Journal of Hydrologic Engineering

Discerning anthropogenic stressors on groundwater is critical for climate change adaptation to reduce risks and increase resiliency. Long-term groundwater level trends are forecasted and examined at three sites in North Florida using a large ensemble of Global Climate Model (GCM) projections under low and medium emission scenarios. The forecasts indicate groundwater levels are likely to decline from 2020 to 2099. However, the declines are expected to accelerate after 2040s, reaching critical levels by the end of this century. Pumping impact constitutes 10% to 45% of future declines but is amplified by enhanced drought. Examination of distinct influence of rainfall, evapotranspiration (ET), and groundwater pumping on future trends shows highly divergent groundwater response to projected hydroclimatic changes. The future long-term rainfall trend may lead to rising groundwater levels, which may be overshadowed by heightened ET loss driven by climate change and increased groundwater pumping, causing steep declines. This study also reveals poor performance of predictions driven by GCM projections in replicating the timing of high and low levels at the sites influenced by Florida’s peninsular climate due to the limitation of downscaling and bias-correction to capture oscillations in climate cycles driving hydrologic memory. However, groundwater levels are predicted well by a few GCMs at one site influenced primarily by continental climate. Additionally, a multidecadal harmonic analysis exposes presence of centennial periodicity in groundwater levels, which opens a new perspective in the understanding of climate change impacts on groundwater resources. Further investigation is needed to better understand the effect of centennial cycles on future groundwater levels and how these cycles can be incorporated into the downscaling methods. Hence, GCM-based forecasts are recommended to be cautiously utilized for groundwater resource planning when they significantly depart from historical long-term cyclic patterns.

Abstract

The unsaturated zone plays a crucial role in the hydrological cycle and is a key factor in modeling and understanding hydrological processes. It regulates water transfer from the land surface to the groundwater while providing protection, transfer, and attenuation of potential groundwater contaminants. This research presents the first results of continuous in situ monitoring of all soil horizons of the Eutric Cambisols and the remaining unsaturated zone of the Zagreb aquifer (Velika Gorica well field). The Zagreb aquifer is the main source of potable water for the inhabitants of the City of Zagreb and Zagreb County, which is protected by the Republic of Croatia. Cross-correlation analysis and water stable isotopes (${\delta }^2\mathrm{H}$ and ${\delta }^{18}\mathrm{O}$) were used to investigate whether there is a hydraulic connection between precipitation and groundwater, the approximate duration of percolation, and whether there are indicators that suggest the presence of preferential flow or processes such as water mixing and/or evaporation. The cross-correlation analysis showed and confirmed the assumption of the presence of preferential flow with average lag times ranging from 1.59 up to 3.63 h. The results also indicate that low organic matter content may contribute to higher lag times associated with precipitation infiltration. It was found that only high-intensity precipitation events allow effective infiltration, and that the amount of infiltration depends on the water content of the soil at the beginning of each precipitation event. The isotopic results showed that the isotopic composition of the soil water in the first three soil horizons follows the one of precipitation, while deuterium excess confirmed that the sampled water is mobile water and that an evaporation process is possible in the A horizon. The results also show that suction cups can be used to determine the isotopic signature of both bulk and mobile water, depending mainly on the granulometric composition of the material in which they are installed.

Practical Applications

Intensive and growing groundwater extraction in past decades now challenges humans to maintain the resource, especially in regions where climate is drying. Knowing which factor and to what extent drives the process of resource replenishment is the key to maintaining and securing the resource and thus sustainable water supply. Knowing what we can measure, and which methodologies we can use for processing the data we have measured, is of value to practitioners and water supply managers. An original combination of well-known methodologies (water stable isotopes analysis and cross-correlation analysis) as presented in this article suggests how can we process measured precipitation data in order to obtain the knowledge of the processes that drive resource replenishment. In addition, it suggests what kind of data we should measure and what kind of equipment we need for such a task.

