Water Flooding Research Articles

Modeling runoff in Bhima River catchment, India: A comparison of artificial neural networks and empirical models

ABSTRACT Effective water resource management in gauged catchments relies on accurate runoff prediction. For ungauged catchments, empirical models are used due to limited data availability. This study applied artificial neural networks (ANNs) and empirical models to predict runoff in the Bhima River basin. Among the tested models, the ANN-5 model, which utilized rainfall and one-day delayed rainfall as inputs, demonstrated superior performance with minimal error and high efficiency. Statistical results for the ANN-5 model showed excellent outcomes during both training (R = 0.95, NSE = 0.89, RMSE = 17.39, MAE = 0.12, d = 0.97, MBE = 0.12) and testing (R = 0.94, NSE = 0.88, RMSE = 11.47, MAE = 0.03, d = 0.97, MBE = 0.03). Among empirical models, the Coutagine model was the most accurate, with R = 0.82, MBE = 74.36, NSE = 0.94, d = 0.82, KGE = 0.76, MAE = 70.01, MAPE = 20.6%, NRMSE = 0.22, RMSE = 87.4, and DRV = −9.2. In contrast, Khosla's formula (KF) significantly overestimated runoff. The close correlation between observed and ANN-predicted runoff data underscores the model's utility for decision-makers in inflow forecasting, water resource planning, management, and flood forecasting.

Wearable ion-selective sensors with rapid conditioning and extended stability achieved through modulation of water and ion transport

Solid-contact (SC) ion-selective electrodes (ISEs) are often employed in wearables for electrolytes detection owing to their simplicity and ease of miniaturization. However, to mitigate their inherently unstable open circuit potential signal, ISEs require long hours of conditioning and frequent calibration prior to and during operation, limiting their practicality in wearable applications. Inspired by strategies to address water crossover and flooding in polyelectrolyte fuel cells, we demonstrated a SCISE with minimal conditioning time and long-term stability by modulating the rate-limiting step between mass transfer of water and hydrated ions and redox kinetics in the conducting polymer (CP). Our strategy comprised a wearable ISE with a superhydrophobic CP, PEDOT:TFPB, which reduced water and ion fluxes within the ISE, resulting in a stable and less-swollen CP and diminished water layer formation while maintaining CP's high capacitance. Our PEDOT:TFPB based ISEs functioned after a short conditioning time of 30 min and exhibited extended stability with a reduced signal deviation of only 0.16 % per hour (0.02 mV h−1) during 48 h of continuous measurement. Through systematic studies, we showed that ISE performance could be further tuned by tailoring the thickness of the ion-selective membrane as well as the hydrophobicity and polymerization charges of the CP. Without the need for recurrent calibration, our ISEs sustain high accuracy and prolonged stability upon integration into a wearable format for on-body perspiration analysis. Our strategy allows wearable ion-selective sensors with minimal maintenance at the user-end for long-term continuous monitoring, unveiling their potential in sports, healthcare, and diagnosis fields.

Assessing the effectiveness of reservoir operation and identifying reallocation opportunities under climate change

Reservoirs are designed and operated to mitigate hydroclimatic variability and extremes to fulfill various beneficial purposes. Existing reservoir infrastructure capacity and operation policies derived from historical records are challenged by hydrologic regime change and storage reduction from sedimentation. Furthermore, climate change could amplify the water footprint of reservoir operation (i.e. non-beneficial evaporative loss), further influencing the complex interactions among hydrologic variability, reservoir characteristics, and operation decisions. Disentangling and quantifying these impacts is essential to assess the effectiveness of reservoir operation under future climate and identify the opportunities for adaptive reservoir management (e.g. storage reallocation). Using reservoirs in Texas as a testing case, this study develops data-driven models to represent the current reservoir operation policies and assesses the challenges and opportunities in flood control and water supply under dynamically downscaled climate projections from the Coupled Model Intercomparison Project Phase 6. We find that current policies are robust in reducing future flood risks by eliminating small floods, reducing peak magnitude, and extending the duration for large floods. Current operation strategies can effectively reduce the risk of storage shortage for many reservoirs investigated, but reservoir evaporation and sedimentation pose urgent needs for revisions in the current guidelines to enhance system resilience. We also identify the opportunities for reservoir storage reallocation through seasonal-varying conservation pool levels to improve water supply reliability with negligible flood risk increase. This study provides a framework for stakeholders to evaluate the effectiveness of the current reservoir operation policy under future climate through the interactions among hydroclimatology, reservoir infrastructure, and operation policy.

