Economic and Environmental Management of Water Resources: Perspective of Groundwater

Subsurface water has a substantial economic value in drinking and irrigation water across the globe Failure to recognise the economic value has led to wasteful and environmentally damaging uses of the resource When the groundwater resource gets depleted groundwater development costs increase and the aquifers capacity to provide the variety of environmental services decreases with sinking groundwater level and diminished natural discharge The cost of abstracting the fresh water increases with the need to lift groundwater from increasingly greater depths and hence the cost-benefit ratio of groundwater use changes over time The procedure of discounting adjusts for future values of related services by accounting for time differences Environmental costs are rather difficult to assess and incorporate in groundwater resources management Environmental damage costs refer to non-use values attached to a healthy functioning aquatic ecosystem while the costs to those who use the water environment refer to the corresponding use values This paper highlights the aspects relevant for decisions to groundwater management and rate of storage depletion and its financial implication

Growth and Variability of Major Food Crops Production in Khyber Pakhtunkhwa, Pakistan

The present study was undertaken to investigate the growth and variability in major food crops production of Khyber Pakhtunkhwa. The study was based on secondary data, covers a period of about 30 years i.e. starting from 1984-85 to 2013-14, Whereas, the growth models has been employed to fit the best growth model and Cuddy Della Vella Index was applied to find variability in major food crops production i.e. wheat, maize, sugarcane and rice. Based on the results of analyzed data, it was found that in major food crops (wheat, maize, sugarcane, rice) Production, the growth models i.e.  Cubic growth model, power growth model, cubic growth model, cubic growth model respectively were found suitable, based on the R2 criteria and fitted trend line. After selecting best fitted model for each major food crop, the growth rate was calculated by using the selected fitted models which were found to be 10.97%, 8.00%, 45.31% and 1.19% respectively.  Moreover, the variability for each major food crop production was found to be 1.53%, 1.23%, 0.44% and 0.79% respectively.

Nile Rivers Basin Dispute: Perspectives of the Grand Ethiopian Renaissance Dam (GERD)

Transboundary river basins are under increasing pressure due to population growth agricultural and industrial developments and climate change as well as river pollution Water scarcity is on the increase due to the increasing gap between water demands and supply This will result in more tensions disputes conflicts and deadlocks in negotiations over water distribution length of time it takes to fill the reservoir and allocation Ethiopia is building the Grand Ethiopian Renaissance Dam GERD on the Blue Nile River with a hydropower capacity of 6 000 MW The total estimated cost of the project is US 4 8 Billion and will be the largest dam in Africa which is much larger than the Aswan Dam in Egypt Regional controversies have risen over the construction of the dam between Ethiopia and the downstreamcountries Sudan and Egypt The Blue Nile River is a source of around 85 of the Nile River water Egypt claims that GERD will reduce flow of water in the Nile River between 11 and 19 billion m3 BCM which will affect2 million people and will also interrupt electricity supplies 25 to 40 The real scale of the environmental impacts of GERD under construction upstream of the Nile River together with the rising sea levels due to climate change leading to saltwater intrusion downstream are still not clear But for Ethiopians GERD is empowering development and contribution to their future With the Nile no longer Egyptian birthright and the Nile Delta gradually disappearing into the Mediterranean sea millions of Egypt s people will inevitably need to look elsewhere for a livable future The crises therefore necessities the adaptation of a more effective institutional arrangement such as through Rowland-Ostrom Framework for common pool shared water resource management andcooperative approach to address and resolve present and future problems including on other common transboundary resources forest oil gas and minerals The need for expanding traditiona

Seasonal and Annual Trends of Rainfall and Streamflow in the Mae Klong Basin, Thailand

This study examined seasonal and annual trends of rainfall and streamflow data in the Mae Klong Basin, Thailand. Monthly data of eight key rainfall stations and ten streamflow stations were analyzed to detect trends using the non-parametric Mann-Kendall test, whilst the magni-tude of the trends was determined by Sen’s slope method for the period 2000-2015. For 75 % of the analyzed stations, rainfall was found to increase in the wet season and decrease in the dry season. Station 130013 situated in the lower region showed a statistically significant increasing trend with a trend slope of 16.02 mm a-1 in the wet season, while station 130042- also located in the lower region of the basin- showed a statistically significant decreasing trend, with a trend slope of 23.60 mm a-1 in the dry season. On an annual basis, 63 % of the analyzed stations showed increasing rainfall trends, particularly in the central and lower regions of the Mae Klong Basin; however, rainfall trends in the upper region were found to be decreasing, which reflected water contributions to two main reservoirs in the upper part. The trends of naturalized inflow of Srinagarind and Vajiralongkorn Reservoirs were found to be decreasing on both seasonal and annual bases, while two naturalized streamflow stations located in Lam Taphoen and Lampachi sub-basins in the central and lower regions, respectively, showed increasing trends in both dry and wet seasons. The trends of regulated streamflow stations downstream of 4 main dams which were a result of reservoir operation were found to mostly decrease on an annual scale. Results of this study can help water resources managers enhance accuracy of assessment and effective planning of water resources management in the basin