Flood warnings provide information about the timing and magnitude of impending floods, which can help mitigate the adverse impacts of flooding. Flood forecasts are highly influenced by uncertainty associated with rainfall forecasts as well as initial catchment wetness. Event-based models are simple and parsimonious and are widely favored by practitioners for flood estimation. However, these models require loss parameters to be manually specified for each simulated event, and this represents an additional source of uncertainty that needs to be considered along with errors in observations and rainfall forecasts. Little attention has been given to the coupling of updating techniques with event-based models to reduce the uncertainty associated with catchment wetness. To this end, we devised a sequential recalibration scheme to characterize uncertainty in ensemble forecasts derived using an event-based flood model. This scheme uses information on both observation and model errors to filter and update catchment loss estimates to improve the accuracy of the forecasts. Analysis of flood forecasts for 22 events showed that although initially, there was low skill in forecasts derived solely from external estimates of catchment wetness, the reliability and accuracy of the forecasts improved rapidly once the flood event commenced and the flood model was coupled with an updating scheme. Compared with forecasts made without any updating scheme, the conditioning and recalibration steps progressively improved the accuracy of the forecasts as measured by Nash-Sutcliffe efficiency from $-0.14$ to 0.88, bias was reduced by 78%, and root-mean square error reduced by 67%. The use of such schemes thus reinforces the advantages of using parsimonious models that have long been favored by practitioners for design and other purposes.

The objective of this study was to identify and quantify surface depressions on grass-covered land surfaces using a high-resolution terrestrial laser scanning (TLS) point cloud, and a triangulated irregular network (TIN). The entire grassy land surface in the study area was divided into five subwatersheds of different topographic attributes (i.e., depression depth and surface slope). Surface depressions were identified and quantified using a TIN generated from a high-resolution TLS point cloud. The results indicated that microtopography of the grassy land surface was well-characterized within each subwatershed in comparison with field observations. With the terrestrial light detection and ranging (LIDAR) point cloud of 15-mm point spacing and the TIN method, surface depression storage depths of the five subwatersheds ranged from 1.73 to 14.28 mm in the study area. The surface depression storage depth, as expected, increased with the maximum depth of surface depression. It was also found to increase when the land surface slope became milder. A sensitivity analysis indicated that a point cloud with a point spacing of 30 mm was sufficient to obtain an accurate representation of the terrain surface in the study area. This study also indicated the TIN method can represent the ground surface and the surface depression more realistically than the commonly used digital elevation model (DEM) method due to the TIN method’s higher capability of identifying and filtering out surface obstructions such as blades of grass. Moreover, by using the high-resolution TLS technology and the TIN method, our study provides an important and broad range of reference data on the surface depression storage depth commonly needed in application of the Storm Water Management Model (SWMM) and other watershed models.

The damage of extreme precipitation to the social economy and ecological environment has attracted extensive attention. Daily precipitation data of 59 meteorological stations located in Xinjiang Uygur Autonomous Region were selected, where the temporal and spatial distribution and variation characteristics of extreme precipitation from 1961 to 2017 were comprehensively analyzed by percentile threshold method, precipitation tendency rate method, the trend-free pre-whitening Mann Kendall trend test, Mann Kendall mutation test, and other methods. Further, three methods, the continuous wavelet transform, cross wavelet transform, and wavelet coherence analysis, were selected to analyze the correlation between extreme precipitation index and six atmospheric circulation indexes. Results show that from 1961 to 2017, the precipitation in Xinjiang increased, and the extreme precipitation (R99P), extreme precipitation days (R99D), extreme precipitation contribution rate (R99C), and extreme precipitation intensity (R99I) showed a significantly increasing trend. Due to the influence of topography, the spatial distribution of extreme precipitation is bounded by the Tianshan Mountains, decreasing from northwest to southeast, meeting the characteristics of “Northern Xinjiang is greater than Southern Xinjiang,” indicating obvious spatial heterogeneity. Among the selected atmospheric circulation indexes, Western Pacific Subtropical High Intensity Index (WPSHII) and Western Pacific Subtropical High Intensity Index (SCSSHII) contribute significantly to extreme precipitation events in Xinjiang. There is an obvious positive correlation between SCSSHII and extreme precipitation index. In other words, the greater the SCSSHII, the greater the frequency and frequency of extreme precipitation events in the study area, and the more prone it is to natural disasters such as rainstorms and floods.

