# Contemporary climate analogs project north–south polarization of urban
water-energy nexus across US cities under warming climate

Renee Obringer  Department of Energy and Mineral Engineering, Pennsylvania
State University, University Park, PA 16802, United States  National Socio-
Environmental Synthesis Center, University of Maryland, Annapolis, MD 21401,
United States  Ecological Science and Engineering, Purdue University, West
Lafayette, IN 47907, United States  Corresponding authors<EMAIL_ADDRESS><EMAIL_ADDRESS>Roshanak Nateghi  School of Industrial Engineering,
Purdue University, West Lafayette, IN 47907 United States  Jessica Knee
School of Industrial Engineering, Purdue University, West Lafayette, IN 47907
United States  Kaveh Madani  United Nations University Institute for Water,
Environment and Health (UNU-INWEH), Hamilton, ON Ontario L8P 0A1, Canada  The
City University of New York Remote Sensing of Earth Science and Technology
(CUNY-CREST) Institute, New York City, NY 10031, United States  Rohini Kumar
Department of Computational Hydrosystems, Helmholtz Centre for Environmental
Research - UFZ, Leipzig 04318, Germany  Corresponding authors:
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Despite the coupled nature of water and electricity demand, the two utilities
are often managed by different entities with minimal interaction. Neglecting
the water-energy demand nexus leads to to suboptimal management decisions,
particularly under climate change. Here, we leverage state-of-the-art machine
learning and contemporary climate analogs to project the city-level coupled
water and electricity demand of 46 major U.S. cities into the future. The
results show that many U.S. cities may experience an increase in electricity
(water) demand of up to 20% (15%) due to climate change under a high emissions
scenario, with a clear north-south gradient. In the absence of appropriate
mitigation strategies, these changes will likely stress current
infrastructure, limiting the effectiveness of the ongoing grid decarbonization
efforts. In the event that cities are unable to match the increasing demand,
there may be increased occurrence of supply shortages, leading to blackouts
with disproportionate impacts on vulnerable populations. As such, reliable
projections of future water and electricity demand under climate change are
critical not only for preventing further exacerbation of the existing
environmental injustices but also for more effective design and execution of
climate change mitigation and adaptation plans.

## Introduction

Globally, cities are growing at an increasingly fast rate, with nearly 70% of
the world’s population estimated to live in cities by 2050 (United Nations,
2018). As urban populations grow, so will the need for critical services such
as water and electricity. In fact, the provisioning of these services is of
paramount importance to sustainable development (United Nations, 2015). The
compound effects of population growth and anthropogenic climate change will
constrain the supply of water and electricity and yet increase the demand for
these resources, straining the existing water and energy infrastructure
systems (Connell-Buck et al., 2011; Cronin et al., 2018; Auffhammer et al.,
2017; Raymond et al., 2019; Ashraf et al., 2019; Mukherjee et al., 2019;
Obringer et al., 2020). For this reason, there is a major area of research
that focuses on the impact of climate change on the water-energy nexus. These
studies primarily focus on the supply-side, such as the nexus between water
supply and electricity generation through hydropower (Guégan et al., 2012;
Madani et al., 2014) or the use of water by electricity generators for cooling
purposes (Macknick et al., 2012; Dale et al., 2015). There is, however, a
growing body of work that investigates the demand-side of the nexus, looking
in particular at the simultaneous consumption of water and electricity (Bartos
and Chester, 2014; Escriva-Bou et al., 2018; Obringer et al., 2019).

Within the literature on the urban water-energy nexus (Newell et al., 2019;
Newell and Ramaswami, 2020), the focus tends to be on smaller scales, such as
a single city (Dale et al., 2015; Escriva-Bou et al., 2018) or a small group
of cities (Venkatesh et al., 2014; Obringer et al., 2019, 2020). Given that
management of water and electricity systems occurs at multiple scales (e.g.,
ranging from local utilities to federal management entities), there is a need
to expand urban water-energy nexus analyses to consider larger scales.

Additionally, studies that leverage data-driven modeling of climate change
impacts on the climate-sensitive portion of end-use demand primarily involve
first characterizing the relationship between climate and demand in the
observational space, then projecting those relationships into the future using
General Circulation Model- (GCM-) derived climate data (Mukherjee and Nateghi,
2017, 2019; Maia-Silva et al., 2020; Obringer et al., 2020, 2022). While this
practice is beneficial and can provide accurate representations of future
demand, the incorporation of GCM-based data is computationally expensive,
rendering it unsuitable for local utilities without access to high-performance
computing and necessary expertise.

Overall, it is critical that urban water-energy nexus studies account for
anthropogenic climate change, which is one of the most pressing challenges
society faces. To do this, there is a need to create transferable frameworks
that can be readily applied to large-scale analyses. Additionally, it is
beneficial, particularly from a practitioner’s perspective, to evaluate quick,
but reliable techniques for obtaining future climate data that are more user-
friendly and less computationally-intensive, such as the climate analogs
approach.

Climate analogs are determined through statistical processes that identify the
location in which the current climate is analogous to the future climate in
another location. In particular, the climate analogs used in this study, which
were developed by Fitzpatrick and Dunn (2019), used 27 GCMs to evaluate the
climate analogs for 540 cities in North America, based on both temperature and
precipitation. In particular, the original study leveraged minimum and maximum
temperature and total precipitation for the four climatological seasons to
conduct their analysis (Fitzpatrick and Dunn, 2019). These analogs were
calculated based on the sigma dissimilarity between these twelve variables (3
measures $\times$ 4 seasons) in the present-day city’s and the future city’s
climate. Thus, each present-day city has 27 possible analogs using each of the
selected GCMs, plus an ensemble analog. In this study, we used the ensemble
analogs for 46 major US cities.

The analog approach has since been adopted in other locations, such as West
Africa (Fitzpatrick et al., 2020) and China (Yin et al., 2020; Wang et al.,
2022), as well as for communication and policy studies (Luccioni et al., 2021;
Binelli et al., 2022). However, it should be noted that, as with any approach
that leverages future climate simulations, there is uncertainty associated
with the climate analogs, as there is no ‘perfect match’. In fact, Fitzpatrick
and Dunn (2019) found that in some cities, the average analog was in a
completely different climatic zone, indicating that extreme climate change
could lead to unexpected changes.

In this work, we leverage climate analogs to characterize the future climate
in 46 major cities (population $>$ 250,000) across the United States. These
cities were chosen based on data availability out of an initial pool of 80
cities that met our population criterion. Using a state-of-the-art machine
learning model (i.e., multivariate tree boosting (Miller et al., 2016)), we
use the observed climate data in the analogs to project the coupled water and
electricity demand for each city of interest. The goal of this study is to
focus on the impact of climate change on urban utilities. In particular, we
present the ‘worst case scenario’, in which there is no adaptation within the
city to account for the changing climate conditions and no mitigation to
reduce carbon emissions at the global level. Additionally, we present results
using the high emissions (RCP8.5) climate analogs. See Supplementary Figure S2
for the results from a moderate emissions scenario (RCP4.5).

