Land Use Effect on the CN Model Parameters in a Tropical Dry Environment

Dry tropical forests account for over 1,000,000 km², and there is still lack of knowledge on their hydrologic processes. The curve number (CN) hydrologic model developed by the Natural Resources Conservation Service (NRCS) is widely applied for runoff determination in various parts of the world, but not so in tropical semiarid regions. This study analyzes the impact of land use changes on the CN model in a tropical semiarid environment, in two catchments of native dry tropical forest and thinned dry tropical forest land use from 2009 to 2012. The CN model was calibrated and validated for the NRCS recommended initial abstraction ratio λ = 0.2, and for λ evaluated from rainfall and runoff data. A reliability analysis was performed using Monte Carlo simulation. Model goodness-of-fit was assessed with statistical criteria. A total of 42 and 40 rainfall-runoff events were analyzed for the native and thinned dry tropical forest, respectively. Characteristic λ values of 0.15 and 0.11 were determined for the two respective catchments. Although CN values were similar for both land uses, CN_λ=0.20 = 80 and CN_{median λ} = 77, the thinned catchment showed a higher CN model parameters variability. The CN model was more sensitive to variations of CN values than to those of λ. This study showed that no matter the vegetation management in a dry tropical forest environment, modeled runoff is not affected by λ, but rather affected by CN, which represents soil, landuse and management.     

A Scenario Based Impact Assessment of Trace Metals on Ecosystem of River Ganges Using Multivariate Analysis Coupled with Fuzzy Decision-Making Approach

The growing consciousness about the health risks associated with environmental pollutants has brought a major shift in global concern towards prevention of hazardous/trace metals discharge in water bodies. Majority of these trace metals gets accumulated in the body of aquatic lives, which are considered as potential indicators of hazardous content. This results in an ecological imbalance in the form of poisoning, diseases and even death of fish and other aquatic lives, and ultimately affect humans through food chain. Trace metals such as Cd, Cr, Cu, Mn, Ni, Pb and Zn originated from various industrial operations containing metallic solutions and agricultural practices, have been contributing significantly to cause aquatic pollution. The present study develops a novel approach of expressing sustainability of river’s ecosystem based on health of the fish by coupling fuzzy sensitivity analysis into multivariate analysis. A systematic methodology has been developed by generating monoplot, two dimensional biplot and rotated component matrix (using ‘Analyze it’ and ‘SPSS’ software), which can simultaneously identify critical trace metals and their industrial sources, critical sampling stations, and adversely affected fish species along with their interrelationships. A case study of assessing the impact of trace metals on the aquatic life of river Ganges, India has also been presented to demonstrate effectiveness of the model. The clusters pertaining to various water quality parameters have been identified using Principal Component Analysis (PCA) to determine actual sources of pollutants and their impact on aquatic life. The fuzzy sensitivity analysis reveals the cause-effect relationship of these critical parameters. The study suggests pollution control agencies to enforce appropriate regulations on the wastewater dischargers responsible for polluting river streams with a particular kind of trace metal(s).     

Sustainable Basin-Scale Water Allocation with Hydrologic State-Dependent Multi-Reservoir Operation Rules

This study extends the PSO-MODSIM model, integrating particle swarm optimization (PSO) algorithm and MODISM river basin decision support system (DSS) to determine optimal basin-scale water allocation, in two aspects. The first is deriving hydrologic state-dependent (conditional) operating rules to better account for drought and high-flow periods, and the second is direct, explicit consideration of sustainability criteria in the model’s formulation to have a better efficiency in basin-scale water allocation. Under conditional operating rules, the operational parameters of reservoir target storage levels and their priority rankings were conditioned on the hydrologic state of the system in a priority-based water allocation scheme. The role of conditional operating rules and policies were evaluated by comparing water shortages associated with objective function values under unconditional and conditional operating rules. Optimal basin-scale water allocation was then evaluated by incorporating reliability, vulnerability, reversibility and equity sustainability indices into the PSO objective function. The extended model was applied for water allocation in the Atrak River Basin, Iran. Results indicated improved distribution of water shortages by about 7.5% using conditional operating rules distinguishing dry, normal and wet hydrologic states. Alternative solutions with nearly identical objective function values were found with sustainability indices included in the model.     

