Publications

Simulation of Extreme Hydrographs in Heterogeneous Catchments with Limited Data

Rainfall-based methods have been employed for computing hydrographs in urban drainage systems. However, their implementation often introduces uncertainty in various aspects, such as the selection of a unit hydrograph, the choice of abstraction methods, and the formulas used to calculate the time of concentration, among others. Conventional consultancy studies tend to oversimplify catchment representation by treating it as a homogeneous unit or discretizing it into a few segments with simplified flood routing. This research proposes a streamlined methodology for computing hydrographs, considering the sub-basins’ heterogeneity. The methodology is based on the principles of proportionality and superposition. A sensitivity analysis of the proposed methodology is conducted, considering both homogeneous and heterogeneous catchments and the temporal distribution of rainfall. The proposed methodology is applied to the catchment of the Ricaurte channel, located in Cartagena de Indias (Colombia), with a watershed area of 728.8 ha. It has proven effective in representing a recorded simultaneous rainfall-runoff event, achieving a Root Mean Square Error of 3.93% in estimating the total volume of the measured hydrographs. A key advantage of the methodology, compared to traditional rainfall–runoff approaches, is that it does not require an extensive number of parameters to be calibrated. It may be utilized to estimate extreme flood events in urban areas with limited data availability, relying on minimal data inputs.

Optimization Tool of Hybrid Energy Systems Toward a New Integrated Solution to Improve the Fish Sector’s Effectiveness

A techno-economic-environmental assessment tool was tailored to a fish sector case study. The fish sector, combined with two renewable components (wind and hydro), was analysed, and sensitivity analyses were carried out to integrate other renewables in a specifically developed optimization model (i.e., HY4RES-AHS). The model used an evolutionary method and resulted in the following conclusions: Scenario 2 excels financially, with the highest IRR (42%), shortest payback (4 years), and lowest investment (EUR 14,500), though it suffers from high energy losses (27.4%) due to a limited grid feed-in (120 kW). Scenario 4 is the most sustainable, with the highest SSR (97.8%) and SCR (63.4%) and lowest grid emissions (12.83 t CO2 eq.), supported by 600 kW PV and strong biomass use, but it has the lowest NPV (EUR 2241) and longest payback (25 years). Scenario 3 offers the best overall balance, achieving the highest NPV (EUR 741,293), solid IRR (20%), low energy losses (2.8%), and strong SSR (94%). Scenarios 5 and 7 prohibit grid feed-in, resulting in the highest energy losses (46.7% and 48.4%) and poor sustainability. Scenario 6 is financially strong (NPV EUR 602,280) but lacks biomass and biogas, reducing system resilience and autonomy. In summary, Scenario 2 is cost-efficient, Scenario 4 leads to sustainability, and Scenario 3 exhibits balanced performance.

Modelling the Spatial Distribution of Soil Organic Carbon Using Machine Learning and Remote Sensing in Nevado de Toluca, Mexico

Accurate soil organic carbon (SOC) estimation is critical for assessing ecosystem services, carbon budgets, and informing sustainable land management, particularly in ecologically sensitive mountainous regions. This study focuses on modelling the spatial distribution of SOC within the heterogeneous volcanic landscape of the Nevado de Toluca (NdT), central Mexico, an area spanning 535.9 km2 and characterised by diverse land uses, altitudinal gradients, and climatic regimes. Using 29 machine learning algorithms, we evaluated the predictive capacity of three key variables: land use, elevation, and the Normalised Difference Vegetation Index (NDVI) derived from satellite imagery. Complementary analyses were performed using the Bare Soil Index (BSI) and the Modified Soil-Adjusted Vegetation Index 2 (MSAVI2) to assess their relative performance. Among the tested models, the Quadratic Support Vector Machine (SVM) using NDVI, elevation, and land use emerged as the top-performing model, achieving a coefficient of determination (R2) of 0.84, indicating excellent predictive accuracy. Notably, 14 models surpassed the R2 threshold of 0.80 when using NDVI and BSI as predictor variables, whereas MSAVI2-based models consistently underperformed (R2 < 0.78). Validation plots demonstrated strong agreement between observed and predicted SOC values, confirming the robustness of the best-performing models. This research highlights the effectiveness of integrating multispectral remote sensing indices with advanced machine learning frameworks for SOC estimation in mountainous volcanic ecosystems

