Journal of Hydrologic Engineering

Abstract

Accurate rainfall fields are important for several hydrological and meteorological applications. In poorly instrumented basins, the lack of rain gauges heavily affects the spatial rainfall estimation, yet neither remote sensed nor climate model estimates are good enough for management applications. To tackle this problem, we investigate the impacts of combining both sources of information by varying the kernel bandwidth value of the double smoothing merging algorithm and analyzing the error of the rainfall fields. We explored the correlation between rain gauge density and bandwidth and compared the results against classical geostatistical interpolation methods based solely on in-situ measurements. Propagation of rainfall error into hydrological modelling is usual, and therefore we evaluated the influence of the bandwidth in streamflow simulations implementing two hydrological models. The hydrological evaluation considered the analysis of hydrological signatures rather than just performance metrics. We found that there is a clear correlation between kernel bandwidth and monitoring network density and that the bandwidth also affects hydrological performance. Simple bilinear downscaling did not produce a significant difference in meteorological or hydrological errors, and rain gauge network configuration also impacts the error of the field. We conclude that merging outperforms the results of classical interpolation methods, in some cases by 20% or 50%, suggesting the suitability of the method for being applied in data-scarce domains.

Practical Applications

Reliable rainfall fields are important for estimating water resource availability and other related applications. However, using just rain gauges can misrepresent the estimated spatial variability of the field. Around the world, the number of rainfall gauges has been decreasing, making this task more challenging. Satellite and reanalysis data can help to overcome these problems, but their direct use is also insufficient. Merging the two mentioned data sources (in-situ and reanalysis data) is the option the authors implemented in this work. The authors focused on evaluating the advantages and disadvantages of the merging by comparing both rainfall and streamflow observations with estimates simulated by a hydrological model. The authors chose the double smoothing algorithm because it is oriented to enhance rainfall estimates in areas with very sparse rain gauge data, yet we evaluate its behavior in two basins with different monitoring conditions. The authors conclude that the merging outperforms the results of classical interpolation methods, in some cases by 20% or 50%, suggesting the suitability of the method for being applied in data-scarce domains to improve hydrological modelling estimates and use their results in water resource management and planning, in climate classification, and in the study of droughts and floods, among others.

In this research, the role of climate variability and weather change in short-term streamflows, including extreme event, was investigated in semiarid climates. The deep learning convolutional neural networks (CNN) were modified by incorporating the imperialist competitive algorithm (ICA) and the grey wolf optimizer (GWO) method to improve hourly runoff predictions at multiple scales, ranging from 100 to over $\mathrm{6,000}{\mathrm{km}}^2$ in the Seybouse Basin, Algeria. The atmospheric reanalysis data set, ECMWF Reanalysis v5 (ERA5) with a 31-km resolution, climate variability indices, and in situ runoff observations were used. The most relevant atmospheric and soil moisture predictors from the reanalysis grids covering the study area were used to represent spatial variability. The prediction performance of the original CNN and modified CNN-ICA and CNN-GWO models were evaluated. The CNN-GWO model outperformed CNN-ICA and the original model in predicting runoff and improved the Nash-Sutcliffe Efficiency score up to 0.99. Results across multiple scales disclose that the models with climate indices as inputs showed higher performance than the models with only atmospheric data as inputs, especially in predicting extreme runoff values in basins with elevations above 670 m, suggesting that climate variability indices need to be considered in flood predictions and infrastructure design in mountainous areas with increasing climate change uncertainties.

Abstract

Reliable estimates of aquifer recharge have the potential to help develop sustainable groundwater management policies. Despite its importance, quantifying this flux continues to be a challenge and remains one of the most uncertain components of the hydrological cycle. Here, we obtain a spatially explicit estimate of recharge using a semi-distributed hydrologic model for a major river basin in the Southeastern United States. A comparison of these process-based estimates with a data-driven recharge product (developed by USGS), which was obtained using a set of empirical regression equations, shows good agreement at the basin scale, but significant discrepancies at finer spatial resolutions. Overall, the semi-distributed model shows a higher degree of spatial heterogeneity across the basin than the USGS study results, which likely indicates that the empirical relationships modeled at the basin scale by the USGS empirical equations might not hold at smaller spatial scales. However, more ground-truthing recharge datasets are necessary to properly evaluate subbasin-scale models and reduce the uncertainty of estimates at these scales.

