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No description available.

Sinusoidal variations in recharge can induce cyclical flows in surface water and groundwater. In this thesis, such time-dependent flows are explored in a coupled lakeaquifer system. The modelling extends previous steady state results and introduces new flow-visualisation techniques. Local responses in a 2D vertical section are illustrated for lakes within a 1D regional groundwater mound. The theory employs complex variables to decouple the periodic groundwater flows into separate steady state and fluctuating components. The time dependent behaviour causes the lake-aquifer flow to change between flowthrough, recharge and discharge regimes. Corresponding fluctuations between inflow and outflow across the lakebed allow interchange of lake water with the aquifer (recycling and recapture). This also gives rise to sinuous flowpaths that can result in apparent dispersion; the number and size of waves, cusps and loops is characterised by a nondimensional waviness ratio. Streakline plots are introduced and provide an intuitive impression of the time-dependent groundwater motion. Such plots are enhanced by animation and illustrate the complex and potentially dispersive nature of the flows. Interplay between the steady state and fluctuating responses determines the type and strength of flow regime transition. Importantly, there is an inverse relationship between head and flow in the fluctuating response. This is characterised by a dimensionless response time; a function of the aquifer geometry, hydraulic properties and period of fluctuation. During fast response, the recharge propagates mainly as fluctuation in flow, with small phase lags; particle trajectories form elliptical paths in the visualised flows. With a slower aquifer response, variation in recharge is manifest mostly as fluctuation in water level; cyclical perturbations in the flows are small and flows are nearly in steady state. The position of a lake within the regional setting, size of the lake, and ratio of lake to aquifer recharge are important to the steady state response. Flow-through regimes occur throughout the regional setting, but dominate when the lake is lower in the system and groundwater flow is greater. Discharge and recharge regimes occur higher in the flow system, when the ratio of lake to aquifer recharge is large in magnitude.

Since the advent of the industrial era atmospheric concentrations of greenhouse gases have been on the rise leading to increasing global mean temperatures. Through increasing temperatures and changes to distributions of precipitation, climate change will intensify the hydrologic cycle which will directly impact surface water sources while the impacts to groundwater are reflected through changes in recharge to the water table. The IPCC (2001) reports that limited investigations have been conducted regarding the impacts of climate change to groundwater resources. The complexity of evaluating the hydrologic impacts of climate change requires the use of a numerical model. This thesis investigates the state of the science of conjunctive surface-subsurface water modeling with the aim of determining a suitable approach for conducting long-term transient simulations at the watershed scale. As a result of this investigation, a coupled modeling approach is adopted using HELP3 to simulate surface and vadose zone processes and HydroSphere to simulate saturated flow of groundwater. This approach is applied to the Alder Creek Watershed, which is a subwatershed of the Grand River Watershed and located near Kitchener-Waterloo, Ontario. The Alder Creek Watershed is a suitable case study for the evaluation of climate change scenarios as it has been well characterized from previous studies and it is relatively small in size. Two contrasting scenarios of climate change (i.e., drier and wetter futures) are evaluated relative to a reference scenario that is based on the historical climatic record of the region. The simulation results show a strong impact upon the timing of hydrologic processes, shifting the spring snow melt to earlier in the year leading to an overall decrease in runoff and increase in infiltration for both drier and wetter future climate scenarios. Both climate change scenarios showed a marked increase to overall evapotranspiration which is most pronounced in the summer months. The impacts to groundwater are more subdued relative to surface water. This is attributed to the climate forcing perturbations being attenuated by the shift of the spring snow melt and the transient storage effects of the vadose zone, which can be significant given the hummocky terrain of the region. The simulation results show a small overall rise of groundwater elevations resulting from the simulated increase in infiltration for both climate change scenarios.

