Journal of Hydrologic Engineering

Variations in precipitation extremes over the relatively small spatial scales of urban areas could be significantly different from those over larger regions. An understanding of such variations is critical for urban infrastructure design and operation. Urban climatology and sparse spatial data lead to uncertainties in the estimates of spatial precipitation. In this study, a Bayesian hierarchical model is used to obtain the spatial distribution of return levels of precipitation extremes in urban areas and quantify the associated uncertainty. The generalized extreme value (GEV) distribution is used for modeling precipitation extremes. A spatial component is introduced in the parameters of the GEV through a latent spatial process by considering geographic and climatologic covariates. A Markov-chain Monte Carlo algorithm is used for sampling the parameters of the GEV distribution and latent-process model. Applicability of the methodology is demonstrated with data from 19 telemetric rain-gauge stations in Bangalore city, India. For this case study, it is inferred that the elevation and mean monsoon precipitation are the predominant covariates for annual maximum precipitation. Variation of seasonal extremes are also examined in the study. For the monsoon maximum precipitation, it is observed that the geographic covariates dominate, whereas for the summer maximum precipitation, elevation and mean summer precipitation are the predominant covariates. A significant variation in spatial return levels of extreme precipitation is observed over the city.

Developing a distributed hydrological model that is applicable to a variety of topographies is a substantial challenge to the hydrological modeler. The primary objective of this study was to develop a distributed hydrological model that has a universal structure and an adjustable core module for small- and medium-scale basins under various topographical and hydrogeological conditions. By analyzing structural characteristics of soil vertical profile and general process of rainfall-runoff calculation under the different topographical conditions, a distributed hydrological model with universal structure was devised by combining the kinematic wave theory with the geographic information system (GIS)–based data on the topographical and hydrogeological conditions of target basins. Model validations were conducted through numerical simulation of the observed flow-discharge for the following three basins with varied topography: (1) the Liudaogou Basin, which is located in the region of the northern Loess Plateau and is abundant in hills and gullies; (2) the Alun River Basin, which flows through eastern Inner Mongolia and the western Heilongjiang Province; and (3) the Bukuro River Basin, which is located in Japan. The results indicated that acceptable simulation results for the rainfall-runoff process were achieved under varying topographical and hydrogeological conditions in each basin and that the calculation accuracy was within the allowable range according to the error criterion (error $<0.03$). The model exhibited relatively high efficiency, and the Nash-Sutcliffe efficiency (NSE) was more than 0.90. Structural universality and core module adjustability of the model are discussed and analyzed for applicability under various topographical and hydrogeological conditions. The results are expected to provide a methodological reference for further studies on the distributed hydrological model for small- and medium-scale basins with varied topographical and hydrogeological conditions.

A calibrated three-dimensional environmental fluid dynamics code model was applied to simulate temperature distributions under various hypothetical weather conditions and daily repeated large releases (DRLRs) from the upstream boundary in a river-reservoir system in Alabama. Both the duration of DRLRs and weather conditions affect and control the formation and spread of density currents along channel bottom at downstream locations, which affect the bottom-layer water temperatures. The daily drop rates of bottom-layer temperature at the Gorgas upstream monitoring station (GOUS) under 6-h DRLRs are 0.3, 0.5, and $0.4°\mathrm{C}/\mathrm{day}$ for assumed $2°\mathrm{C}/\mathrm{day}$ air temperature drop lasting 2, 4, and 6 days, respectively. The average bottom-layer temperature at the river intake under 4-h DRLRs is 2.3°C lower than one under 2-h DRLRs and only 1.1°C higher than one under 6-h DRLRs in the whole simulation period. The daily drop rate and dropping duration of bottom-layer temperature are almost the same for 2-, 4-, and 6-h DRLRs because of the same drop and rise pattern for weather conditions. The maximum differences between the stationary weather scenario and the 11-day drop-rise weather scenario range from 3.1 to 4.2°C under different release durations. The lower bottom-layer temperatures at GOUS and the river intake are primarily because of the lower air temperatures and solar radiation during the 11 days and less affected by the release pattern. Bottom-layer temperature dynamics in the riverine portion are more affected by flow momentum of DRLRs and, in the deeper reservoir portion, primarily controlled by weather conditions.

Most snow hydrology models do not include the long-term effects of interannual snow storage such as glaciers because glacier dynamics have a longer timescale than river flow and seasonal snowmelt. Interannual snow and ice, including inland glaciers, should be treated as systems in dynamic equilibrium that remain constant under a static climate condition. The model introduced here considers the movement of snow/ice from high-elevation areas to valleys by means of wind redistribution, avalanches, and glaciation. The relocated snow and ice may melt faster because of the higher temperatures of low-elevation areas. The reference temperature increase was modeled as a proportion to the snow and ice mass in a computational cell. The proportional coefficient can describe the equilibrating factor of the glacier system. This study discusses the physically based modeling of such a dynamic equilibrium snow system for long-term snow simulations at a regional scale. The developed regional snow (RegSnow) model was coupled with the weather research and forecasting (WRF) model to compute the snow surface-energy fluxes. According to the model implemented for Wyoming in the United States, approximately 82.2% of interannual snow and ice storage may disappear by the end of this century based on the temperature increase projected by general circulation models (GCMs) under the representative concentration pathway (RCP) 4.5 emission scenario.

Drought characterization is crucial for identifying impacts on irrigation, agriculture, hydrologic engineering, and water resources management. This case study demonstrates the scientific relevance of the standardized precipitation-evapotranspiration index (SPEI) as a robust drought metric that incorporates influence of supply-demand balance. Using long-term data, the SPEI was calculated at multiple timescales to identify historical water deficit periods in selected drought-prone case study regions in southeast Queensland, Australia. The drought duration ($D$; number of months with continuously negative SPEI representing below average water resources), severity ($S$; accumulated negative SPEI in a drought-identified period), intensity ($I$; minimum SPEI), and return periods were enumerated for iconic dry events over multiple (1-, 3-, 6-, 9-, 12-, and 24-month) timescales. The SPEI was evaluated with corresponding drought indicators (precipitation and soil moisture) and climatological Rainfall Anomaly Index to yield drought severity information from a meteorological perspective. The results showed disparities in duration, severity, and intensity (D–S–I) of different droughts among the case study regions; reaffirming the significance of SPEI for regional drought impact assessment. Accordingly, this case study advocates SPEI as a convenient metric for detecting drought onsets and terminations, including drought ranking and recurrence evaluations that are vital statistics in hydrologic engineering.