•Future water-energy interactions are modeled in the Phoenix Metropolitan region.•Fast transition to renewable energy will save water and reduce CO2 emissions.•Intense droughts will slightly reduce ...energy used by water infrastructure.•Costs of renewable and business as usual scenarios are comparable.
Current population and climate trends are increasing the need to adopt holistic approaches for managing water and energy systems, especially in water-limited regions like the Southwestern U.S. In this study, we quantify the implications of future energy mix alternatives on the water-energy nexus in the Phoenix, Arizona metropolitan region using the Long-range Energy Alternatives Planning (LEAP) platform. We first show that LEAP is able to simulate historical observations of energy generation and consumption from 2001 to 2018. We then simulate future electricity generation through 2060 under the same demand projections and different energy mix solutions. Results of our simulations are as follows. (i) Water heating accounts for 71% of the total water-related uses and its energy needs are projected to double in 2060, due to population growth; the energy required to treat and move water is instead expected to decrease by 9%, mainly because of declining agricultural water demands. (ii) Energy mix solutions that transition faster to renewable sources are more sustainable than a business as usual scenario that relies more on fossil fuels, because renewable technologies require less water for electricity generation (−35%) and reduce CO2 emissions (−57%). (iii) The aggressive transition to renewable energy is projected to have higher structural costs than the business as usual scenario, but comparable total expenses because of the lower operational cost of renewable technologies. This work complements and expands previous regional studies focused on the Southwestern U.S. and supports current efforts of local stakeholder engagement initiated by the authors.
The Colorado River basin (CRB) is the primary source of water in the southwestern United States. A key step to reduce the uncertainty of future streamflow projections in the CRB is to evaluate the ...performance of historical simulations of general circulation models (GCMs). In this study, this challenge is addressed by evaluating the ability of 19 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and four nested regional climate models (RCMs) in reproducing the statistical properties of the hydrologic cycle and temperature in the CRB. To capture the transition from snow‐dominated to semi‐arid regions, analyses are conducted by spatially averaging the climate variables in four nested sub‐basins. Most models overestimate the mean annual precipitation (P) and underestimate the mean annual temperature (T) at all locations (up to +140% and −4.9 °C, respectively). A group of models capture the mean annual run‐off at all sub‐basins with different strengths of the hydrological cycle, depending on the level of P overestimation. Another set of models overestimate the mean annual run‐off, due to a weak cycle in the evaporation channel. An abrupt increase in the mean annual T of ~0.8 °C is detected at all locations around 1980 from the observed and most of the simulated time series. However, no statistically significant monotonic trends emerge for both P and T. All models simulate the seasonality of T quite well. The phasing of the seasonal cycle of P is reproduced fairly well in one of the upper, snow‐dominated sub‐basins. Model performances degrade in the larger sub‐basins that include semi‐arid areas, because several GCMs are not able to capture the effect of the North American monsoon. Finally, the relative performances of the climate models in reproducing the climatologies of P and T are quantified to support future impact studies in the basin.
Digital elevation model of the Colorado River basin (CRB) with the four nested sub‐basins and their outlet locations at Glenwood (GL), Green River (GR), Lees Ferry (LF), and Imperial Dam (IM). (b)–(d) Grid points of (b) HadCM3, (c) CNRCM, and (d) CanRCM4 climate models, along with the regular grid used to interpolate the data. The interpolated grid has a resolution of 0.1° (~10 km), but, for visualization purposes, it has been plotted in (b)–(d) at 1° (~100 km). The interpolated grid at 0.1° resolution is shown in the inset of panel (b).
