntington , 2006], including the intensification of rainfall extremes [Westra et al. , 2014]. As air temperature rises, the water vapor held in the atmosphere also increases following the Clausius-Clapeyron relation [Clausius , 1850]. This relationship has been documented extensively in the climate literature [Donat et al. , 2013;Guerreiro et al. , 2018; Papalexiou and Montanari , 2019;Westra et al. , 2013], and has led to concerns of a future characterized broadly by an increase in the magnitude of global flood events.
Large scale investigations into changes in floods, however, indicate a broader range of global flood response, with many studies documenting sites with a decrease in flood magnitude [Do et al. , 2017;Do et al. , 2020b; Gudmundsson et al. , 2019; Hodgkins et al. , 2017; Kundzewicz et al. , 2004; Lins and Slack , 1999]. These somewhat unexpected relationships between trends in extreme precipitation and trends in extreme discharge can be attributed to the influence of other flood generation mechanisms such as soil moisture [Ivancic and Shaw , 2015; Wasko et al. , 2020;Ye et al. , 2017] and snow dynamics [Berghuijs et al. , 2016; Blöschl et al. , 2017; Do et al. , 2020a;Ledingham et al. , 2019; Stein et al. , 2020]. Even when floods are triggered by precipitation extremes, the relationship between precipitation magnitude and flood magnitude is likely non-linear [Sharma et al. , 2018], owing to the complex interactions of many variables which have undergone substantial changes such as land cover [Keenan et al. , 2015; Lambin et al. , 2003], river channels [Slater et al. , 2015; Yamazaki et al. , 2014] and evapotranspiration [Bosilovich et al. , 2005;Gronewold and Stow , 2014].
However, there is still a lack of quantitative understanding of the relationship between precipitation extremes and floods, partially due to unavailable discharge observations in many parts of the world [Do et al. , 2018; Do et al. , 2020b]. Even for regions with relatively good streamflow records, empirical investigations have primarily focused on the consistency between the timing of precipitation extremes and that of floods [Berghuijs et al. , 2019;Blöschl et al. , 2017; Do et al. , 2020a; Ivancic and Shaw , 2015; Stein et al. , 2020; Wasko et al. , 2020] rather than co-variation between precipitation extreme intensity and flood magnitude. As a result, it is difficult to promote generalized statements about global relationships between changes in precipitation extremes and changes in floods, which is essential to the design of robust flood prevention and mitigation strategies in a warming climate [Milly et al. , 2008].
We aim to fill in this gap through an empirical assessment of the co-variation of precipitation extremes and flood magnitude using a large sample (671) of catchments across the Contiguous United States (CONUS) (Section 2.1). We used annual maxima streamflow from 1980 to 2014 from these catchments as the flood population, and we used three metrics of annual maxima precipitation to represent precipitation extremes (Section 2.2). Temporal changes in floods and precipitation extremes were then estimated at each catchment and the correlation between the spatial patterns of these trends was assessed (Section 2.3). The ordinal date of precipitation extreme events was then compared to that of annual flood events (Section 2.4) to assess potential causal relationships between these hydro-climatic extremes. Finally, the co-variation between the intensity of precipitation extremes and flood magnitude across catchments was assessed (Section 2.5) to evaluate the appropriateness of using changes in extreme precipitation as a proxy for changes in floods.
2 Data and Methods
2.1 Data
Data for our analysis was derived from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset [Addor et al. , 2017a; Newman et al. , 2015]. The CAMELS database aggregates a variety of hydrometeorological variables (primarily derived from other studies) for 671 catchments across the CONUS (the outlets of CAMELS catchments are shown in Figure 1). The catchments in the CAMELS database are intended to reflect relatively natural hydrologic conditions (the impervious surface area of each catchment is less than 5% of the total catchment area; see Newman et al. [2015] for more information). These catchments have a relatively small size (the median catchment area is 340.7 km2) and cover a range of climatic conditions (e.g., dry, temperate, and continental climates) as well as geographic features (e.g., mountains and deserts). Other variables in the CAMELS database include daily streamflow (originally obtained from the United States Geological Survey), catchment-average daily precipitation and temperature (derived from the Daymet dataset [Thornton et al. , 1997]), and daily evapotranspiration, simulated by the conceptual SAC-SMA model [Burnash et al. , 1973].
In addition to the hydro-meteorological data available through CAMELS, we also obtained soil moisture data from the NOAA Climate Prediction Center [Van den Dool et al. , 2003]. This dataset provides monthly soil moisture water height equivalent, simulated by a leaky bucket model, with a spatial resolution of 0.5-degree latitude x 0.5-degree longitude (i.e., a cell-size of about 2,500 km2). We used monthly soil moisture from the cell containing each catchment outlets as a proxy for catchment-wide to obtain soil moisture conditions from 1980 to 2014 for each of the CAMELS catchments. We judged this approach to be appropriate for the CAMELS catchments, which have a relatively small size.