Groundwater for People and the Environment: A Globally Threatened Resource.

The intensity of global groundwater use rose from 124 m3 per capita in 1950 to 152 m3 in 2021, for a 22.6% rise in the annual per capita use. This rise in global per capita water use reflects rising consumption patterns. The global use of groundwater, which provides between 21% and 30% of the total freshwater annual consumption, will continue to expand due to the sustained population growth projected through most of the 21st century and the important role that groundwater plays in the water-food-energy nexus. The rise in groundwater use, on the other hand, has inflicted adverse impacts in many aquifers, such as land subsidence, sea water intrusion, stream depletion, and deterioration of groundwater-dependent ecosystems, groundwater-quality degradation, and aridification. This paper projects global groundwater use between 2025 and 2050. The projected global annual groundwater withdrawal in 2050 is 1535 km3 (1 km3 = 109 m3 = 810,713 acre-feet). The projected global groundwater depletion, that is, the excess of withdrawal over recharge, in 2050 equals 887 km3 , which is about 61% larger than in 2021. This projection signals probable exacerbation of adverse groundwater-withdrawal impacts, which are worsened by climatic trends and the environmental requirement of groundwater flow unless concerted national and international efforts achieve groundwater sustainability.

Resolving the Water Crisis: There's a Way, But Is There the Will?

In this issue paper, the authors refine the definition of water sustainability to account for temporal dynamics and spatial variability, identify specific challenges that must be resolved in the very near future to avoid catastrophic outcomes on levels ranging from economic disruption to survival of mankind, discuss related policy changes and potential effectiveness, and describe several technologies available to achieve water security and sustainability. While water quality certainly poses formidable challenges, in this piece we emphasize and address challenges associated with dynamic water supply availability. Our future as a society will depend upon how well and how rapidly we navigate these challenges in the coming years. As such, the main objective is to encourage private and public sector practitioners to consider revising existing programs, and to update current industry business models in a manner that promotes expedited solutions, alignment of beneficial goals, and motivates the biggest consumers of water to adopt modern data collection and decision support technologies.

Integrated strategic planning and multi-criteria decision-making framework with its application to agricultural water management.

Sustainable water resources management involves social, economic, environmental, water use, and resources factors. This study proposes a new framework of strategic planning with multi-criteria decision-making to develop sustainable water management alternatives for large scale water resources systems. A fuzzy multi-criteria decision-making model is developed to rank regional management alternatives for agricultural water management considering water-resources sustainability criteria. The decision-making model combines hierarchical analysis and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The management alternatives were presented spatially in the form of zoning maps at the level of irrigation zones of the study area. The results show that the irrigation management zone No.3 (alternative A3) was ranked first based on agricultural water demand and supply management in five among seven available scenarios, in which the scenarios represents a possible combination of weights assigned to the weighing criteria. Specifically, the results show that irrigation management zone No.3 (alternative A3) achieved the best ranking values of 0.151, 0.169, 0.152, 0.174 and 0.164 with respect to scenarios 1, 4, 5, 6 and 7, respectively. However, irrigation management zone No.2 (alternative A2) achieved the best values of 0.152 and 0.150 with respect to the second and third scenarios, respectively. The model results identify the best management alternatives for agricultural water management in large-scale irrigation and drainage networks.

The effect of reducing per capita water and energy uses on renewable water resources in the water, food and energy nexus.

This study assesses the feedbacks between water, food, and energy nexus at the national level with a dynamic-system model, taking into account the qualitative and quantitative environmental water needs. Surface and groundwater resources are considered jointly in the water resources subsystem of this dynamic system. The developed model considers the effects of reducing the per capita use water and energy on its system's components. Results indicate that due to feedbacks the changes in per capita uses of water and energy have indirect and direct effects. About 40% of the total water savings achieved by the per capita change policy was related to energy savings, in other words, it is an indirect saving. Implementation of per capita use reductions compensates for 9% of the decline of Iran's groundwater reservoirs (non-renewable resources in the short term) that occur during the five-year study period. The Manageable and Exploitable Renewable Water Stress Index (MRWI) corresponding to water and energy savings equals 214.5%, which is better than its value under the current situation (which is equal to 235.1%).