•Short-duration precipitation (P) analyzed with unique high-density network•Space-time correlation structure and marginal distribution of P vary seasonally•Parametric models for correlation structure ...and marginal distribution•Seasonal differences increase with the P resolution•Multisite stochastic simulations reveal insights into space-time P variability
The statistical characterization of precipitation (P) at short durations (≤ 24 h) is crucial for practical and scientific applications. Here, we advance the knowledge of and ability to model the space-time correlation structure (STCS) and marginal distribution of short-duration P using a network of rain gages in central Arizona with one of the largest densities and spatial coverages in the world. We separately analyze summer and winter P sampled at multiple durations, Δt, from 0.5 to 24 h. We first identify an analytical model and a three-parameter distribution that robustly capture the empirical STCS and marginal distribution of P, respectively, across Δt’s. We then conduct Monte Carlo experiments consisting of multisite stochastic simulations of P time series to explore the spatial and seasonal variability of these properties. Significant seasonal differences emerge, especially at low Δt. Summer (winter) P exhibits weak (strong) correlation structure and heavy- (light-)tailed distributions resulting from short-lived, isolated thunderstorms (widespread, long-lasting frontal systems). The STCS of P is most likely homogeneous and isotropic except for winter at Δt ≥ 3 h, where anisotropy could be introduced via the motion of frontal storms. The spatial variability of the marginal distribution is reproduced by a regional parameterization dependent on elevation in all cases except, again, for winter at Δt ≥ 3 h where additional factors are needed to explain the variability of the mean P intensity. This work provides insights to improve stochastic P models and validate convection-permitting models used to investigate the mechanisms driving changes in short-duration P.
The application of physics‐based distributed hydrologic models (DHMs) at hyperresolutions (~100 m) is expected to support several water‐related applications but is still prevented by critical data, ...model validation, and computational challenges. In this study, we address some of these challenges by applying the TIN‐based Real‐time Integrated Basin Simulator DHM at a nominal resolution of ~88 m in the Río Sonora basin, a regional watershed of ~21,000 km2 in northwest Mexico. First, we generate reliable high‐resolution (1‐km) hydrometeorological forcings by bias correcting reanalysis products with ground observations and applying downscaling routines that use terrain information at high resolution, which is available globally. Second, we develop a strategy to obtain high‐resolution (250‐m) grids of soil parameters by integrating a coarse‐resolution soil map based on the Food and Agriculture Organization classification with recently released high‐resolution global data sets. Third, we apply the model over a decadal period (2004–2013) and use a set of complementary tools, including Taylor diagrams, connectivity analysis, and empirical orthogonal function analysis, to assess its ability to simulate spatial patterns of land surface temperature through comparison with daily remotely sensed products. We find that (i) the hyperresolution‐simulated patterns capture the spatial variability of land surface temperature quite well and (ii) vegetation properties are the major physical factors controlling the discrepancies between simulated and remotely sensed products. The strategies presented here are based on global data sets and robust statistical techniques that can be utilized in different settings with other DHMs, and thus, they provide valuable support for the scientific community focused on hyperresolution hydrologic modeling.
Key Points
Long‐term (10 years) hyperresolution (88 m) hydrologic simulations are performed in a regional watershed (21,000 km2)
Global and local data sets are integrated to generate high‐resolution hydrometeorological forcings and soil properties
Simulated and remotely sensed spatial patterns of land surface temperature are compared to validate the model and diagnose its deficiencies
Pluvial flooding in urban regions is a natural hazard that has been rarely investigated. Here, we evaluate the utility of three radar (Stage IV, Multi-Radar Multi-Sensor or MRMS, and gauge-corrected ...MRMS or GCMRM) quantitative precipitation estimates (QPEs) and the Storm Water Management Model (SWMM) hydrologic-hydraulic model to simulate pluvial flooding during the North American monsoon in Phoenix. We focus on an urban catchment of 2.38 km
2
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 gauge 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.