Influence of binder content on gas-water two-phase flow and displacement phase diagram in the gas diffusion layer of PEMFC: A pore network view

Understanding the gas-water flow dynamics in the gas diffusion layer (GDL) is one of the keys to improving the proton exchange membrane fuel cell's (PEMFC) overall performance and avoiding water flooding. Yet, the relationship between the two-phase flow and micro-scale GDL structures (including binder, PTFE, and fiber skeleton) is not well understood. In this study, three-dimensional GDL structures with different fractions of binder contents (φb=0%∼50%) and a fixed porosity of 75.6 % are confidently reconstructed based on high-resolution images from the confocal microscope, implying that the addition of binder will increase the number of large pores and cause a bimodal throat size distribution through morphology analysis. Thereafter, drainage simulations are performed under a wide range of capillary number (−9 < log Ca <0) and viscosity ratio (−3 < log M < 2) by the pore network model (PNM), which robustly predicts the displacement phase diagram of viscous fingering, capillary fingering, stable displacement, and transitional regime. In the viscous fingering and stable displacement regime, the nonwetting phase saturation (Snw) is nearly the same for different φb, implying the fluid invading pathway does not change much with the addition of binder. However, in the capillary fingering regime (refer to the actual operating conditions), Snw increases rapidly with φbat the breakthrough time and its transient value continues to increase until the steady state, which may be attributed to the increased fractions of pores above the capillary threshold attributed by the bimodal throat size distribution. In addition, the effective water permeability increases sharply with φb, whereas the gas permeability remains almost constant, which means such pore structure alteration by adding the binder is beneficial to water disposal in PEMFC. This research provides insights into delineating the effect of pore and throat size distribution alteration by adding binder on two-phase dynamics in GDL.

Experimental Investigation of the Effect of Surfactant–Polymer Flooding on Enhanced Oil Recovery for Medium Crude Oil

High technical and financial risks are involved in exploring and exploiting new fields; hence, greater focus has placed on the development of environmentally friendly, cost-effective, and enhanced oil recovery (EOR) options for existing fields. For reservoirs producing high-density crudes and those with high interfacial tensions, water flooding is usually less effective due to density differences—hence the advent of polymer and surfactant flooding. For cost-effective and eco-friendly EOR solutions, a biopolymer and a surfactant synthesized from Jatropha seeds are used in this study to determine their effectiveness in increasing the oil recovery during core flooding analysis. The experiment involved an initial water flooding that served as the base cases of three weight percentages of polymers and polymeric surfactant solutions. The results for the polymer flooding of 1 wt%, 1.5 wt%, and 2 wt% showed an incremental oil recovery in comparison to water flooding of 16.8%, 17%, and 26%, while the polymeric surfactant mixtures of 5 wt% of surfactant and 1 wt%, 1.5 wt%, and 2 wt% of a polymer recorded 16.5%, 22.3%, and 28.8%, and 10 wt% of surfactant and 1 wt%, 1.5 wt%, and 2 wt% of a polymer recorded incremental oil recoveries of 20%, 32.9%, and 38.8%, respectively.

A Novel Online Hydrological Data Quality Control Approach Based on Adaptive Differential Evolution

The quality of hydrological data has a significant impact on hydrological models, where stable and anomaly-free hydrological time series typically yield more valuable patterns. In this paper, we conduct data analysis and propose an online hydrological data quality control method based on an adaptive differential evolution algorithm according to the characteristics of hydrological data. Taking into account the characteristics of continuity, periodicity, and seasonality, we develop a Periodic Temporal Long Short-Term Memory (PT-LSTM) predictive control model. Building upon the real-time nature of the data, we apply the Adaptive Differential Evolution algorithm to optimize PT-LSTM, creating an Online Composite Predictive Control Model (OCPT-LSTM) that provides confidence intervals and recommended values for control and replacement. The experimental results demonstrate that the proposed data quality control method effectively manages data quality; detects data anomalies; provides suggested values; reduces reliance on manual intervention; provides a solid data foundation for hydrological data analysis work; and helps hydrological personnel in water resource scheduling, flood control, and other related tasks. Meanwhile, the proposed method can also be applied to the analysis of time series data in other industries.

