Water Productivity
Binata Roy; G M Tarekul Islam; A.K.M. Saiful Islam; Biswa Bhattacharya; Md. Jamal Uddin Khan
Abstract
Introduction: Monsoon rains in August 1917 in Bangladesh affected close to 6.9 million people in Bangladesh. Rising waters began on August 11 and by August 15, one third of the country was submerged.The floods killed at least 115 people and forced close to 200,000 people from their homes. In some parts ...
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Introduction: Monsoon rains in August 1917 in Bangladesh affected close to 6.9 million people in Bangladesh. Rising waters began on August 11 and by August 15, one third of the country was submerged.The floods killed at least 115 people and forced close to 200,000 people from their homes. In some parts of the country, up to 80% of sanitation and water facilities were affected. Many shelter centres lacked the ability to provide adequate food, water, hygiene and protection, putting children, adolescent girls and women at risk. Most of the affected people were farm labourers, relying on agriculture to meet their basic needs. The extent of the disaster increased the risks of serious disease outbreaks; children dropping out of school; and violence, neglect, abuse and exploitation of children and women.Pre-monsoon (March-May) flash flood in the northeast Haor region of Bangladesh has drawn much attention due to its early onset, high frequency, and adverse impact on the Boro crop. To understand its past changes and future occurrences, a trend analysis is carried out on the observed 3 - hourly water level data and daily rainfall data of the Haor region using the Mann-Kendall test, Trend-Free Pre-Whitening test, and Sens slope estimator.Material and methods: This study was conducted in the north-eastern region of Bangladesh covers approximately 24,500 km2, bounded by the international border with India to the north and east, the Old Brahmaputra River to the west, and the Nasirnagar to Madhabpur and Meghna River to the south. This region is comprised of the floodplains of the Meghna River and its tributaries. The larger portion of this region is the Haor basin and is characterized by numerous large, deeply flooded depressions.The Mann Kendall Trend Test is used to analyze data collected over time for consistently increasing or decreasing trends (monotonic) in Y values. It is a non-parametric test, which means it works for all distributions, but your data should have no serial correlation. If your data does follow a normal distribution, you can run simple linear regression instead. The test can be used to find trends for as few as four samples. However, with only a few data points, the test has a high probability of not finding a trend when one would be present if more points were provided. The more data points you have the more likely the test is going to find a true trend (as opposed to one found by chance). The minimum number of recommended measurements is therefore at least 8 to 10.Results: Later, statistical trend analysis is conducted using the Mann-Kendall test and Sen’s slope. However, Lag-1 autocorrelation was determined before statistical trend analysis. The stations having no autocorrelation were directly investigated by the MK test, whereas the stations showing significant autocorrelation were analyzed through TFPWMK. Here, TFPWMK was preferred to PWMK because PWMK deals with the influence of serial correlation on the MK test but does not address the interaction between a trend and autocorrelation process. There can be a case where a trend exists in a time series even though the time series does not comprise an autocorrelation process, and in such a situation, the use of PWMK can be erroneous.Conclusion: Although trend analysis can be extremely helpful in many applications—from climate change to sociological analysis—it’s important to keep in mind that it is not foolproof. In particular:All data (unless gathered through a population census) is liable to sampling error. The extent of this problem will increase when coarse sampling methods (e.g. convenience sampling) are used. Data is likely subject to measurement error; random, systematic, or external; trends in this error may be mistaken as trends in the actual data. “Phantom”, short term trends exist even in the most random of number sequences, so trends should be followed out as long as possible. Also, finding no trend may mean there is no trend, but it may just as likely mean that your data is insufficient to illuminate a trend which does in fact exist.A statistically significant increasing trend is found for the relative water level. The trend in rainfall is increasing, though it is not statistically significant. From the observed record, the peak of the flash floods is found to be arriving early in late March-early April (instead of late April-early May), coinciding with the harvesting period of the Boro crop. The early arrival of the flash flood can cause catastrophic damage to the Boro crop in future flash floods. None of the current Boro varieties BRRI dhan28, BRRI 36, BRRI dhan69, BRRI dhan88 are safer to save Boro from early flash floods experienced in recent years. To escape the Boro crop from an early flash flood, Boro varieties with a shorter growth duration should be introduced. This helps crop productivity.
