scholarly journals Groundwater Flow Modeling: A Case Study of the Lower Rusizi Alluvial Plain Aquifer, North-Western Burundi

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3376
Author(s):  
Pierre Claver Ngenzebuhoro ◽  
Alain Dassargues ◽  
Tarik Bahaj ◽  
Philippe Orban ◽  
Ilias Kacimi ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Groundwater Flow ◽  
Integrated Management ◽  
Regional Scale ◽  
Pumping Test ◽  
Groundwater Potential ◽  
Alluvial Plain ◽  
Predictive Tool ◽  
Groundwater Levels

The study area, in northwestern Burundi, is an alluvial plain consisting of fine clayey sands and coarse sands with mixed lithology. The aquifer of the lower Rusizi plain could be considered as confined under a clay layer. A 2D horizontal groundwater flow model was developed under steady-state conditions using the Modflow software. The study aims to determine the most productive areas of this confined alluvial aquifer and the main aquifer inflow and outflow values together with the recharge and river–aquifer interactions. The groundwater potential is dependent on the spatial distribution of hydraulic conductivity and aquifer thickness values providing the local transmissivity values. The calibrated model made it possible to assess the spatial distribution of the hydraulic conductivity values at the regional scale, which ranged from 6 × 10−6 (contact between alluvial plain and Precambrian basement) to 7.5 × 10−3 m/s (coastal barriers). The results also provided the computed groundwater flow directions, and an estimation of the groundwater levels in areas not yet investigated by drilling. The results of the computed groundwater flow budget allowed us to deduce that recharge and river–aquifer interaction constitute the main inflow while the downwards boundaries (where piezometric heads could be prescribed) are the main zones where outflows occur. The results of this model can be used in the planning of pumping test programs, locating areas with high groundwater potential to plan water supply for different private and public users. This predictive tool will contribute to the resolution of problems related to the use and integrated management of the groundwater resource in this part of Burundi.

scholarly journals Assessment of the heterogeneity of hydraulic properties in gravelly outwash plains: a regionally scaled sedimentological analysis in the Munich gravel plain, Germany

2020 ◽  
Vol 28 (8) ◽  
pp. 2657-2674
Author(s):  
Markus Theel ◽  
Peter Huggenberger ◽  
Kai Zosseder
Keyword(s):  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Large Scale ◽  
Regional Scale ◽  
Pumping Test ◽  
Small Scale ◽  
Significant Heterogeneity ◽  
Conductivity Distribution ◽  
Braided Rivers ◽  
Sedimentary Deposits

AbstractThe favorable overall conditions for the utilization of groundwater in fluvioglacial aquifers are impacted by significant heterogeneity in the hydraulic conductivity, which is related to small-scale facies changes. Knowledge of the spatial distribution of hydraulically relevant hydrofacies types (HF-types), derived by sedimentological analysis, helps to determine the hydraulic conductivity distribution and thus contribute to understanding the hydraulic dynamics in fluvioglacial aquifers. In particular, the HF-type “open framework gravel (OW)”, which occurs with the HF-type “bimodal gravel (BM)” in BM/OW couplings, has an intrinsically high hydraulic conductivity and significantly impacts hydrogeological challenges such as planning excavation-pit drainage or the prognosis of plumes. The present study investigates the properties and spatial occurrence of HF-types in fluvioglacial deposits at regional scale to derive spatial distribution trends of HF-types, by analyzing 12 gravel pits in the Munich gravel plain (southern Germany) as analogues for outwash plains. The results are compared to the reevaluation of 542 pumping tests. Analysis of the HF-types and the pumping test data shows similar small-scale heterogeneities of the hydraulic conductivity, superimposing large-scale trends. High-permeability BM/OW couples and their dependence on recognizable discharge types in the sedimentary deposits explain sharp-bounded small-scale heterogeneities in the hydraulic conductivity distribution from 9.1 × 10−3 to 2.2 × 10−4 m/s. It is also shown that high values of hydraulic conductivity can be interpolated on shorter distance compared to lower values. While the results of the HF-analysis can be transferred to other fluvioglacial settings (e.g. braided rivers), regional trends must be examined with respect to the surrounding topography.

scholarly journals Simulation of the Spatial Distribution of Hydraulic Conductivity in Porous Media through Different Methods

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Zengguang Xu ◽  
Xue Wang ◽  
Junrui Chai ◽  
Yuan Qin ◽  
Yanlong Li
Keyword(s):  
Grain Size ◽  
Porous Media ◽  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Pumping Test ◽  
Wide Area ◽  
Test Results ◽  
Seepage Field ◽  
Water Conservancy ◽  
Geological Research

