Journal of Hydrologic Engineering

One of the most important impacts of a future warmer climate is the projected increase in the frequency and intensity of extreme rainfall events. This increasing trend in extreme rainfall is seen in both the observational record and climate model projections. However, a thorough review of the recent scientific literature paints a complex picture in which the intensification of rainfall extremes depends on a multitude of factors. While some projected rainfall indices follow the Clausius-Clapeyron relationship scaling of an $\sim 7\%$ increase in rainfall per 1°C of warming, there is substantial evidence that this scaling depends on rainfall extremes frequency, with longer return period events seeing larger increases, leading to super Clausius-Clapeyron scaling in some cases. The intensification of extreme rainfall events is now well documented at the daily scale but is less clear at the subdaily scale. In recent years, climate model simulations at a finer spatial and temporal resolution, including convection-permitting models, have provided more reliable projections of subdaily rainfall. Recent analyses indicate that rainfall scaling may also increase as a function of duration, such that shorter-duration, longer return period events will likely see the largest rainfall increases in a warmer climate. This has broad implications on the design and the use of rainfall intensity–duration–frequency (IDF) curves, for which both an overall increase in magnitude and a steepening can now be predicted. This paper also presents an overview of measures that have been adopted by various governing bodies to adapt IDF curves to the changing climate. Current measures vary from multiplying historical design rainfall by a simple constant percentage to modulating correction factors based on return periods and to scaling them to the Clausius-Clapeyron relationship based on projected temperature increases. All of these current measures fail to recognize a possible super Clausius-Clapeyron scaling of extreme rainfall and, perhaps more importantly, the increasing scaling toward shorter-duration rainfall and the most extreme rainfall events that will significantly impact stormwater runoff in cities and in small rural catchments. This paper discusses the remaining scientific gaps and offers technical recommendations for practitioners on how to adapt IDF curves to improve climate resilience.

Computer models and simpler methods must be used to reliably determine total maximum daily loads (TMDLs) for impaired waters of the US. Models are also useful to develop implementation plans that allocate load reductions to meet water quality standards. During model selection, practical considerations sometimes override the fundamental requirements of parsimony and defensibility. For the first time, a rationale for efficiently selecting reliable models for TMDL determinations is presented in this paper, based on deliberations of the TMDL Analysis and Modeling Task Committee of the Environmental and Water Resources Institute of ASCE. This protocol is based on technical criteria (waterbody, water quality, and data requirements) and management constraints (state plans and priorities, stakeholder and expert engagement, and state authority to manage pollution sources and state allocation priorities). Uncertainties due to changes in economic activity, population, land use, climate, sea level, and policies are also included. To reduce wasteful trial-and-error iterations during model selection, an optimal conceptual modeling framework is identified and subsequently refined in a flowchart based on practical considerations. The proposed protocol holistically defines the state of the science in model selection and is anticipated to soon define the state of the practice for TMDL determination.

In reservoirs with relatively low capacity-inflow ($\mathrm{C}/I$) ratios, real-time daily operations provide the possibility of minimizing the risk of failure and meeting downstream demands sustainably even during extreme droughts. In the present study, it has been attempted to generate synthetic daily precipitation and maximum and minimum temperature data using the widely applied weather generator Long Ashton Research Station weather generator (LARS-WG). The synthetic meteorological data were then inserted into the Arno River (ARNO) daily rainfall–runoff (R-R) model to reproduce long-term daily streamflow scenarios and subsequently used to determine optimal daily releases and operating policies. The daily R-R model can also produce appropriate predictions of future inflows to the reservoir, which can be applied to implementing real-time operations in daily time steps. The methodology used in this study provides two main advantages: (1) the capability to determine optimal daily releases for streamflow sequences, which can be applied to make optimal real-time decisions even by considering the unregulated downstream flow regime, and (2) the ability to predict future inflows and determine real-time decisions, which can be used to decrease predictive uncertainties of future releases.

The variation in flow for the Xiaonanhai spring and its main controlling factors were statistically examined using Mann-Kendall trend analysis, rescaled range (R/S) analysis, Morlet wavelet analysis, and cross-wavelet analysis. The annual spring flow has exhibited a continuous and significant downward trend since 1997, which varies temporally, with main periods of 14–17 years and 25–28 years. There is a positive correlation between the spring flow and precipitation ($p<0.01$). The results of the cross-wavelet analysis and the wavelet coherence analysis show that the main resonance periods of the precipitation and spring flow were present in 1983–1995 and 1998–2015, respectively. A stepwise regression model and a back propagation neural network model were established based on the analysis of the flow changes in order to predict the spring flow under the expected changes in precipitation. The simulation precision of the neural network structure is better than that of the stepwise regression model. Therefore, the neural network model can be used to predict the dynamic flow of the Xiaonanhai spring under the effects of future climate change.

This article focuses on the assessment of climate change impacts on the hydroclimatology of the Subansiri River basin, which is the largest tributary of the Brahmaputra River in the northeastern part of India. Three representative concentration pathway (RCP) emission scenarios, namely, RCP2.6, RCP6.0, and RCP8.5, of three general circulation models (GCM) archived by the Geophysical Fluid Dynamics Laboratory (GFDL), were utilized for the projection of climatic variables (such as precipitation and temperature). Long-term (2011–2100) projections of precipitation and temperature for different emission scenarios were made using the statistical downscaling technique. The soil and water assessment tool (SWAT) hydrological model was used for hydrological modeling of the river basin. The observed streamflow series for the period of 2002–2013 has been utilized for calibration and validation of the hydrological model. Parameterization, uncertainty analysis, and parameter sensitivity analysis of the model were performed using a sequential uncertainty fitting (SUFI2) program. The coefficient of determination ($R^2$) for the calibration and validation of the hydrological model on the monthly streamflow time series was found to be 0.86 and 0.80, respectively. Future projections of the precipitation and temperature suggest an increase in the annual average maximum temperature ($T\max$), annual average minimum temperature ($T\min$), and annual precipitation of the river basin. These projected climatic variables were used as the primary input in the hydrological model for the projection of the streamflow for the period of 2016–2100. The flow duration curve analysis of streamflow projections reveals an increase in the discharge for a particular percent of the dependable flow in the case of all the RCP scenarios. Water yield analysis also suggests an increase in the annual average water yield in all cases of emission scenarios.

A quantification of the spatial dependence among extremes of rainfall events is important for investigating the properties of intense, extreme weather-related hazards. Extreme value theory has been widely applied to weather variables, and rigorous approaches have also been employed to investigate dependence structures among extreme values in space. To investigate the joint dependence of extreme rainfall events in space, spatial dependence modeling through max-stable process models has been considered to analyze extreme rainfall data across selected weather stations in South Africa. The analysis was also used to illustrate how the geographic and temporal covariates can affect the extreme rainfall field and subsequently the distribution of spatial random variables. The results revealed significant trends in the time-heterogeneous spatially fitted generalised extreme value (GEV) distribution. In addition, the max-stable process model predicted the probability of annual maximum rainfall and spatial contrasts of extreme rainfall characteristics across selected weather stations in South Africa. The results indicated that the annual extreme rainfall across selected weather stations in South Africa exhibits noticeable spatial variability. This study also depicted the significance of spatial max-stable process models over the univariate modeling and how models of spatial extremes with dependence can be used to better understand the probability of extreme rainfall events and to account for the influence of temporal covariates. Results obtained in this study have essential scientific and practical applications in monitoring hydrological-related risks for mitigation and adaptation strategies.