Building a mathematical model for the free groundwater Resources of the plain area in the lattakia basin: بناء نموذج رياضي لموارد المياه الجوفية الحرة للمنطقة السهلية في حوض اللاذقية

The results of the stratigraphic modeling of the study area showed that the stratigraphic formations constitute a hydrogeological system that is hydrodynamically linked to each other represented by a water layer with a free underground flow. The results of the mathematical model that was built for the region also showed that the high values of the leaching factor were concentrated in the northern and central part of the research area, where the faults were located, the difference between the input values and the calibration results in most areas of the hydraulic conductivity distribution of the layer did not exceed 10%. The water resources in the northeastern part of the study area are of good quality, large quantities, and easy to invest, with a total intake of (134607.3) m3/day. The thickness of the carrier ranged about 50 m in the northern section and increased towards the area of the falcon band within the course of the Arab River to reach about 125 m. As for the thickness of the carrier in the southern section, it ranged between (75 – 60) m. the groundwater system in the study area is stable, and the largest part of the amount of water resources formed in the region to the lateral inflow of the ground and and amounted to about 90% of total value of the resources.

Machine learning for flux regression in discrete fracture networks

In several applications concerning underground flow simulations in fractured media, the fractured rock matrix is modeled by means of the Discrete Fracture Network (DFN) model. The fractures are typically described through stochastic parameters sampled from known distributions. In this framework, it is worth considering the application of suitable complexity reduction techniques, also in view of possible uncertainty quantification analyses or other applications requiring a fast approximation of the flow through the network. Herein, we propose the application of Neural Networks to flux regression problems in a DFN characterized by stochastic trasmissivities as an approach to predict fluxes.

Evaluation of the quality of groundwater in the municipality of Ji-Paraná, Rondônia, in the Brazilian Amazon region

Groundwater quality can be affected by many factors. The objective of this study was to assess the quality of groundwater and map the flow direction, to identify possible sources of contamination in the districts of Nova Londrina and Nova Colina (Ji-Paraná, Rondônia). Water samples were collected in September 2015 and February 2016 in 20 wells in Nova Londrina and in December 2016 and April 2017 in 15 wells in Nova Colina. The turbidity, pH and electrical conductivity (EC) were measured in loco. The microbiological parameters were determined by the membrane filtration method in a chromogenic medium, and the total dissolved solids (TDS) were measured by the gravimetric method. The nutrients ammonia, nitrite, nitrate, dissolved phosphorus and total phosphorus were ascertained by the spectrophotometric method and the underground flow was obtained by applying the Surfer 8.0 software. Through principal component analysis (PCA) it was possible to detect that the TDS, total coliforms, pH and EC had important contributions to formation of the groundwater axes in Nova Londrina, while the variables EC, nitrate and well and cesspit depths contributed in Nova Colina. The groundwater flow did not differ among the months studied, and was mainly in the northwest-southeast direction in Nova Londrina and from the southeast to other regions in Nova Colina.

Performance Analysis of Multi-Task Deep Learning Models for Flux Regression in Discrete Fracture Networks

In this work, we investigate the sensitivity of a family of multi-task Deep Neural Networks (DNN) trained to predict fluxes through given Discrete Fracture Networks (DFNs), stochastically varying the fracture transmissivities. In particular, detailed performance and reliability analyses of more than two hundred Neural Networks (NN) are performed, training the models on sets of an increasing number of numerical simulations made on several DFNs with two fixed geometries (158 fractures and 385 fractures) and different transmissibility configurations. A quantitative evaluation of the trained NN predictions is proposed, and rules fitting the observed behavior are provided to predict the number of training simulations that are required for a given accuracy with respect to the variability in the stochastic distribution of the fracture transmissivities. A rule for estimating the cardinality of the training dataset for different configurations is proposed. From the analysis performed, an interesting regularity of the NN behaviors is observed, despite the stochasticity that imbues the whole training process. The proposed approach can be relevant for the use of deep learning models as model reduction methods in the framework of uncertainty quantification analysis for fracture networks and can be extended to similar geological problems (for example, to the more complex discrete fracture matrix models). The results of this study have the potential to grant concrete advantages to real underground flow characterization problems, making computational costs less expensive through the use of NNs.

Influence of Hydrogeochemistry on Tunnel Drainage in Evaporitic Formations: El Regajal Tunnel Case Study (Aranjuez, Spain)

Many of the large number of underground works constructed or under construction in recent years are in unfavorable terrains facing unusual situations and construction conditions. This is the case of the subject under study in this paper: a tunnel excavated in evaporitic rocks that experienced significant karstification problems very quickly over time. As a result of this situation, the causes that may underlie this rapid karstification are investigated and a novel methodology is presented in civil engineering where the use of saturation indices for the different mineral specimens present has been crucial. The drainage of the rock massif of El Regajal (Madrid-Toledo, Spain, in the Madrid-Valencia high-speed train line) was studied and permitted the in-situ study of the hydrogeochemical evolution of water flow in the Miocene evaporitic materials of the Tajo Basin as a full-scale testing laboratory, that are conforms as a whole, a single aquifer. The work provides a novel methodology based on the calculation of activities through the hydrogeochemical study of water samples in different piezometers, estimating the saturation index of different saline materials and the dissolution capacity of the brine, which is surprisingly very high despite the high electrical conductivity. The circulating brine appears unsaturated with respect to thenardite, mirabilite, epsomite, glauberite, and halite. The alteration of the underground flow and the consequent renewal of the water of the aquifer by the infiltration water of rain and irrigation is the cause of the hydrogeochemical imbalance and the modification of the characteristics of the massif. These modifications include very important loss of material by dissolution, altering the resistance of the terrain and the increase of the porosity. Simultaneously, different expansive and recrystallization processes that decrease the porosity of the massif were identified in the present work. The hydrogeochemical study allows the evolution of these phenomena to be followed over time, and this, in turn, may facilitate the implementation of preventive works in civil engineering.

