Techno-ecological synergies of hydropower plants: Insights from GHG mitigation

Hydropower is a source of climate-friendly energy; however, its ecological impacts have been criticized. Few studies have considered the greenhouse gas (GHG) emissions resulting from ecosystem restoration. This study proposes a techno-ecological synergy framework based on life cycle assessment (LCA) to evaluate 34 hydropower plants (HPs) in the upper reaches of the Yangtze River from GHG supply and demand side perspectives. Our results show that the demand unit carbon footprint of the 34 HPs ranged from 5.43 to 49.36 g CO₂-eq/kWh, while the imputed GHG emissions from ecosystem restoration occupied 1.22 % to 30.35 %. The unit carbon footprint of large HPs were larger than those of small HPs, and both were positively correlated with the installed capacity of the HPs. All the HPs were unsustainable at the local scale and relied on regional ecosystem supplies. The Sobol' sensitivity analysis and Monte Carlo simulations demonstrated the reliability of our results. Finally, our results were used to consider the related policy implications.

The impact of a power law-induced memory effect on the SARS-CoV-2 transmission

It is well established that COVID-19 incidence data follows some power law growth pattern. Therefore, it is natural to believe that the COVID-19 transmission process follows some power law. However, we found no existing model on COVID-19 with a power law effect only in the disease transmission process. Inevitably, it is not clear how this power law effect in disease transmission can influence multiple COVID-19 waves in a location. In this context, we developed a completely new COVID-19 model where a force of infection function in disease transmission follows some power law. Furthermore, different realistic epidemiological scenarios like imperfect social distancing among home-quarantined individuals, disease awareness, vaccination, treatment, and possible reinfection of the recovered population are also considered in the model. Applying some recent techniques, we showed that the proposed system converted to a COVID-19 model with fractional order disease transmission, where order of the fractional derivative ( α ) in the force of infection function represents the memory effect in disease transmission. We studied some mathematical properties of this newly formulated model and determined the basic reproduction number (R 0 ). Furthermore, we estimated several epidemiological parameters of the newly developed fractional order model (including memory index α ) by fitting the model to the daily reported COVID-19 cases from Russia, South Africa, UK, and USA, respectively, for the time period March 01, 2020, till December 01, 2021. Variance-based Sobol's global sensitivity analysis technique is used to measure the effect of different important model parameters (including α ) on the number of COVID-19 waves in a location (W C ). Our findings suggest that α along with the average transmission rate of the undetected (symptomatic and asymptomatic) cases in the community (β 1 ) are mainly influencing multiple COVID-19 waves in those four locations. Numerically, we identified the regions in the parameter space of α andβ 1 for which multiple COVID-19 waves are occurring in those four locations. Furthermore, our findings suggested that increasing memory effect in disease transmission ( α → 0) may decrease the possibility of multiple COVID-19 waves and as well as reduce the severity of disease transmission in those four locations. Based on all the results, we try to identify a few non-pharmaceutical control strategies that may reduce the risk of further SARS-CoV-2 waves in Russia, South Africa, UK, and USA, respectively.

Employing sensitivity analysis to catchments having scanty data

Sensitivity analysis (SA) is generally desirable for parameter optimization, mapping, and calibration in hydrological models, yet the implementation of SA in data-sparse regions is usually avoided due to a lack of continuous data. The present study proposes the novel concept of "minimum continuous data period" to overcome this constraint. It analyses the sensitivity profile of two data-suffice sub-catchments of a data-sparse watershed using data at various timescales to determine the minimum data period required for the SA. The results suggest that the SA employing a minimum data period (2 years in this study) replicated the actual sensitivity profile by an average of 77.5% while replicating the most sensitive and insensitive parameters by 100%. The study encourages the use of the data from sub-catchments to determine the sensitivity profile of the data-sparse catchment. It would benefit in improving the use of SA for rainfall-runoff modelling in data-scanty regions.