Conceptual Hybrid Energy Model for Different Power Potential Scales: Technical and Economic Approaches

This research attempts to address the gap between the theoretical fundamentals of hybrid renewable energy systems and their practical implementation at different scales through a new Conceptual Hybrid Energy Model (COHYBEM). The main objective was to develop a multi-variable model to allow a new complete and comprehensive techno-economic analysis of the performance of possible hybrid renewable power systems at different scales. The purpose is to evaluate the influence of critical parameters by changing key parameters in the developed model and identifying their impacts. It covers big data analyses, simulation and optimization of hybrid energy solutions, combining wind, solar and hydropower energy sources with the energy storage technology of pump hydropower storage. The research also denoted the Pareto front with the increasing power installed, for the maximum efficiency and total satisfied demand by Wind + PVSolar and by Hydro converges to a higher percentage, while a minimum waste by Wind + PVSolar is also progressing towards the increasing scales. In terms of investment costs for the 243 analyzed case studies, it varies between 45 k€ to 2.1 M€, resulting in a net present value (NPV) between 18 and 600 k€ and a payback period around 6–17 years depending on the power scale analyzed.

Predictive modelling of extreme air pocket pressures during filling and emptying processes in water pipelines using the transformer linear regression algorithm

Quasi-static Flow Model for Predicting the Extreme Values of Air Pocket Pressure in Draining and Filling Operations in Single Water Installations

Inertial models have been used by researchers to simulate the draining and filling processes in water pipelines, based on the evolution of the main hydraulic and thermodynamic variables. These models use complex differential equations, which are solved using advanced numerical codes. In this study, a quasi-static flow model is developed to study these operations in hydraulic installations. The quasi-static flow model represents a simplified formulation compared with inertial flow models, in which its numerical resolution is easier because only algebraic equations must be addressed. Experimental measurements of air pocket pressure patterns were conducted in a 4.36 m long single pipeline with an internal diameter of 42 mm. Comparisons between measured and computed air pocket pressure oscillations indicate how the quasi-static flow model can predict extreme values of air pocket pressure for experimental runs, demonstrating the possibility of selecting stiffness and pipe classes in actual pipelines using this model. Two case studies were analysed to determine the behaviour of the quasi-static flow model in large water pipelines.

Numerical modelling for analysing drainage in irregular profile pipes using OpenFOAM

Different methods of two-dimensional and three-dimensional numerical resolution models have been used to predict the air–water interaction in pipe systems in the early twenty-first century, where reliable and adequate results have been obtained when compared with experimental results. However, the study of the drainage process in pressurized systems with air admitted through openings has not been studied using this type of model due to the complexity that this represents. In this research, a two-dimensional numerical model is developed in the open-source software OpenFOAM; this model represents the drainage of an irregular pipe with air admitted by an air valve, defined by a structured mesh. A validation of the numerical model related to the air admitted by the variation of the air valve diameter is also performed.

Three-Dimensional Analysis of Air-Admission Orifices in Pipelines during Hydraulic Drainage Events

Air valves operate as protection devices in pipelines during drainage processes in order to mitigate vacuum pressures and control the transient flows. Currently, different authors have proposed one-dimensional models to predict the behaviour of orifices during filling and draining events, which offer good numerical results. However, the three-dimensional dynamic behaviour of air-admission orifices during drainage processes has not been studied in depth in the literature. In this research, the effects of air inflow on an orifice installed in a single pipe during drainage events are analysed using a three-dimensional computational fluid dynamics model by testing orifices with diameters of 1.5 and 3.0 mm. This model was validated with different experimental measurements associated to the vacuum pressure, obtaining good fits. The three-dimensional model predicts additional information associated to the aerodynamic effects that occur during the air-admission processes, which is studied. Subsonic flows are observed in different orifices with Mach numbers between 0.18 and 0.30. In addition, it is shown that the larger-diameter orifice ensures a more effective airflow control compared to the smaller-diameter orifice.