Seepage problems exist in water conservancy projects, groundwater research, and geological research, and hydraulic conductivity is an important factor that affects the seepage field. This study investigates the heterogeneity of hydraulic conductivity. Kriging methods are used to simulate the spatial distribution of hydraulic conductivity, and the application of resistivity and grain size is used to obtain hydraulic conductivity. The results agree with the experimental pumping test results, which prove that the distribution of hydraulic conductivity can be obtained economically and efficiently and in a complex and wide area.

scholarly journals Hydraulic conductivity estimation via the AI-based numerical model optimization using the harmony search algorithm

2018 ◽  
Vol 49 (5) ◽  
pp. 1669-1683 ◽  
Author(s):  
Alireza Docheshmeh Gorgij ◽  
Ozgur Kisi ◽  
Mohammad Mehdi Moayeri ◽  
Asghar Asghari Moghaddam
Keyword(s):  
Hydraulic Conductivity ◽  
Numerical Model ◽  
Hybrid Model ◽  
Search Algorithm ◽  
Harmony Search ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Pumping Test ◽  
Groundwater Levels ◽  
Gp Model

Abstract Groundwater as a vital resource for humankind is being debilitated by enormous over-extraction and intensifying contamination. Insightful advancement and protection of this significant resource needs a careful understanding of aquifer parameters. In the present study, the groundwater level was predicted at first, using a hybrid wavelet artificial neural networks and genetic programming (wavelet-ANN-GP) model. The hybrid model results were then evaluated using the performance evaluation criteria including R square, root mean square error (RMSE), mean absolute error and Nash–Sutcliffe efficiency, respectively ranged from 0.81 to 0.97, 0.070 to 4.45, 0.016 to 3.036 and 0.74 to 0.96, which revealed the high applicability of the hybrid model. The groundwater levels were predicted using wavelet-ANN-GP and then entered into the numerical model. Harmony search (HS) was used for the optimization of the numerical model. Hydraulic conductivity (HC) was estimated during the optimization process. Then, the estimated HC was extended throughout the aquifer domain by the empirical Bayesian kriging (EBK) method. Eventually, estimated hydraulic conductivity was compared by defined hydraulic conductivity through the pumping test. The plotted map of the estimated hydraulic conductivity showed about 87.5% conformity to points with distinct hydraulic conductivities obtained from the pumping test. The results proved the applicability of AI-based meta-heuristic optimization models in water resource studies.

scholarly journals Spatial variability of saturated hydraulic conductivity and its links with other soil properties at the regional scale

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Boguslaw Usowicz ◽  
Jerzy Lipiec
Keyword(s):  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Organic Carbon ◽  
Spatial Variability ◽  
Soil Properties ◽  
Regional Scale ◽  
Total Porosity ◽  
The Other ◽  
Study Region ◽  
The Mean

AbstractSaturated hydraulic conductivity (K) is a key property for evaluating soil water movement and quality. Most studies on spatial variability of K have been performed soil at a field or smaller scale. Therefore, the aim of this work was to assess (quantify) the spatial distribution of K at the larger regional scale in south-eastern Poland and its relationship with other soil properties, including intrinsic sand, silt, and clay contents, relatively stable organic carbon, cation exchange capacity (CEC) and temporally variable water content (WC), total porosity (FI), and dry bulk density (BD) in the surface layer (0–20 cm). The spatial relationships were assessed using a semivariogram and a cross-semivariogram. The studied region (140 km2) with predominantly permeable sandy soils with low fertility and productivity is located in the south-eastern part of Poland (Podlasie region). The mean sand and organic carbon contents are 74 and 0.86 and their ranges (in %) are 45–95 and 0.002–3.75, respectively. The number of individual samples varied from 216 to 228 (for K, WC, BD, FI) to 691 for the other soil properties. The best fitting models were adjusted to the empirical semivariogram (exponential) and the cross-semivariogram (exponential, Gaussian, or linear) used to draw maps with kriging. The results showed that, among the soil properties studied, K was most variable (coefficient of variation 77.3%) and significantly (p < 0.05) positively correlated with total porosity (r = 0.300) and negatively correlated with soil bulk density (r = – 0.283). The normal or close to the normal distribution was obtained by natural logarithmic and root square transformations. The mean K was 2.597 m day−1 and ranged from 0.01 up to 11.54 m day−1. The spatial autocorrelation (range) of K in the single (direct) semivariograms was 0.081° (8.1 km), while it favourably increased up to 0.149°–0.81° (14.9–81 km) in the cross-semivariograms using the OC contents, textural fractions, and CEC as auxiliary variables. The generated spatial maps allowed outlining two sub-areas with predominantly high K above 3.0 m day−1 in the northern sandier (sand content > 74%) and less silty (silt content < 22%) part and, with lower K in the southern part of the study region. Generally, the spatial distribution of the K values in the study region depended on the share of individual intrinsic textural fractions. On the other hand, the ranges of the spatial relationship between K and the intrinsic and relatively stable soil properties were much larger (from ~ 15 to 81 km) than between K and the temporally variable soil properties (0.3–0.9 km). This knowledge is supportive for making decisions related to land management aimed at alteration of hydraulic conductivity to improve soil water resources and crop productivity and reduce chemical leaching.