Downwelling longwave radiation and sensible heat flux observations are critical for surface temperature and emissivity estimation from flux tower data

Land surface temperature (LST) is a preeminent state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. At the landscape-scale, LST is derived from thermal infrared radiance measured using space-borne radiometers. In contrast, plot-scale LST estimation at flux tower sites is commonly based on the inversion of upwelling longwave radiation captured by tower-mounted radiometers, whereas the role of the downwelling longwave radiation component is often ignored. We found that neglecting the reflected downwelling longwave radiation leads not only to substantial bias in plot-scale LST estimation, but also have important implications for the estimation of surface emissivity on which LST is co-dependent. The present study proposes a novel method for simultaneous estimation of LST and emissivity at the plot-scale and addresses in detail the consequences of omitting down-welling longwave radiation as frequently done in the literature. Our analysis uses ten eddy covariance sites with different land cover types and found that the LST values obtained using both upwelling and downwelling longwave radiation components are 0.5–1.5 K lower than estimates using only upwelling longwave radiation. Furthermore, the proposed method helps identify inconsistencies between plot-scale radiometric and aerodynamic measurements, likely due to footprint mismatch between measurement approaches. We also found that such inconsistencies can be removed by slight corrections to the upwelling longwave component and subsequent energy balance closure, resulting in realistic estimates of surface emissivity and consistent relationships between energy fluxes and surface-air temperature differences. The correspondence between plot-scale LST and landscape-scale LST depends on site-specific characteristics, such as canopy density, sensor locations and viewing angles. Here we also quantify the uncertainty in plot-scale LST estimates due to uncertainty in tower-based measurements using the different methods. The results of this work have significant implications for the combined use of aerodynamic and radiometric measurements to understand the interactions and feedbacks between LST and surface-atmosphere exchange processes.

The sensitivity of simulated streamflow to individual hydrologic processes across North America

Streamflow sensitivity to different hydrologic processes varies in both space and time. This sensitivity is traditionally evaluated for the parameters specific to a given hydrologic model simulating streamflow. In this study, we apply a novel analysis over more than 3000 basins across North America considering a blended hydrologic model structure, which includes not only parametric, but also structural uncertainties. This enables seamless quantification of model process sensitivities and parameter sensitivities across a continuous set of models. It also leads to high-level conclusions about the importance of water cycle components on streamflow predictions, such as quickflow being the most sensitive process for streamflow simulations across the North American continent. The results of the 3000 basins are used to derive an approximation of sensitivities based on physiographic and climatologic data without the need to perform expensive sensitivity analyses. Detailed spatio-temporal inputs and results are shared through an interactive website.

This work investigates the sensitivity of streamflow simulations to individual hydrologic processes at 3316 locations across North America, revealing common sensitivities across watersheds.

Investigating the potential of Morris algorithm for improving the computational constraints of global sensitivity analysis

Sensitivity analysis (SA) is widely acknowledged as advantageous and worthwhile in recognizing parameters for model calibration and optimization, especially in complex hydrological models. Although Sobol global SA is an efficient way to evaluate the sensitivity indices, the computational cost is a constraint. This study analyzes the potential of Morris global SA to achieve results tantamount to Sobol SA, at a much cheaper computational expense, using a new approach of increasing the number of replications for the Morris algorithm. SA for two catchments is performed on a coupled hydrological model using Morris and Sobol algorithms. Two target functions are used for each of the algorithms. Sobol SA required 660000 model simulations accounting for about 400 computing hours, whereas increasing the replications from 1000 to 3000, the Morris method called for 63000 runs and 06 computing hours to produce significantly similar results. The Morris parameter ranking improved 50% with respect to Sobol SA by a three-fold increase in replications with a small 5-h increase in the computational expense. The results also suggest that target functions and catchments influence parameter sensitivity. The new approach to employ the Morris method of SA shows promising results for highly parameterized hydrological models without compromising the quality of SA, specifically if there are time constraints. The study encourages the use of SA, which is mainly skipped because of higher computational demands.

Inverse Modeling of Thermal Decomposition of Flame-Retardant PET Fiber with Model-Free Coupled with Particle Swarm Optimization Algorithm

The thermal decomposition model of flame-retardant polyethylene terephthalate (FRPET) fiber is essential for predicting its fire behavior and do relevant fire simulation. In this work, the thermal decomposition character of FRPET is investigated via thermogravimetric analysis at four heating rates. Two kinetic schemes are proposed based on the analysis of experimental data and model-free methods. The model-free methods (Friedman and advanced Vyazovkin methods) are employed to determine possible search range for particle swarm optimization algorithm with constriction factor (CFPSO). Thus, this coupled method could evaluate the kinetic parameters for two proposed schemes without initial guess. Both models could reasonably predict the experimental data with obtained parameters, and the second two-step consecutive model shows better performance. The performance of CFPSO on the second model is further compared with improved generalized simulated annealing algorithm, and CFPSO was found to be more effective. Furthermore, global sensitivity analysis was conducted via the Sobol method to investigate the influence of kinetic parameters for the second model on predicted results. The most influential parameters are ln A and E _α of the second reaction and reaction order n of the third reaction.

