La tecnología del internet de las cosas (IoT) a través de los años ha tenido una constante evolución empezando primero con el envío de información básica para posteriormente ser empleada en la mayoría de campos partiendo desde lo más básico como prender un televisor hasta lo más complejo como el monitoreo en tiempo real de las condiciones climáticas, del suelo y obtención de información de los cultivos para que haya una producción más eficiente en la agricultura. Para el presente proyecto se empleó la tecnología IoT a través de sensores de bajo costo. Estos primeramente fueron instalados en la reserva hídrica de Paluguillo donde se tomaron mediciones de distintas variables meteorológicas. Posterior a esto estas mismas variables fueron analizadas para dos cultivos de ciclo corto en un terreno ubicado en la parroquia de Conocoto, donde se calculó el balance hídrico para una optimización del riego partiendo de datos como la evapotranspiración (ETc). La cual fue calculada mediante el método del tanque evaporímetro Clase A empleando el sensor de nivel del agua. Para la distribución de los cultivos, el terreno fue dividido en dos secciones una con riego controlado por medio de los valores obtenidos del balance hídrico y de los sensores. Y la otra sección con un riego no controlado para evidenciar si se da un desarrollo eficiente de los cultivos sin emplear la tecnología IoT. Mediante los resultados obtenidos se estima que el desarrollo de los cultivos es eficiente mediante un monitoreo constante de las variables climáticas de la zona. | The Internet of Things (IoT) technology over the years has had a constant evolution, starting first with the sending of basic information to later be used in most fields, starting from the most basic such as turning on a television to the most complex. such as real-time monitoring of climatic and soil conditions and obtaining information on crops so that there is more efficient production in agriculture. For this project, IoT technology was used with low-cost sensors. These were first installed in the Paluguillo water reserve where measurements of different meteorological variables were taken. After this, these same variables were analyzed for two short-cycle crops in a plot of land located in the parish of Conocoto, where the water balance was calculated for irrigation optimization based on data such as evapotranspiration (ETc). Which was calculated using the Class A evaporimeter tank method using the water level sensor. For the distribution of the crops, the land was divided into two sections, one with irrigation controlled by means of the values obtained from the water balance and from the sensors. And the other section with uncontrolled irrigation to show if there is an efficient development of crops without using IoT technology. Through the results obtained, it is estimated that the development of crops is efficient through constant monitoring of the climatic variables of the area. | Manciati Jaramillo, Carla Paola, director.

Faults may impact regional groundwater flow and transport, so it is important to include them during aquifer-system conceptualization and while constructing groundwater flow models. For the Neogene aquifer in Flanders (Belgium), three-dimensional groundwater-flow and solute-transport models have been developed in the framework of safety and feasibility studies for the underlying Boom Clay Formation as a potential host for geological disposal of radioactive waste. The model outcomes are subject to uncertainties as they are typically constrained only by hydraulic heads, and their current conceptualization does not differentiate the fault zones from the undisturbed hydrostratigraphic formations. A groundwater flow model has been developed using MODFLOW-2005 to investigate how groundwater flow in the sedimentary Neogene aquifer may be disturbed by the Rauw Fault—a 55-km-long normal fault—across the Nete catchment. The observed hydraulic gradient across the fault zone appears significant, with head differences of 1.5–2.0 m over a horizontal distance of 60 m. A simulated hydraulic-head difference of 2.4 m was achieved largely corresponding to the observed behavior. The Neogene aquifer, within the Nete catchment, seems to be composed of several local flow systems and potentially with a deeper more semiregional/intermediate flow system. Testing different fault configurations shows the impact on the local/semiregional flow system, with pronounced effects in the fault’s vicinity, and extending or narrowing the flow systems further away. These results demonstrate the importance of considering faults, or any other hydrogeological subsurface barrier/conduit, and suggests they should be accounted for in the general practice of subsurface activity impact assessment.

Groundwater recharge (GR) is a complex process that is difficult to quantify. Increasing attention has been given to unsaturated zone modeling to estimate GR and better understand the processes controlling it. Continuous soil-moisture time series have been shown to provide valuable information in this regard. The objectives of this study were to (i) analyze the processes and factors controlling GR in an unconfined granular aquifer in a cold and humid environment and (ii) assess the uncertainties associated with the use of data from different sources. Soil moisture data monitored over three years at three experimental sites in southern Quebec (Canada) were used to calibrate the HYDRUS-1D model and to estimate ranges of possible GR in a region where groundwater is increasingly used as a source of fresh water. The simulations identified and quantified important factors responsible for the near-surface water balance that leads to GR. The resulting GR estimates from 2016 to 2018 showed marked differences between the three sites, with values ranging from 347 to 735 mm/y. Mean GR for the three sites was 517 mm/y for 2016–2018 and 455 mm/y for the previous 12-year period. GR was shown to depend on monthly variations in precipitation and on soil textural parameters in the root zone, both controlling soil-water retention and evapotranspiration. Monthly recharge patterns showed distinct preferential GR periods during the spring snowmelt (38–45% of precipitation) and in the fall (29% of precipitation). The use of different meteorological datasets was shown to influence the GR estimates.

