Journal of Hydrologic Engineering

The Ogallala Aquifer, located in the Central Plains of the United States, is essential for agricultural irrigation and public water supply. Indiscriminate pumping from the aquifer has caused several negative impacts, such as deterioration of water quality and depletion of groundwater levels, which urgently demand better management. This paper applies hierarchical cluster analysis (HCA) and artificial neural networks (ANNs) for predicting annual groundwater levels in 403 wells of the Ogallala Aquifer. First, the methodology employed HCA to cluster homogeneous wells based on the time series of groundwater levels. Then, the study calibrated an ANN model for each cluster (composed of one or more wells) using previous annual values of groundwater levels as input. The HCA results showed a particular pattern in the spatial distribution of the 30 found clusters, revealing that the Ogallala Aquifer holds higher groundwater levels in the western part, which gradually decrease, advancing to the east. The ANN models provided proper predictions even for wells outside of the calibration data set. This investigation concludes that the integration of HCA and ANN enabled single models to accurately forecast annual groundwater levels for sets of wells in the Ogallala Aquifer.

High urbanization adversely affects the hydrological behavior of watersheds. Therefore, different stormwater management strategies have been put forward to mitigate these negative effects. Grass swales are one of the environmentally friendly practices that are widely used today as an alternative to traditional infrastructure systems to reduce the peak flow rates, increase infiltration, and the time of concentration. The determination of the effective parameters on the hydrological characteristics of grass swales plays an important role in identification of proper design criteria. To this end, hydrological experiments were conducted by designing a grass swale module and integrating it into a large-scale rainfall-watershed-swale (RWS) experimental setup. The potential effective parameters (rainfall, soil, grass type, drainage area, and grass height) on the hydrological performance of the grass swales were tested with six different swale configurations by the controlled laboratory experiments. Experimental results show that swales decrease the peak flow significantly. In addition, hydrological performance of swales decreases with the increase in rainfall intensity or rainfall duration. Moreover, the soil and grass type have a significant effect on the peak flow reduction and drainage performance of the swales.

The hydrologic performance of permeable interlocking concrete pavers (PICP) and a dome concrete forming system (DCFS) was quantified and evaluated at a retrofitted residential laneway in Toronto, Ontario. The quantification and evaluation were performed by comparing their monitored performance over 14 months with an adjacent concrete pavement. Their monitored performance was then used to assess their ability to mimic predevelopment hydrologic behavior, estimated through modeling. The PICP and DCFS achieved (1) an average runoff volume reduction of 33% and 85%; (2) an average runoff coefficient of 0.48 and 0.12; and (3) an average peak flow reduction of 43% and 89%, respectively. The outflow duration from the DCFS was extended 4.1 times greater than the inflow duration, indicating its potential to attenuate flashy events. The peak flows produced by the DCFS were delayed by nearly 2.5 h from those occurring on the PICP. Correlation analyses indicated a more significant influence of rainfall depth and intensity on the performance of PICP than that of the DCFS. Compared with predevelopment levels, PICP did not match runoff volumes for smaller events ($<10\mathrm{mm}$) but produced less runoff for larger events ($>30\mathrm{mm}$). However, the PICP did not match predevelopment peak flows in 100% of occurrences. The DCFS reduced runoff volumes and peak flows to magnitudes lower than all predevelopment levels. Surface infiltration testing and subsurface hydraulic conductivity estimations indicated that infiltration capability in both systems could be reduced over time. The study demonstrated that the DCFS might need to be prioritized over permeable pavements, especially when installed over low-permeability soils.

A coupled atmospheric-hydrologic system models the complex interactions between the land surface and the atmospheric boundary layer and the water-energy cycle from groundwater across the land surface to the top of the atmosphere. A regional climate model called weather research forecasting (WRF) was coupled with a land-surface scheme (Noah) to simulate intensive storms in Alberta of Canada. Accounting for the land-atmosphere feedback enhances the predictability of the fine-tuned WRF-Noah system. Soil moisture, vegetation, and land-surface temperature influence latent and sensible heat fluxes and modulate both thermal and dynamical characteristics of land and the lower atmosphere. WRF was set up in a two-way, 3-domain nested framework so that the output of the outermost domain (D1) was used to run the 2nd domain (D2), and the output of D2 was used to run the innermost, 3rd domain (D3). By this 2-way nesting, D3 and D2 provide the feedback to their outer domains (D2 and D1), respectively. D3 was set at a 3-km resolution adequate to simulate convective storms. WRF-Noah was forced with climate outputs from global climate models (GCMs) for the baseline period 1980–2005. A regional frequency analysis and a quantile-quantile bias correction method were applied to develop intensity-duration-frequency (IDF) curves using precipitation data simulated by WRF-Noah. The simulated baseline precipitation of central Alberta agrees well with observed data from a 13-rain gauge network of the City of Edmonton. The 5th-generation mesoscale atmospheric model (MM5) of National Center for Atmospheric Research was also set up in a 3-domain, but 1-way nesting configuration. As expected, after bias correction, precipitation simulated by MM5 was less accurate than that simulated by WRF-Noah. For storms of short durations and return periods of more than 25 years, both MM5 driven by special reports on emission scenario climate scenarios of coupled model inter-comparison project (CMIP3) and WRF-Noah driven by representative concentration pathway climate scenarios of CMIP5 projected storm intensities in central Alberta to increase from the base period to the 2050s and to the 2080s.

Several approximations are used in deriving the National Oceanic and Atmospheric Administration (NOAA) Atlas 14 depth-duration-frequency (DDF) functions from annual maximum series (AMS) of rainfall amounts over various durations. Analyses reported in this paper show that the return periods in Atlas 14 DDF functions for Florida are likely overestimated at about 10% of the base stations for durations of 15 min, 1 h, and 24 h. This relatively high incidence of return-period overestimation is also evidenced by measurements at several stations having very high Atlas 14-estimated return periods. Comparison of the Atlas 14 generalized extreme value (GEV) distributions of rainfall with sample distributions derived from AMS data show agreement at only 68%, 64%, and 30% of all base stations for durations of 15 min, 1 h, and 24 h, respectively. It is concluded that the expected rainfall amounts for given durations are not always reliable over the Atlas 14 range of return periods.