Computational fluid dynamics for sub-atmospheric pressure analysis in pipe drainage

The occurrence of sub-atmospheric pressure in the drainage of pipelines containing an air pocket has been known as a major cause of several serious problems. Accordingly, some system malfunction and pipe buckling events have been reported in the literature. This case has been studied experimentally and numerically in the current research considering objectives for a better understanding of: (i) the emptying process, (ii) the main parameters influencing the drainage, and (iii) the air-water interface deformation. Also, this research demonstrates the ability of a computational fluid dynamic (CFD) model in the simulation of this event. The effects of the air pocket size, the percentage and the time of valve opening on the pressure variation have been studied. Results show the pipeline drainage mostly occurs due to backflow air intrusion. The worst case scenario is associated with a fast valve opening when a tiny air pocket exists in the pipeline.

Explicit Scheme for a Hydrological Channel Routing: Mathematical Model and Practical Application

The computation of hydrographs in large watersheds necessitates utilizing channel routing, which calculates the movement of hydrographs along channel branches. Routing methods rely on an implicit scheme to facilitate numerical resolution, which requires more computational time than the explicit scheme. This study presents an explicit scheme channel routing model that offers a versatile approach to open channel flow analysis. The model is based on mass conservation principles and Manning equations, and it can accommodate varying bed slopes, making it highly adaptable to diverse hydraulic scenarios. In addition, the proposed model considers backwater effects, which enhances its applicability in practical scenarios. The model was tested in a practical application on a rectangular channel with a width of 7 m, and the results showed that it can accurately predict outflow hydrographs and handle different flow conditions. Comparative analyses with existing models revealed that the proposed model’s performance in generating water flow oscillations was competitive. Moreover, sensitivity analyses were performed, which showed that the model is highly responsive to parameter variations, such as Manning’s coefficient, bed slope, and channel width. The comparison of peak flows and peak times between the proposed model and existing methods further emphasized the model’s reliability and efficiency in simulating channel routing processes. This research introduces a valuable addition to the field of hydrology by proposing a practical and effective channel routing model that integrates essential hydraulic principles and parameters. The results of the proposed model (lumped routing) are comparable with the solution provided by the Muskingum–Cunge method (distributed routing). It is of utmost importance to note that the proposed model applies to channel branches with bed slopes below 6°.

Sensitivity of Empirical Equation Parameters for the Calculation of Time of Concentration in Urbanized Watersheds

The time of concentration is the time it takes a drop of water in a basin to travel from the most distant point to the outlet, and is one of the most important parameters, along with the morphometric characteristics, for determining the design flow rate in rainfall-runoff models. This study aims to determine the sensitivity of the parameters included in different equations for the calculation of the time of concentration. A case study was conducted on small, urbanized watersheds in the city of Montería, Colombia. The study uses information obtained through field work using GPS equipment and electronic total station, supplemented by geographic information contained in the city drawings of the local sewage company, which includes data on elevations above sea level with sub-metric precision. The time of concentration determined by the 12 empirical equations was compared to the results obtained from the equation proposed by the Natural Resources Conservation Service (NRCS), which was considered as a baseline formulation for the intricacy of calculation. Based on this comparison, it was found that the Carter equation is the one that best fits the results obtained from the NRCS equation because it displayed highly significant goodness of fit values. Even though the equations by Kirpich, Ventura, California Culvert Practice, Simas-Hawkins and TxDOT provide a relatively good fit compared to other empirical equations, they tend to over-estimate time of concentration values, which could lead to the under-estimation of the design flow rates. For this reason, sensitivity analysis of the parameters of these equations represents an alternative for improving the calculation of the time of concentration. The current research analyses deepen the influence of some parameters in the estimation of time of concentration. The research can also be used by designers and engineers in the city of Montería, Colombia, as an important reference to compute time of concentrations in urbanized watersheds.

Use of Cedrela odorata L. as a Biomaterial for Dye Adsorption in Wastewater: Simulation and Machine Learning Approaches for Scale-Up Analysis

