Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America

Abstract

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.

Snow Persistence Explains Stream High Flow and Low Flow Signatures with Differing Relationships by Aridity and Climatic Seasonality

“…The multivariate regression analysis was conducted in R version 4.1.0 (R Core Team, 2021) with the R interactions package developed by Long (2019) and the R iml package by Molnar et al (2018). Multivariate regression models serve as a classical method to predict streamflow characteristics (Lull and Sopper, 1966;Shu and Ouarda, 2012;Eurich et al, 2021;Janssen et al, 2021). Here, multivariate linear regression models were developed between each streamflow signature (denoted as response 205 variables, 𝑌 𝑘 ) and climatic metrics including SP (𝑥 1 ), AI (𝑥 2 ), and SI (𝑥 3 ) as model predictors (Eq.…”

“…At the extremes, -1 (out-of-phase) signifies winter-dominant precipitation, 0 signifies uniform precipitation throughout the year, and +1 (in-phase) signifies summer-dominant precipitation (Berghuijs et al, 2014b). Catchmentscale long-term average climate aridity and seasonality indices used in this paper were calculated in Janssen and Ameli (2021).…”

“…Shape-based streamflow signatures can represent the shape and flashiness of hydrographs to help elucidate relationships in the baseflow of streams, human impacts on flow pathways, and relationships with storage of water in catchments (McMillan, 2020;Janssen and Ameli, 2021). In this study, five shape-based streamflow signatures were used to assess the ability of SP to predict streamflow flashiness at low and high flow conditions.…”

“…Q5 and Q95 were normalized against Qmean to produce normalized Q5 and normalized Q95, respectively. Both signatures serve as dimensionless shape-based indices to broadly describe catchments across different regions that are agnostic to the impacts of flow magnitudes (Janssen and Ameli, 2021). The Flow Duration Curve (FDC) aggregates recorded streamflow values against their exceedance probability and is a compact representation of streamflow data (Searcy, 1959).…”

“…In practice, streamflow signatures have a wide array of applications in classical hydrologic research, such as process elucidation, environmental decision making, and analyses of land-use or climate change impacts on catchment hydrology (Smakhtin, 2001;Wagener et al, 2007;McMillan, 2020;Sawicz et al, 2011;McMillan, 2021). While there are many different types of streamflow signatures, shape-based signatures seek to represent the shape and flashiness of hydrographs 30 and they can reveal important insights into baseflow, catchment storage, and anthropogenic impacts on catchment flow paths (McMillan, 2020;Janssen and Ameli, 2021). Changes to global water systems are predicted to occur due to global climate change (IPCC, 2018).…”