Informational Entropy: a Failure Tolerance and Reliability Surrogate for Water Distribution Networks

Evolutionary algorithms are used widely in optimization studies on water distribution networks. The optimization algorithms use simulation models that analyse the networks under various operating conditions. The solution process typically involves cost minimization along with reliability constraints that ensure reasonably satisfactory performance under abnormal operating conditions also. Flow entropy has been employed previously as a surrogate reliability measure. While a body of work exists for a single operating condition under steady state conditions, the effectiveness of flow entropy for systems with multiple operating conditions has received very little attention. This paper describes a multi-objective genetic algorithm that maximizes the flow entropy under multiple operating conditions for any given network. The new methodology proposed is consistent with the maximum entropy formalism that requires active consideration of all the relevant information. Furthermore, an alternative but equivalent flow entropy model that emphasizes the relative uniformity of the nodal demands is described. The flow entropy of water distribution networks under multiple operating conditions is discussed with reference to the joint entropy of multiple probability spaces, which provides the theoretical foundation for the optimization methodology proposed. Besides the rationale, results are included that show that the most robust or failure-tolerant solutions are achieved by maximizing the sum of the entropies.     

Modeling of Water Resources Allocation and Water Quality Management for Supporting Regional Sustainability under Uncertainty in an Arid Region

In this study, a scenario-based interval-stochastic fraticle optimization with Laplace criterion (SISFL) method is developed for sustainable water resources allocation and water quality management (WAQM) under multiple uncertainties. SISFL can tackle uncertainties presented as interval parameters and probability distributions; meanwhile, it can also quantify artificial fuzziness such as risk-averse attitude in a decision-making issue. Besides, it can reflect random scenario occurrence under the supposition of no data available. The developed method is applied to a real case of water resources allocation and water quality management in the Kaidu-kongque River Basin, where encounter serve water deficit and water quality degradation simultaneously in Northwest China. Results of water allocation pattern, pollution mitigation scheme, and system benefit under various scenarios are analyzed. The tradeoff between economic activity and water-environment protection with interval necessity levels and Laplace criterions can support policymakers generating an effective and robust manner associated with risk control for WAQM under multiple uncertainties. These discoveries avail local policymakers gain insight into the capacity planning of water-environment to satisfy the basin’s integrity of socio-economic development and eco-environmental sustainability.     

Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation

Water resources allocation problems are mainly categorized in two classes of simulation and optimization. In most cases, optimization problems due to the number of variables, constraints and nonlinear feasible search space are known as a challenging subject in the literature. In this research, by coupling particle swarm optimization (PSO) algorithm and a network flow programming (NFP) based river basin simulation model, a PSO-NFP hybrid structure is constructed for optimum water allocation planning. In the PSO-NFP model, the NFP core roles as the fast inner simulation engine for finding optimum values for a large number of water discharges in the network links (rivers and canals) and nodes (reservoirs and demands) while the heuristic PSO algorithm forms the outer optimization cover to search for the optimum values of reservoirs capacities and their storage priorities. In order to assess the performance of the PSO-NFP model, three hypothetical test problems are defined, and their equivalent nonlinear mathematical programs are developed in LINGO and the results are compared. Finally, the PSO-NFP model is applied in solving a real river basin water allocation problem. Results indicate that the applied method of coupling PSO and NFP has an efficient ability for handling river basin-scale water resources optimization problems.     

