Journal of Hydrologic Engineering

Porous asphalt is used in Permeable Pavement Systems, but it is sensitive to surface clogging, which leads to a loss in its infiltration capacity. Test methods based on the use of permeable pavement models, which are manufactured in a laboratory and assessed under different clogging conditions, such as slope, rain, and runoff, have been widely applied to the study of permeable pavements with concrete blocks but not to the study of porous bituminous mixtures. The Cantabrian Fixed (CF) Infiltrometer has been used for the study of porous asphalt with void percentages between 20 and 33%. Three clogging scenarios were studied: 1) newly placed surface, 2) surface with an average maintenance level, and 3) clogged surface. Each clogging scenario was tested with five different slopes: 0, 2, 5, 8, and 10% and three repetitions. The direct rainfall simulation was produced by five lines of bubblers over the $0.25\mathrm{\text{-}}{\mathrm{m}}^2$ piece, and the runoff was simulated by one perforated pipe over a plastic ramp at the beginning of the surface. From the analysis of the results, it was concluded that a suitable design of a porous bituminous mixture, with a void percentage that increases with depth, along with surface brushing are enough to ensure and maintain a good infiltration capacity. Finally, an empirical, conservative model for estimating the porous asphalt infiltration capacity, based on the length, the clogging scenario, and the surface slope, is proposed.

The transportation and urban infrastructure relies heavily on impervious surfaces. Unmitigated rainfall runoff from impervious surfaces can lead to a myriad of environmental problems in downgradient areas. To address this issue, novel stormwater control measures (SCMs) are being emphasized and implemented widely to mitigate some of the impacts of impervious surface. Bioretention is a soil/media-based SCM that is often used for this purpose, but current design practices are highly empirical. This study compiles work from three research sites in three states to provide some fundamental underpinnings to bioretention design. Although all sites demonstrate different levels of performance, water volumetric performance trends are common to all. These trends are based on the available storage in the bioretention cell, termed herein as the Bioretention Abstraction Volume (BAV). The BAV is directly related to available media porosity and storage in the surface bowl. A finite capacity to completely store all runoff from smaller events is defined by the BAV. Normalization for this storage provides prediction for volumetric performance. Recommendations for bioretention design are provided.

The climate impact studies in hydrology often rely on climate change information at fine spatial resolution. Because general circulation models (GCMs) operate on a coarse scale, the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, downscaling models are developed using the state-of-the-art nonparametric method $K$-Nearest Neighbor ($K$-NN) approach, with an emphasis on optimal choice in selection of nearest neighbors for obtaining simultaneous projections of mean monthly maximum and minimum temperatures ($T_{\max }$ and $T_{\min }$) as well as monthly precipitation and pan evaporation to lake-basin scale in a semiarid region that is considered to be a climatically sensitive region in India. The performance of the $K$-NN approach was evaluated based on several statistical performance indicators. A comparison of $K$-NN has been made with a linear multiple regression (LMR)-based downscaling model. Also, the prevailing view in the literature regarding optimal choice of selection of nearest neighbors is checked with different perturbations. A simple multiplicative shift was used for correcting predictand values. The $K$-NN-based models are found to be superior to LMR-based models, and, subsequently, the $K$-NN-based model is applied to obtain future climate projections of the predictands. An increasing trend is observed for $T_{\max }$ and $T_{\min }$ for A1B, A2, and B1 scenarios, and the precipitation is projected to increase in the future for A2 and A1B scenarios, whereas no trend has been observed for pan evaporation in future.