Long-term hydrological forecasting based on sea surface temperature (SST) fields faces the large $p$ and small $n$ problem, i.e., too many potential predictors and a limited number of samples. Considering the selection of predictors will also enhance the complexity of models and lead to overfitting, in this study, two strategies are used for building forecasting models for long-term streamflow forecasting. The first strategy is to downsample the SST field and optimize its spatial resolution; the second is to shrink model coefficients based on L1 regularization. We build models based on the downsampled SST fields with different spatial resolutions. It is found that the model based on a proper spatial resolution always performs better than the model based on the raw SST field. This result suggests that it is better to treat the spatial resolution of the predictor field as a hyperparameter, which is similar to hyperparameters for controlling the complexity of many machine learning models. For applying the second strategy, L1 norm regularization models, including (1) least absolute selection and shrinkage operator (LASSO), (2) relaxed LASSO, and (3) two-step approach of LASSO and ordinary least squares regression ($\mathrm{LASSO}+\mathrm{OLS}$) are explored. We have found that the relaxed LASSO model always performs better than the ordinary LASSO, indicating that relaxed LASSO is a better shrinkage approach. Furthermore, the skills of the presented models are compared with the stepwise regression, and the lower skill of the stepwise regression based on SST fields with a high spatial resolution suggests that one should not select predictors based on fields with high resolutions considering the limited number of samples.

Understanding the flood-generating mechanisms that influence flood seasonality in a region provides information on setting up relevant contingency measures. Although former studies estimated flood seasonality at regional/continental scale, limited/no studies have investigated the climate/basin drivers that influence the changes in flood seasonality. Considering this, the current study performed two analyses: (1) estimated the changes in the seasonality of annual maximum floods (AMF) between pre- and post-1970 across Hydroclimate Data Network basins over the conterminous US, and (2) identified the predictors that influence the change in the seasonality from a set of climate and geomorphic variables. Significant changes in the AMF seasonality were noted for approximately half of the basins in the eastern US, but low to no change was found in most basins in the central/western US. We found, except in the Northeast and mid-Atlantic basins, a decrease in the seasonality index, indicating floods arriving more uniformly is typically associated with an increase in the precipitation days in basins. On the other hand, increase in the seasonality index, indicating floods occurring more concentrated in time, is typically associated with an increase in the extreme precipitation in basins. Among the basin characteristics, elevation has a more dominant role than the drainage area in changing the flood seasonality. Elevation affects the form of precipitation, particularly in the western US, because floods arrive more distributed over the year (i.e., decrease in flood seasonality index), which potentially indicates increased warming resulting in early snowmelt.

Heterogeneously layered river-basin or reservoir-bank aquifers are widely distributed, and the two-layer aquifer system is a simple representative of such aquifers. In this research, a conceptual model of vertical groundwater flux induced by a rising prescribed head boundary in a two-layer aquifer system (without an aquitard in between) is developed. A semianalytical solution is derived using Green’s function method (GFM) and compared with finite-element numerical solutions. The result indicates that differences of hydraulic parameters have profound effects on the hydraulic head distribution in both layers and dynamics of vertical mass exchange between the layers. A larger media anisotropy ($A_m$) leads to a smaller buildup contrast between the two layers and a greater peak value of vertical groundwater flux across the interface of two layers. The buildup in the upper layer is insensitive to $K_{LD}$ [which is the permeability ratio of the lower (less permeable) layer to the upper (more permeable) layer], whereas the buildup in the lower layer is sensitive to $K_{LD}$. A greater permeability contrast between the two layers causes a larger groundwater flux across the interface and a greater peak flux. A larger specific storage contrast leads to smaller buildups, and a smaller specific storage contrast leads to a greater peak vertical groundwater flux. Among many applications, the proposed solutions have profound chemical, biological, and thermal transport implications because tracer elements or heat of different layers can dynamically interact with each other, driven by the vertical groundwater flux of different layers.