System dynamics modeling of lake water management under climate change.

Lake Urmia, the twentieth largest lake in the world, is the most valuable aquatic ecosystem in Iran. The lake water level has decreased in recent years due to human activities and climate change. Several studies have highlighted the significant roles of climatic and anthropogenic factors on the shrinkage of the lake. Management policies for water resources harvesting must be adopted to adapt to climate change and avoid the consequent problems stemming from the drought affecting Lake Urmia, and rationing must be applied to the upstream water demands. This study analyzes strategies and evaluates their effectiveness in overcoming the Urmia Lake crisis. Specifically, system dynamics analysis was performed for simulating the water volume of Lake Urmia, and the Hadley Centre coupled model was applied to project surface temperature and precipitation for two future periods: 2021-2050 and 2051-2080. Six management scenarios were considered for decreasing the allocation of agricultural water demand corresponding to two options: (1) one-reservoir option (Bukan reservoir only), and (2) six-reservoir option. The net inflow of Urmia Lake was simulated for the two future periods with the IHACRES model and with artificial neural network models under the six management scenarios. The annual average volumes of Lake Urmia would be 30 × 109 and 12 × 109 m3 over the first and second future periods, respectively, without considering the management scenarios. The lake volumes would rise by about 50% and 75% for the first and second periods, respectively under the management scenarios that involve strict protective measures and elimination of the effect of all dams and their reservoirs. Implementing strict measures would increase the annual average lake volume to 21 × 109 m3 in the second period; yet, this volume would be less than the long-term average and strategic volume. The human water use would be completely eliminated under Scenario 6. Nevertheless, Lake Urmia would experience a considerable loss of storage because of drought.

Evaluation of cooperative and non-cooperative game theoretic approaches for water allocation of transboundary rivers.

Efficient water allocation in a transboundary river basin is a complex issue in water resources management. This work develops a framework for the allocation of transboundary river water between the countries located in the river basin to evaluate the characteristics of allocation approaches. The allocation of river water is obtained based on initial-water conditions, cooperative, and non-cooperative game-theoretic approaches. The initial-conditions water allocation approach assigns 34, 40, and 26% of the Harirud River flow to Afghanistan, Iran, and Turkmenistan, respectively. The game-theoretic cooperative approach assigns 36, 42, and 22% of the river flow to Afghanistan, Iran, and Turkmenistan, respectively. The non-cooperative game-theoretic approach establishes that the most stable water allocation was 42, 38, and 20% of the Harirud River flow for Afghanistan, Iran, and Turkmenistan, respectively. Human and agricultural water-stress criteria are used to evaluate the water allocations in the Harirud River basin. The criterion of human water stress has the largest influence in Iran, and the criterion of agricultural water stress has the smallest influence in Afghanistan. This work's results indicate the initial-conditions water allocation approach favors Turkmenistan, whereas the cooperative and the non-cooperative game-theoretic approaches favors Iran and Afghanistan, respectively. The results show that the priorities of each country governs water allocation, and cooperation is shown to be necessary to achieve sustainable development.

Sensitivity of non-conditional climatic variables to climate-change deep uncertainty using Markov Chain Monte Carlo simulation.

