Influence of Temperature on Streamflow Dynamics: A Multi-Catchment Analysis Using the PCMCI+ Causal Discovery Algorithm

While precipitation is the primary driver of streamflow variability, temperature also plays a significant role. Temperature influences streamflow by modifying precipitation, evapotranspiration, and soil moisture. While this relationship is often studied using hydrological or black-box models, the causal effect of temperature dynamics on streamflow at the catchment scale is not fully understood. This study investigates the causal relationship between precipitation, temperature, and streamflow time series using the PCMCI+ causal discovery method. Having the causal structure, the total causal effect of temperature on stream flow is estimated. The analysis is conducted on CAMELS-GB (671 catchments) and LamaH (859 catchments) datasets to study the causal effects of temperature on streamflow across a wide range of catchments with different climate and physiographic characteristics. Preliminary results indicate that temperature significantly influences streamflow within a specific range, which changes over time for most catchments. The changes in the range within which the temperature has high causal effects on the temperature might be due to the shift in catchment storage and precipitation patterns, leading to a change in catchment response to temperature. These findings highlight the importance of identifying a relationship between temperature streamflow variability from a cause-and-effect perspective. This suggests that incorporating causal information can improve the modelling of the hydrological systems under changing climate.