Journal of Hydrologic Engineering

In recent years, the stationary assumption of hydroclimatic variables as well as their temporal and spatial dependence structure have been challenged due to anthropogenic and climate changes. This study developed a stepwise and dynamic C-vine copula–based conditional model (SDCVC) to incorporate the nonstationarity into a monthly streamflow prediction, which depicted the predictor–predictand association on the basis of monthly streamflow and rainfall series of upstream and downstream stations in the Yangtze River basin of China. The model consists of (1) nonstationary modeling of the margins and nonstationary temporal and spatial dependence structure by the generalized additive models with location, scale, and shape (GAMLSS) and C-vine copula incorporating climate-related indexes as covariates during training time frames; and (2) a four-dimensional C-vine copula–based conditional quantile function to generate the simulated series during validation time frames. Three kinds of nonstationary models corresponding to different degrees were investigated to show the impact of nonstationarity on the streamflow forecasting. The proposed SDCVC model considering highest degree of nonstationarity outperformed the other two nonstationary models in terms of the performance metrics, because the SDCVC model not only described the dynamic change of time-varying connections between parameter and the large-scale climate forcings, but the stepwise strategy, by selecting the optimum time horizon, helped increase forecasting accuracy. Furthermore, the SDCVC model is superior, to some extent, to classical data-driven approaches [support vector regression (SVR) and adaptive-network-based fuzzy inference system (ANFIS)] in terms of the performance metrics.

Laws of conservation of mass, momentum, and energy lead, respectively, to the equations of continuity, momentum, and energy, which are used to mathematically represent hydrologic flow systems as well as analogous systems (physical or nonphysical). For solving a range of problems, the momentum and energy equations are often either simplified or replaced by what are called flux or constitutive laws (linear or nonlinear). When a flux law is coupled with the continuity equation, the resulting equation can be called a governing equation. Depending on the type of flux law and the problem at hand, numerous governing equations exist, but have not been brought under a single framework yet. In this paper, we (1) illustrate a unified framework from which 26 governing equations are derived, each of which is a differential equation common in physics, such as Euler, diffusion, Laplace, Poisson, Boussinesq, Riccati, or others, encompassing partial differential equations (PDEs) of all three types, namely parabolic, hyperbolic, and elliptic; (2) derive 12 hydrologic problems from our unified framework, namely overland flow, surface runoff, snowmelt runoff, glacial movement, flow routing, infiltration, unsaturated flow, subsurface flow, groundwater flow, groundwater recharge, pollutant transport, and sediment transport; (3) show how this framework also applies to two nonhydrologic analogous problems describing a physical system (traffic flow on long highways) and a nonphysical one (flood frequency analysis in statistical hydrology); and (4) conclude with a strategy for analytical treatment of the error history in continuous time or space in an approximate model. Taken together, the unified framework helps establish a connection between numerous seemingly disparate flow problems that can aid in engineering education, research, and design.

The objective of the study was to identify areas subject to the implementation of payment for hydrological environmental services projects, aiming to provide solutions for planners and decision makers. Multicriterial analysis (MCA) was used, with the integration of criteria that were aggregated through methods called ordered weighted averaging (OWA) and weighted linear combination (WLC). The alternatives WLC and OWA medium-to-low risk-taking were viable solutions to identify areas with potential for improving water availability, identifying and combining areas with high surface runoff, low soil water storage, high soil loss, low concentration time, and close to rivers. The same alternatives were good solutions to identify areas with potential for improving water quality, pointing out priority areas with a high concentration of nitrogen, high surface runoff, and soil loss. For the areas with potential to erosion control and sediment production, the alternatives WLC and OWA medium-to-high risk-taking proved to be good solutions, indicating areas that coincide with high soil loss, high surface runoff, high slope, and close to rivers. With the established MCA procedures, it is possible to use the same criteria, weights, and methods used to define key areas for the qualification of decision-making and management processes, especially in matters involving instruments and measures for the conservation of watersheds.

Due to frequent pluvial flooding in different urban and peri-urban catchments worldwide, understanding the flooding situation is necessary for effective flood management. However, limited hydrological data availability, for example, lack of discharge information at required spatiotemporal scale or unavailability of local rating curve, can hinder modeling urban pluvial flooding and extracting useful information for flood mitigation. To deal with this situation, the physically-based Variable Parameter Muskingum Stage-routing (VPMS) module was coupled with the popular Storm Water Management Model (SWMM) using the GIS-derived morphological features of a typical urban and peri-urban catchment in eastern India. The study further explored the effect of urban channel representation in the SWMM-VPMS coupled model on the catchment response for the monsoon periods of the years 2009, 2011, and 2014 and confirmed the better performance of high-resolution channel representation with a Nash-Sutcliffe efficiency (NSE) of 0.86, mean absolute error (MAE) of 0.24 m, and percentage bias (PBias) of $-2.33\%$ compared to low-resolution channel representation with NSE, MAE, and PBias of 0.85, 0.23 m, and $-2.95\%$, respectively. Further, this coupled model was applied to understand the flooding situation across various channels considering conditions without Low-impact Development (LID) for 2-h and 3-h design rainfalls under 1-year, 2-year, 5-year, and 10-year return periods. Subsequently, the effect of implementing green roof (GR) and bioretention cell (BRC) as LID techniques was evaluated in mitigating pluvial urban flooding. The results reveal that, although both the BRC and GR perform well in reducing flooding across various critical locations, the BRC is more effective in reducing flooding than the GR.