There is substantial evidence suggesting climate change is having an adverse impact on the world's water resources. One must remember, however, that climate change is beset by uncertainty. It is therefore meaningful for climate change impact assessments to be conducted with stochastic-based frameworks. The degree of uncertainty about the nature of a stochastic phenomenon may differ from one another. Deep uncertainty refers to a situation in which the parameters governing intervening probability distributions of the stochastic phenomenon are themselves subjected to some degree of uncertainty. In most climatic studies, however, the assessment of the role of deep-uncertain nature of climate change has been limited. This work contributes to fill this knowledge gap by developing a Markov Chain Monte Carlo (MCMC) analysis involving Bayes' theorem that merges the stochastic patterns of historical data (i.e., the prior distribution) and the regional climate models' (RCMs') generated climate scenarios (i.e., the likelihood function) to redefine the stochastic behavior of a non-conditional climatic variable under climate change conditions (i.e., the posterior distribution). This study accounts for the deep-uncertainty effect by evaluating the stochastic pattern of the central tendency measure of the posterior distributions through regenerating the MCMCs. The Karkheh River Basin, Iran, is chosen to evaluate the proposed method. The reason for selecting this case study was twofold. First, this basin has a central role in ensuring the region's water, food, and energy security. The other reason is the diverse topographic profile of the basin, which imposes predictive challenges for most RCMs. Our results indicate that, while in most seasons, with the notable exception of summer, one can expect a slight drop in the temperature in the near future, the average temperature would continue to rise until eventually surpassing the historically recorded values. The results also revealed that the 95% confidence interval of the central tendency measure of computed posterior probability distributions varies between 0.1 and 0.3 °C. The results suggest exercising caution when employing the RCMs' raw projections, especially in topographically diverse terrain.

Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages.

Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.

Developing water, energy, and food sustainability performance indicators for agricultural systems.

Water use by the agricultural sector along with inefficient irrigation methods and climate change has led to the depletion and insecurity of water resources and consequent instability of the agricultural system. Defining benchmarks and comparing them is essential for sustainable system management performance. The sustainability performance of an agricultural system depends on various factors related to water, energy, and food. This study selects and ranks sustainability performance indicators (SPIs) of agricultural systems with the analytical hierarchy process (AHP). Expert opinions on agricultural sustainability were obtained from Iran's Regional Water Organization. The factors and variables affecting the management of water resources in agricultural systems in a basin area are evaluated with 17 SPIs (10 indicators of water resources sustainability, 3 energy sustainability indicators, and 4 food sustainability indicators) that measure the sustainability of agricultural systems. The AHP reduced the number of indicators to a small number of effective indicators. Results of pairwise comparison and the subsequent determination of the weight of each indicator show that the indicators of water consumption, groundwater level stability, vulnerability of water resources, and water stress have the largest weights (i.e., importance) for agricultural system sustainability at the basin scale. These selected indicators can be applied to agricultural water systems (AWSs).

Analysis of long-term strategies of riparian countries in transboundary river basins.

Transboundary river basins give rise to complex water-sharing decision making that can be analyzed as a game in the sense of dynamic game theory, as done in this work. The sharing of transboundary water resources depends on the long-term shifting interactions between upstream and downstream countries, which has received limited research attention in the past. The water-sharing strategy of a riparian country depends on the strategies of other countries over time. This paper presents an evolutionary game method to analyze the long-term water-sharing strategies of countries encompassing transboundary river basins over time. The method analyzes the evolutionary strategies of riparian countries and investigates evolutionary stable strategies (ESSs) considering the payoff matrix. The evolutionary game method is applied to a river basin shared by three countries assuming two types of benefits and one type of cost to countries as decision variables of a game that reflects water use, economic and political gains, and socio-economic losses of countries. Numerical examples illustrate the strategies resulting from the evolutionary game processes and the role of several parameters on the interaction between riparian countries. The countries' strategies are analyzed for several levels of benefits and costs, and the convergence of the strategies to a stable point is assessed. Results demonstrate the role that the upstream country's potential benefits and the cost of conflict (i.e., non-cooperation) to other countries has on reaching a stable point in the game. This work's results show the potential benefit to the upstream country under cooperative strategy must exceed its benefits from water use under non-cooperative strategy to gain the full stable cooperation of downstream countries. This work provides a method to resolve water-sharing strategies by countries sharing transboundary river basins and to evaluate the implications of cooperation or non-cooperation.