Rainfall-Runoff Simulation for Ungauged Watershed: A Case of Bessre Watershed, Duhok Province, Iraq

The scarcity of measured hydrological data poses a challenge in many developing countries, stemming from insufficiently established gauging stations. Due to the mentioned issue, it is crucial to develop models capable of conducting reliable simulations of runoff behavior, particularly for ungauged catchments. Understanding the intricate relationships in rainfall-runoff modeling is essential for estimating peak flows, a critical aspect in formulating water resources management strategies, which can aid in water resource management and planning. In areas prone to floods performing, an extensive hydrological study becomes necessary. This study determined the outflow discharge at the outlet point of the Bessre Valley Ungauged Catchment (41.4 km2) using the Watershed Modeling System, used by reliable hydrological standards as a graphical interface integrating with the Hydrologic Modeling System (HEC-HMS). Bessre Valley watershed is one of the flood-prone watersheds in the Duhok governorate, mainly due to the terrain’s steep slopes at the upper north and east of the catchment. The catchment was delineated by a Geographic Information System (GIS). Its properties were extracted from a 12.5 m × 12.5 m Digital Elevation Model (DEM), which evaluates the hydrological response of a watershed to two significant storm events: a real rainfall event in March 2020 and a hypothetical 100-year return period event by dividing the watershed into ten sub-basins. Achieving a Nash-Sutcliffe efficiency of 0.895 indicates a high accuracy between observed and simulated peak flows of the real rainfall event of March 2020, underscoring the model's reliability for hydrological predictions. Also, comparing the HEC-HMS model and the Rational Method of (100 YRP event) for calculating peak discharges revealed a mere 2.2% error. Furthermore, the study explores the potential for building additional dams based on discharge volumes from specific sub-basins to enhance flood control and water storage capabilities.

Reservoir Optimization Scheduling Driven by Knowledge Graphs

As global climate change intensifies, the challenges of water scarcity and flood disasters become increasingly severe. This severity makes efficient reservoir scheduling management crucial for the rational utilization of water resources. Due to the diverse topological structures and varying objectives of different watersheds, existing optimization models and algorithms are typically applicable only to specific watershed environments. This specificity results in a “one watershed, one model” limitation. Consequently, optimization of different watersheds usually requires manual reconstruction of models and algorithms. This process is not only time-consuming but also limits the versatility and flexibility of the algorithms. To address this issue, this paper proposes a knowledge graph-driven method for reservoir optimization scheduling. By improving genetic algorithms, this method allows for the automatic construction of optimization models tailored to specific watershed characteristics based on knowledge graphs. This approach reduces the dependency of the optimization model on manual modeling. It also integrates hydrodynamic simulations within the watershed to ensure the effectiveness and practicality of the genetic algorithms. Furthermore, this paper has developed an algorithm that directly converts optimized reservoir outflow into actionable dispatch instructions. This method has been applied in the Pihe River Basin, optimizing flood control and resource management strategies according to different seasonal demands. It demonstrates high flexibility and effectiveness under varying hydrological conditions, significantly enhancing the operational efficiency of reservoir management.