This study aims to fill a significant gap in the literature by presenting a
computationally inexpensive projection of the climate-related changes in
future water and electricity demand across a number of major U.S. cities,
while also emphasizing the importance of considering the water-electricity
nexus in such analyses. Additionally, by using climate analogs as the primary
source of future climate data we aim to communicate climate change impacts to
US citizens and decision-makers in a more intuitive way than traditional GCM-
based studies that often do not have a comparative analysis basis.

In the following sections we will discuss the results of using climate analogs
to project water and electricity demand across the United States. We will
first focus on the large-scale regional trends before delving into specific
cases of interest, such as exploring the north-south gradient and evaluating
the impact of various shared socioeconomic pathways on the total water and
electricity demand. We will wrap up with a brief discussion of the
implications of the results and the limitations of this study.

## Methods

### Data

In this study there were two types of data: consumption data and climate data.
The consumption data included both water and electricity consumption, which
were obtained from different sources. The water consumption data were
collected directly from the water utilities in the selected cities via Freedom
of Information Act (FOIA) requests. Initially, cities with populations greater
than 250,000 were selected, resulting in 80 initial cities. Of these 80
cities, only 46 cities (see Supplementary Table S1 or Figure 2a) responded
with adequate data for our analysis. In particular, some cities simply did not
respond the the request, while others were unable to provide monthly water
consumption for the time period of interest—2007 to 2018. The electricity
consumption data were obtained from the US Energy Information Administration
(EIA), which keeps values of monthly electricity use for a number of utilities
across the US (US Energy Information Administration, 2019). It should be noted
that in the case of Texas, many of the utilities were not included in the EIA
dataset, which led to some Texas cities being removed from the analysis.
Finally, both water consumption and electricity consumption were normalized by
service population and de-trended to remove the impact non-climatic factors
(i.e., technology, socioeconomic status) following a method by Sailor and
Muñoz (Sailor and Muñoz, 1997). The normalization process was important to
allow for a closer comparison between cities, as well as the water and
electricity consumption within a city, as many water and electric utilities
have different jurisdictions. The de-trending process, allowed us to focus on
the climate-sensitive portion of the water and electricity consumption, rather
than including the technological and socioeconomic factors that also influence
the demand profiles.

The climate data were collected from the North American Regional Reanalysis
(Mesinger et al., 2006). The variables included dry bulb temperature, wet bulb
temperature, dew point temperature, relative humidity, wind speed, and
precipitation. Initially, the data were collected as daily values for the
entire time period in each city, but were later aggregated to monthly
variables. In particular, the temperature, humidity, and wind speed variables
were aggregated to form minimum, maximum, and mean monthly values, while the
precipitation as aggregated to be the accumulated monthly value, as well as
the number of days within the month in which there was precipitation. Previous
work has leveraged the NARR dataset for observational climate data, following
a similar collection process as this study (Maia-Silva et al., 2020). Since we
used the climate analog approach, there was no need to run any simulations to
obtain the future climate variables. Rather, the climate variables from the
various analog cities were used to represent the future climate of the cities
of interest. See the Supplementary Information for more information on the
development of the climate analogs.

Figure 1: (a) Total water ($m^{3}$) and electricity (MWh) consumption compared
to the summer-only consumption for each city. (b) A selection of the initial
input variables. (c) The predicted climate-sensitive portion of the water and
electricity use plotted against the actual data, with a 45° line for
reference. (d) Measure of error, plotted as a box plot to represent the
variance across the 46 cities. The lower bound represents the minimum value,
the upper bound represents the maximum value, and the crossbar represents the
median.

### Methods

The modeling framework was adapted from Obringer et al. (2020), who projected
the water and electricity demand into the future for six cities in the
Midwestern United States. The framework is centered around a statistical
learning algorithm, known as multivariate tree boosting (Miller et al., 2016),
which is capable of simultaneously predicting multiple variables. See the
Supplementary Information for an algorithmic representation of this method.
The algorithm also accounts for the interdependency between the response
variables to improve the predictive accuracy, making the water-electricity
nexus an ideal study application for the model. This work expanded the study
by Obringer et al. (2020) to the entire continental United States, as well as
changed several procedures within the framework. Namely, the model was first
developed at a regional scale, grouping the cities based on the NOAA Climate
Zones (Karl and Koss, 1984). Within this step, we performed variable selection
to reduce the number of variables within each region. Given that climatic
patterns often evolve at the region-level, this step was important for
determining which variables were important throughout a region, before delving
into the city-level analysis. This regional analysis resulted in 4-6 important
variables per region, rather than the original 17—reducing the complexity and
improving model accuracy. See Supplementary Figures S1 and S5 for the
important variables selected for each region.

Following the region-level variable selection, we performed individual city
analyses with the previously selected variables. First, to avoid overfitting,
the model was trained and rigourously tested via conducting randomized 5-fold
cross-validation using observational climate data collected from NARR and
water and electricity consumption data from local utilities. Then, using this
trained model, we substituted the observed climate data with NARR climate data
from the analogs, creating projections of water and electricity demand. The
projections were conducted for two scenarios: RCP8.5 (high warming) and RCP4.5
(moderate warming), based on the ensemble analogs presented by (Fitzpatrick
and Dunn, 2019). Finally, we evaluated the percent change between the analog-
based projection and observed model results for each city. For more
information on the methods used in this study, see the Supplementary
Information, as well as Figure S4.

These projections represent the most likely climate (based on the ensemble of
27 GCMs) for a given city in the year 2080 (Fitzpatrick and Dunn, 2019). The
results represent the change to the per capita water and electricity demand in
the selected cities, if everything else (e.g., technology, socioeconomic
status, demographics, etc.) should stay the same. Although this is a large
assumption, understanding the climate-only impacts can provide information on
how much society needs to do to offset the changes. In other words, any
increase in demand caused by climate needs to be met by the supply, or offset
by increases to efficiency or conservation behaviors. This knowledge is
beneficial to utility managers and policymakers that are implementing
equitable policies to reduce climate impacts to infrastructure and seek to
protect the most vulnerable groups. Moreover, the work is relevant for climate
communication, as the use of climate analogs is easily understood by
researchers, practitioners, and citizens alike. Finally, although we focused
on 46 US cities, this model can be applied to a number of cities and regions,
provided the data is available. The framework itself can be scaled up to
regions or transferred to other cities, states, or countries.

## Results

The methodology of this work was composed to two modeling steps: (1) using
observational data for model training, and (2) using climate analog data to
investigate the impact of future climate change. Throughout the remainder of
this paper, we will refer to the first step as a ‘predictive model’ and the
second step as a ‘projection model’ to differentiate between the two steps.
The predictive model was built using observational data from the North
American Regional Reanalysis (see Methods and Figure 1b for input variables).
In particular, we considered the summer months (June - September), which
account for 30-50% of the total annual water and electricity demand (Figure
1a).