Optimal virtual water flows for improved food security in water-scarce countries.

The worsening water scarcity has imposed a significant stress on food production in many parts of the world. This stress becomes more critical when countries seek self-sufficiency. A literature review shows that food self-sufficiency has not been assessed as the main factor in determining the optimal cultivation patterns. However, food self-sufficiency is one of the main policies of these countries and requires the most attention and concentration. Previous works have focused on the virtual water trade to meet regional food demand and to calculate trade flows. The potential of the trade network can be exploited to improve the cropping pattern to ensure food and water security. To this end, and based on the research gaps mentioned, this study develops a method to link intra-country trade networks, food security, and total water footprints (WFs) to improve food security. The method is applied in Iran, a water-scarce country. The study shows that 781 × 106 m3 of water could be saved by creating a trade network. Results of the balanced trade network are input to a multi-objective optimization model to improve cropping patterns based on the objectives of achieving food security and preventing water crises. The method provides 400 management scenarios to improve cropping patterns considering 51 main crops in Iran. Results show a range of improvements in food security (19-45%) and a decrease in WFs (2-3%). The selected scenario for Iran would reduce the blue water footprint by 1207 × 106 m3, and reduce the cropland area by 19 × 103 ha. This methodology allows decision makers to develop policies that achieve food security under limited water resources in arid and semi-arid regions.

Assessment of global hydro-social indicators in water resources management.

Water is a vital element that plays a central role in human life. This study assesses the status of indicators based on water resources availability relying on hydro-social analysis. The assessment involves countries exhibiting decreasing trends in per capita renewable water during 2005-2017. Africa, America, Asia, Europe, and Oceania encompass respectively 48, 35, 43, 20, and 5 countries with distinct climatic conditions. Four hydro-social indicators associated with rural society, urban society, technology and communication, and knowledge were estimated with soft-computing methods [i.e., artificial neural networks, adaptive neuro-fuzzy inference system, and gene expression programming (GEP)] for the world's continents. The GEP model's performance was the best among the computing methods in estimating hydro-social indicators for all the world's continents based on statistical criteria [correlation coefficient (R), root mean square error (RMSE), and mean absolute error]. The values of RMSE for GEP models for the ratio of rural to urban population (PRUP), population density, number of internet users and education index parameters equaled (0.084, 0.029, 0.178, 0.135), (0.197, 0.056, 0.152, 0.163), (0.151, 0.036, 0.123, 0.210), (0.182, 0.039, 0.148, 0.204) and (0.141, 0.030, 0.226, 0.082) for Africa, America, Asia, Europe and Oceania, respectively. Scalable equations for hydro-social indicators are developed with applicability at variable spatial and temporal scales worldwide. This paper's results show the patterns of association between social parameters and water resources vary across continents. This study's findings contribute to improving water-resources planning and management considering hydro-social indicators.

Developing a novel parameter-free optimization framework for flood routing.

The Muskingum model is a popular hydrologic flood routing technique; however, the accurate estimation of model parameters challenges the effective, precise, and rapid-response operation of flood routing. Evolutionary and metaheuristic optimization algorithms (EMOAs) are well suited for parameter estimation task associated with a wide range of complex models including the nonlinear Muskingum model. However, more proficient frameworks requiring less computational effort are substantially advantageous. Among the EMOAs teaching-learning-based optimization (TLBO) is a relatively new, parameter-free, and efficient metaheuristic optimization algorithm, inspired by the teacher-student interactions in a classroom to upgrade the overall knowledge of a topic through a teaching-learning procedure. The novelty of this study originates from (1) coupling TLBO and the nonlinear Muskingum routing model to estimate the Muskingum parameters by outflow predictability enhancement, and (2) evaluating a parameter-free algorithm's functionality and accuracy involving complex Muskingum model's parameter determination. TLBO, unlike previous EMOAs linked to the Muskingum model, is free of algorithmic parameters which makes it ideal for prediction without optimizing EMOAs parameters. The hypothesis herein entertained is that TLBO is effective in estimating the nonlinear Muskingum parameters efficiently and accurately. This hypothesis is evaluated with two popular benchmark examples, the Wilson and Wye River case studies. The results show the excellent performance of the "TLBO-Muskingum" for estimating accurately the Muskingum parameters based on the Nash-Sutcliffe Efficiency (NSE) to evaluate the TLBO's predictive skill using benchmark problems. The NSE index is calculated 0.99 and 0.94 for the Wilson and Wye River benchmarks, respectively.