Research on the Law of Crack Propagation in Oil Well Fracturing Process

In the field of oilfield fracturing development, a profound understanding of the evolution and propagation of damage during the fracturing process is crucial for preventing well water coning and channeling. This study aimed to unravel the complexity of damage evolution during fracturing and elucidate the causes of well water flooding phenomena. To accurately describe the damage propagation laws, a damage constitutive model considering compaction and post-peak correction parameters was established in this research. The model, through parameter adjustment, enhances the precision of stress calculation during the rock compaction phase and accounts for the stress degradation pattern subsequent to damage. This model was applied to simulate the damage evolution under various conditions in oil layer profiles and wellbore cross-sections, including the impact of different perforation angles, natural fracture patterns, and the ratio of longitudinal to transverse boundary pressures. The research concludes that well water channeling and flooding are primarily caused by damage propagation and the connectivity with adjacent water-bearing formations. The proposed rock damage constitutive model demonstrated an accuracy improvement of more than 3% compared to previous studies. Additionally, the study discovered that when the angle between the perforation section and the formation exceeds 30°, the risk of fracture propagation into adjacent layers increases, leading to an elevated risk of post-fracturing water flooding. The presence of natural fractures in the oil layer provides a conduit for damage propagation, accelerating the process of damage in the oil layer. Furthermore, the perforation angle and the ratio of boundary pressure loads during the fracturing process were identified as the main factors influencing the direction change of fracture propagation. The conclusions drawn from this study provide a scientific basis for preventing post-fracturing water channeling and flooding issues and offer new perspectives for the development of well fracturing technology, aiding in the resolution of water flooding problems associated with well fracturing.

Topological Characterization and Uncertainty Visualization of Atmospheric Rivers

AbstractAtmospheric rivers (ARs) are long, narrow regions of water vapor in the Earth's atmosphere that transport heat and moisture from the tropics to the mid‐latitudes. ARs are often associated with extreme weather events in North America and contribute significantly to water supply and flood risk. However, characterizing ARs has been a major challenge due to the lack of a universal definition and their structural variations. Existing AR detection tools (ARDTs) produce distinct AR boundaries for the same event, making the risk assessment of ARs a difficult task. Understanding these uncertainties is crucial to improving the predictability of AR impacts, including their landfall areas and associated precipitation, which could cause catastrophic flooding and landslides over the coastal regions. In this work, we develop an uncertainty visualization framework that captures boundary and interior uncertainties, i.e., structural variations, of an ensemble of ARs that arise from a set of ARDTs. We first provide a statistical overview of the AR boundaries using the contour boxplots of Whitaker et al. that highlight the structural variations of AR boundaries based on their nesting relationships. We then introduce the topological skeletons of ARs based on Morse complexes that characterize the interior variation of an ensemble of ARs. We propose an uncertainty visualization of these topological skeletons, inspired by MetroSets of Jacobson et al. that emphasizes the agreements and disagreements across the ensemble members. Through case studies and expert feedback, we demonstrate that the two approaches complement each other, and together they could facilitate an effective comparative analysis process and provide a more confident outlook on an AR's shape, area, and onshore impact.

Low salinity water flooding: estimating relative permeability and capillary pressure using coupling of particle swarm optimization and machine learning technique

The reservoir’s properties are required for proper reservoir simulation, which also involves uncertainties. Experimental methods to estimate the relative permeability and capillary pressure data are expensive and time-consuming. This study aims to determine the relative permeability and capillary pressure functions of a sandstone core in the presence and absence of clay during low-salinity water floods. The data were provided by automatic history matching the results from previously lab-reported studies through coupling a simulator with the particle swarm optimization algorithm. Correlations were proposed using multiple-linear regression for relative permeability and capillary pressure parameters at low-salinity conditions. They were validated against experimental results of no clay and clayey formation with regression of 95% and 97%. To assign one curve of relative permeability and capillary pressure to the grid cells of the simulator, averaging techniques were implemented. The effect of salinity and clay content on the obtained curves was investigated. Changing salinity from 42000 to 4000 ppm, the reduction in water relative permeability appeared to be higher than the oil relative permeability increment. Moreover, a noticeable shift in the relative permeability curves toward the highest saturations related to the clay content was observed. The proposed hybrid method could be a suitable tool to estimate the relative permeability and capillary pressure functions of the water-based EOR methods.

