Encoded within catchment hydrology are complex interactions among catchments' climatic and physical (e.g., soil, geology, and topography) attributes. Large spatial-temporal variabilities of these attributes, and diverse interactions among them, give rise to diverse catchment-scale streamflow generation mechanisms, in catchments around the world (Blöschl et al., 2014). This diversity complicates the understanding of the dominant streamflow generation mechanisms in a given catchment (Wu et al., 2021), which are central to obtaining the "right streamflow predictions for the right reasons" (Kirchner, 2006). As qualitatively showed by Dunne (1978) and Dooge (1986), there could be a generalizable catchment-scale scientific framework that explains and synthesizes the baffling diversity of streamflow generation mechanisms. Such a framework could allow the transfer and regionalization of the mechanisms (McDonnell et al., 2007;Wagener et al., 2007), essentially required to predict streamflow responses in poorly gauged regions (McDonnell & Woods, 2004). There is, however, little in the way of a scientific framework to quantitatively explain why variations in dominant streamflow generation mechanisms occur between catchments (Li et al., 2014). This quantitative framework, to be generalizable, should be developed based on globally available catchment data and should explain how interactions among catchment attributes influence the way a catchment transforms (or filters) climatic variability into streamflow variability (Troch, Berne, et al., 2013). This study takes a small step toward developing and testing (the application of) one such generalizable quantitative framework.Several studies showed the efficiency of a small set of interactive indices to describe a catchment's hydrologic behavior and predict the signatures of the catchment water balance, within the context of large-sample hydrology (LSH). LSH uses (globally) available datasets of catchment physical and climatic attributes and

While anthropogenic climate change poses a risk to freshwater resources across the globe through increases in evapotranspiration and temperature, it is essential to quantify the risks at local scales in response to projected trends in both freshwater supply and demand. In this study, we use empirical modeling to estimate the risks of municipal water shortages across North America by assessing the effects of climate change on streamflow and urban water demand. In addition, we aim to quantify uncertainties in both supply and demand predictions. Using streamflow data from both the US and Canada, we first cluster 4,290 streamflow gauges based on hydrograph similarity and geographical location. We develop a set of multiple linear regression (MLR) models, as a simplified analog to a distributed hydrological model, with minimum input data requirements. These MLR models are calibrated to simulate streamflow for the 1993–2012 period using the ERA5 climate reanalysis data. The models are then used to predict streamflow for the 2080–2099 period in response to two climate scenarios (RCP4.5 and RCP8.5) from five global climate models. Another set of MLR models are constructed to project seasonal changes in municipal water consumption for the clustered domains. The models are calibrated against collected data on urban water use from 47 cities across the study region. For both streamflow and water use, we quantified uncertainties in our predictions using stochastic weather generators and Monte Carlo methods. Our study shows the strong predictive power of the MLR models for simulating both streamflow regimes (Kling-Gupta efficiency >0.5) and urban water use (correlation coefficient ≈0.7) in most regions. Under the RCP4.5 (RCP8.5) emissions scenario, the West Coast, the Southwest, and the Deep South (South-Central US and the Deep South) have the highest risk of municipal water shortages. Across the whole domain, the risk is the highest in the summer season when demand is high. We find that the uncertainty in projected changes to the water demand is substantially lower than the uncertainty in the projected changes to the supply. Regions with the highest risk of water shortages should begin to investigate mitigation and adaptation strategies.

<p>Expanding the scientific understanding of global hydrological processes is a key research area for hydrologists. Research in this area can allow hydrologists to make better predictions in ungauged basins and catchments under climate change scenarios. Though hydrological processes are largely understood at a laboratory-scale, catchment-scale processes are much more complex and unknown. Previous studies at the catchment-scale have shown catchment geology is largely irrelevant in determining components of streamflow. Laboratory-scale experiments, however, have revealed that this is unlikely. This contradiction indicates the current techniques for determining hydrological variable importance in the literature are insufficient. In this paper, we quantify the influence of the interaction amongst climatic, geological, and topographical features on a large set of hydrological signatures in snow-dominated regions across North America, using Stable Extrapolative Marginal Contribution Feature Importance. The preliminary results show that when we consider interaction effects among climatic and geophysical features, and remove the influence of correlation, geological features show considerable importance at the catchment scale. We contend that this study contributes to the scientific understanding of catchment-scale hydrological processes, especially in cold, ungauged basins.</p>

<p>Recent research showed that, snow persistence, defined here as the fraction of time that snow is present on the ground, can play an important role in explaining spatial variability of average annual streamflow in moderately snowmelt-dominated regions. Here, we extend this work and explore the following questions: 1) whether globally available snow persistence data is useful for estimating a suite of streamflow signatures explaining the shape, flashiness and components of streamflow hydrograph, and 2) whether snow persistence could be useful for reconstructing streamflow patterns in ungauged watersheds, both spatially and temporally. We explore these questions across a spectrum of climatic dryness, snowiness, and geological settings. The motivations for the study are the need to understand how loss of snow may affect the components of streamflow in different climatic and geological settings, as well as the need for simple methods to predict components of streamflow in snow-dominated ungauged basins.</p>