Inter-basin hydropolitics for optimal water resources allocation.

Efficient, just, and sustainable water resources' allocation is difficult to achieve in multi-stakeholder basins. This study presents a multi-objective optimization model for water resources allocation and reports its application to the Sefidrud basin in Iran. Available water resources are predicted until 2041with the artificial neural network algorithm (ANN). This is followed by multi-objective optimization of water resource allocation. The first objective function of the optimization model is maximization of revenue, and the second objective function is the achievement of equity in water resources allocation in the basin. This study considers two scenarios in the optimization scheme. The first scenario concerns the water allocation with existing dams and dams under construction. The second scenario tackles water allocation adding dams currently in the study stage to those considered in Scenario 1. The Gini coefficient is about 0.1 under the first scenario, indicating the preponderance of economic justice in the basin. The Gini coefficient is about 0.4 under the second scenario, which signals an increase of injustice in water allocation when considering the future operation of dams currently under study.

Reservoir water quality simulation with data mining models.

Water pollution is a concern in the management of water resources. This paper presents a statistical approach for data mining of patterns of water pollution in reservoirs. Genetic programming (GP), artificial neural network (ANN), and support vector machine (SVM) are applied to reservoir quality modeling. Input data for GP, ANN, and SVM were derived with the CE-QUAL-W2 numerical water quality simulation model. A case study was carried out using measured reservoir inflow and outflow, temperature, and nitrate concentration to the Amirkabir reservoir, Iran. Data mining models were evaluated with the MAE, NSE, RMSE, and R2 goodness-of-fit criteria. The results indicated that using the SVM model for determining nitrate pollution is time saving and more accurate in comparison with GP, ANN, and particularly CE-QUAL-W2. The SVM model reduces the runtime of nitrate concentration simulation by 581, 276, and 146 s compared with CE-QUAL-W2, GP, and ANN, respectively. The goodness-of-fit results showed that the highest values (R2 = 0.97, NSE = 0.92) and the lowest values (MAE = 0.034 and RMSE = 0.007) corresponded to SVM predictions, indicating higher model accuracy. This study demonstrates the potential for application of data mining tools to solute concentration simulation in reservoirs.

Integrated virtual water trade management considering self-sufficient production of strategic agricultural and industrial products.

The uneven distribution of water on earth causes its scarcity in many countries, hindering economic and human development. Virtual water trading takes place by the export and import of agricultural and industrial products whose production involves water and, so, is one of the methods to cope with water scarcity. This work investigates the integrated management of the virtual water trade of strategic agricultural and industrial products with the goals of maximizing economic revenue and minimizing the consumption of virtual water. Bi-objective optimization is performed with the non-dominated sorting genetic algorithm (NSGA)-II algorithm under two scenarios. One of the scenarios applies production constraints to ensure a minimum of internal or domestic production of agricultural and industrial goods, thus providing a degree of self-sufficiency. This paper applies its methodology to Iran and establishes that the imports of industrial products should be terminated and exports of these products should increase. The results show that the export amount of iron ore increases by 71% due to the application of the self-sufficiency constraint, which shows the profitability of this product in addition to its low water consumption. This paper demonstrates that the optimal solutions for agricultural products (except for potatoes and tomatoes) is achieved by producing at least 50% of the domestic demand, but rarely calls for a higher level of production. This work illustrates through the case study that it is possible to increase economic revenue under water scarcity by proper management of the virtual water trade.

