Journal of Hydrologic Engineering

Despite their importance in social, economic, and hydroecological terms, numerous dams and reservoirs spread worldwide lack bathymetric or even storage data of any kind. This scarcity of minimal storage and morphology data represents an obstacle to evaluating the current functions and impacts of these artificial reservoirs, hindering planning efforts for their future development or removal and limiting hydrologic, hydrodynamic, or water quality modeling efforts on dammed watersheds. Although some global databases provide records with basic information on the location and surface extent of large reservoirs—including attributes such as dam height, total storage capacity, or even storage curves—bathymetries remain rarely available. Surveying bathymetric data is challenging and expensive, especially in the least-developed countries, regions experiencing war conflicts, or remote areas exposed to natural risks. This article presents a method for deriving synthetic bathymetries using cubic spline spatial interpolation. The method uses a digital terrain model (DTM) surrounding the reservoir as the only required input, assuming geomorphological and hydrological continuity and imposing longitudinal depth controls to the interpolation process. In comparison with a sample of 12 vessel-based reservoir bathymetric surveys, available from the United States Bureau of Reclamation (USBR), the synthetic bathymetries resulting from this interpolation method show bias performances within a range of $\pm 25\%$ and Kling–Gupta efficiencies (KGE) over 0.5. Except for vessel-based bathymetric surveys, any indirect depth estimation method that applies remote sensing or interpolation is susceptible to some level of uncertainty and varied reliability. However, cubic spline spatial interpolation can produce bathymetric models with a reasonable level of accuracy depending on the projected application. The performance levels suggest that the proposed method can produce synthetic bathymetries that offer valuable information for early-stage hydrological or environmental studies or assessments. Some potential applications include initial evaluations involving problems like dam failure and dam removal or restoration, where bathymetric data play a relevant role as an input of hydrological, hydraulic, environmental, and risk simulations. In prefeasibility or conceptual stages, these types of projects and assessments can tolerate higher levels of uncertainty in the input data before making decisions to proceed to advanced stages of assessment, design, construction, restoration, or demolition. The method offers a low-cost and time-efficient alternative solution if vessel-based bathymetric surveys or remote sensing techniques turn out to be unfeasible for future studies or assessments in data-scarce regions. Reliable synthetic bathymetric interpolation turns out to be especially valuable in remote or dangerous sites posing physical risks, social conflicts, or any other safety threats for field surveys.

Time of concentration is an important parameter in rainfall–runoff models, such as the rational method and the unit hydrograph method, used for drainage design. There are several equations for computing the time of concentration. Many of these equations are empirical and are mostly based on length and slope, and some equations depend also on drainage area. Equations have also been derived using the kinematic wave theory, which accounts for length, slope, roughness coefficient, quality of flow (laminar, transient or turbulent) and shape (rectangular, converging, diverging, or diverging-converging), and rainfall or rainfall excess intensity and duration. In all of the equations, it is assumed that the rainstorm is stationary, but rainstorms are often dynamic and move in a particular direction and for a particular period of time, which significantly affect the time of concentration. There seems to have been no attempt to evaluate the impact of storm direction and duration as well as watershed shape on the time of concentration. The objective of this paper therefore is to revisit the concept of time of concentration and empirically derived equations, present the time of concentration equations for rectangular, converging, and diverging planes under stationary storms reported in the literature, examine the impact of moving storms for a rectangular plane, and compare these equations with empirically derived equations. The objective here is not to test or validate these equations for different watersheds.

High temporal resolution rainfall data are among the most demanded resources for water resource engineers. In modern times, this need has only multiplied day by day due to the need for training large parameter-heavy models for the prediction of climatic features, analysis of extreme rainfall, etc. However, the availability of such high temporal resolution data is low, which can cause hindrances in research or development projects in several regions. It is therefore imperative to find newer and better models for the disaggregation of rainfall data from lower to higher temporal resolutions, such as a model that uses deep learning neural networks. The main issue with such a model is the requirement for historical rainfall data at different time scales for training, testing, and validating prior to use in practical scenarios, data that may not always be available for all regions necessary. In this paper, an attempt has been to test the accuracy and applicability of pretrained models for the purpose of disaggregating rainfall in other geographical locations, thus reducing the requirement for historical rainfall data for training and validation purposes. A large data set comprising rainfall data from 68 rain gauge stations across the Indian subcontinent has been used to test models pretrained using rainfall data from seven major stations in India (Bikaner, Chennai, Cherrapunji, Delhi, Kolkata, Mumbai, and Mangalore). The pretrained models are tested in their ability to conserve extreme rainfall characteristics by comparing intensity–duration–frequency (IDF) curves generated from observed and disaggregated rainfall, further which the errors in these IDF curves are used to generate heatmaps for the country using the inverse distance weighted interpolation method. At the end of this paper, a map is provided that covers the entire country of study, detailing that a pretrained model can be used for a certain region based on its accuracy of disaggregation and proximity to the city of pretraining data.

We employed a dataset of 1,229 rainfall-runoff events in 47 watersheds in Texas to explore the performance of the Natural Resources Conservation Service (NRCS) Curve Number Method in determining peak discharges given varying initial abstraction ratios ($\lambda$). For each watershed, a curve number was calibrated from rainfall-runoff events using the least-squares approach, and the standard error ratio was used to evaluate calibration performance. Curve numbers and standard error ratios were compared for both $\lambda =0.20$ and $\lambda =0.05$. Hypothesis testing found no significant difference in the standard error ratios within the five Texas regions studied, highlighting the need for some alternative criteria on which to select the most appropriate $\lambda$ value for modeling and design. An equation was developed that computes equivalent curve number between $\lambda =0.20$ and $\lambda =0.05$. Finally, we presented a method to predict whether small or large $\lambda$ will estimate larger runoff and recommended a design approach that selects the $\lambda$ value such that the larger runoff is estimated, leading to the more conservative hydrologic design.

Numerical groundwater modelers often encounter challenges in computing and implementing regional unconfined aquifer models that match real-world water table undulations and local features. A commonly-practiced simplifying assumption in groundwater models states that incorporating extensive real-world detail does not correlate to better model results. This study discusses how this assumption may lead to unrepresentative models due to oversimplification. Sometimes, when determining the appropriate complexity of regional models, important elements of the conceptual model may be oversimplified, resulting in a nonrepresentative model or part of a model. This study tests the hypothesis that, by incorporating specific areas of greater hydrogeologic complexity into modeling a regional-sized unconfined aquifer, simulated heads will significantly better match observed conditions, especially in areas where unconfined groundwater discharges through aquitard breaches and where dry cells are a natural (realistic) condition of the system. Three surface processes (additional recharge, stream density, and evapotranspiration) were tested in multiple sets of impact models. Model outcomes were compared against observed conditions of the shallow unconfined aquifer beneath Memphis, TN, US, and against the parent fully calibrated groundwater model of the aquifer systems beneath Shelby County, TN. Results indicate that an increase in stream detail better mimics observed water table undulations. These undulations occur at the same scale as aquitard breaches and improve appropriate gradients and flow surrounding the breaches. Results further indicate that the addition of greater hydrogeologic complexity to the shallow unconfined aquifer improves models like that of Shelby County, TN, by better representing realistic gradients, conveying appropriate quantities of water through breaches, and offering an opportunity for improved contaminant transport modeling from the shallow unconfined aquifer to the deeper semiconfined Memphis aquifer via aquitard breaches.