Evaluating SWAT model performance, considering different soils data input, to quantify actual and future runoff susceptibility in a highly urbanized basin

The Soil and Water Assessment Tool (SWAT) is a physical model designed to predict the hydrological processes that could characterize natural and anthropized watersheds. The model can be forced using input data of climate prediction models, soil characteristics and land use scenarios to forecast their effect on hydrological processes. In this study, the SWAT model has been applied in the Aspio basin, a small watershed, highly anthropized and characterized by a short runoff generation. Three simulations setup, named SL1, SL2 and SL3, were investigated using different soil resolution to identify the best model performance. An increase of space requirement and calibration time has been registered in conjunction with the increasing soil resolution. Among all simulations, SL1 has been chosen as the best one in describing watershed streamflow, despite it was characterized by the lower soil resolution. A map of susceptibility to runoff for the entire basin was so created reclassifying the runoff amount of four years in five classes of susceptibility, from very low to very high. Eleven sub-basins, coinciding with the main urban settlements, were identified as highly susceptible to runoff generation. Considering future climate predictions, a slight increase of runoff has been forecasted during summer and autumn. The map of susceptibility successfully identified as highly prone to runoff those sub-basins where extreme flood events were yet recorded in the past, remarking the reliability of the proposed assessment and suggesting that this methodology could represent a useful tool in flood managing plan.

Time Variant Sensitivity Analysis of Hydrological Model Parameters in a Cold Region Using Flow Signatures

The complex terrain, seasonality, and cold region hydrology of the Nelson Churchill River Basin (NCRB) presents a formidable challenge for hydrological modeling, which complicates the calibration of model parameters. Seasonality leads to different hydrological processes dominating at different times of the year, which translates to time variant sensitivity in model parameters. In this study, Hydrological Predictions for the Environment model (HYPE) is set up in the NCRB to analyze the time variant sensitivity analysis (TVSA) of model parameters using a Global Sensitivity Analysis technique known as Variogram Analysis of Response Surfaces (VARS). TVSA can identify parameters that are highly influential in a short period but relatively uninfluential over the whole simulation period. TVSA is generally effective in identifying model’s sensitivity to event-based parameters related to cold region processes such as snowmelt and frozen soil. This can guide event-based calibration, useful for operational flood forecasting. In contrast to residual based metrics, flow signatures, specifically the slope of the mid-segment of the flow duration curve, allows VARS to detect the influential parameters throughout the timescale of analysis. The results are beneficial for the calibration process in complex and multi-dimensional models by targeting the informative parameters, which are associated with the cold region hydrological processes.

Seasonal Adaptation of the Thermal-Based Two-Source Energy Balance Model for Estimating Evapotranspiration in a Semiarid Tree-Grass Ecosystem

The thermal-based two-source energy balance (TSEB) model has accurately simulated energy fluxes in a wide range of landscapes with both remote and proximal sensing data. However, tree-grass ecosystems (TGE) have notably complex heterogeneous vegetation mixtures and dynamic phenological characteristics presenting clear challenges to earth observation and modeling methods. Particularly, the TSEB modeling structure assumes a single vegetation source, making it difficult to represent the multiple vegetation layers present in TGEs (i.e., trees and grasses) which have different phenological and structural characteristics. This study evaluates the implementation of TSEB in a TGE located in central Spain and proposes a new strategy to consider the spatial and temporal complexities observed. This was based on sensitivity analyses (SA) conducted on both primary remote sensing inputs (local SA) and model parameters (global SA). The model was subsequently modified considering phenological dynamics in semi-arid TGEs and assuming a dominant vegetation structure and cover (i.e., either grassland or broadleaved trees) for different seasons (TSEB-2S). The adaptation was compared against the default model and evaluated against eddy covariance (EC) flux measurements and lysimeters over the experimental site. TSEB-2S vastly improved over the default TSEB performance decreasing the mean bias and root-mean-square-deviation (RMSD) of latent heat (LE) from 40 and 82 W m−2 to −4 and 59 W m−2, respectively during 2015. TSEB-2S was further validated for two other EC towers and for different years (2015, 2016 and 2017) obtaining similar error statistics with RMSD of LE ranging between 57 and 63 W m−2. The results presented here demonstrate a relatively simple strategy to improve water and energy flux monitoring over a complex and vulnerable landscape, which are often poorly represented through remote sensing models.