scholarly journals Modeling Spatial Distribution of 2D Anisotropic Hydraulic Conductivity Tensor of Fractured Groundwater Flow Media using Neural Network Case Study Grasberg Open Pit of PTFI and Surrounding

2017 ◽  
Author(s):  
Adam Verdyansyah Putra ◽  
Tedy Agung Cahyadi ◽  
Lilik Eko Widodo ◽  
Eman Widijanto
Keyword(s):  
Neural Network ◽  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Groundwater Flow ◽  
Geological Structure ◽  
Conductivity Tensor ◽  
Open Pit ◽  
Two Dimensional ◽  
Fracture Mapping ◽  
Anisotropic Hydraulic Conductivity

Highly fractured rocks in Grasberg open pit and surrounding of PT Freeport Indonesia (PTFI) result in fractured groundwater flow media. It is due to the complex geological structure and lithological condition. Accordingly, it leads to anisotropic distribution of hydraulic conductivity. The paper will be devoted tothe modeling of two dimensional (2D) spatial distribution of hydraulic conductivity using neural network. Surface fracture mapping database will be used to estimate 2D equivalent anisotropic hydraulic conductivity tensor based on the Oda et al (1996) approach. Modeled anisotropic hydraulic conductivity is then checked at some points where the slug tests for isotropic conductivity are observed. Co-relation, validation and training between modeled and observed hydraulic conductivity is then carried out using transformation of vector anisotropic hydraulic conductivity into the scalar isotropic hydraulic conductivity. Following training step, neural network will then generate two dimensional model of anisotropic hydraulic conductivity distribution. It is beneficial for modeling of shallow anisotropic flow of groundwater distribution

scholarly journals A high-resolution global-scale groundwater model

2015 ◽  
Vol 19 (2) ◽  
pp. 823-837 ◽  
Author(s):  
I. E. M. de Graaf ◽  
E. H. Sutanudjaja ◽  
L. P. H. van Beek ◽  
M. F. P. Bierkens
Keyword(s):  
Groundwater Flow ◽  
Land Surface ◽  
Regional Scale ◽  
Unconfined Aquifer ◽  
Global Scale ◽  
Water Levels ◽  
Natural State ◽  
Groundwater Model ◽  
Groundwater Levels ◽  
Aquifer Systems

Abstract. Groundwater is the world's largest accessible source of fresh water. It plays a vital role in satisfying basic needs for drinking water, agriculture and industrial activities. During times of drought groundwater sustains baseflow to rivers and wetlands, thereby supporting ecosystems. Most global-scale hydrological models (GHMs) do not include a groundwater flow component, mainly due to lack of geohydrological data at the global scale. For the simulation of lateral flow and groundwater head dynamics, a realistic physical representation of the groundwater system is needed, especially for GHMs that run at finer resolutions. In this study we present a global-scale groundwater model (run at 6' resolution) using MODFLOW to construct an equilibrium water table at its natural state as the result of long-term climatic forcing. The used aquifer schematization and properties are based on available global data sets of lithology and transmissivities combined with the estimated thickness of an upper, unconfined aquifer. This model is forced with outputs from the land-surface PCRaster Global Water Balance (PCR-GLOBWB) model, specifically net recharge and surface water levels. A sensitivity analysis, in which the model was run with various parameter settings, showed that variation in saturated conductivity has the largest impact on the groundwater levels simulated. Validation with observed groundwater heads showed that groundwater heads are reasonably well simulated for many regions of the world, especially for sediment basins (R2 = 0.95). The simulated regional-scale groundwater patterns and flow paths demonstrate the relevance of lateral groundwater flow in GHMs. Inter-basin groundwater flows can be a significant part of a basin's water budget and help to sustain river baseflows, especially during droughts. Also, water availability of larger aquifer systems can be positively affected by additional recharge from inter-basin groundwater flows.