To measure model performance of the prediction model, we calculated $R^{2}$
and normalized root mean squared error (NRMSE). In terms of $R^{2}$, the
median value for climate-driven portion of electricity (water) consumption was
0.70 (0.56), suggesting that the model does explain a notable percentage of
variance for the majority of the cities. However, the values did range from
0.36 to 0.90 (0.31 to 0.84) for electricity (water) consumption indicating the
varying degrees of climate sensitivity of demand across the US. In other
words, it indicates that there are some cities that are not completely
explained by the climate variables. Figure 1c shows that in terms of water
consumption, some cities in the Southeast, as well as the Southwest do not
have as close of a fit as other areas. However, similar work has shown lower
values of $R^{2}$ (Obringer et al., 2019, 2020), suggesting that it is not
uncommon to see low $R^{2}$ values when modeling the climate-sensitive portion
of the water-electricity nexus. Nonetheless, $R^{2}$ is not a measure of error
and is therefore rarely used to evaluate predictive performance, which is
usually characterized through measures such as RMSE or MAE. Here, we consider
NRMSE as the primary measure of predictive error.

Overall, the predictive model reliably characterized the historical records of
the climate-sensitive portion of the coupled water and electricity demand,
with a median NRMSE of 0.12 (0.15) for electricity (water) demand (see Figure
1d). This suggests that the median error for both water and electricity
consumption was below 15%. In general, there was less variation in the NRMSE
than the $R^{2}$, indicating that the predictive performance of the model is
relatively stable throughout the cities studied. This further indicates the
transferability of the model, particularly if the goal is to predict the
climate-sensitive portion of the water and electricity consumption. Going
forward, this model was used to project the coupled water and electricity
demand under the high emissions climate change scenario.

The demand projections were made by leveraging climate analogs as a source of
future climate data. The analogs were initially determined by Fitzpatrick and
Dunn (2019) by calculating the sigma dissimaliarity for temperature (minimum
and maximum) and precipitation across 540 North American cities (Fitzpatrick
and Dunn, 2019). The authors used an ensemble of 27 GCMs to determine the
analog for each city under RCP4.5 and and RCP8.5 (Fitzpatrick and Dunn, 2019).
Generally, the authors aimed to find the location with less than $2\sigma$
difference between the cities, although some cities did have differences up to
$8\sigma$, depending on the GCM (Fitzpatrick and Dunn, 2019). For example, New
York City’s climate analog for the year 2080 (RCP8.5) is Jonesboro, Arkansas
(Fitzpatrick and Dunn, 2019). This means that in 2080, New York City can
expect a climate similar to present-day Jonesboro, if there is no global
reduction in emissions. In order to obtain future climate data for New York
City, therefore, we collected observed climate data from Jonesboro (2007-2018)
using the NARR dataset. In this way, we circumvent the need for downscaled GCM
data, while making reasonable future projections of water and electricity
demand. The results of the projections are shown in Figure 2b and d. See
Supplementary Figure S2 for the projections under a moderate emissions
scenario (RCP 4.5).

Figure 2: (a) Target cities and their high warming analogs (Fitzpatrick and
Dunn, 2019). (b) Projected changes to electricity demand across the 46 cities
(sized by population). (c) Total projected electricity demand increase by 2080
in three major cities under the five Shared Socioeconomic Pathways (SSPs)
(Hauer, 2019). (d) Projected changes to the water demand across the 46 cities
(sized by population).

### Regional trends in the projected water and electricity demand

The majority of this increase is occurring in the Midwestern cities,
particularly with respect to electricity use. Previous work has shown
significant increases in electricity use, as well as air conditioning use in
particular, within the Midwest region (Obringer et al., 2020, 2022). Our
results align with these previous findings, indicating close to 20% increase
in the climate-sensitive portion of electricity demand by 2080, solely due to
climate warming. Likewise, water consumption is expected to increase in many
Midwestern cities. These projected increases are likely due to the analogs,
which are generally located in the Southern US. This means that the average
future under high warming for the Midwest will be warmer and drier, leading to
increased demand for water and electricity within the residential sector.
Previous work in the Midwest found similar results.

In particular, an analysis by Obringer et al. (2020) projected a 20%
($\pm$15%) increase in Chicago’s electricity demand after surpassing the 2.0°C
threshold, following the same water-energy nexus approach. The present
analysis projected an increase of 12% for the city of Chicago’s electricity
use, which is within the estimated range previously reported. The primary
difference between the two methodologies was the use of climate analogs rather
than downscaled GCM data. The fact that the present results for Chicago are
within the range of GCM-based projections provides greater confidence in the
reliability of this method, at least in Chicago. Nevertheless, additional work
is needed to confirm the results for the rest of the cities, however, given
that this method is significantly less computationally expensive and more
user-friendly, it does signify the possibility of using climate analogs to
discuss climate change impacts.

In the Northeast, the electricity consumption is projected to increase under
the high emissions scenario, while water is projected to remain relatively
constant or increase slightly. This indicates that the average high emissions
future has higher temperatures (which are often associated with higher
electricity consumption), with limited changes to precipitation (which is
often the primary climate driver of water consumption). In terms of water
consumption, the mountain region and Western US are projected to see
increases. Given the ongoing issues of drought within the Colorado River basin
(as well as other Western US basins) (Guégan et al., 2012; Madani et al.,
2014; Udall and Overpeck, 2017; Fleck and Udall, 2021; Gangopadhyay et al.,
2022; Lisonbee et al., 2022), this increase could create further stress on the
water system. Further, the increased consumption of electricity paired with
this increase in water consumption could negatively impact hydropower in the
Western US. Specifically, during periods of drought, the increased water
consumption could create issues for the reservoirs, which may then limit the
effectiveness of hydroelectric plant operations (Guégan et al., 2012; Madani
et al., 2014). Understanding these interconnections are critical for building
resilience and planning for future climate change.

Finally, there are some cities that are projected to see reductions in the
climate-sensitive water and electricity demand, primarily in the Southern
region, which tend to have analogs in more tropical regions (see Figure 1a).
In general, the results indicate that under the high emissions scenario, the
majority of the cities studied are projected to see increases in water and
electricity demand up to 15% and 20% above current levels, respectively;
although some cities are projected to see reductions in demand. These latter
cases are found in the Southern regions of the country, demonstrating a north-
south gradient to projected changes in water and electricity demand, which
will be further discussed below.

### Southern cities show decreases in climate-sensitive water-electricity
demand

Within the country as a whole, there is a gradient that separates the Southern
cities from from the central and Northern cities. In particular, we see
reductions in water and electricity demand below this gradient, with increases
above it. This gradient is perhaps counterintuitive, as we would expect the
warmer regions to get warmer (and potentially drier), which would lead to
higher levels of electricity and water demand. However, by investigating the
analogs of the Southern cities evaluated in this analysis, we find that the
analogs are often more tropical, leading to increased precipitation and
slightly cooler (average) dry bulb temperatures. Based on the relationship
between climate and the water-electricity demand nexus, this leads the model
to project reductions in the climate-sensitive portion of water and
electricity demand in these cities. It should be noted, however, that these
results are based on the ensemble of 27 GCMs, thus there is some uncertainty
in the initial climate analogs. The authors themselves discuss this
uncertainty and point out that these counterintuitive results indicate that
there are possible futures in which cities experience completely new climate
zones (Fitzpatrick and Dunn, 2019).