Groundwater safe yield powered by clean wind energy.

Wind energy has been used by humans for thousands of years. Yet, the relatively low economic cost and availability of fossil fuels upstaged the use of wind power. Fossil fuel resources are not renewable and will decline until exhaustion in the future. At the same time, humans have become aware of the adverse effects on the environment caused by reliance on fossil fuel energy. Wind, on the other hand, is a renewable energy source with minimal adverse environmental impacts that does not involve greenhouse gas emissions. Agricultural irrigation systems use fossil fuel energy resources in various forms. Groundwater withdrawal is central to supplying agricultural water demand in arid and semi-arid regions. Such withdrawal is mostly based on water extraction with pumps powered by diesel, gasoline, or electricity (which is commonly produced by fossil fuels). This paper coupled the non-sorted genetic algorithm (NSGA-II) as the optimization tool to the mathematical formulation of the wind-powered groundwater production problem to determine the potential of wind energy for groundwater withdrawal in an arid area. The optimal safe yield and the optimal size of regulation reservoir are determined considering two objectives: (1) maximizing total extraction of groundwater and (2) minimizing the cost of reservoir construction. The safe yield and the two objectives are optimized for periods lasting 1, 2, 3, 4, and 6 months over a 1-year planning horizon. This paper's methodology is evaluated with groundwater and wind-power data pertinent to Eghlid, Iran. The optimal safe yield increases by increasing the period length. Specifically, increasing the period length from 1 to 6 months increases the safe yield from 12 to 29 m3. Application of the proposed NSGA-II-based optimization of groundwater production identifies the best design and operational variables with computational efficiency and accuracy.

Application of particle swarm optimization to water management: an introduction and overview.

Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO's applications and performance analysis in water resources engineering optimization problems. Our literature review revealed 22 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall-runoff modeling, water quality modeling, and groundwater modeling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., simulated annealing, differential evolution, genetic algorithm, and shark algorithm) and mathematical methods (e.g., support vector machine and differential dynamic programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.

State-of-art of genetic programming applications in water-resources systems analysis.

Evolutionary algorithms (EAs) have become competitive solvers of a wide variety of water-resources optimization problems. Genetic programming (GP) has become a leading EA since its inception in 1985. This paper reviews the state-of-the-art of GP and its applications in water-resources systems analysis. A comprehensive knowledge about GP's theory and modeling approach is essential for its successful application in water-resources systems analysis. This review presents variants of GP that have been proven useful in various applications to water resources problems. Several examples of applications of GP in water-resources systems analysis are herein presented. This review reveals GP's capability and superiority compared to other conventional methods, which makes it suitable for solving a wide variety of water-related problems including rainfall-runoff modeling, streamflow sediment prediction, flood prediction and routing, evaporation and evapotranspiration forecasting, reservoir operation, groundwater modeling, water quality modeling, water demand forecasting, and water distribution systems.

Analysis of the effect of inputs uncertainty on riverine water temperature predictions with a Markov chain Monte Carlo (MCMC) algorithm.

Water temperature is a key characteristic defining chemical, physical, and biologic conditions in riverine systems. Models of riverine water quality require many inputs, which are commonly beset by uncertainty. This study presents an uncertainty analysis of inputs to the stream-temperature simulation model HFLUX. This paper's assessment relies on a Markov chain Monte Carlo (MCMC) analysis with the DREAM algorithm, which has fast convergence rate and good accuracy. The inputs herein considered are the river width and depth, percent shade, view to sky, streamflow, and the minimum and maximum values of inputs required for uncertainty analysis. The results are presented as histograms for each input specifying the input's uncertainty. A comparison of the observational data with the DREAM algorithm estimates yielded a maximum error equal to 7.5%, which indicates excellent performance of the DREAM algorithm in ascertaining the effect of uncertainty in riverine water quality assessment.