scholarly journals Minimum dissipation of potential energy by groundwater outflow results in a simple linear catchment reservoir

2017 ◽  
Author(s):  
Axel Kleidon ◽  
Hubert H. G. Savenije
Keyword(s):  
Potential Energy ◽  
Groundwater Flow ◽  
Groundwater Potential ◽  
Channel Network ◽  
Groundwater Levels ◽  
Groundwater Flux ◽  
Linear Behaviour ◽  
Linear Reservoir ◽  
Groundwater Outflow ◽  
Do So

Abstract. Streamflow recessions of catchments during periods of no recharge can often be reproduced by a simple, linear reservoir despite the complexity of the catchments. Here we show that such a simple linear behaviour can result from the assumption that groundwater drains from smaller units within the catchment into the stream in such a way that the potential energy of groundwater of the whole catchment is dissipated at the minimum possible rate. To do so, we consider the mass balances of groundwater of two connected sub-catchments that form a hypothetical catchment and consider the depletion of potential energy as groundwater drains into the channel network. We show analytically that the catchment-level depletion of groundwater potential energy has a minimum with respect to a groundwater flux that connects the sub-catchments. The catchment-level minimisation results in equal groundwater levels in the sub-catchments with respect to their channels, which then results in a simple, linear reservoir model for the whole catchment. We then discuss the requirements for such a minimum dissipation state to exist and propose possible mechanisms by which groundwater flow can organise and evolve to such a state. We conclude that the simple, linear response in streamflow recession can be interpreted as the outcome of groundwater flow within the catchment organised to dissipate potential energy at the minimum possible rate. Hence, it would seem that energetic considerations provide an important, additional constraint in the dynamics of water flow networks within catchments that potentially reduces the problem of equifinality in hydrology.

scholarly journals Geological and groundwater flow model of a submarine groundwater discharge site at Hanko (Finland), northern Baltic Sea

2021 ◽  
Author(s):  
Samrit Luoma ◽  
Juha Majaniemi ◽  
Arto Pullinen ◽  
Juha Mursu ◽  
Joonas J. Virtasalo
Keyword(s):  
Hydraulic Conductivity ◽  
Groundwater Flow ◽  
Baltic Sea ◽  
Submarine Groundwater Discharge ◽  
Flow Model ◽  
Groundwater Discharge ◽  
Coarse Grained ◽  
Local Geometry ◽  
Groundwater Flow Model ◽  
Submarine Groundwater

AbstractThree-dimensional geological and groundwater flow models of a submarine groundwater discharge (SGD) site at Hanko (Finland), in the northern Baltic Sea, have been developed to provide a geological framework and a tool for the estimation of SGD rates into the coastal sea. The dataset used consists of gravimetric, ground-penetrating radar and shallow seismic surveys, drill logs, groundwater level monitoring data, field observations, and a LiDAR digital elevation model. The geological model is constrained by the local geometry of late Pleistocene and Holocene deposits, including till, glacial coarse-grained and fine-grained sediments, post-glacial mud, and coarse-grained littoral and aeolian deposits. The coarse-grained aquifer sediments form a shallow shore platform that extends approximately 100–250 m offshore, where the unit slopes steeply seawards and becomes covered by glacial and post-glacial muds. Groundwater flow preferentially takes place in channel-fill outwash coarse-grained sediments and sand and gravel interbeds that provide conduits of higher hydraulic conductivity, and have led to the formation of pockmarks on the seafloor in areas of thin or absent mud cover. The groundwater flow model estimated the average SGD rate per square meter of the seafloor at 0.22 cm day−1 in autumn 2017. The average SGD rate increased to 0.28 cm day−1 as a response to an approximately 30% increase in recharge in spring 2020. Sensitivity analysis shows that recharge has a larger influence on SGD rate compared with aquifer hydraulic conductivity and the seafloor conductance. An increase in recharge in this region will cause more SGD into the Baltic Sea.

scholarly journals Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

2021 ◽  
Vol 13 (12) ◽  
pp. 2300
Author(s):  
Samy Elmahdy ◽  
Tarig Ali ◽  
Mohamed Mohamed
Keyword(s):  
Machine Learning ◽  
Regional Scale ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Groundwater Potential ◽  
Machine Learning Algorithms ◽  
Conditioning Factors ◽  
Potential Mapping ◽  
Classification And Regression ◽  
Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

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