Methylene blue and safranin are dyes that may have harmful effects on both aquatic ecosystems and human health. This research aims to simulate an industrial-scale operational adsorption column for competitively removing these dyes from wastewater, employing Cedrela odorata L. as a bioadsorbent material. Aspen Adsorption (v.1) software simulated an industrial-scale packed-bed adsorption column under various configurations. Moreover, machine learning algorithms were applied to predict the results generated by Aspen, representing an advancement in the development of new strategies in this field. The kinetic model employed was the Linear Driving Force (LDF) model. Adsorption efficiencies of 96.1% were achieved for both methylene blue and safranin using the Langmuir–LDF model. The Freundlich–LDF model showed efficiencies of 94.8% for methylene blue and 96% for safranin. Meanwhile, the Langmuir–Freundlich–LDF model achieved up to 96.1% for methylene blue and 94.8% for safranin. This study demonstrated the feasibility of simulating the competitive adsorption of dyes in solution at an industrial scale using Cedrela odorata L. as a bioadsorbent. The application of LDF kinetic models and adsorption isotherms (Langmuir, Freundlich, and Langmuir–Freundlich) resulted in high adsorption efficiencies, highlighting the potential of this approach for the remediation of dye-contaminated effluents as a viable method for predicting the performance of full-scale packed columns. Machine learning algorithms were implemented in this research, obtaining R2 higher than 0.996 for validation and testing stages for the responses of the model.

The Development of a Hydrological Method for Computing Extreme Hydrographs in Engineering Dam Projects

Engineering dam projects benefit society, including hydropower, water supply, agriculture, and flood control. During the planning stage, it is crucial to calculate extreme hydrographs associated with different return periods for spillways and diversion structures (such as tunnels, conduits, temporary diversions, multiple-stage diversions, and cofferdams). In many countries, spillways have return periods ranging from 1000 to 10,000 years, while diversion structures are designed with shorter return periods. This study introduces a hydrological method based on data from large rivers which can be used to compute extreme hydrographs for different return periods in engineering dam projects. The proposed model relies solely on frequency analysis data of peak flow, base flow, and water volume for various return periods, along with recorded maximum hydrographs, to compute design hydrographs associated with different return periods. The proposed method is applied to the El Quimbo Hydropower Plant in Colombia, which has a drainage area of 6832 km2. The results demonstrate that this method effectively captures peak flows and evaluates hydrograph volumes and base flows associated with different return periods, as a Root Mean Square Error of 11.9% of the maximum volume for various return periods was achieved during the validation stage of the proposed model. A comprehensive comparison with the rainfall–runoff method is also provided to evaluate the relative magnitudes of the various variables analysed, ensuring a thorough and reliable assessment of the proposed method.

Techno-Economic Evaluation for Renewable Deployment in Southern Chile: Expanding the Green Hydrogen Frontier

Chile stands out for its renewable energy resources and its commitment to developing green hydrogen. However, achieving cost parity with gray hydrogen remains an obstacle, mainly due to high capital costs and sensitivity to scale. This study assesses the technical and economic feasibility of green hydrogen production, using five different plants located in the Magallanes region in the south of the country as a reference. The model integrates a detailed framework of wind generation, PEM electrolysis, compression, and high-pressure storage subsystems, as well as a stochastic economic layer that combines the CAPEX, NPV, and LCOH assessments using Monte Carlo simulations. It also incorporates real-world capacity distributions and probabilistic fluctuations in systems. A sensitivity analysis confirms production scale as the main factor affecting profitability, with a break-even threshold of 0.5 MW. The results show that the LCOH decreases from 7.1 USD to 3.4 USD/kgH2 as capacity increases. The analysis reveals that only 23.88% of small-scale configurations yield positive NPV, underscoring the need for scaling to achieve economic viability.

Assessment of Baseflow Separation Methods Used in the Estimations of Design-Related Storm Hydrographs Across Various Return Periods

Accurately estimating storm hydrographs for various return periods is crucial for planning and designing hydrological infrastructure, such as dams and drainage systems. A key aspect of this estimation is the separation of baseflow from storm runoff. This study proposes a method for deriving storm hydrographs for different return periods based on hydrological station records. The proposed approach uses three baseflow separation methods: constant, linear, and master recession curve. A significant advantage of the proposed method over traditional rainfall–runoff approaches is its minimal parameter requirements during calibration. The methodology is tested on records from the Lengupá River watershed in Colombia, using data from the Páez hydrological station, which has a drainage area of 1090 km2. The results indicate that the linear method yields the most accurate hydrograph estimates, as demonstrated by its lower root mean square error (RMSE) of 0.35%, compared to the other baseflow separation techniques, the values of which range from 2.92 to 3.02%. A frequency analysis of hydrological data was conducted using Pearson Type III and Generalized Extreme Value distributions to identify the most suitable statistical models for estimating extreme events regarding peak flow and maximum storm hydrograph volume. The findings demonstrate that the proposed methods effectively reproduce storm hydrographs for return periods ranging from 5 to 200 years, providing valuable insights for hydrological design, which can be employed using the data from stream gauging stations in rivers.