Simulation of Nonstationary Spring Discharge Using Time Series Models

We present a detailed analysis and comparison of two time series models, i.e., ARIMA and ARIMA-GARCH, to simulate the discharge of a karst spring (Niangziguan Springs (NS) complex) in the northern China. Statistical tests for the residuals are applied to examine the reasonability of the models. Statistically, both models are reasonably good to simulate the mean value of the discharge of the NS complex. The statistical test shows that the residual discharge data have conditional time-varying variance and volatility clustering, known as heteroscedasticity of the data. Calibration test shows that the ARIMA-GARCH model gives a varying confidence interval, which can more effectively capture the heteroscedasticity of the data, comparing with a constant confidence interval in the ARIMA model. In the validation and application process, we applied two approaches to simulate the discharge data: (1) fixed models, and (2) evolving models. The confidence interval width monotonically increases in both fixed models, and the fixed ARIMA-GARCH model has faster increasing confidence interval width than the fixed ARIMA model. This suggests that the fixed time series models are only suitable for short-term prediction. However, we found that this drawback can be compensated by updating the model once new data become available. Our evolving models show more reasonable confidence interval width for both models. In addition, the application shows that the ARIMA-GARCH model is very sensitive to the data fluctuation. We also found the evolving ARIMA-GARCH model was able to return to the narrow confidence interval width once the fluctuation diminished. Hence, we conclude that the ARIMA-GARCH model is more suitable for the sequences with strong heteroscedasticity.     

Participatory Modeling Workshops in a Water-Stressed Basin Result in Gains in Modeling Capacity but Reveal Disparity in Water Resources Management Priorities

Participatory modeling workshops were held in Sonora, México, with the goal of developing water resources management strategies in a water-stressed basin. A model of the water resources system, consisting of watershed hydrology, water resources infrastructure, and groundwater models, was developed deliberatively in the workshops, along with scenarios of future climate and development. Participants used the final version of the water resources systems model to select management strategies. The performance of the strategies was based on the reliability of meeting current and future demands at a daily time scale over a year’s period. Pre- and post-workshop surveys were developed and administered. The survey questions focused on evaluation of participants’ modeling capacity and the utility and accuracy of the models. The selected water resources strategies and the associated, expected reliability varied widely among participants. Most participants could be clustered into three groups with roughly equal numbers of participants that varied in terms of reliance on expanding infrastructure vs. demand modification; expectations of reliability; and perceptions of social, environmental, and economic impacts. The wide range of strategies chosen and associated reliabilities indicate that there is a substantial degree of uncertainty in how future water resources decisions could be made in the region. The pre- and post-survey results indicate that participants believed their modeling abilities increased and beliefs in the utility of models increased as a result of the workshops.     

Ecosystem Service Impacts of Urban Water Supply and Demand Management

Utilities face the challenge of enhancing long-term water security while minimising undesirable economic, social and environmental impacts of supply and demand management options. This paper provides an example of how the ecosystem services concept can be used to enumerate and organise broad impacts of water supply options. A case study of Adelaide, South Australia, is used to examine costs and benefits associated with different sources of water and source-water mix scenarios. Ecosystem service impacts are estimated using estimates from the literature. Seven water supply and demand management options are considered for Adelaide: 1) the River Murray, 2) Mt. Lofty Ranges catchments, 3) wastewater reuse, 4) desalination, 5) stormwater harvesting, 6) groundwater and 7) water conservation. The largest costs are associated with sourcing water from conservation measures such as water restrictions on outdoor watering estimated at $1.87/kL. Salinity damage costs associated with residential uses are estimated at up to $1.54/kL. Salinity damage costs of wastewater reuse were estimated at $1.16/kL. The largest benefit is coastal amenity services associated with stormwater harvesting and treatment estimated at $1.03/kL. Results show that there is a trade-off between financial costs and ecosystem services impacts with source-water mix scenarios with the highest ecosystem services cost having the lowest financial O&M cost and vice versa. This highlights the importance of taking ecosystem services into account when evaluating water supply options.