For example, our results indicate that the city of Phoenix (Arizona) may
experience significant reductions in residential water and electricity
demand—approximately 15% in both sectors—due to the relationship between
climate and the water-energy nexus, without accounting for changes in
population, socioeconomic status, cultural norms, or other anthropogenic
impacts, such as urban heat islands. This is a case in which the ensemble
climate analog (Esperanza, Mexico) has a different climate than the city of
interest. Given the growing population in Phoenix, as well as the ongoing
mega-drought and periods of extreme heat, it represents an opportunity to
evaluate the impact of model uncertainty on our results.

In particular, Esperanza, Mexico is both cooler and wetter in the summer when
compared to Phoenix. Thus, in our model, which leverages the relationships
between climate variables and water-electricity demand, the occurrence of
lower temperatures and more precipitation leads to lower consumption of both
water and electricity. In particular, our analysis found that the most
important variable for predicting the climate-sensitive portion of the water-
energy nexus in Phoenix was the average dry bulb temperature (see
Supplementary Figures S1 and S5 for important variables by city and region,
respectively), which was nearly 20% lower in Esperanza than Phoenix during the
observation period. Given that higher average dry bulb temperatures tend to
lead to higher electricity consumption within the observed values for
Southwestern region (see Supplementary Figure S3), it is likely that this
reduction in temperature leads to a projection of reduced electricity demand
due to the climatic conditions. Similarly, an increase in precipitation would
lead to a reduction in water consumption due to a reduction in the need for
outdoor landscaping.

While the model itself is built on these relationships, it is important to
note that the uncertainty in the future climate analogs could lead to
different values. For example, Fitzpatrick and Dunn (2019) presented the
results from 27 individual GCM-based analyses plus the ensemble. If we
investigate the individual analogs for Phoenix, they range from Buckeye,
Arizona (about 40 miles southwest of Phoenix) to several locations in the
Sonora state of Mexico (including the cities of Nacozari de García,
Hermosillo, and Esperanza). While the ensemble analog resulted in a projected
15% decrease in water and electricity consumption, the individual analogs lead
to different results. In particular, if we consider Buckeye, Arizona as the
analog for Phoenix, the result is a 4.5% (1.8%) reduction in water
(electricity) consumption. Likewise, if we evaluate the other Mexican cities,
the result is a 6.3% (10.0%) reduction and a 3.5% (12.5%) reduction in water
(electricity) consumption for Hermasillo and Nacozari de García, respectively.
Thus, we can see that the different analogs lead to different magnitudes of
change for the city of Phoenix, depending on the GCM used to derive the
analog. This is representative of the uncertainty present within studies that
leverage multiple GCMs.

Likewise, the cities in Florida are projected to see reductions in our
modeling results. In particular, the city of Tampa is projected to see close
to a 10% decrease in water demand, with only a slight decrease in electricity.
The climate analog of Tampa is Ciudad Mante, Mexico (Fitzpatrick and Dunn,
2019), which receives more precipitation in the summer than Tampa. This
increase in precipitation would likely lead to less outdoor water use, the
main source of urban water demand during the summer months. Looking that the
possible analogs for Tampa, they all fall along the Gulf coast in Mexico,
ranging from Ciudad Mante (the ensemble analog) to Palenque in the state of
Chiapas. Comparing Ciudad Mante to Palenque, both are tropical climates,
however, the Köppen climate designation for Ciudad Mante is tropical savanna
while that of Palenque is tropical monsoon. These differences in climate are
likely to cause differences in the projection of the climate-sensitive water
and electricity demand in the city of Tampa. In fact, if we consider Palenque
as the analog for Tampa, the resulting change is a 6.5% (3.1%) reduction in
water (electricity) consumption. Although we did not investigate all 1,242
analogs (27 GCMs $\times$ 46 cities), it is likely that there would be similar
results across the US, suggesting the importance of quantifying uncertainty in
future climate analogs analyses.

Ultimately, the projections of water and electricity consumption that we
presented here are based on the observed relationship between the climatic
conditions and the water-electricity demand nexus in a given city, which may
change in the future due to a number of factors (e.g., technology, cultural
norms, or demographics). In this sense, it is important to acknowledge the
uncertainty associated with the GCM-derived analogs, which were used to obtain
these projections. The uncertainty associated with the 27 possible analogs
could lead to some differences in projections, as discussed for the cities of
Phoenix and Tampa. As the GCMs continue to evolve and improve, it is likely
that the range of possible climate analogs will be reduced, leading to more
certain results. Additionally, this approach does not account for extreme
events, since we are focusing on projecting the monthly demand for an average
month in future summers, nor does it assume any changes to the current
demographics or cultures of the cities. One way to explore these additional
impacts is through the Shared Socioeconomic Pathways (SSPs), which provide
scenarios of future population growth.

### Exploring the impacts of shared socioeconomic pathways

As shown in Figure 2b, the majority of the cities included in the analysis are
projected to see increases in electricity demand, including the most populous
cities in the country: New York City (New York), Chicago (Illinois), and Los
Angeles (California). These cities show little to no change in the water
demand, likely due to their dense nature, which does not allow for much
outdoor use, but with large increases in electricity use (6-12%) per capita.
To understanding the impact of population growth, we can evaluate the increase
in electricity use based on the SSPs of the major cities mentioned above.

In terms of the total change in electricity demand, a 6-12% increase in per
capita electricity demand in these three major cities represents an increase
of 2.5-9 million MWh in a given summer month, depending on the city and
population growth scenario (see Figure 2c). In Figure 2c, we show the total
monthly electricity increases in the counties where Chicago, Los Angeles, and
New York City reside, based on the five SSPs (Hauer, 2019). The SSP data is
aggregated by county, not city, so the total electricity values are for the
counties as a whole (Cook County for Chicago, the combination of Bronx County,
King County, New York County, Queens County, and Richmond County for New York
City, and Los Angeles County for Los Angeles). We make the assumption that the
residents beyond the city limits will have the same increase in electricity
demand, given the similarity in climatic changes that will be experienced.
Within these pathways, SSP 1 represents sustainable development, while SSP 5
represents fossil-fueled development (Hauer, 2019). As SSP 5 is more in line
with the worst-case warming scenario, we can use the total electricity from
that scenario to better understand the possible changes to the electricity
demand patterns under extreme climate change. In particular, the results
presented in Figure 2c show that in 2080, Chicago will require 5.7 million MWh
in a given summer month compared to the 4.3 million MWh generated now.
Likewise, Los Angeles will require 9.2 million MWh (compared to 5.4 million
MWh today) and New York City will need to provide 9.0 million MWh, compared to
the 4.2 million MWh required currently.