Since the advent of the industrial era atmospheric concentrations of greenhouse gases have been on the rise leading to increasing global mean temperatures. Through increasing temperatures and changes to distributions of precipitation, climate change will intensify the hydrologic cycle which will directly impact surface water sources while the impacts to groundwater are reflected through changes in recharge to the water table. The IPCC (2001) reports that limited investigations have been conducted regarding the impacts of climate change to groundwater resources. The complexity of evaluating the hydrologic impacts of climate change requires the use of a numerical model. This thesis investigates the state of the science of conjunctive surface-subsurface water modeling with the aim of determining a suitable approach for conducting long-term transient simulations at the watershed scale. As a result of this investigation, a coupled modeling approach is adopted using HELP3 to simulate surface and vadose zone processes and HydroSphere to simulate saturated flow of groundwater. This approach is applied to the Alder Creek Watershed, which is a subwatershed of the Grand River Watershed and located near Kitchener-Waterloo, Ontario. The Alder Creek Watershed is a suitable case study for the evaluation of climate change scenarios as it has been well characterized from previous studies and it is relatively small in size. Two contrasting scenarios of climate change (i.e., drier and wetter futures) are evaluated relative to a reference scenario that is based on the historical climatic record of the region. The simulation results show a strong impact upon the timing of hydrologic processes, shifting the spring snow melt to earlier in the year leading to an overall decrease in runoff and increase in infiltration for both drier and wetter future climate scenarios. Both climate change scenarios showed a marked increase to overall evapotranspiration which is most pronounced in the summer months. The impacts to groundwater are more subdued relative to surface water. This is attributed to the climate forcing perturbations being attenuated by the shift of the spring snow melt and the transient storage effects of the vadose zone, which can be significant given the hummocky terrain of the region. The simulation results show a small overall rise of groundwater elevations resulting from the simulated increase in infiltration for both climate change scenarios.

The availability and abundance of surface water on the Manyeleti Game Reserve was quantified to provide information towards the development of a water provision policy. A total of 696 water source sites were located with a mean distance of 223.3 m apart. The water source sites (natural and artificial) were monitored seasonally to describe the seasonal availability of surface water on the Manyeleti Game Reserve. There were significant relationships between seasonal rainfall and the number of water source sites and maximum distance between sites. The large number of water sources is regulated by climatic progression and thus water provision on the Manyeleti Game Reserve follows a natural cycle linked primarily to rainfall. Water sources that dry up towards the dry seasons need to be supplied with water during drought periods in order to maintain game numbers without causing rangeland degradation. A water provision model that incorporates all the variables of the Greater Kruger Park Conservation Area, with particular reference to the smaller conservation areas within it, should become a research priority.

Diss. (sammanfattning) Umeå : Sveriges lantbruksuniv. / Härtill 5 uppsatser.

For an agricultural watershed, best management practice (BMP) is a conservational way to prevent non-point source pollution, soil and water loss and mitigate groundwater declination. In this dissertation, several BMPs of tail water recovery system, conservation tillage system and crop rotation were selected and evaluated in order to demonstrate the impacts of those activities on stream water quality and quantity. Besides, a land use change scenario was also evaluated. In order to evaluate the scenarios comprehensively, Soil and Water Assessment Tool (SWAT) and Annualized Agricultural Non-point Source Pollution (AnnAGNPS) were applied to simulate surface hydrology scenarios, and Modular flow (MODFLOW) models was used to simulate groundwater level change. This dissertation contains several novel methods regarding to model simulation including (i) using satellite imagery data to detect possible tail water recovery ponds, (ii) simulating surface and groundwater connected, (iii) selecting land use change area based on local trend and spatial relationship, (iv) comparing scenarios between two models. The outcomes from this dissertation included scenarios comparison on surface water quantity and quality, groundwater level change for long term simulation, and comparison between surface water models.

No description available.

