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Two-sample tests are widely used in hydrologic and climate studies to investigate whether two samples of a variable of interest could be considered drawn from different populations. Despite this, the information on the power (i.e., the probability of correctly rejecting the null hypothesis) of these tests applied to hydroclimatic variables is limited. Here, this need is addressed considering four popular two-sample tests applied to daily and extreme precipitation, and annual peak flow series. The chosen tests assess differences in location (t-Student and Wilcoxon) and distribution (Kolmogorov–Smirnov and likelihood-ratio). The power was quantified through Monte Carlo simulations relying on pairs of realistic samples of the three variables with equal size, generated with a procedure based on suitable parametric distributions and copulas. After showing that differences in sample skewness are monotonically related to differences in spread, power surfaces were built as a function of the relative changes in location and spread of the samples and utilized to interpret three case studies comparing samples of observed precipitation and discharge series in the U.S. It was found that (1) the t-Student applied to the log-transformed samples has the same power as the Wilcoxon test; (2) location (distribution) tests perform better than distribution (location) tests for small (moderate-to-large) differences in spread and skewness; (3) the power is relatively lower (higher) if the differences in location and spread or skewness have concordant (discordant) sign; and (4) the power increases with the sample size but could be quite low for tests applied to extreme precipitation and discharge records that are commonly short. This work provides useful recommendations for selecting and interpreting two-sample tests in a broad range of hydroclimatic applications. 

Abstract Intensity–duration–frequency (IDF) analyses of rainfall extremes provide critical information to mitigate, manage, and adapt to urban flooding. The accuracy and uncertainty of IDF analyses depend on the availability of historical rainfall records, which are more accessible at daily resolution and, quite often, are very sparse in developing countries. In this work, we quantify performances of different IDF models as a function of the number of available high-resolution (Nτ) and daily (N24h) rain gauges. For this aim, we apply a cross-validation framework that is based on Monte Carlo bootstrapping experiments on records of 223 high-resolution gauges in central Arizona. We test five IDF models based on (two) local, (one) regional, and (two) scaling frequency analyses of annual rainfall maxima from 30-min to 24-h durations with the generalized extreme value (GEV) distribution. All models exhibit similar performances in simulating observed quantiles associated with return periods up to 30 years. When Nτ > 10, local and regional models have the best accuracy; bias correcting the GEV shape parameter for record length is recommended to estimate quantiles for large return periods. The uncertainty of all models, evaluated via Monte Carlo experiments, is very large when Nτ ≤ 5; however, if N24h ≥ 10 additional daily gauges are available, the uncertainty is greatly reduced and accuracy is increased by applying simple scaling models, which infer estimates on subdaily rainfall statistics from information at daily scale. For all models, performances depend on the ability to capture the elevation control on their parameters. Although our work is site specific, its results provide insights to conduct future IDF analyses, especially in regions with sparse data. 

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

Phoenix, an Active Management Area in the desert Southwest US, is the 5th most populated city in the US. Scarce local groundwater and water transported from external resources must be managed in the presence of different types of energy sources. Local and regional decision-makers are faced with answering challenging questions on managing water, energy supply, and demand over a few years to several decades. Prediction and planning for the interdependency of these entities can benefit from modeling the water and energy systems as well as their interactions with one another. In this paper, the integrated WEAP and LEAP tools and a modeling framework that externalizes their hidden linkage to an interaction model are described and compared using the Phoenix AMA. Loose coupling enabled by interaction modeling is a key for decision-policies that should be grounded at the nexus of the water-energy system of systems