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Abstract. The Columbia River Treaty (CRT) signed between the United States and Canada in 1961 is known as one of the most successful transboundary watertreaties. Under continued cooperation, both countries equitably share collective responsibilities of reservoir operations and flood control andhydropower benefits from treaty dams. As the balance of benefits is the key factor of cooperation, future cooperation could be challenged byexternal social and environmental factors which were not originally anticipated or change in the social preferences of the two actors. To understandthe robustness of cooperation dynamics, we address two research questions. (i) How does social and environmental change influence cooperationdynamics? (ii) How do social preferences influence the probability of cooperation for both actors? We analyzed infrastructural, hydrological,economic, social, and environmental data to inform the development of a socio-hydrological system dynamics model. The model simulates the dynamicsof flood control and hydropower benefit sharing as a function of the probability to cooperate, which in turn is affected by the share ofbenefits. The model is used to evaluate scenarios that represent environmental and institutional change and changes in political characteristicsbased on social preferences. Our findings show that stronger institutional capacity ensures equitable sharing of benefits over the long term. Under the current CRT, the utility of cooperation is always higher for Canada than non-cooperation, which is in contrast to the United States. The probability tocooperate for each country is lowest when they are self-interested but fluctuates in other social preference scenarios. 

Pluvial flooding in urban regions is a natural hazard that has been rarely investigated. Here, we evaluate the utility of three radar (Stage IV, MRMS, and GCMRMS) quantitative precipitation estimates (QPEs) and the SWMM hydrologic-hydraulic model to simulate pluvial flooding during the North American Monsoon in Phoenix. We focus on an urban catchment of 2.38 km2 and, for four storms, we simulate a set of flooding metrics using the original QPEs and an ensemble of 100 QPEs characterizing radar uncertainty through a statistical error model. We find that Stage IV QPEs are the most accurate, while MRMS QPEs are positively biased and their utility to simulate flooding increases with the gage correction done for GCMRMS. For all radar products, simulated flood metrics have lower uncertainty than QPEs as a result of rainfall-runoff transformation. By relying on extensive precipitation and basin datasets, this work provides useful insights for urban flood predictions.