Short-duration extreme rainfall events are the main trigger of flash and pluvial floods in cities. Depending on the local climate zone and urban fabric that affect meteorological variables such as ...air temperature, humidity, and aerosol concentration, the built environment can either intensify or reduce extreme rainfall intensity. This study examined how urbanization in a large metropolitan area characterized by open low-rise buildings, affected sub-daily extreme rainfall intensities over the period between 2000 and 2018. The research was conducted in the metropolitan region of Phoenix, Arizona, which is supported by a large and dense rain-gauge network (168 stations). The built area increased by 6% between 2001 and 2016 and the number of residences by 300,000. Over the study period, sub-daily extreme rainfall intensities intensified both in the urbanized area and in its rural surroundings but the intensification trend within the built area was considerably larger (3 times larger). We calculated a negative trend in aerosol concentration (−0.005 AOD y−1) but a positive trend in near-surface air temperature that was considerably larger in the urban areas (0.15 °C y−1) as compared to the rural counterpart (0.09 °C y−1) for the period between 2005 and 2018. Although built surfaces and open low-rise buildings contributed to an increase in air temperature, they did not affect air humidity. Changes in rainfall extremes approximately follow the Clausius–Clapeyron relation within the urban area with an increase at a rate of 7% °C−1. These results demonstrate that the warming effect associated with a low-rise urban area can cause an intensification of sub-daily rainfall extremes that is significantly larger than in nearby rural areas.
•The urban fabric role in intensifying sub-daily rainfall extremes was investigated.•Rainfall extremes intensify at a much higher rate within the city than in its surroundings.•Urban temperatures in the past 15 years increased more than rural temperatures.•Clausius–Clapeyron scaling of rainfall extremes holds in the city and outside.
The effects of forest treatments on watershed hydrology have often been studied in isolation from climate change. Consequently, under a warming climate, it is unclear how forest thinning will impact ...snowpacks, evapotranspiration, and streamflow availability. In this study, we used a distributed hydrologic model to provide insight into the effects of warming and forest treatment on the hydrologic response of the Beaver Creek watershed (∼1,100 km2) of central Arizona. Prior to the numerical experiments, confidence in the hydrologic model performance was established by comparisons to long‐term observations (2003–2018) of snow water equivalent and streamflow using station observations and through spatially distributed estimates. Results indicated that warming during the 21st century could increase mean annual streamflow by 1.5% for warming levels up to +1°C, followed by a −29% decrease for continued warming up to +6°C, due to the varying effects of warming on snow sublimation, soil evaporation, and plant transpiration. On average, forest thinning increased streamflow by +12% (or 7 mm/yr) through lower plant transpiration by −19% (or −18 mm/yr), while also increasing the change in soil water storage by +42% (or 11 mm/yr). Forest thinning delayed the detrimental effects of warming on streamflow until +4°C, as compared to +2°C without forest treatment. Furthermore, model results suggested that forest cover reductions laterally displace water availability and evapotranspiration to downstream sites. These model‐derived mechanisms provide insights on the potential for water resilience toward warming effects afforded through treatment projects in southwestern US ponderosa pine forests.
Plain Language Summary
The effects of forest thinning on watershed hydrology have often been studied in isolation from warming. Thus, it is unclear if forest thinning conducted under different warming levels will have hydrologic impacts on snow and streamflow conditions. Here, we used a hydrologic model to study these interactions within the Beaver Creek watershed in Arizona. First, we built confidence in the hydrologic model through comparisons to snow and streamflow observations. Then, we conducted a range of different warming scenarios with and without forest thinning. We found that warming could increase streamflow up to +1°C, but then led to larger decreases in streamflow up to +6°C. In contrast, forest thinning increased streamflow and delayed the negative effects of warming up to +4°C. The application of the hydrologic model with the warming and forest thinning scenarios provides new insights on how warming effects could be reduced through management projects in ponderosa pine forests.