These increases represent a significant portion of the existing electricity
generation capacity. For example, Chicago will have to provide an additional
1.4 million MWh in a given summer month, this the equivalent power output of
the Hoover Dam over 127 days (Ritchie and Roser, 2017). In terms of carbon
emissions, this increase in electricity demand would lead to an additional
605,000 metric tons of CO2e month, based on the 2022 national electricity mix
(US EPA, 2020). Similarly, following an increase in Los Angeles (county)
electricity demand of 3.8 million MWh, nearly 1.6 million metric tons of CO2e
would be emitted (US EPA, 2020). Finally, New York City is projected to
experience an increase of 4.8 million MWh in electricity demand by 2080, this
would emit 2.1 million metric tons of CO2e in a given summer month (US EPA,
2020). Together, these three cities would emit an additional 4.3 million
metric tons of CO2e a month, assuming there are no mitigation efforts. To
sequester these additional emissions, the U.S. would need add 20,717 km2 of
forested land (US EPA, 2020), enough to nearly cover the state of New Jersey.
It is important to note that these comparisons assume no mitigation carbon
emissions through the electricity mix, which is an unlikely scenario,
particularly given these three cities’ commitment to renewable energy (Johnson
et al., 2008; Garcetti, 2019; de Blasio et al., 2019). Additionally, as the
SSPs represent different pathways for population growth, it is possible that
society will follow a more sustainable path, limiting the total emissions, as
shown in Figure 2c. Nonetheless, there is still value to assessing the worst-
case scenario, as we will discuss below.

## Discussion

Within this manuscript, we have primarily focused on a worst-case, high
emissions scenario. This means that we not only used the RCP8.5 scenario for
future global emissions, but also did not account for technological advances
or adaptation (e.g., to reduce consumption). The primary aim of this study was
to focus on the impact of climate change on water-electricity demand, so this
worst-case scenario provides an opportunity to (1) evaluate the impacts solely
attributable to climate change and (2) understand the magnitude of change that
will be needed to avoid those impacts. Additionally, while the idea that there
will be no technological advancements in the next 60 years is not plausible,
the chance of a high emissions scenario remains possible (Schwalm et al.,
2020). In other words, if there is not global action to avert the climate
crisis, then it is unlikely to matter what cities or even states, like
California, do in terms of mitigation. Reducing emissions is a global problem,
so even if localities implement climate adaptation and mitigation plans,
without large-scale action around the world, emissions will continue to rise
and the climate crisis will worsen. As such, it is critical that cities
account for potential climate-induced shifts in water and electricity demand
when developing future plans in addition to working towards mitigation. In
particular, the unaccounted for change in demand could make it more difficult
for cities to reach renewable energy targets. For example, Los Angeles plans
to achieve 100% renewable energy by 2050 (Garcetti, 2019). An increase of 3.8
million MWh across the county would require 14,000 additional 1.5 MW wind
turbines, approximately 20% of the current operational stock in the United
States, just for the relatively small area servicing Los Angeles County. If
cities are not prepared for these changes, they may fall back on carbon-
intensive electricity generation, which will ultimately exacerbate the climate
crisis.

While a detailed overview of the injustice implications of our findings is
beyond the scope of this study, it is important to note that the impacts of
unmitigated climate change to the water and electricity grid are likely to
have a greater effect on vulnerable groups, exacerbating existing
environmental injustices (Bednar and Reames, 2020; Obringer et al., 2022). The
results presented here represent the average increase over the course of a
single summer month. There will be, however, extreme events, such as droughts
or heatwaves, that lead to peaks in water and electricity consumption beyond
our analysis. These events put a strain the infrastructure systems, with
managers and operators often resorting to conservation mandates (Mehran et
al., 2017; Wendt et al., 2021) or, in the case of the electric grid, rolling
blackouts (van Vliet et al., 2012; Yalew et al., 2020). Previous research has
shown that vulnerable groups, such as lower income families, the elderly, or
racially/ethnically marginalized people, experience more extreme effects
during heatwaves (Khosla et al., 2020). This increased vulnerability to heat-
related events can be attributed to a number of factors, from the urban heat
island effect to the inability to afford rising bills (Drehobl and Ross, 2016;
Berry et al., 2018; Sanchez-Guevara et al., 2019; Graff and Carley, 2020;
Bednar and Reames, 2020). Should utilities and management agencies fail to
account for the climate-induced shifts in demand, as the results presented
here suggest are likely, the resulting supply deficits should result in
increased vulnerability to heat-related issues, as well as other impacts. In
this sense, it is critical that society work to not only mitigate the climate
crisis, but also to prepare our infrastructure for the worst case scenario to
ensure equitable access to water and electricity for future generations. To do
this effectively, we need to continue to expand our modeling capabilities, as
well as focus on finding user-friendly and computationally inexpensive ways to
model climate impacts on infrastructure systems.

### Study limitations and areas for future work

In this analysis, we focused on the summer months, showing a north-south
gradient in terms of climate-induced shifts in water and electricity
consumption. Given that climate change is likely to lead to warmer winters,
there is a need to further understand how the climate-sensitive portion of
water and electricity consumption will change during this season as well. In
fact, in many locations, the higher temperatures might lead to an increased
need for electricity-based space-cooling, where previously, there was a
greater need for natural gas-based space-heating. The difference between
summer and winter months may be particularly evident below the gradient, in
the Southern states. For example, while Esperanza, Mexico is cooler and wetter
than Phoenix in the summer, it is warmer and drier in the winter. This could
lead to an unbalanced demand structure, which may further stress
infrastructure systems. Likewise, Tampa could see wetter summers and drier
winters, given their ensemble analog of Ciudad Mante, Mexico. This indicates a
need to further evaluate the climate change impacts during winter on the
climate-sensitive portion of the water-electricity nexus, particularly in the
Southern cities considered in this study.

Additionally, the climate analogs were initially developed using the CMIP5
suite of GCMs (Fitzpatrick and Dunn, 2019). As the CMIP6 models become more
commonplace and downscaled, bias-corrected data becomes more readily available
from these next generation GCMs, it might be beneficial to develop new climate
analogs. Additionally, in the original study, the authors only considered
maximum temperature, minimum temperature, and precipitation to determine the
most likely analog. However, recent work has indicated that relative humidity
and other measures of temperature play a major role in climate change impacts
(Maia-Silva et al., 2020; Kumar et al., 2020; Obringer et al., 2022)
therefore, including these climatic variables might improve the climate
analogs development, particularly for use in impact assessments. While the aim
of this study was not to criticize or improve upon the climate analogs, making
these improvements would like help to build up the use of climate analogs as
both a modeling and communication tool, as well as provide greater certainty
around future projections in the climate change impact space.

## Conclusion

In this study, we build a scalable and transferable model for projecting the
coupled water and electricity demand into the future under climate change. In
particular, we leverage climate analogs as a modeling tool to make future
projections across 46 cities in the United States under a high emissions
climate change scenario. Moreover, we use a water-energy nexus framing, which
models the two sectors simultaneously and has been shown to be more
representative of the real world systems (Bartos and Chester, 2014; Obringer
et al., 2019). The results indicate increases in climate-sensitive water and
electricity demand up to 15% and 20%, respectively. In the absence of
proactive mitigation strategies, the unanticipated increases in demand could
lead to supply shortages and complicate the transition to clean energy.
Moreover, previous research has indicated that vulnerable groups, such as low
income families or the elderly, are likely to experience more intense impacts
in the event of supply shortages. As such, preparing for the climate-induced
shifts in water and electricity demand is not only an issue of climate change
adaptation, but also one of environmental justice.