In nonstationary hydrological frequency analysis, the nonstationarity of hydrological series is often characterized by expressing the distribution parameters as functions of some covariates. The distribution parameters with the stochastic covariates such as climatic indices could result in time variations in forms of wide random fluctuations, which would fail to give a clear-cut description and explanation for the nonstationarity. In this paper, a hierarchical model is proposed to derive the nonstationary hydrological distribution with smooth changes by characterizing stochastic covariates as random processes. Under this model, the conditional distribution of the interested hydrological variable given its covariates is first constructed, and then the distribution of the interested hydrological variable is derived by combining its conditional distribution with the probability distribution of the stochastic covariates. The results of a simulation study indicate satisfactory performance of the proposed hierarchical model in fitting the generated series. The annual runoff series of the Weihe River is employed to perform a case study. The results reveal that the hierarchical model is able to give an intuitive description of the nonstationarity of the annual runoff. On the basis of the smoothly changing hydrological distribution derived by the hierarchical model, the nonstationarity of annual runoff series can be clearly described and explicitly associated with the changes of the covariates. It is found that the decline in the mean of annual runoff of the Weihe River is attributed to precipitation decline, climate warming, and agricultural irrigation. The hierarchical model with stochastic covariates is more applicable in engineering practice than the theoretical distribution with time-varying parameters.

A comprehensive examination of regional errors in Satellite Precipitation Products (SPPs) is crucial for accurate hydrometeorological modelling. In this study, a multiplicative error-based approach was used for correcting systematic bias in the SPPs at Western Ghats (WG) region of India. Most of the SPPs available so far underestimate the monsoon rainfall in WG. Quality controlled gridded rain gauge data from the Indian Meteorological Department (IMD) was used as the ground data for bias correction. Bias correction of three multi-satellite precipitation products, namely, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 Version 7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Prediction Center (CPC) MORPHed (CMORPH) precipitation, were performed in this study. The results show that bias correction remarkably reduced the bias between the SPPs and IMD rainfall measurements. The efficacy of bias-corrected SPPs in hydrologic modelling was investigated with the help of two conceptual rainfall runoff models, GR4J and HYMOD. The bias-corrected SPPs were able to provide improved streamflow simulations with daily Nash-Sutcliffe efficiency (NSE) and correlation coefficients greater than 0.4 and 0.7, respectively. It was also found that the performance of the model HYMOD was marginally better than that of GR4J in predicting streamflow in terms of NSE, linear correlation coefficient, and p-factor for all five validation catchments in the WG region. This study contributes to the ongoing research on error characterization of SPPs for improved global hydrometeorological modelling.

Modeling of extreme events is important in many scientific fields, including environmental and civil engineering, and impacts and risk assessments. Among available methods, statistical models that allow estimating extremes’ frequency and intensity are regularly used in procedures to anticipate potential changes in extreme events. Extreme value theory provides a theoretical basis for statistical estimation of extreme events using frequency analysis. The challenge in modeling is knowing when to use the block maxima method or the peaks-over-threshold (POT) method. Each has its drawbacks. POT describes the main characteristics of the observed extreme series; the threshold selection is always challenging and might affect the accuracy of the simulated results and the credibility of changes in extreme values. To encompass this challenge, mixture models offer more flexibility to represent samples with nonhomogeneous data. This study presents the gamma generalized Pareto (GGP) mixture model for estimating risk occurrence of hydroclimatic extremes. The model was developed in its general form, whereas the observed hydrometeorological extreme events depend on multidimensional covariates. A maximum likelihood algorithm is proposed to estimate the parameters with a constraint on the shape parameter of the generalized Pareto (GP) distribution. A Monte Carlo (MC) simulation compared the proposed model with the classical POT approach, with a fixed threshold, and the annual maximum series of streamflow. The approach was applied using a daily hydrological data set from an observed station located in the Saint John River at Fort Kent (01AD002), New Brunswick, Canada. The results show a flexibility to model extremes for dependent or nonstationary time series and adequately describes the central part of the observed frequencies, as well as the tails.