Pathline Density Distributions in a Null-Space Monte Carlo Approach to Assess Groundwater Pathways

A null-space Monte-Carlo (NSMC) approach was applied to account for uncertainty in the calibration of the hydraulic conductivity (K) field for a three-dimensional groundwater flow model of a major water supply system in Switzerland. The approach generates different parameter realizations of the K field using the pilot point methodology. Subsequently, particle tracking (PT) was applied to each calibrated model, and the resulting particles are interpreted as the spatial pathline density distribution of multiple sources. The adopted approach offers advantages over classical PT which does not provide a means for treating uncertainty originating from the incomplete description of subsurface heterogeneity. Uncertainty in the K field is shown to strongly influence the spatial pathline distribution. Pathline spreading is particularly evident in locations where the information content of the head observations does not sufficiently constrain the estimated parameters. Despite the predictive uncertainty, the pumped drinking water at the study site is most likely dominated by artificially-infiltrated groundwater originating from the local infiltration canals. The model suggests that within the well field, the central pumping wells could be extracting regional groundwater, although the probability is relatively low. Nevertheless, a rigorous uncertainty assessment is still required since only a few realizations resulted in flow paths that support the field observations. Model results should therefore not be based on only one model realization; rather, an uncertainty analysis should be carried out to provide a sufficiently large suite of equally probable simulations that include all potential sources and pathways.

Impact of aerosols on reservoir inflow: A case study for Big Creek Hydroelectric System in California

Accurate and reliable reservoir inflow forecast is instrumental to the efficient operation of the hydroelectric power systems. It has been discovered that natural and anthropogenic aerosols have a great influence on meteorological variables such as temperature, snow water equivalent, and precipitation, which in turn impact the reservoir inflow. Therefore, it is imperative for us to quantify the impact of aerosols on reservoir inflow and to incorporate the aerosol models into future reservoir inflow forecasting models. In this paper, a comprehensive framework was developed to quantify the impact of aerosols on reservoir inflow by integrating the Weather Research and Forecasting model with Chemistry (WRF-Chem) and a dynamic regression model. The statistical dynamic regression model produces forecasts for reservoir inflow based on the meteorological output variables from the WRF-Chem model. The case study was performed on the Florence Lake and Lake Thomas Alva Edison of the Big Creek Hydroelectric Project in the San Joaquin Region. The simulation results show that the presence of aerosols results in a significant reduction of annual reservoir inflow by 4-14%. In the summer, aerosols reduce precipitation, snow water equivalent, and snowmelt that leads to a reduction in inflow by 11-26%. In the spring, aerosols increase temperature and snowmelt which leads to an increase in inflow by 0.6-2%. Aerosols significantly reduce the amount of inflow in the summer when the marginal value of water is extremely high and slightly increase the inflow in the spring when the run-off risk is high. In summary, the presence of aerosols is detrimental to the optimal utilization of hydroelectric power systems.

Estimation of Sobol's Sensitivity Indices under Generalized Linear Models

We derive explicit formulas for Sobol's sensitivity indices (SSIs) under the generalized linear models (GLMs) with independent or multivariate normal inputs. We argue that the main-effect SSIs provide a powerful tool for variable selection under GLMs with identity links under polynomial regressions. We also show via examples that the SSI-based variable selection results are similar to the ones obtained by the random forest algorithm but without the computational burden of data permutation. Finally, applying our results to the problem of gene network discovery, we identify though the SSI analysis of a public microarray dataset several novel higher-order gene-gene interactions missed out by the more standard inference methods. The relevant functions for SSI analysis derived here under GLMs with identity, log, and logit links are implemented and made available in the R package SobolSensitivity.