Application of non-parametric approaches to identify trends in streamflow for the Mae Klong River Basin, Thailand

Mean monthly streamflow records of ten gauging stations in the Mae Klong River Basin, Thailand, were analyzed for trends using non-parametric approaches for the period 2000–2015. The statistical significance of trends was tested on monthly, seasonal, and annual scales using three non-parametric tests; Mann–Kendall (MK), Spearman's rho (SR), and innovative trend analysis (ITA) on a 5% significance level. The streamflow stations were grouped into two categories: naturalized streamflow (four stations) and regulated streamflow (six stations). In both the dry and wet seasons, decreasing trends were observed in the naturalized inflows to the Srinagarind and Vajiralongkorn Dams. On an annual basis, all the naturalized streamflow stations showed decreasing trends. For the regulated streamflow stations, in the dry season, four out of six stations showed increasing trends, while in the wet season, five out of six stations indicated decreasing trends. On an annual basis, five stations showed decreasing trends by both the MK and SR tests, while the ITA method indicated decreasing trends for four regulated streamflow stations. The results of this study can help water resources managers for better assessment and planning of water resources in the basin. HIGHLIGHTS This study examined streamflow trends in the Mae Klong River Basin in Thailand.; On the seasonal scale, mixed trends (increasing and decreasing) trends were found for the naturalized streamflow stations. Regulated stations showed decreasing trends for the majority of the stations.; On the annual basis, most of the stations showed decreasing trends.; The ITA method showed comparable findings with that of statistical methods.

Alpha-Power Exponentiated Inverse Rayleigh distribution and its applications to real and simulated data.

The main goal of the current paper is to contribute to the existing literature of probability distributions. In this paper, a new probability distribution is generated by using the Alpha Power Family of distributions with the aim to model the data with non-monotonic failure rates and provides a better fit. The proposed distribution is called Alpha Power Exponentiated Inverse Rayleigh or in short APEIR distribution. Various statistical properties have been investigated including they are the order statistics, moments, residual life function, mean waiting time, quantiles, entropy, and stress-strength parameter. To estimate the parameters of the proposed distribution, the maximum likelihood method is employed. It has been proved theoretically that the proposed distribution provides a better fit to the data with monotonic as well as non-monotonic hazard rate shapes. Moreover, two real data sets are used to evaluate the significance and flexibility of the proposed distribution as compared to other probability distributions

Alpha-Power Pareto distribution: Its properties and applications.

In Statistical theory, inclusion of an additional parameter to standard distributions is a usual practice. In this study, a new distribution referred to as Alpha-Power Pareto distribution is introduced by including an extra parameter. Several properties of the proposed distribution, including moment generating function, mode, quantiles, entropies, mean residual life function, stochastic orders and order statistics are obtained. Parameters of the proposed distribution have been estimated using maximum likelihood estimation technique. Two real datasets have been considered to examine the usefulness of the proposed distribution. It has been observed that the proposed distribution outperforms different variants of Pareto distribution on the basis of model selection criteria

An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan.

Identifying the abnormally high-risk regions in a spatiotemporal space that contains an unexpected disease count is helpful to conduct surveillance and implement control strategies. The EigenSpot algorithm has been recently proposed for detecting space-time disease clusters of arbitrary shapes with no restriction on the distribution and quality of the data, and has shown some promising advantages over the state-of-the-art methods. However, the main problem with the EigenSpot method is that it cannot be adapted to detect more than one spatiotemporal hotspot. This is an important limitation, since, in reality, we may have multiple hotspots, sometimes at the same level of importance. We propose an extension of the EigenSpot algorithm, called Multi-EigenSpot that is able to handle multiple hotspots by iteratively removing previously detected hotspots and re-running the algorithm until no more hotspots are found. In addition, a visualization tool (heatmap) has been linked to the proposed algorithm to visualize multiple clusters with different colors. We evaluated the proposed method using the monthly data on measles cases in Khyber-Pakhtunkhwa, Pakistan (Jan 2016- Dec 2016), and the efficiency was compared with the state-of-the-art methods: EigenSpot and Space-time scan statistic (SaTScan). The results showed the effectiveness of the proposed method for detecting multiple clusters in a spatiotemporal space

Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach

Ability to detect potential space-time clusters in spatio-temporal data on disease occurrences is necessary for conducting surveillance and implementing disease prevention policies. Most existing techniques use geometrically shaped (circular, elliptical or square) scanning windows to discover disease clusters. In certain situations, where the disease occurrences tend to cluster in very irregularly shaped areas, these algorithms are not feasible in practise for the detection of space-time clusters. To address this problem, a new algorithm is proposed, which uses a co-clustering strategy to detect prospective and retrospective space-time disease clusters with no restriction on shape and size. The proposed method detects space-time disease clusters by tracking the changes in space–time occurrence structure instead of an in-depth search over space. This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. The results of the annual data analysis showed that the most likely hotspot emerged in three sub-regions in the years 2013-2014. The most likely hotspots in monthly data appeared in the month of July to October in each year and showed a strong periodic trend