Practical Applications

Groundwater recharge information at local scales is essential for various tasks: It is critical in the assessment of groundwater contamination from point sources, determining rates of change in response to pumping, quantifying local scale climate-induced storage change effects, assessing climate impacts on land cover changes and water supply, to name a few (Scanlon and Cook 2002) (Reitz et al. 2017). Because precipitation, pumping rates, land cover changes, and other important factors that affect groundwater recharge can vary significantly at a local scale (on the order of 1 to $10{\mathrm{km}}^2$), having recharge estimates at a similarly fine scale will be useful for groundwater managers to evaluate the effectiveness of various practices that impact different stakeholders within the basin, and use this information to develop more effective water management plans.

Analysis of the various hydrologic features responsible for the spatial and temporal variability in large scale subsurface systems is a critical component of water resources engineering and management. Traditionally, modeling large-scale groundwater flow patterns includes simulations with underlying parameter estimations and associated overarching assumptions. More recently, subsurface hydrologists are using empirical orthogonal function analysis to separate and quantify the primary hydrologic components contributing to observed regional and local-scale groundwater hydrodynamics. In this case study, modern spatiotemporal geostatistics plus primary hydrologic component analysis were conducted on spatially and temporally distributed groundwater levels measured in the Yellow River Delta. The results demonstrate the significant interplay between surface water hydrodynamics, overall groundwater quality, and groundwater utilization at the local scale, plus the significant impact of regional-scale aquifer recharge on groundwater level seasonal variation, largely driven by precipitation events. With increased accessibility to remote sensing data, additional research using these or similar statistical approaches to interpolate, compress, and decompose those features driving subsurface fluid-flow variability will prove to be highly beneficial to water resource engineers and managers.

Intensity-duration-frequency (IDF) relationships are frequently required for hydraulic design and risk assessment in small catchments. Nonetheless, the usual scarcity of subdaily rainfall data may render the derivation of such models unfeasible in the locations of interest. For addressing this problem, in this study, we discuss a regional IDF relationship based on the principle of scale invariance, and apply this model to gauged sites in the metropolitan region of Belo Horizonte, Brazil. Our results indicated that a single regional-scale exponent, as derived from the average of estimates at training sites, is accurate enough (mean absolute error of 15%) for describing the probabilistic behavior of extreme rainfall across a large set of subdaily durations in the study area. In addition, the quantile estimates derived from our model are similar to those obtained with more complex regionalization methods, which suggests that the scale invariance principle may be an effective and parsimonious tool for extreme rainfall frequency analysis in poorly gauged areas.

Abstract

River bank filtration (RBF), as one of the sustainable natural treatment techniques, can provide risk-free drinking water supply to rural villages where piped water supply through conventional treatment schemes is very expensive. The main purpose of this paper is to report the results of a RBF production well implemented in a complex hydrogeological setting under the ephemeral Varaha river influenced by the saline coastal aquifer in the Visakhapatnam district of Andhra Pradesh (India) for the supply of drinking water to the nearby rural habitats that have no organized water supply. To understand and identify the hydraulics of the river–aquifer exchanged water, particle tracking analysis to determine the travel times of river water path lines, capture zones, the proportion of bank filtrate and river water, and groundwater into RBF production well consequent to pumping were carried out using modular finite difference groundwater flow model (MODFLOW) in conjunction with a particle-tracking model for MODFLOW (MODPATH). The river–aquifer and well field interaction were studied by developing a two-dimensional two-layer groundwater flow model with a partially penetrating well in an unconfined aquifer. The flow modeling simulation results together with MODPATH particle tracking analyses revealed that the installed RBF production well could yield about 61% river water through bank filtration and the remainder from the groundwater. The flow model was also used to optimize the pumping duration from the RBF production well without compromising the water quality of the bank filtrate water. The optimum pumping duration was estimated to be 5 h a day at the rate of $31.2{\mathrm{m}}^3/\mathrm{h}$ from continuous pumping during the monsoon period. The performance and quality of the bank filtrate from the well demonstrated that the RBF technique could also be a potential and viable approach for the supply of safe drinking water from an ephemeral river-induced bank filtrate influenced by the saline coastal aquifer.

Practical Applications

River bank filtration (RBF), a process to extract river water and groundwater through RBF wells, is one of the sustainable natural treatment techniques successfully providing risk-free drinking water supply in many urban, periurban, and rural areas in different countries. In India, it is gaining popularity, particularly in rural clusters where piped water supply through conventional treatment schemes has a remote chance of being effective. RBF wells are very effective in removing suspended particles, pathogenic compounds, and microorganisms present in the source water. The quantity of RBF water depends on the user requirements; for a town with a series of RBF wells or a small village using a single RBF well, RBF techniques can be employed. For establishing RBF wells in coastal regions, extra care would be required to fix the pumping rate and its duration to avoid mixing of saline with fresh water. The present study demonstrates the successful implementation of a RBF well on an ephemeral river (Varaha river in Andhra Pradesh State, India) in a saline coastal aquifer. The RBF technique can be employed to any other river reach or waterbody in the region where there is a need for potable water supply with minimum water treatment cost.