A practical methodology of design of off-line reservoirs for reducing maximum water levels in urban channels

Flooding can often occur in many places due to the increasing trend of rainfall events (climate change), the urbanization process of a catchment area, or by an undersized urban channel. Reservoirs are used for limiting flooding since they can store an enough water volume. This research presents a practical procedure for sizing off-line reservoirs with an application in urban channels for places with space limitations. A complete formulation is presented, which can be easily used for designers and engineers for computing dimensions of off-line reservoirs. The formulation is applied to a case study located at the Cartagena de Indias, Colombia.

Challenges in Aquaculture Hybrid Energy Management: Optimization Tools, New Solutions, and Comparative Evaluations

A novel methodology for hybrid energy management in aquaculture is introduced, aimed at enhancing self-sufficiency and optimizing grid-related cash flows. Wind and solar energy generation are modeled using calibrated turbine performance curves and PVGIS data, respectively, with a photovoltaic capacity of 120 kWp. The system also incorporates a 250 kW small hydroelectric plant and a wood drying kiln that utilizes surplus wind energy. This study conducts a comparative analysis between HY4RES, a research-oriented simulation model, and HOMER Pro, a commercially available optimization tool, across multiple hybrid energy scenarios at two aquaculture sites. For grid-connected configurations at the Primary site (base case, Scenarios 1, 2, and 6), both models demonstrate strong concordance in terms of energy balance and overall performance. In Scenario 1, a peak power demand exceeding 1000 kW is observed in both models, attributed to the biomass kiln load. Scenario 2 reveals a 3.1% improvement in self-sufficiency with the integration of photovoltaic generation, as reported by HY4RES. In the off-grid Scenario 3, HY4RES supplies an additional 96,634 kWh of annual load compared to HOMER Pro. However, HOMER Pro indicates a 3.6% higher electricity deficit, primarily due to battery energy storage system (BESS) losses. Scenario 4 yields comparable generation outputs, with HY4RES enabling 6% more wood-drying capacity through the inclusion of photovoltaic energy. Scenario 5, which features a large-scale BESS, highlights a 4.7% unmet demand in HY4RES, whereas HOMER Pro successfully meets the entire load. In Scenario 6, both models exhibit similar load profiles; however, HY4RES reports a self-sufficiency rate that is 1.3% lower than in Scenario 1. At the Secondary site, financial outcomes are closely aligned. For instance, in the base case, HY4RES projects a cash flow of 54,154 EUR, while HOMER Pro estimates 55,532 EUR. Scenario 1 presents nearly identical financial results, and Scenario 2 underscores HOMER Pro’s superior BESS modeling capabilities during periods of reduced hydroelectric output. In conclusion, HY4RES demonstrates robust performance across all scenarios. When provided with harmonized input parameters, its simulation results are consistent with those of HOMER Pro, thereby validating its reliability for hybrid energy management in aquaculture applications.

Hydraulic modeling during filling and emptying processes in pressurized pipelines: a literature review

Filling and emptying processes are common maneuvers while operating, controlling and managing water pipeline systems. Currently, these operations are executed following recommendations from technical manuals and pipe manufacturers; however, these recommendations have a lack of understanding about the behavior of these processes. The application of mathematical models considering transient flows with entrapped air pockets is necessary because a rapid filling operation can cause pressure surges due to air pocket compressions, while an uncontrolled emptying operation can generate troughs of sub-atmospheric pressure caused by air pocket expansion. Depending on pipe and installation conditions, either situation can produce a rupture of pipe systems. Recently, reliable mathematical models have been developed by different researchers. This paper reviews and compares various mathematical models to simulate these processes. Water columns can be analyzed using a rigid water column model, an elastic water model, or 2D/3D CFD models; air–water interfaces using a piston-flow model or more complex models; air pockets through a polytropic model; and air valves using an isentropic nozzle flow or similar approaches. This work can be used as a starting point for planning filling and emptying operations in pressurized pipelines. Uncertainties of mathematical models of two-phases flow concerning to a non-variable friction factor, a polytropic coefficient, an air pocket sizes and an air valve behavior are identified.