A Novel Method to Calculate Water Influx Parameters and Geologic Reserves for Fractured-Vuggy Reservoirs with Bottom/Edge Water

In practical oilfield production, the phenomenon of water influx typically shortens the water-free recovery period of wells, leading to water flooding and causing a sharp decline in the production well yields, bringing great harm to production. Water invasion usually occurs as a result of the elastic expansion of the water as well as the compaction of the aquifer pore space. However, it can be due to the special characteristics of fractured-vuggy reservoirs such as non-homogeneity and the discrete distribution of the pore spaces. It is challenging to use traditional seepage flow theories to analyze the characteristics of water influx. Also, reservoir numerical simulation methods require numerous parameters which are difficult to obtain, which significantly reduces the accuracy of the results. In this study, considering the driving energy for water influx, a water influx characteristic model was obtained by fitting a graph plate. Subsequently, an iterative calculation method was used to simultaneously obtain water influx volume and OOIP. The aquifer to hydrocarbon ratio was determined by fitting the water influx curve with the graphic plate. Results show that the calculation method is sensitive to the values of reservoir pressure and the crude oil formation volume factor. After applying the method to one field case, it was discovered that water influx performance can be characterized into two types, i.e., linear water influx and logarithmic water influx. In the early stages, the water influx rate of logarithmic water influx is greater compared to linear water influx. However, the volume and energy of waterbody are limited, and the water invasion phenomenon occurs almost exclusively within a short period after the invasion. On the other hand, the volume of waterbody invaded by linear water influx is larger, and it can maintain a stable rate of water influx. The results of the study can provide theoretical support for the waterbody energy evaluation and dynamic analysis of water influx, as well as the control and management of water in these types of reservoirs.

Numerical optimization of duration time for the gravitational sedimentation process of produced water in oilfield

Following water flooding, polymer flooding and alkali/surfactant/polymer (ASP) flooding have become the main measures to enhance oil recovery in mature oilfields. However, it is the fact that the development of mature oilfields has continuously entered the ultra-high water cut stage, the amount of produced water has increased year by year. The characteristics of water quality have become increasingly complex, posing challenges for green and efficient separation. Taking China's mature oilfield, Daqing Oilfield, as an example, gravitational sedimentation is a basic process for produced water treatment before filtration. The duration time has always been determined based on standardized design parameters, and the adjustment of duration time depends on experience. This paper distinguishes the characteristics of produced water, and the relationship between treatment performance and production conditions is comprehensively considered. The numerical simulation method is employed to optimize the duration time for gravitational sedimentation of produced water. For produced water with different characteristics of water flooding, polymer flooding and ASP flooding, the reasonable duration time for primary sedimentation is 5 h, 12 h and 14 h respectively, and the reasonable duration time for secondary sedimentation is 3 h, 8 h and 10 h respectively. This recommendation has been validated by experiments.

The fate of 17β-estradiol in snowmelt from a field with a history of manure application: A laboratory simulation and field study

17β-estradiol is a naturally occurring estrogen, and livestock manure applied to agricultural fields is a major source to the environment. Liquid swine manure is widely applied to agricultural fields in the Canadian Prairies, a region where the majority of the annual runoff occurs during a brief snowmelt period over frozen soil. Transport of estrogens from manure amendments to soil during this important hydrological period is not well understood but is critical to mitigating the snowmelt-driven offsite transport of estrogens. This study quantified the concentration and load of 17β-estradiol in snowmelt from an agricultural field with a history of manure application under manure application methods: no manure applied, manure applied on the sub-surface, and on the surface, using a laboratory simulation study with flooded intact soil cores and a field study during snowmelt. A higher concentration of 17β-estradiol was in the laboratory simulation than in the field (mean laboratory pore water = 1.65 ± 1.2 μg/L; mean laboratory flood water = 0.488 ± 0.58 μg/L; and mean field snowmelt = 0.0619 ± 0.048 μg/L). There were no significant differences among manure application methods for 17β-estradiol concentration. Laboratory pore water concentrations significantly increased over time, corresponding with changes in pH. In contrast, there was no significant change in the field snowmelt concentrations of 17β-estradiol over time. However, for both laboratory simulation experiments and field-based snowmelt experiments, mean concentrations of 17β-estradiol were higher with subsurface than surface-applied manure, and the cumulative load of 17β-estradiol was significantly higher in the sub-surface than in surface applied. The mean cumulative load from the field study across all treatments (6.91 ± 3.7 ng/m2) approximates the magnitude of 17β-estradiol that could be mobilized from manured fields. The sub-surface application of manure seems to increase the persistence of 17β-estradiol in soil, thus enhancing the potential loss to snowmelt runoff.