The intensity-duration-frequency (IDF) curve is a critical input for designing hydraulic infrastructure such as stormwater drainage systems. The last few decades have witnessed drastic changes in rainfall patterns and changes in their extremes, mostly associated with the impact of climate change. Therefore, it is important to study the possible drift in the IDF curve associated with the changing trends in historical rainfall data and future climatic conditions. Based on the understanding of the negative correlation between rainfall intensity and duration, this study used a bivariate copula-based approach for IDF curve development (considering historical rainfall data) and compared it with the conventional empirical models and univariate frequency analysis. The study identified the Frank copula (from 24 candidate copulas) with its parameters estimated by Bayesian inference and a hybrid-evolution Monte Carlo Markov Chain. In addition, the IDF curve was developed for four future climate scenarios corresponding to three different time periods. The proposed methodology was demonstrated for an urban catchment in Northeast India, for which the future climate scenarios were not considered for previous IDF curve development. The rainfall intensity from one of the empirical models and univariate analysis compared well with the corresponding values obtained using the IDF developed using bivariate analysis for short durations ($\le 3\mathrm{h}$). Both the time periods and different climate scenarios had a significant influence on the rainfall intensities compared to the historical bivariate IDF data. The recommendation from this study for this area is that to account for the influence of near future climate change on infrastructure with a design life of 50 years and $<50\mathrm{years}$, the representative concentration pathway (RCP) 6.0 scenario would yield the critical IDF curve. For long-term planning with a design life $>50\mathrm{years}$, it is desirable to consider the IDF curve based on the RCP 8.5 scenario. For the intermittent period P2 (2048–2074), both RCP 6.0 and RCP 8.5 exhibited a similar trend in terms of rainfall intensity. The results from this study and the literature recommend comprehensive studies on specific urban catchments to incorporate the impact of climate change on the IDF curve for assessing the adequacy of hydraulic structures from a future perspective.

Identifying the footprint of controlling factors on the spring discharge is challenging under the impacts of human activities and climate change. The challenge can be addressed by (1) exploring the temporal scaling features of spring discharge; and (2) disentangling and quantifying the effects of latent factors on the spring discharge. Taking an example of the Niangziguan Spring in China, the study explores the spring discharge over a 49-year (1959–2007) time window. The results show that the spring discharge is controlled by the positive feedback mechanism before the beginning of groundwater exploitation, while human activities cause variability in spring discharge. The effects of latent factors on the spring discharge characterize the obvious temporal scale-dependent. The quantitative analysis indicates that the local recharge of groundwater possesses the largest contribution (43%) to spring discharge on the temporal scale of 4–16 months. The western North Pacific monsoon (WNPM) and Indian Summer monsoon (ISM) cause the smallest effect (3%) on spring discharge. Human activities have become one of the most important factors (27%) in controlling spring discharge. The results are useful to predict or simulate the groundwater dynamic processes in the study reported here considering that spring discharge is a natural outlet of groundwater.

The hydroclimatic extremes such as floods and droughts have been causing damage and losses with rising frequency than ever before. The human-induced and internal climate variability create extreme events and local hydrometeorological changes influencing climate-sensitive sectors. This research is aimed at analyzing the recent changes in the hydrometeorological extremes using indices over the Bilate basin in Ethiopia. Mann-Kendall and Sen’s slope estimator were used to examine changes in hydrometeorological extreme indices. The rainy days’ rate of change falls between $+10.64\mathrm{mm}$ in the downstream to $-10.67\mathrm{mm}$ in the upstream north. The wet day rainfall and heavy rainfall day indices were stronger in the basin’s southwest, implying more likely flood events. The consecutive dry days show a rising tendency with more variability, while the consecutive wet days show no trend with less variability. The change point analysis revealed inconsistencies for the majority of the extreme indices. The stations’ average warmest nights and days significantly increased at a rate of 0.0358°C and 0.0320°C per annum, respectively. The coldest nights in most of the stations show a significant and negligible rise in the basin while on the coldest days more than half of the stations declining. The peak flow in the annual and seasonal time series shows a rising trend and a dominant rise in most low flow indices, which possibly flashes downstream flooding. The global and local climate anomalies revealed a weak correlation, but with overlap of wet and drought years. Basin water resource plans may benefit from identified overlap cross of threshold years for improved flood control and drought monitoring.