Groundwater pollution source identification (GPSI) provides information about the temporal and spatial distribution of pollution sources and helps decision makers design pollution remediation plans to protect the groundwater environment. The Bayesian approach based on the Markov Chain Monte Carlo (MCMC) approach provides an efficient framework for GPSI. However, MCMC sampling entails multiple model calls to converge to the posterior probability distribution of unknown pollution source parameters and entails a massive computational load if the simulation model is directly called. This study aimed to develop an innovative framework in which an improved MCMC approach was linked to a surrogate model. Sensitivity analysis was incorporated into the MH-MCMC approach, named SAMH-MCMC (sensitivity analysis based Metropolis Hastings-Markov Chain Monte Carlo), to speed up the convergence of the posterior distribution in a novel way to control the search step size. Three computationally inexpensive surrogate models for the simulation model were proposed: support vector regression, Kriging (KRG), and multilayer perceptron, and the most accurate model was chosen. The feasibility and advantages of the developed framework were evaluated and validated through two hypothetical numerical cases with homogenous and heterogeneous media. The proposed approach has strong convergence robustness as it considers the sensitivities of the unknown parameters that characterise groundwater pollution sources and can achieve high identification accuracy. Furthermore, the KRG surrogate model has a higher accuracy than other surrogate models, owing to its linear unbiased estimation characteristic. Overall, the framework developed in this study is a promising solution for identifying groundwater pollution source parameters.

Large-scale studies of the spatial and temporal variation of groundwater drought status require complete inventories of groundwater levels on regular time steps from many sites so that a standardised drought index can be calculated for each site. However, groundwater levels are often measured sporadically, and inventories include missing or erroneous data. A flexible and efficient modelling framework is developed to fill gaps and regularise data in such inventories. It uses linear mixed models to account for seasonal variation, long-term trends and responses to precipitation and temperature over different temporal scales. The only data required to estimate the models are the groundwater level measurements and freely available gridded weather products. The contribution of each of the four types of trends at a site can be determined and thus the causes of temporal variation of groundwater levels can be interpreted. Validation reveals that the models explain a substantial proportion of groundwater level variation and that the uncertainty of the predictions is accurately quantified. The computation for each site takes less than 130 s and requires little supervision. Hence, the approach is suitable to be upscaled to represent the variation of groundwater levels in large datasets consisting of thousands of boreholes.

Enhanced bioremediation combined with in-situ chemical oxidation has the potential to remediate groundwater contaminated with organics. To explore the remediating effects of these two approaches and to evaluate their combined feasibility, traditional gasoline (no ethanol) and ethanol-gasoline (10% ethanol, v/v) were released into experimental sand tanks (TG-tank and EG-tank, respectively) under the same water-flow conditions. Nitrate and sulfate were added to enhance bioremediation and then persulfate was injected to encourage chemical oxidation. Two push–pull tests, using persulfate and bromide respectively, were conducted to compare their behavior. The results showed that nitrate reduction, rather than sulfate reduction, enhanced BTEX (benzene, toluene, ethylbenzene, and xylene) biodegradation, but the presence of ethanol inhibited these processes. The detected concentration of BTEX in the TG-tank was lower than that in the EG-tank, and the first-order decay rate constants of BTEX in the TG-tank and EG-tank under nitrate-adjusted conditions were 0.0058 and 0.0016 d⁻¹, respectively. The first persulfate injection (10 g L⁻¹) resulted in 86 and 94% concentration decreases of BTEX in the TG-tank and EG-tank, respectively, at first-order decay rates of 0.0180 and 0.0181 d⁻¹, respectively. However, the subsequent persulfate injections at 20 and 50 g L⁻¹ had no significant removal effect on BTEX. Persulfate oxidation made pH decrease (but it quickly recovered) and did not significantly inhibit nitrate reduction. This study suggests that enhanced nitrate reduction can be combined with persulfate oxidation for the in-situ remediation of groundwater contaminated by petroleum hydrocarbons.