Going forward, it is important to continue to develop future projections of
water and electricity demand that not only account for the interdependencies
between the two sectors, but also the impact that climate change will have on
the coupled demand structure. Ultimately, this coupled demand will feedback
into the supply side, which is also deeply interconnected (e.g., water is
necessary for many modes of electricity generation, so higher demand for
electricity not only leads to more water demand at the end-use, but also at
the generator). In this sense, by making these interconnected projections, we
can also encourage interconnected action, such as the electricity utilities
working to encourage water conservation as a way to maintain adequate
reservoir levels for hydropower generation. This is also critical for the
shift to renewable energy, since planning electricity generation requires
accurate representations of the demand structure. For example, energy system
plans that do not account for the climate impact on demand or that this impact
is likely to effect the water sector, which in turn will effect the
electricity sector, may not achieve the necessary capacity to provide adequate
supply for future generations. In this sense, the modeling of climate impacts
through a water-energy nexus framing is critical to furthering climate change
mitigation and adaptation efforts. Moreover, the use of climate analogs as a
proxy for future climate data can greatly reduce the computational needs,
potentially providing practitioners with a more interpretable way to model and
communicate the impacts of climate change on their local systems. Finally,
although this work focused on the climate-sensitive portion of water and
electricity demand, the methods can be easily integrated with work
investigating the impact of technological change and population growth,
through SSPs, for example, as was demonstrated for three major cities above.
Additionally, the model can be scaled to encompass regions or transferred to
other cities, states, or countries, provided the data is available. This type
of interdisciplinary work is critical for building resilience to climate
change and finding equitable solutions that are accessible to local
governments and organizations.