Quantitative estimates on the magnitude and dynamics of vadose and groundwater zones in subarctic conditions are rare. Furthermore, knowledge on the constraints of different data on numerical groundwater recharge estimates (i.e., data worth analyses) is limited. We built a process-based three-dimensional hydrological model to describe and analyze the dynamics and magnitude of groundwater recharge, vadose zone, and related key hydrological components in subarctic conditions. Three-year time series of daily groundwater level and soil moisture (five different depths) data were used to identify plausible model realizations. Thereafter, long-term simulations over 6 years were conducted. The model adequately corresponded with the magnitude and dynamics of the observations, even though the simulations and observations occasionally differed. Simulations with several different plausible parameterizations showed how soil moisture and groundwater level fluctuations, as well as recharge estimates, are sensitive to water retention parameter variations, whereas precipitation and evapotranspiration controlled the recharge magnitude. Recharge showed clear interannual variability (annual mean 0.28–0.60 m). The fraction of annual mean recharge over precipitation was 39%–66% during our simulation period. We showed how groundwater level data can set more constraints on the recharge estimates than soil moisture data, and how both of the data types together constrained the recharge estimates the most. We showed how the vadose zone had modest dynamics and storage properties (typically 2%–8% of the total soil water) in terms of the bulk soil water storage. However, the average amount of the air-filled pore space (0.70 m) and water (0.38 m) in the vadose zone were proportional to annual precipitation and evapotranspiration, respectively. Thus, the vadose zone can form an important boundary between the soil surface and groundwater zone, and its role is not highly sensitive to hydrometeorological fluctuations. Data worth analyses provide a potential opportunity to move toward increasingly reliable model-based groundwater recharge estimates and system understanding.

The groundwater level observed from a monitoring well is a mixed signal resulting from different stimulations. It is challenging to obtain a specific component from this mixed signal. This is the case for obtaining the pumping component from a monitoring well in an area with many pumping wells. This study developed a signal processing method to extract the average magnitude of pumping (AMP) from the observed groundwater signals. AMP represents the variation over time in the groundwater level caused by pumping surrounding a monitored well. It is obtained from a digital filtering procedure. The use of a Gaussian high-pass filter removes the long-term trends from the groundwater signal and reveals a high-frequency signal that is usually hidden. After applying frequency analysis and verifying it by fast Fourier transformation (FFT), the pumping frequency was identified. The pumping frequency is set as the center frequency for a band-pass filter to obtain AMP. To validate and understand the factor influencing AMP, a three-dimensional (3D) numerical model was constructed to simulate artificial groundwater fluctuations. The synthetic case study showed that the pumping rate and the distance between the pumping wells and the monitoring well are factors that control AMP. We further applied this methodology to evaluate AMP for three monitoring wells in Tuku Township, located in the Chou-Shui Chi alluvial fan in central Taiwan. The results showed a negative correlation between the AMP variation and rainfall events, describing long-term trends for pumping and the influence of pumping on the groundwater level variation with time. Furthermore, the results revealed the scale of the AMP at each monitoring well, and the area with a higher AMP had a higher drawdown magnitude daily. Finally, the average pumping rate of a region can be estimated using AMP.