Journal of Hydrologic Engineering

A state-of-the-art review on the probable maximum precipitation (PMP) as it relates to climate variability and change is presented. The review consists of an examination of the current practice and the various developments published in the literature. The focus is on relevant research where the effect of climate dynamics on the PMP are discussed, as well as statistical methods developed for estimating very large extreme precipitation including the PMP. The review includes interpretation of extreme events arising from the climate system, their physical mechanisms, and statistical properties, together with the effect of the uncertainty of several factors determining them, such as atmospheric moisture, its transport into storms and wind, and their future changes. These issues are examined as well as the underlying historical and proxy data. In addition, the procedures and guidelines established by some countries, states, and organizations for estimating the PMP are summarized. In doing so, attention was paid to whether the current guidelines and research published literature take into consideration the effects of the variability and change of climatic processes and the underlying uncertainties.

Infiltration excess runoff, i.e., runoff as a result of the rainfall intensity exceeding the infiltration capacity of the soil surface, has traditionally been considered the only contributor to the surface runoff from urban pervious areas. However, recent studies show that subsurface throughflow also can be a significant contributor to urban stormwater runoff. Although rainfall-runoff from urban pervious areas can contribute with large quantities of runoff, only little knowledge exists on this topic. In this study, experimental field observations of subsurface throughflow from the literature are used to assess the capability of different models to simulate this type of runoff. It is investigated how well three new modeling approaches in urban drainage engineering (linear reservoir, regression, and shallow neural network models) performs in simulating subsurface throughflow compared to two commonly used models (the time-area and kinematic wave model). The models are compared with the measured runoff rate and evaluated by the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and Bayesian likelihood ($\mathit{L}$). Generally, a neural network containing 60 neurons and using up to 180 min of data back in time produces the best results ($\mathrm{RMSE}=0.59\mathrm{L}{\min }^{-1}$, $\mathrm{NSE}=0.91$, and $\mathit{L}=0.92$). However, both the kinematic wave ($\mathrm{RMSE}=1.06\mathrm{L}{\min }^{-1}$, $\mathrm{NSE}=0.71$, and $\mathit{L}=0.76$) and linear reservoir model ($\mathrm{RMSE}=0.98\mathrm{L}{\min }^{-1}$, $\mathrm{NSE}=0.75$, and $\mathit{L}=0.78$) generate reasonable results despite their significantly simpler modeling approaches.

The peak flow of a flash flood is an important factor concerning flood risk management. Empirical approaches based on measured hydrological data at gauging sites can be applied to estimate flood peaks; however, they are not suitable for catchments with limited available rainfall and streamflow data, especially ungauged small mountainous catchments. This paper presents a novel method for estimating flash flood peaks in small mountainous catchments based on derived distributed geomorphologic unit hydrographs (DGUHs) using fuzzy $c$-means (FCM) clustering. Data from 25,130 small mountainous catchments in Gansu Province, located in northwest China, are selected for this study. The results show that (1) the derived DGUHs can reflect the strong nonlinearity of the hydrologic process over small mountainous catchments; (2) the FCM clustering method performs well in the clustering of the DGUHs, and the optimal number of clusters in this case is insensitive to the excess rainfall intensity; and (3) empirical formulas based on catchment characteristics (including drainage area, length, and the longitudinal gradient of main channel networks) can estimate flash flood peaks accurately in small mountainous catchments.

An analytical solution is proposed for a two-dimensional, fully-penetrating ditch drainage system by considering the boundaries at the ditch face and at the soil surface to change with time. The general assumption of instantaneous boundary impositions at the ditch face and at the surface of the soil is replaced by continuous time-varying boundary impositions at these faces, as they are expected to be more realistic and readily realized in actual field situations, as compared to static and instantaneous boundaries at the ditches and at the surface of the soil. However, the proposed analytical model is a versatile one, capable of tackling both instantaneous and continuous boundary impositions alike. To ascertain the validity of the solution, a few experiments on them have also been carried out. Further, the correctness of the proposed model is also checked for a simplified flow scenario by comparing it with an existing analytical solution to the problem. The study highlights that with the imposition of time-dependent boundaries, the maximum top discharge value gets reduced by multiple folds as compared to ponded drainage situations in which boundaries have been instantaneously imposed. It is also observed that the time taken by a ponded drainage system to reach a steady-state has a direct correlation with the time required to create steady water depths at the different boundaries of the problem. Further, the pathline and travel time of water particles are both found to be sensitive to the nature and distribution of the time-dependent boundaries of the problem.

Characterization of nonlinear trends in time series of hydroclimatic variables exhibiting nonstationarity is necessary for more realistic projections of climate change and for optimal design of hydraulic structures. The present study was conducted to demonstrate the applicability of a novel Monte-Carlo-based singular spectrum analysis (SSA) to characterize nonlinear trends in historical time series of rainfall characteristics. Long-term (1960–2015) rainfall records for 17 gauges located in the Malaprabha River Basin, India, were used to analyze spatiotemporal variabilities of trends in rainfall totals and number of rainy days for annual and seasonal time periods. While the traditional Sen’s Slope and Mann–Kendall (MK) trend tests indicated statistically nonsignificant decreasing monotonic trends at most gauge stations, SSA revealed the existence of steep nonlinear trends and distinct change points in the direction of the trend over the period of record for both rainfall and rainy days. Results of this study demonstrate the potential for SSA to extract crucial information on the trajectories of nonlinear trends and change points in time series of hydroclimatic variables that exhibit nonstationarity.