## References

  * Ashraf et al. [2019] Samaneh Ashraf, Amir AghaKouchak, Ali Nazemi, Ali Mirchi, Mojtaba Sadegh, Hamed R. Moftakhari, Elmira Hassanzadeh, Chi-Yuan Miao, Kaveh Madani, Mohammad Mousavi Baygi, Hassan Anjileli, Davood Reza Arab, Hamid Norouzi, Omid Mazdiyasni, Marzi Azarderakhsh, Aneseh Alborzi, Mohammad J. Tourian, Ali Mehran, Alireza Farahmand, and Iman Mallakpour.  Compounding effects of human activities and climatic changes on surface water availability in Iran.  _Climatic Change_ , 152(3):379–391, March 2019\.  ISSN 1573-1480.  doi: 10.1007/s10584-018-2336-6.
  * Auffhammer et al. [2017] Maximilian Auffhammer, Patrick Baylis, and Catherine H. Hausman.  Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States.  _Proceedings of the National Academy of Sciences_ , 114(8):1886–1891, February 2017.  ISSN 0027-8424, 1091-6490.  doi: 10.1073/pnas.1613193114.
  * Bartos and Chester [2014] Matthew D. Bartos and Mikhail V. Chester.  The Conservation Nexus: Valuing Interdependent Water and Energy Savings in Arizona.  _Environmental Science & Technology_, 48(4):2139–2149, February 2014.  ISSN 0013-936X.  doi: 10.1021/es4033343.
  * Bednar and Reames [2020] Dominic J. Bednar and Tony G. Reames.  Recognition of and response to energy poverty in the United States.  _Nature Energy_ , 5(6):432–439, June 2020.  ISSN 2058-7546.  doi: 10.1038/s41560-020-0582-0.
  * Berry et al. [2018] Chip Berry, Carolyn Hronis, and Maggie Woodward.  One in three U.S. households faces a challenge in meeting energy needs.  Technical report, US Energy Information Administration, September 2018\.
  * Binelli et al. [2022] Chiara Binelli, Matthew Loveless, and Brian F. Schaffner.  Explaining Perceptions of Climate Change in the US.  _Political Research Quarterly_ , page 10659129211070856, May 2022\.  ISSN 1065-9129.  doi: 10.1177/10659129211070856.
  * Connell-Buck et al. [2011] Christina R. Connell-Buck, Josué Medellín-Azuara, Jay R. Lund, and Kaveh Madani.  Adapting California’s water system to warm vs. dry climates.  _Climatic Change_ , 109(1):133–149, December 2011\.  ISSN 1573-1480.  doi: 10.1007/s10584-011-0302-7.
  * Cronin et al. [2018] Jennifer Cronin, Gabrial Anandarajah, and Olivier Dessens.  Climate change impacts on the energy system: A review of trends and gaps.  _Climatic Change_ , 151(2):79–93, November 2018\.  ISSN 1573-1480.  doi: 10.1007/s10584-018-2265-4.
  * Dale et al. [2015] Larry L. Dale, Nihan Karali, Dev Millstein, Mike Carnall, Sebastian Vicuña, Nicolas Borchers, Eduardo Bustos, Joe O’Hagan, David Purkey, Charles Heaps, Jack Sieber, William D. Collins, and Michael D. Sohn.  An integrated assessment of water-energy and climate change in sacramento, california: How strong is the nexus?  _Climatic Change_ , 132(2):223–235, September 2015.  ISSN 1573-1480.  doi: 10.1007/s10584-015-1370-x.
  * de Blasio et al. [2019] Bill de Blasio, Dean Fuleihan, Dominic Williams, and Daniel A Zarrilli.  OneNYC 2050: Building A Strong and Fair City.  Technical report, The City of New York, 2019.
  * Drehobl and Ross [2016] Ariel Drehobl and Lauren Ross.  How Energy Efficiency Can Improve Low Income and Underserved Communities.  Technical report, American Council for an Energy-Efficient Economy (ACEEE), 2016.
  * Escriva-Bou et al. [2018] A. Escriva-Bou, J. R. Lund, and M. Pulido-Velazquez.  Saving Energy From Urban Water Demand Management.  _Water Resources Research_ , 54(7):4265–4276, 2018.  ISSN 1944-7973.  doi: 10.1029/2017WR021448.
  * Fitzpatrick and Dunn [2019] Matthew C. Fitzpatrick and Robert R. Dunn.  Contemporary climatic analogs for 540 North American urban areas in the late 21st century.  _Nature Communications_ , 10(1):1–7, February 2019.  ISSN 2041-1723.  doi: 10.1038/s41467-019-08540-3.
  * Fitzpatrick et al. [2020] Rory G. J. Fitzpatrick, Douglas J. Parker, John H. Marsham, David P. Rowell, Lawrence S. Jackson, Declan Finney, Chetan Deva, Simon Tucker, and Rachael Stratton.  How a typical West African day in the future-climate compares with current-climate conditions in a convection-permitting and parameterised convection climate model.  _Climatic Change_ , 163(1):267–296, November 2020\.  ISSN 1573-1480.  doi: 10.1007/s10584-020-02881-5.
  * Fleck and Udall [2021] John Fleck and Brad Udall.  Managing Colorado River risk.  _Science_ , 372(6545):885–885, May 2021.  doi: 10.1126/science.abj5498.
  * Gangopadhyay et al. [2022] Subhrendu Gangopadhyay, Connie A. Woodhouse, Gregory J. McCabe, Cody C. Routson, and David M. Meko.  Tree Rings Reveal Unmatched 2nd Century Drought in the Colorado River Basin.  _Geophysical Research Letters_ , 49(11):e2022GL098781, 2022.  ISSN 1944-8007.  doi: 10.1029/2022GL098781.
  * Garcetti [2019] Eric Garcetti.  L.A.’s Green New Deal: Sustainable City pLAn.  Technical report, Mayor’s Office of Sustainability, Los Angeles, CA, 2019.
  * Graff and Carley [2020] Michelle Graff and Sanya Carley.  COVID-19 assistance needs to target energy insecurity.  _Nature Energy_ , 5(5):352–354, May 2020.  ISSN 2058-7546.  doi: 10.1038/s41560-020-0620-y.
  * Guégan et al. [2012] Marion Guégan, Cintia B. Uvo, and Kaveh Madani.  Developing a module for estimating climate warming effects on hydropower pricing in California.  _Energy Policy_ , 42:261–271, March 2012.  ISSN 0301-4215.  doi: 10.1016/j.enpol.2011.11.083.
  * Hauer [2019] Mathew E. Hauer.  Population projections for U.S. counties by age, sex, and race controlled to shared socioeconomic pathway.  _Scientific Data_ , 6(1):1–15, February 2019\.  ISSN 2052-4463.  doi: 10.1038/sdata.2019.5.
  * Johnson et al. [2008] Sadhu A Johnson, Adele Simmons, Ellen Alberding, Michael Berkshire, Scott Bernstein, Timothy H Brown, Mary Gade, Bill Gerwing, Karen Greenbaum, Geoffrey Hewings, Karen Hobbs, Richard Lanyon, Mary Laraia, Jack Lavin, Howard Learner, Kevin Lynch, Suzanne Malec-McKenna, Jim Mann, Ronald E Meissen, Charles L Owen, Raymond T Pierrehumbert, Patrick Sarb, Doug Scott, Rebecca Stanfield, and Donald Wuebbles.  Chicago Climate Action Plan.  Technical report, City of Chicago, 2008.
  * Karl and Koss [1984] Thomas R Karl and Walter James Koss.  Regional and National Monthly, Seasonal, and Annual Temperature Weighted by Area, 1895-1983.  Technical report, National Climatic Data Center, Asheville, NC, 1984\.
  * Khosla et al. [2020] Radhika Khosla, Nicole D. Miranda, Philipp A. Trotter, Antonella Mazzone, Renaldi Renaldi, Caitlin McElroy, Francois Cohen, Anant Jani, Rafael Perera-Salazar, and Malcolm McCulloch.  Cooling for sustainable development.  _Nature Sustainability_ , pages 1–8, October 2020.  ISSN 2398-9629.  doi: 10.1038/s41893-020-00627-w.
  * Kumar et al. [2020] Rohini Kumar, Benjamin Rachunok, Debora Maia-Silva, and Roshanak Nateghi.  Asymmetrical response of California electricity demand to summer-time temperature variation.  _Scientific Reports_ , 10(1):10904, July 2020\.  ISSN 2045-2322.  doi: 10.1038/s41598-020-67695-y.
  * Lisonbee et al. [2022] Joel Lisonbee, Elizabeth Ossowski, Meredith Muth, Veva Deheza, and Amanda Sheffield.  Preparing for Long-Term Drought and Aridification.  _Bulletin of the American Meteorological Society_ , 103(3):E821–E827, March 2022.  ISSN 0003-0007, 1520-0477.  doi: 10.1175/BAMS-D-21-0321.1.
  * Luccioni et al. [2021] Alexandra Luccioni, Victor Schmidt, Vahe Vardanyan, and Yoshua Bengio.  Using Artificial Intelligence to Visualize the Impacts of Climate Change.  _IEEE Computer Graphics and Applications_ , 41(1):8–14, January 2021.  ISSN 1558-1756.  doi: 10.1109/MCG.2020.3025425.
  * Macknick et al. [2012] J. Macknick, S. Sattler, K. Averyt, S. Clemmer, and J. Rogers.  The water implications of generating electricity: Water use across the United States based on different electricity pathways through 2050.  _Environmental Research Letters_ , 7(4):045803, December 2012.  ISSN 1748-9326.  doi: 10.1088/1748-9326/7/4/045803.
  * Madani et al. [2014] Kaveh Madani, Marion Guégan, and Cintia B. Uvo.  Climate change impacts on high-elevation hydroelectricity in California.  _Journal of Hydrology_ , 510:153–163, March 2014.  ISSN 0022-1694.  doi: 10.1016/j.jhydrol.2013.12.001.