Water Productivity
Fiaz Hussain; Amir Iqbal
Abstract
The impacts of water variation differ in their magnitude in different canal irrigation systems, mainly due to variations in water availability (timings and amounts), crop types and soil fertility status. These necessitates conducting site-specific research and studies to evaluate the impact of variations ...
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The impacts of water variation differ in their magnitude in different canal irrigation systems, mainly due to variations in water availability (timings and amounts), crop types and soil fertility status. These necessitates conducting site-specific research and studies to evaluate the impact of variations in water availability at the farm level for specific crops. The findings of such studies will then be utilized to rationalize the irrigation supplies at the farm level keeping in view the level of variation. Accordingly, this study was undertaken for the assessment of inequality in canal water distribution and its impacts on the yield and water productivity of maize in the command area of the Khikhi distributary, Pakistan. For this purpose, three water courses at the head, middle and tail reaches of the distributary were selected. On each of the selected watercourse, three maize fields at the head, middle and tail were chosen. Discharges measurements were taken and the yield of the maize crop was recorded. A significant variation in design and measured discharges were observed in the head reaches (inlet point) of watercourses off taking from the head, middle and tail of distributary that was 13.79%, 12.0% and 7.30% reduction in the flow against the allocated discharges, respectively. The discharge variation along the distributary varies from 0 to 38% from head to tail end, similarly, the variation in discharge for the watercourse located at the head of the distributary was from (100 %) 2.90 cfs to (85.86 %) 2.49 cfs i.e. 0.41 cfs (14.14%) reduction in discharge from head to tail end and for the watercourses located at the middle (WCM) and tail (WCT) the discharge reduction was (31.72%) 1.02 cfs and (37.08 %) 0.66 cfs, respectively. These variations in discharge ultimately reduced the maize crop yield and production from 11 to 54%. The percentage gap in yield from head to tail was up to 54% and the water productivity decreased up to 26% for tail end section of watercourses. These results clearly showed the inconsistency in canal water distribution at tertiary level (watercourse) as well as secondary (distributary) irrigation system leading to reduce the crop production of tail end farmers.
Water Productivity
Josephine Treacy
Abstract
The Organisation for Economic Co-operation and Development (OECD) have predicted from the years 2000 to 2050 the industrial demand for water will increase by 400 %. This manuscript will discuss water stewardship as an aid to water productivity. The benefits of the integration of water stewardship and ...
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The Organisation for Economic Co-operation and Development (OECD) have predicted from the years 2000 to 2050 the industrial demand for water will increase by 400 %. This manuscript will discuss water stewardship as an aid to water productivity. The benefits of the integration of water stewardship and water productivity will be portrayed in this paper. The fundamentals of water productivity will be outlined. The stages of water stewardship namely operational, context, strategy and engagement will be introduced. The concept of the green economy and ecolabel products will be discussed. Other synergies including the life cycle analysis, water footprint assessment, multibarrier designs, citizen science and policy development as core needs within the integration will be outlined. The bigger goal of aiding the sustainable development goals (SDGs) to achieve clean water and water security as the main reason for society and corporate business to move in the direction of this integration will be highlighted. Water hydrology and catchment understanding are also the core benefits of the integration of water stewardship with water productivity. Improving water productivity by integrating water stewardship into its practices can improve business practices, environmental water flows, supply chain sourcing, policies, and water-efficient technologies. This manuscript highlights the range of different synergies that can strengthen the integration of water stewardship and water productivity. Water stewardship as an aid to water productivity can place water as a game changer for more eco-economical and environmental practices.