Simulation of Groundwater Flow in an Aquiclude for Designing a Drainage System during Urban Construction: A Case Study in Madrid, Spain

A detailed hydrogeological study was carried out due to the recent occurrence of unexpected problems associated with the flooding of the water table during excavations in the area of a major urbanization work in Madrid. The numerous exploratory drilling excavations carried out allowed for the development of a conceptual model of the complex hydrogeological functioning in clay formations in an urban area. The clays have very little natural recharge, and the underground flow is highly conditioned by the topography and a fold-fault. Modelling with MODDFLOW confirmed and quantified this conceptual model and also allowed for the design of an efficient network of 1.5 km-long drainage trenches. The design of this drainage network was influenced by the difficult balance that must be respected in order not to contaminate the water with sulphates from the nearby gypsum substrate. This is to guarantee the quantitative and qualitative sustainability of the groundwater. The follow-up and monitoring of the water tables and the quality of the groundwater for more than a year after the excavation of the drainage trenches guaranteed the results of the research.

Modeling the Effectiveness of Cooling Trenches for Stormwater Temperature Mitigation

Due to elevated runoff stormwater temperatures from impervious areas, one management strategy to reduce stormwater temperature is the use of underground flow through rock media termed a cooling trench. This paper examines the governing equations for the liquid phase and media phases for modeling the temperature leaving a cooling trench assuming that changes in temperature occurred longitudinally through the cooling trench. This model is dependent on parameters such as the media type, porosity, media initial temperature, inflow rate, and inflow temperature. Several approaches were explored mathematically for evaluating the change in temperature of the water and the cooling trench media. Typical soil–water heat transfer coefficients were summarized. Examples of predictions of outflow temperatures were shown for different modeling assumptions, such as well-mixed conditions, batch mixing and subsequent release, and steady-state and dynamic conditions. Several of these examples evaluated how long rock media would cool following a stormwater event and how the cooling trench would respond to multiple stormwater events.

Review: Advances in the methodology and application of tracing in karst aquifers

Tracer methods have been widely used in many fields of environmental and natural sciences, and also in human health sciences. In particular, tracers are used in the study of karst hydrogeology, typically focusing on phenomena such as sinkholes, sinking rivers and large karst springs. It is known that tracers have been used since antiquity. The aim of tracer tests has been to investigate underground flow paths, transport processes and water–rock interactions, and to get an insight into the functioning of a karst aquifer. In karst hydrogeology, tracer methods are the most important investigation tools beside conventional hydrological methods. In early times, tracer methods were applied only to investigate underground flow-paths. Later they were also used to elucidate transport processes associated with water flow, and today they are often the basis, together with detailed hydrological information, of groundwater protection investigations and aquifer modelling. Many substances (spores, microspheres, bacteriophages, salt tracers, fluorescent dyes, radioactive substances) have been investigated for their properties and potential usage in environmental investigations, in particular the often unknown and inaccessible underground systems of karst areas. A great number of analytical techniques is available. This includes instrumentation for laboratory applications and direct online, on-site or in-situ field measurements. Modern instruments have a high capability for data acquisition, storage and transmission in short intervals, as a basis for quantitative evaluation and modelling. This enables research on the hydrological and hydrochemical dynamics of aquifers and their response to different natural or anthropogenic impacts.

Modern changes of the runoff characteristics of the rivers of the Greater Caucasus

The article is devoted to the analysis of modern changes in annual runoff, its main components – underground and surface runoff, as well as the minimum winter and summer-autumn runoff rivers of the Greater Caucasus within Azerbaijan. A brief review of previous studies on flow changes in the country is given. It is noted that in these works the method of geographical comparison and linear trend analysis were used. It is concluded that the changes in the surface and under ground runoff of the rivers of the Greater Caucasus due to climate change have not yet been analysed. The data on the runoff of 17 hydrological observation points covering 1934–2017 were analysed. All these hydrological observation points are located in the mountainous part of the river basins, i. e. runoff indices characterise the natural or conditionally natural regime of rivers. The annual values of the underground flow were determined as the arithmetic average of the monthly average minimum winter and summer-autumn water discharges. The annual values of surface runoff are calculated as the difference between annual and underground runoff. The method of geographical comparison is used. Calculations and generalisations of the results obtained are performed for different periods, according to the recommendations of the World Meteorological Organisation. It was revealed that, for 1981–2010 and 2001–2017 surface runoff of the rivers of the studied region decreased compared to runoff for the base period (1961–1990), due to a decrease in the amount of snow precipitation and a decrease in the volume of spring flood. However, there was a more significant increase in the underground flow of rivers and, therefore, an increase in annual flow was observed throughout the region. The dynamics of changes in the minimum river flow, especially in the winter season, is also positive, since over the past decades the snow cover has been melting earlier than usual, and favorable conditions are being created for the formation of groundwater that feeds the rivers during periods of minimal runoff. It is noted that the revealed nature of changes in various indices of river flow in the studied region is associated with climate change.