  * Maia-Silva et al. [2020] Debora Maia-Silva, Rohini Kumar, and Roshanak Nateghi.  The critical role of humidity in modeling summer electricity demand across the United States.  _Nature Communications_ , 11(1):1–8, April 2020\.  ISSN 2041-1723.  doi: 10.1038/s41467-020-15393-8.
  * Mehran et al. [2017] Ali Mehran, Amir AghaKouchak, Navid Nakhjiri, Michael J. Stewardson, Murray C. Peel, Thomas J. Phillips, Yoshihide Wada, and Jakin K. Ravalico.  Compounding Impacts of Human-Induced Water Stress and Climate Change on Water Availability.  _Scientific Reports_ , 7(1):6282, July 2017.  ISSN 2045-2322.  doi: 10.1038/s41598-017-06765-0.
  * Mesinger et al. [2006] Fedor Mesinger, Geoff DiMego, Eugenia Kalnay, Kenneth Mitchell, Perry C. Shafran, Wesley Ebisuzaki, Dušan Jović, Jack Woollen, Eric Rogers, Ernesto H. Berbery, Michael B. Ek, Yun Fan, Robert Grumbine, Wayne Higgins, Hong Li, Ying Lin, Geoff Manikin, David Parrish, and Wei Shi.  North American Regional Reanalysis.  _Bulletin of the American Meteorological Society_ , 87(3):343–360, March 2006.  ISSN 0003-0007, 1520-0477.  doi: 10.1175/BAMS-87-3-343.
  * Miller et al. [2016] Patrick J. Miller, Gitta H. Lubke, Daniel B. McArtor, and C. S. Bergeman.  Finding structure in data using multivariate tree boosting.  _Psychological Methods_ , 21(4):583–602, 2016\.  ISSN 1939-1463(Electronic),1082-989X(Print).  doi: 10.1037/met0000087.
  * Mukherjee and Nateghi [2017] Sayanti Mukherjee and Roshanak Nateghi.  Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States.  _Energy_ , 128:688–700, June 2017.  ISSN 0360-5442.  doi: 10.1016/j.energy.2017.04.034.
  * Mukherjee and Nateghi [2019] Sayanti Mukherjee and Roshanak Nateghi.  A Data-Driven Approach to Assessing Supply Inadequacy Risks Due to Climate-Induced Shifts in Electricity Demand.  _Risk Analysis_ , 39(3):673–694, 2019.  ISSN 1539-6924.  doi: 10.1111/risa.13192.
  * Mukherjee et al. [2019] Sayanti Mukherjee, C. R. Vineeth, and Roshanak Nateghi.  Evaluating regional climate-electricity demand nexus: A composite Bayesian predictive framework.  _Applied Energy_ , 235:1561–1582, February 2019.  ISSN 0306-2619.  doi: 10.1016/j.apenergy.2018.10.119.
  * Newell and Ramaswami [2020] Joshua P Newell and Anu Ramaswami.  Urban food–energy–water systems: Past, current, and future research trajectories.  _Environmental Research Letters_ , 15(5):050201, May 2020.  ISSN 1748-9326.  doi: 10.1088/1748-9326/ab7419.
  * Newell et al. [2019] Joshua P Newell, Benjamin Goldstein, and Alec Foster.  A 40-year review of food–energy–water nexus literature and its application to the urban scale.  _Environmental Research Letters_ , 14(7):073003, July 2019.  ISSN 1748-9326.  doi: 10.1088/1748-9326/ab0767.
  * Obringer et al. [2019] R. Obringer, R. Kumar, and R. Nateghi.  Analyzing the climate sensitivity of the coupled water-electricity demand nexus in the Midwestern United States.  _Applied Energy_ , 252:113466, October 2019.  ISSN 0306-2619.  doi: 10.1016/j.apenergy.2019.113466.
  * Obringer et al. [2020] Renee Obringer, Rohini Kumar, and Roshanak Nateghi.  Managing the water–electricity demand nexus in a warming climate.  _Climatic Change_ , 159(2):233–252, March 2020\.  ISSN 1573-1480.  doi: 10.1007/s10584-020-02669-7.
  * Obringer et al. [2022] Renee Obringer, Roshanak Nateghi, Debora Maia-Silva, Sayanti Mukherjee, Vineeth CR, Douglas Brent McRoberts, and Rohini Kumar.  Implications of Increasing Household Air Conditioning Use Across the United States Under a Warming Climate.  _Earth’s Future_ , 10(1):e2021EF002434, 2022.  ISSN 2328-4277.  doi: 10.1029/2021EF002434.
  * Raymond et al. [2019] Leigh Raymond, Douglas Gotham, William McClain, Sayanti Mukherjee, Roshanak Nateghi, Paul V. Preckel, Peter Schubert, Shweta Singh, and Elizabeth Wachs.  Projected climate change impacts on Indiana’s Energy demand and supply.  _Climatic Change_ , January 2019.  ISSN 1573-1480.  doi: 10.1007/s10584-018-2299-7.
  * Ritchie and Roser [2017] Hanna Ritchie and Max Roser.  A sense of units and scale for electrical energy production and consumption, November 2017.
  * Sailor and Muñoz [1997] David J. Sailor and J. Ricardo Muñoz.  Sensitivity of electricity and natural gas consumption to climate in the U.S.A.—Methodology and results for eight states.  _Energy_ , 22(10):987–998, October 1997.  ISSN 0360-5442.  doi: 10.1016/S0360-5442(97)00034-0.
  * Sanchez-Guevara et al. [2019] Carmen Sanchez-Guevara, Miguel Núñez Peiró, Jonathon Taylor, Anna Mavrogianni, and Javier Neila González.  Assessing population vulnerability towards summer energy poverty: Case studies of Madrid and London.  _Energy and Buildings_ , 190:132–143, May 2019.  ISSN 0378-7788.  doi: 10.1016/j.enbuild.2019.02.024.
  * Schwalm et al. [2020] Christopher R. Schwalm, Spencer Glendon, and Philip B. Duffy.  RCP8.5 tracks cumulative CO2 emissions.  _Proceedings of the National Academy of Sciences_ , 117(33):19656–19657, August 2020.  doi: 10.1073/pnas.2007117117.
  * Udall and Overpeck [2017] Bradley Udall and Jonathan Overpeck.  The twenty-first century Colorado River hot drought and implications for the future.  _Water Resources Research_ , 53(3):2404–2418, 2017.  ISSN 1944-7973.  doi: 10.1002/2016WR019638.
  * United Nations [2015] United Nations.  Transforming Our World: The 2030 Agenda for Sustainable Development.  Technical report, 2015.
  * United Nations [2018] United Nations.  World Urbanization Prospects: The 2018 Revision.  Technical report, 2018.
  * US Energy Information Administration [2019] US Energy Information Administration.  Form EIA-861M Sales and Revenue Data.  2019\.
  * US EPA [2020] US EPA.  Greenhouse Gas Equivalencies Calculator.  https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator, March 2020.
  * van Vliet et al. [2012] Michelle T. H. van Vliet, John R. Yearsley, Fulco Ludwig, Stefan Vögele, Dennis P. Lettenmaier, and Pavel Kabat.  Vulnerability of US and European electricity supply to climate change.  _Nature Climate Change_ , 2(9):676–681, September 2012.  ISSN 1758-6798.  doi: 10.1038/nclimate1546.
  * Venkatesh et al. [2014] G. Venkatesh, Arthur Chan, and Helge Brattebø.  Understanding the water-energy-carbon nexus in urban water utilities: Comparison of four city case studies and the relevant influencing factors.  _Energy_ , 75:153–166, October 2014.  ISSN 0360-5442.  doi: 10.1016/j.energy.2014.06.111.
  * Wang et al. [2022] Shasha Wang, Wenfeng Zhan, Huilin Du, Chenguang Wang, Long Li, Sida Jiang, Huyan Fu, Shiqi Miao, and Fan Huang.  Identifying analogs of future thermal comfort under multiple projection scenarios in 352 Chinese cities.  _Sustainable Cities and Society_ , 82:103889, July 2022\.  ISSN 2210-6707.  doi: 10.1016/j.scs.2022.103889.
  * Wendt et al. [2021] Doris E. Wendt, John P. Bloomfield, Anne F. Van Loon, Margaret Garcia, Benedikt Heudorfer, Joshua Larsen, and David M. Hannah.  Demonstrating the impact of integrated drought policies on hydrological droughts.  _Natural Hazards and Earth System Sciences_ , April 2021.  doi: 10.5194/nhess-2021-129.
  * Yalew et al. [2020] Seleshi G. Yalew, Michelle T. H. van Vliet, David E. H. J. Gernaat, Fulco Ludwig, Ariel Miara, Chan Park, Edward Byers, Enrica De Cian, Franziska Piontek, Gokul Iyer, Ioanna Mouratiadou, James Glynn, Mohamad Hejazi, Olivier Dessens, Pedro Rochedo, Robert Pietzcker, Roberto Schaeffer, Shinichiro Fujimori, Shouro Dasgupta, Silvana Mima, Silvia R. Santos da Silva, Vaibhav Chaturvedi, Robert Vautard, and Detlef P. van Vuuren.  Impacts of climate change on energy systems in global and regional scenarios.  _Nature Energy_ , 5(10):794–802, October 2020\.  ISSN 2058-7546.  doi: 10.1038/s41560-020-0664-z.
  * Yin et al. [2020] Cong Yin, Fei Yang, and Juanle Wang.  Analogs of Future Climate in Chinese Cities Identified in Present Observations.  _IEEE Access_ , 8:219151–219159, 2020.  ISSN 2169-3536.  doi: 10.1109/ACCESS.2020.3042548.