Groundwater contamination source estimation (GCSE) involves an inverse process to match time-series monitoring data in sparse observation wells. It is commonly accompanied by a search task in high-dimensional space and huge computational burden brought about by massive callings of the simulation model. Particle filters can provide accurate estimation for a high-dimensional search task in source estimation, but the process suffers from particle degradation and huge computational load brought about by repeatedly solving the transport simulation model. To tackle the particle degradation, an iterative ensemble smoother was introduced to provide a proper proposal distribution, improving the search efficiency of the traditional particle filter. Moreover, to relieve the computational burden, a deep residual neural network was proposed to perform the surrogate task for the highly nonlinear and long-running-time original simulation model. In general, a refined particle filter with a deep-learning-method surrogate was proposed as an inverse framework for GCSE, which was evaluated by estimation tasks for a point-source contamination case and an areal-source contamination case, respectively, under different levels of observation errors. The results indicated that the deep-residual-neural-network surrogate model achieved the performance R² of 0.993 and 0.995, respectively for point-source and aerial-source contamination, to substitute the simulation models with a swift invoking process. Furthermore, the iterative ensemble smoother evidently improved the estimation efficiency of the particle filter. The proposed inverse framework can provide reliable and stable estimation of the groundwater contamination source and aquifer hydraulic conductivity.

The low-resolution characteristic of Gravity Recovery and Climate Experiment (GRACE) satellite data greatly limits their application in many fields at regional or local scales. Aiming to overcome this limitation, the partial least squares regression (PLSR) model is firstly utilized to assess the importance of some independent variables that are commonly employed in GRACE downscaling research. Three kinds of downscaling models are chosen to improve the resolution of GRACE-based water storage estimates from 1 to 0.25°, namely: multivariable linear regression, random forest (RF), and NoahV2.1. Results indicate that terrestrial water storage anomalies are more closely related to four independent variables in the Haihe River Basin, China: these variables are evapotranspiration, land surface temperature, air temperature, and soil moisture. With respect to the spatial distribution, the downscaled results based on the NoahV2.1 and RF models can effectively capture the subgrid heterogeneity while preserving the water storage characteristics at the original scale. By verifying the downscaled results with measured groundwater levels, it can be observed that the correlation coefficient between the RF-based downscaled groundwater storage anomalies (GWSA) and in-situ measurements is increased by 20.55% (Beijing), 9.13% (Tianjin), and 10.48% (Hebei) relative to the downscaled results based on the NoahV2.1 model. The cross wavelet transform illustrates that the meteorological factors have a strong influence on the GWSA series in the Haihe River Basin with an approximately 12-month signal during 2003–2016. This study can provide high-resolution GWSA datasets for water resources management and also provide a reference for the selection of dominant independent variables.

Microbes live throughout the soil profile. Microbial communities in subsurface horizons are impacted by a saltwater–freshwater transition zone formed by seawater intrusion (SWI) in coastal regions. The main purpose of this study is to explore the changes in microbial communities within the soil profile because of SWI. The study characterizes the depth-dependent distributions of bacterial and archaeal communities through high-throughput sequencing of 16S rRNA gene amplicons by collecting surface soil and deep core samples at nine soil depths in Longkou City, China. The results showed that although microbial communities were considerably impacted by SWI in both horizontal and vertical domains, the extent of these effects was variable. The soil depth strongly influenced the microbial communities, and the microbial diversity and community structure were significantly different (p < 0.05) at various depths. Compared with SWI, soil depth was a greater influencing factor for microbial diversity and community structure. Furthermore, soil microbial community structure was closely related to the environmental conditions, among which the most significant environmental factors were soil depth, pH, organic carbon, and total nitrogen.

Domestic supply wells meet much of the world’s potable water demand. These wells tend to fail as regional groundwater levels decline from intensive agricultural groundwater use, especially during drought when additional pumping occurs. This work examines approaches for addressing impacts on domestic wells in much of the San Joaquin Valley in California, USA, where groundwater management is now required. Mitigation actions and their costs are considered to allow continued well operations as groundwater levels decline to target levels specified in groundwater management plans. The estimated total mitigation cost for groundwater-level declines to the planned management targets ranges from $42 to $96 million depending upon well retirement age. If groundwater levels decline further to defined limits below the management targets allowed during drought, costs increase by $78 to $153 million. There will likely be competition for specialized labor to implement the mitigation actions since agricultural wells will also be affected. Unless current groundwater management plans become more stringent and specify shallower groundwater depth targets, proactive mitigation should be considered for the most vulnerable areas to prevent impacts from growing beyond the capacity for timely mitigation and to avoid widespread failure of rural domestic water supplies. The cost of mitigation for impacted wells is estimated to be less than 2% of the benefit to agriculture from being allowed to pump groundwater in excess of management targets during a multiyear drought.