Urban development and the loss of natural streams leads to increased flooding

Developing countries with hard pressed economies and a rapidly growing population have a myriad of issues to face. Karachi is one of the largest cities of Pakistan and is rapidly growing. The city has been hit by floods in the year 2016, 2018, 2019 and 2020, recently. Since the early 1990s Karachi has seen a rapid spike in development and in the process many natural streams have been built over. This has led reduced space for rainwater runoff and has created problems for urban flood managers. To ascertain this, topographic records of 1940s and 1980s and Landsat imagery of 2000, 2008, 2015 and 2020 were used to map historical urban extent. Sentinel—1, Synthetic Aperture Radar (SAR) imagery was used to extract flooded areas during 2015, 2017, 2019 and 2020 floods. In the year 2020, 64.3 km2 of area was flooded which was the highest. There has been a growth of 286% in urban area observed between 1980 and 2000. The city was built with several natural flood water carrying streams, north and west of it. It was found that that out of 3600 km of these streams, 870 km of streams have either been blocked or removed and a 100% in core urban area. DEM of difference (DOD) prepared by subtracting 1945 DEM from SRTM DEM indicated that most of embankments along these streams were in the areas that show erosion in DOD surface, thus explaining the expansion of built areas along historical stream channels. Forcing floods to enter these built areas.

Model-based interpretation of bottomhole pressure records during matrix treatments in layered formations

During injection treatments, bottomhole pressure measurements may significantly mismatch modeling results. We devise a computationally effective technique for interpretation of fluid injection in a wellbore interval with multiple geological layers based on the bottomhole pressure measurements. The permeability, porosity and compressibility in each layer are initially setup, while the skin factor and partitioning of injected fluids among the zones during the injection are found as a solution of the problem. The problem takes into account Darcy flow and chemical interactions between the injected acids, diverter fluids and reservoir rock typical in modern matrix acidizing treatments. Using the synchronously recorded injection rate and bottomhole pressure, we evaluate skin factor changes in each layer and actual fluid placement into the reservoir during different pumping jobs: matrix acidizing, water control, sand control, scale squeezes and water flooding. The model is validated by comparison with a simulator used in industry. It gives opportunity to estimate efficiency of a matrix treatment job, role of every injection stage, and control fluid delivery to each layer in real time. The presented interpretation technique significantly improves accuracy of matrix treatments analysis by coupling the hydrodynamic model with records of pressure and injection rate during the treatment.

Stage–discharge prediction in the multi-stage ice-covered compound channel

The multi-stage compound channel, which is a common pattern in natural alluvial rivers and the regulation projects of urban rivers, inevitably freezes in winter when it is situated in cold northern areas with high latitudes. Given that ascertaining the stage–discharge relationship for rivers is the foundation for the development of flood control schemes and water resources management, this study concentrates on proposing an analytical model for predicting the stage–discharge curves of multi-stage ice-covered compound channels. In deducing the analytical model, the cross section of the channel is first segmented into several homogeneous subregions that can be grouped into seven categories according to the geometric characteristics. Through analyzing the momentum transfer between adjacent subregions, the force balance equation for each subregion is then established to get the bulk mean velocity for the corresponding subregion, thereby obtaining the discharge by solving a tridiagonal matrix. Subsequently, measurements from two-stage and three-stage ice-covered compound channel experiments and three sets of experimental data from the literature are used to validate the performance of the proposed model. Good agreement between the predictions and the measured data suggests that the deduced model can accurately estimate the discharge for the multi-stage ice-covered compound channels when the flow depth is given. Finally, sensitivity analysis indicates that Manning's roughness coefficient of the channel bed has a more pronounced impact on the stage–discharge relationship than that of the ice cover. Moreover, when compared to the two-stage ice-covered compound channel, the multi-stage ice-covered compound channel offers greater potential for water resource utilization.