This work focuses on the use of Artificial Intelligence (AI), and in particular Machine Learning (ML) to tackle quality and quantity aspects of both surface water and groundwater. Traditionally, river water quality modelling and contaminant transport in groundwater studies resort to the solution of physical-based (PB) equations, which aim to define a conceptual model of reality. The complexity of the processes involved, in some cases undisclosed or indiscernible, calls for a sensitive parameterization by the modeler. For such reason, the PB models can be limited by the complexity of the system, the availability of data, and the consequent need for simplifying assumptions. On the other hand, ML models are data-driven and rely on algorithms to identify patterns in data. These techniques aim to extract a surrogate representation of the reality by learning existing correlations in data. They can handle complex and non-linear relationships between variables and can be more flexible and adaptable to new environments. However, they are directly affected by the quality and quantity of available data, requiring larger datasets than PB models. To explore the potential of these methods in addressing surface and groundwater challenges,we experimented with different algorithms in three distinct applications. First, we compared two ML techniques for a water quality catchment-scale model and the most performing was then employed to fill the gaps in environmental time series and to enhance the prediction of a PB model in the groundwater context. Therefore, in the first part of this work, a water quality model of the Adige River Basin is presented and discussed. For this purpose, Random Forest (RF) and Dense feed-forward Neural Network (DNN) were applied and compared to a standard linear regression (LR) approach and an Importance Features Assessment (IFA) of the drivers was performed. DNN showed to be more flexible and effective in detecting non-linear relationships than RF. LR performed at a satisfactory level, similar to RF and DNN, only when drivers linearly correlated to the observational variable were used, and a limited fraction of variability was explained. However, important drivers, non-linearly related to the water quality variables of interest introduced a significant gain when DNN was used. Regarding the variables investigated, water temperature and dissolved oxygen were modeled accurately, using RF or DNN, and sufficient accuracy was obtained by using the minimum information available, represented here by the Julian day of the measurements embodying the seasonality. The other variables showed instead a more balanced influence by the complete set of drivers, appreciable in the IFA procedure for DNN and RF, and a geogenic origin and anthropogenic disturbances were confirmed for chemical contaminants. The proposed analysis, by means of ML algorithms and through the IFA of the drivers, can be applied to predict spatial and temporal variability of contaminant concentrations and physical parameters and to identify the external forcing exerting the most relevant impacts on the dynamics of water quality variables. The second part of the thesis investigated the use of the DNN algorithm to gap-fill time series measurements, for daily flow rate and daily water temperatures from different sites downstream of the Careser glacier, in Pejo valley (northeastern Italy). Thus, an in-depth analysis of the streamflow response to the hydrological regime alterations of the glacier was carried out, through the reconstruction of the time series of the flow rate measured at a gauging station downstream the glacier, in the period 1976-2019. The water temperature time series, instead, were correlated to the macro-invertebrate population’s statistics in the same period at four sites along the Careser stream from the glacier to the reservoir immediately downstream the Careser Baia gauging station. In the first step, the water temperature was modelled just through the Julian day and air temperature information and, subsequently, precipitation, reconstructed flow rate, and evapotranspiration were introduced for sensitivity analysis of the features. With air temperature projections, the DNN model of the water temperature was also applied to simulate future scenarios up to 2050, considering different emission pathways. In this case, DNN proved to be a reliable tool for gap-filling the observational time series, even for time series with many gaps. The reconstructions of the water temperature allowed us to estimate the delay between the warming in air and water temperature and the effect on the biological invertebrate species in the glacier streams. The sensitivity analysis of the features was again key in underlining the contributions of the forcing available, unveiling the combined effects of the warming in air temperature and the decline of flow rate on the water temperature increase. The in-depth analysis of the flow rate revealed, besides the dramatic reduction of streamflow, the anticipation of the summer peak and the negligible influence of the precipitation in these alterations. Lastly, the framework for an ML-PB hybrid model in the context of contaminant transport by groundwater was presented. In this procedure, the contaminant concentration at several sampling locations was associated with physical parameters characterizing the aquifer. Through a synthetic case, a DNN model was employed to predict the physical parameters and a simplified PB equation was used to project the concentration into the future. The analysis demonstrated the capability of DNN to predict physical parameters by capitalizing on the information contained in the available concentration measurements. The thesis is articulated through 7 chapters. In Chapter 1, a broad overview of Machine Learning is presented, with its specific applications in Water sciences and the consequent motivations and objectives of this research. In Chapter 2 the main Machine Learning basic concepts are clarified and presented, in order to set the floor for the successive developments in which ML is applied to surface and subsurface hydrology. Chapter 3 covers the Machine Learning and statistical algorithms employed for modeling in the current research. In Chapter 4, Adige water catchment case study is presented and discussed. In Chapter 5, the gap-filling time series procedure for Careser case study is presented for both the variables investigated. In Chapter 6 the results of the hybrid Machine-Learning Physics-Based application of a groundwater model on synthetic data are presented. Finally, remarks and conclusions are summarized in Chapter 7, which provides also perspective work for these applications.