Impact of Spatial Data Availability on Climate Change Prediction in the Weyib River Basin in Ethiopia

Impact of spatial data availability on the temperature and precipitation prediction characteristics of Weyib River basin in Ethiopia has been investigated using CMIP5-CanESM2 model for the RCP8.5, RCP4.5 and RCP2.6 scenarios. The objective of the present study is to characterize how future temperatures and precipitation prediction under CMIP5-CanESM2 model output varies against diverse averaged arbitrary spatial weather stations found in the basin. The statistical downscaling model tested and verified using the observed daily data for twelve, six and three averaged arbitrary spatial weather stations as well as for a single weather station was used to predict the future climate scenarios. The results revealed that the mean annual daily maximum and minimum temperature and precipitation for twelve, six and three arbitrary spatial stations have revealed an increasing trend in the upcoming periods until the end of the century. In single station analysis, the trend itself has changed from increasing trend to decreasing trend in case of maximum and minimum temperature. In case of precipitation, no visible trend has been observed in case of single station analysis. Therefore, the variation in amount and distribution of precipitation and temperature among the four averaged spatial stations in the same study area might affect the water resources and agriculture of the basin and also instead of using a single weather station to predict future climate variables for a particular study basin, it is more reliable using averages of numerous spatial weather stations data.     

An Optimization Approach for Assessing the Reliability of Rainwater Harvesting

This study was aimed at developing an optimization approach to rainwater harvesting (RWH) considering three (3) water consumption scenarios (WCS). These scenarios which include basic water need (BWN), pour flush (PF) and full plumbing connection (FPC) corresponding to 50 litres per capita per day (lpcd), 75(lpcd) and 150(lpcd) respectively were simulated for different categories of buildings. Reliability of supply was determined by first obtaining composite surplus/deficit of rainwater followed by optimizing the redistribution of surplus rainwater harvested to deficient buildings. Results showed that when total annual rainfall intercepted by roof exceeded total demand, 100% reliability of water supply was guaranteed. Reliability was found to be a linear function of storage. When reliability of supply is possible, the optimized storage bears an inverse exponential relationship to the roof plan area per capita. The relationship between surplus/deficit and roof plan area per capita follows a one-phase decay pattern. An optimal redistribution of surplus water from self-sufficient buildings to deficient ones gave an improvement in supply reliability from 64 to 87% for basic water need, 47 to 58% for pour flush and 28 to 29% for full plumbing connection.     

Water Bloom Precursor Analysis Based on Two Direction S-Rough Set

In the field of water quality management, it is vital to determine the main precursory anomalies from the precursor of intricate water bloom in the context of a given area. In this paper, a water bloom precursor analysis method, based on two direction singular rough set, was proposed. This approach was produced on the basis of the different sections and pre-water bloom of water bloom precursor anomalies and characteristic of elements transferred in singular rough set. For testing the validity of two direction singular rough set application in water bloom precursor analysis, Xiangxi River, which is one of the typical tributaries of Three Gorges Reservoir in China, was selected as study area. The result showed that compared with other indexes, pH and dissolved oxygen (DO) are the most valuable indicators of water bloom in the precursory anomalies. Furthermore, regarding with water bloom precursory anomalies in Xiangxi River, most of the nutrient loading and biological community are the key indicators. Hence, this method can determine the main precursory anomaly for water bloom in the study area, which provides powerful knowledge support to water quality specialists for them to comprehensively analyze precursory anomaly so as to find out its relationship with occurrence law of water bloom.     

Reliability Analysis Applied on Land Subsidence Effects of Groundwater Remediation: Probabilistic vs. Deterministic Approach

The quantification of soil variability is one of the most important aspects in the geo-engineering context. The uncertainty analysis is the main part of the reliability assessment for which a quantitative evaluation was performed in this study. The Reliability Index and the Probability of Failure using the First-Order Reliability Method (FORM) represents both, an effective method which is easy to implement at the same time. This work analyzes possible effects of compaction induced into the aquifer of the Scarlino Plain, caused by the extension of the hydraulic barrier for groundwater remediation. The currently implemented vertical barrier is composed of 12 wells which reach the depth of 10 m. The improvement of the project involves the construction of a further 40 clusters, each consisting of a doublet which intercepts different depths (10 and 18 m). The models of the subsoil stratigraphy and of the groundwater were built using a numerical model. The groundwater flow and the piezometric surface in the current configuration of the barrier were studied and the project configuration was evaluated. Using the Aquitard drainage model, the land subsidence was estimated to calculate the maximum admissible displacement related to exhibited goods, the so called territorial vulnerability. The evaluation analysis was performed using a traditional deterministic approach, followed by a reliability method based on probabilistic models. Finally, the respective results were reported in a soil mapping with overlapping layers.     

Overview of Water Policy Developments: Pre- and Post-2015 Development Agenda

The year 2015 marked the end of some important universal decisions regarding water developments. The International Decade for Action ‘Water for Life’ (2005–2015) was concluded so that a “Post-2015 Development Agenda” is now defined. 2015 was also the year when UN Millenium Goals (MDGs) came to an end to convert into the new Agenda, which is a process led by the United Nations (UN) to define the future global development framework. The proposed goal is now referred to as SDG’s or Sustainable Development Goals, that extend existing commitments such as the MDGs and the priorities of Rio + 20. SDG’s will balance the economic, social and environmental dimensions of sustainable development with a strong linkage between environment and socio-economic goals. They converge with the post-2015 development agenda, now called Agenda 30 as these goals will be valid until 2030. One of the major concepts of SDG’s is water security, which is the basic element of the Global Goal on Water. Water security is the capacity of a population to safeguard sustainable access to adequate quantities of acceptable quality of water for sustaining livelihoods, human well-being, and socioeconomic development, for ensuring protection against pollution and waterrelated disasters, and for preserving ecosystems in a climate of peace and political stability. This paper discusses the developments in water management within the last 30 years, which eventually led to the above concepts. A summary is provided on key events and documents of these past years to point out how the international community has reacted towards present and emerging needs of the society.     

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).     

Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks

The success of hydraulic simulation models of water distribution networks is associated with the ability of these models to represent real systems accurately. To achieve this, the calibration phase is essential. Current calibration methods are based on minimizing the error between measured and simulated values of pressure and flow. This minimization is based on a search of parameter values to be calibrated, including pipe roughness, nodal demand, and leakage flow. The resulting hydraulic problem contains several variables. In addition, a limited set of known monitored pressure and flow values creates an indeterminate problem with more variables than equations. Seeking to address the lack of monitored data for the calibration of Water Distribution Networks (WDNs), this paper uses a meta-model based on an Artificial Neural Network (ANN) to estimate pressure on all nodes of a network. The calibration of pipe roughness applies a metaheuristic search method called Particle Swarm Optimization (PSO) to minimize the objective function represented by the difference between simulated and forecasted pressure values. The proposed method is evaluated at steady state and over an extended period for a real District Metering Area (DMA), named Campos do Conde II, and the hypothetical network named C-town, which is used as a benchmark for calibration studies.     

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.