The interfacial diffusion-controlled models of chemical transport across the interface of the soil surface and overland flow are physically based and can be easily expanded to include other functional modules to predict chemical loads from soil to overland flow. The efficiency of the model predictions and the veracity of the parameters are critical for accurate estimation of the chemical loads. In this study, the interfacial diffusion-controlled model was employed to simulate the transport process of a dissolved chemical (bromide, ${\mathrm{Br}}^{-}$) from saturated soil to overland flow. The model parameters were optimized by fitting the analytical solution of the model with experimental data. Comparison between model simulations and experimental observations showed that the model is efficient for predicting (${\mathrm{Br}}^{-}$) transport in the soil and overland system. It predicted runoff-concentration data very well and predicted the short-term change in the upper soil profile better than the long-term change in the lower profile. When the mass transfer coefficient estimated by the existing equation was applied in the model, the model underestimated the chemical loads in overland flow by a relative error of 21.5% in this study, which was attributed to the neglect of rainfall impact on the solute transfer from the soil surface to overland flow.

This paper presents a detailed modeling of rainfall-runoff processes and flow routing along a complex large-scale region, the Upper Paraguay River Basin (UPRB), encompassing a drainage area of approximately $600,000{\mathrm{km}}^2$, which extends over Brazil, Paraguay, and Bolivia. Within the UPRB lies the Pantanal, the world’s largest wetland, with extraordinary biodiversity and great ecologic value, but which currently is threatened by anthropogenic activities. A conceptual model was applied with two main components: (1) simulation of the basin and part of the Paraguay River tributaries by means of the distributed large-scale hydrological model MGB-IPH using simpler flow routing methods; and (2) simulation of the main drainage network, approximately 4,800 km of river reaches, with a one-dimensional hydrodynamic model. Despite the data scarcity, complexity, and the intricate river drainage network of the region, the coupled model was able to represent the hydrological regime of the basin. Comparisons between observed and calculated hydrographs showed a good model skill in representing the flow regime of the upper Paraguay River and its tributaries, highlighting its value as a tool for understanding and predicting the system behavior. The proposed modeling of the hydrological processes of the UPRB, with a detail never presented before, provides a valuable tool for understanding ecosystem functioning and assessing its resilience to anthropogenic pressure, climate change, and climate variability.

This study was conducted in the Black Volta basin, Ghana, to determine how Tropical Rainfall Measuring Mission (TRMM) satellite-derived rainfall compares with ground-measured values and the possibility of using it to complement ground-measured rainfall. Bilinear interpolation was used to resample $0.25°\times 0.25°$ TRMM 3B42V6 monthly rainfall grids to determine site-specific rainfall of the basin based on rain gauge locations and analyzed statistically. The study showed that the correlation between the monthly datasets ranged from 0.73 to 0.88. A plot of the average monthly data of all the stations gave a correlation coefficient ($R$) of 0.94. The overall catchment rainfall was well represented by TRMM data. However, the annual station rainfalls were either underestimated or overestimated. The underestimations and overestimations were mostly below 20% and 10%, respectively, of the raingauge measurements. Although the TRMM rainfall data did not perfectly match with the ground measurements, it can still be used to supplement ground measurements and for estimating rainfalls in ungauged basins.

In this paper, we propose a novel neural modeling methodology for forecasting daily river discharge that makes use of neural units with higher-order synaptic operations (NU-HSOs). For hydrologic forecasting, conventional rainfall-runoff models based on mechanistic approaches in the literature have shown limitations attributable to their overparameterization and complexity. With the use of neural units with quadratic synaptic operation (NU-QSO) and cubic synaptic operation (NU-CSO), as suggested in this paper, the refined neural modeling methodology can overcome the intricacy and inefficiency of conventional models. In this paper, neural network (NN) models with NU-HSO are compared with conventional NNs with neural units with linear synaptic operation (NU-LSO) for forecasting river discharge. This study was conducted using 1- to 5-day lead time forecasting in the Mahanadi River basin at the Naraj gauging site to evaluate the effectiveness of the higher-order neural networks (HO-NNs). Performance indices for the prediction of daily discharge forecasting indicated that NNs with NU-CSO and NNs with NU-QSO achieved better performance than NNs with NU-LSO even with a lower number of hidden neurons. Thus, this study shows that HO-NNs can be effective in hydrologic forecasting.