Boundary of ecosystem services: Differentiating between ecosystem services and geosystem services is needed

Geosystem services (GSs) and ecosystem services (ESs) are interconnected, both representing nature's contributions to people. Whether GSs are a subset of ESs depends on the definition of ESs. The answer would be “not necessarily” (i.e., some GSs are, while other GSs are not), if ESs are the benefits humans derive from ecological functions, processes, or characteristics. The boundary proposed by Chen et al. (2023) to differentiate ESs from other ecosystem-related benefits adopted this definition, and suggested that ESs are renewable and affected by biotic elements to occur. Gray et al. (2024) criticized this boundary for separating out bits of nature and ignoring the contributions of GSs and abiotic elements to ESs and human wellbeing. In fact, highlighting that ESs are affected by biotic elements to occur does not deny that ESs' occurrence is also affected by abiotic elements. However, ESs' dependence on abiotic elements cannot be a criterion to differentiate ESs from other benefits because abiotic elements are integral to geosystems, ecosystems, and many other natural and artificial systems, as well as to these systems' services. Conversely, while geosystems might persist without biotic elements, ecosystems cannot. Chen et al. (2023) only excluded those (not the whole) abiotic benefits, such as wind energy, that may occur independently of biotic elements, while allowing for integrating certain GSs into ESs. For example, geological structures can offer flood protection and water storage as GSs, which can also be classified as ESs when their qualities or quantities are affected by biotic elements. Differentiation between GSs and ESs should not be misinterpreted as splitting their interconnections or undervaluing or dividing nature. Instead, such differentiation and classification of nature's benefits serve to facilitate communication, management, education, research, and policy-making associated with nature's benefits, while also highlighting the richness and diversity of nature's benefits.

Evaluation of Different Numerical Approaches to Modeling Flood Flows Over Groynes

AbstractThe hydraulic resistance of groynes is an important factor in the determination of design flood water levels on rivers and the assessment of how much these levels are lowered by modifying the groynes. In standard one‐ or two‐dimensional numerical hydrodynamic models for flood risk management, groynes are commonly represented as subgrid features with a local energy loss according to a weir formula. We tested this representation by using a two‐dimensional horizontal mesh at various groyne submergence degrees by comparing the results with those of flume experiments. We also compared the results with simulations using different 2D and 3D approaches on finer grids that incorporate groynes in the bed topography. In one of the two tested 3D models, complete Reynolds‐averaged Navier‐Stokes equations were solved. The second tested 3D model was constructed simpler by assuming hydrostatic pressure distribution in the vertical direction. We employed Delft3D software in construction and execution of all models. One of the 3D models did predict the hydraulic resistance at low submergence better than the standard model, but it slightly underestimated the resistance at higher submergences. Despite differences in flow characteristics, weirs and groynes were found to produce similar flow resistances for the same height and boundary conditions. Simulations of groyne modifications showed that hydraulic resistance decreased nonlinearly with groyne lowering and streamlining.

Modeling arboreal vegetation-induced friction and its influence on river flooding using a two-dimensional model

Extensive research on vegetation flow resistance at the laboratory scale has yielded numerous formulations for determining vegetation friction coefficients. However, implementing and validating these equations at the river scale is often complex due to the lack of data on vegetation features and flow characteristics during floods, resulting in a limited number of field application studies. In this research, we use the Telemac-2D hydrodynamic model in a new case study to evaluate the performance of five vegetation friction formulations applied to arboreal vegetation. Employing vegetation data acquired through field surveys and LiDAR, along with flow data collected during one of the highest peak floods recorded on the Piave River, we built a two-dimensional (2D) river model. This model considers existing arboreal vegetation and terrain conditions to compare observed water surface elevations (WSE) during the flood with those simulated using vegetation friction equations. Our findings reveal that all tested equations perform similarly in all simulations. While these equations overestimated WSE throughout most of the flood, they accurately predicted levels at the highest peak and its proximity. Additionally, we explore several vegetation scenarios, varying density and percentage of area covered by arboreal vegetation, to assess their effect on water levels and flood risk.