Key Points
Distributed simulations in mountainous watershed show good match with warm and cold season hydrologic data over 16‐year period
Independent and combined scenarios showed that forest thinning reduces the streamflow impact of warming up to a 4°C increase
Forest treatment effects propagated downstream by displacing evapotranspiration from forested uplands to near channel regions
As climate change affects precipitation patterns, urban infrastructure may become more vulnerable to flooding. Flooding mitigation strategies must be developed such that the failure of infrastructure ...does not compromise people, activities, or other infrastructure. “Safe-to-fail” is an emerging paradigm that broadly describes adaptation scenarios that allow infrastructure to fail but control or minimize the consequences of the failure. Traditionally, infrastructure is designed as “fail-safe” where they provide robust protection when the risks are accurately predicted within a designed safety factor. However, the risks and uncertainties faced by urban infrastructure are becoming so great due to climate change that the “fail-safe” paradigm should be questioned. We propose a framework to assess potential flooding solutions based on multiple infrastructure resilience characteristics using a multi-criteria decision analysis (MCDA) analytic hierarchy process algorithm to prioritize “safe-to-fail” and “fail-safe” strategies depending on stakeholder preferences. Using urban flooding in Phoenix, Arizona, as a case study, we first estimate flooding intensity and evaluate roadway vulnerability using the Storm Water Management Model for a series of downpours that occurred on September 8, 2014. Results show the roadway types and locations that are vulnerable. Next, we identify a suite of adaptation strategies and characteristics of these strategies and attempt to more explicitly categorize flooding solutions as “safe-to-fail” and “fail-safe” with these characteristics. Lastly, we use MCDA to show how adaptation strategy rankings change when stakeholders have different preferences for particular adaptation characteristics.
Mediterranean region is characterized by high precipitation variability often enhanced by orography, with strong seasonality and large inter-annual fluctuations, and by high heterogeneity of terrain ...and land surface properties. As a consequence, catchments in this area are often prone to the occurrence of hydrometeorological extremes, including storms, floods and flash-floods. A number of climate studies focused in the Mediterranean region predict that extreme events will occur with higher intensity and frequency, thus requiring further analyses to assess their effect at the land surface, particularly in small- and medium-sized watersheds. In this study, climate and hydrologic simulations produced within the Climate Induced Changes on the Hydrology of Mediterranean Basins (CLIMB) EU FP7 research project were used to analyze how precipitation extremes propagate into discharge extremes in the Rio Mannu basin (472.5km2), located in Sardinia, Italy. The basin hydrologic response to climate forcings in a reference (1971–2000) and a future (2041–2070) period was simulated through the combined use of a set of global and regional climate models, statistical downscaling techniques, and a process based distributed hydrologic model. We analyzed and compared the distribution of annual maxima extracted from hourly and daily precipitation and peak discharge time series, simulated by the hydrologic model under climate forcing. For this aim, yearly maxima were fit by the Generalized Extreme Value (GEV) distribution using a regional approach. Next, we discussed commonality and contrasting behaviors of precipitation and discharge maxima distributions to better understand how hydrological transformations impact propagation of extremes. Finally, we show how rainfall statistical downscaling algorithms produce more reliable forcings for hydrological models than coarse climate model outputs.
•Statistical analysis in a basin in Sardinia shows high uncertainty of climate projections of precipitation extremes.•Soil properties and topography control the basin response to extreme storms.•Statistical downscaling of precipitation is useful to improve accuracy of physically-based hydrologic simulations.
Surface soil moisture plays a crucial role on the terrestrial water, energy, and carbon cycles. Characterizing its variability in space and time is critical to increase our capability to forecast ...extreme weather events, manage water resources, and optimize agricultural practices. Global estimates of surface soil moisture are provided by satellite sensors, but at coarse spatial resolutions. Here, we show that the resolution of satellite soil moisture products can be increased to scales representative of ground measurements by reproducing the scale invariance properties of soil moisture derived from hydrologic simulations at hyperresolutions of less than 100 m. Specifically, we find that surface soil moisture is scale invariant over regimes extending from a satellite footprint to 100 m. We use this evidence to calibrate a statistical downscaling algorithm that reproduces the scale invariance properties of soil moisture and test the approach against 1-km aircraft remote sensing products and through comparisons of downscaled satellite products to ground observations. We demonstrate that hyperresolution hydrologic models can close the loop of satellite soil moisture downscaling for local applications such as agricultural irrigation, flood event prediction, and drought and fire management.
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