468 research outputs found
Water Footprint, Blue Water Scarcity, and Economic Water Productivity of Irrigated Crops in Peshawar Basin, Pakistan
Get PDFPakistan possesses the fourth largest irrigation network in the world, serving 20.2 million hectares of cultivated land. With an increasing irrigated area, Pakistan is short of freshwater resources and faces severe water scarcity and food security challenges. This is the first comprehensive study on the water footprint (WF) of crop production in Peshawar Basin. WF is defined as the volume of freshwater required to produce goods and services. In this study, we assessed the blue and green water footprints (WFs) and annual blue and green water consumption of major crops (maize, rice, tobacco, wheat, barley, sugarcane, and sugar beet) in Peshawar Basin, Pakistan. The Global Water Footprint Assessment Standard (GWFAS) and AquaCrop model were used to model the daily WF of each crop from 1986 to 2015. In addition, the blue water scarcity, in the context of available surface water, and economic water productivity (EWP) of these crops were assessed. The 30 year average blue and green WFs of major crops revealed that maize had the highest blue and green WFs (7077 and 2744 m3/ton, respectively) and sugarcane had the lowest blue and green WFs (174 and 45 m3/ton, respectively). The average annual consumption of blue water by major crops in the basin was 1.9 billion m3, where 67% was used for sugarcane and maize, covering 48% of the cropland. The average annual consumption of green water was 1.0 billion m3, where 68% was used for wheat and sugarcane, covering 67% of the cropland. The WFs of all crops exceeded the global average. The results showed that annually the basin is supplied with 30 billion m3 of freshwater. Annually, 3 billion m3 of freshwater leaves the basin unutilized. The average annual blue water consumption by major crops is 31% of the total available surface water (6 billion m3) in the basin. Tobacco and sugar beet had the highest blue and green EWP while wheat and maize had the lowest. The findings of this study can help the water management authorities in formulating a comprehensive policy for efficient utilization of available water resources in Peshawar Basin
Correction to: The ‘can do, do do’ concept in COPD; quadrant interpretation, affiliation and tracking longitudinal changes
No full textFollowing publication of the original article [1], the authors identified a mistake in the author names, as both forename and initials were stated. Initially published author names: A. J. Alex van ’t Hul, E. H. Noortje Koolen, H. W. Jeroen van Hees, B. Bram van den Borst and M. A. Martijn Spruit Correct author names: Alex J. van ‘t Hul, Noortje H. Koolen, Jeroen W. van Hees, Bram van den Borst, Martijn A. Spruit. The original article has been corrected.</p
Quantification and modelling of organic micropollutant removal by reverse osmosis (RO) drinking water treatment
Get PDFReverse osmosis (RO) is the most promising membrane technology in organic micropollutants (MPs) removal of drinking water treatment. For 78 MPs, passage and removal were evaluated with an ESPA3 RO membrane and the robustness of RO against MPs was studied. The MPs were classified according to their charge and hydrophobicity. The results showed that the size of neutral compounds was negatively correlated with their passage. This correlation was weaker for neutral hydrophobic MPs than neutral hydrophilic MPs. The lowest passage (0.2%–4%) was displayed by anionic MPs because of electrostatic repulsion between the negatively charged solute and negatively charged membrane surface. Cationic MPs showed a higher passage (around 0.4%–40%) due to electrostatic sorption and Donnan exclusion. The relationship between physical-chemical properties of MPs and their passage was evaluated by the one-way analysis of variance (ANOVA). We performed a qualitative analysis of variables using Principal Component Analysis (PCA) in order to examine the physical-chemical properties of compounds that affect the membrane removal of MPs. After analysis with Multiple Linear Regression (MLR), we concluded that properties such as molecular width, equivalent molecular width, pKa and solubility can be considered as significant descriptors for prediction of the membrane removal. The influence of feed water temperature on MPs passage was also assessed. The results revealed that a rise of water temperature from 5 to 19 °C, increases the average passage of MPs by 6.5%.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Sanitary Engineerin
Outcome novelty in Exploratory Modellingand Analysis: A research into the value of novelty search for exploratory modelling
No full textNovelty search is a state-of-the-art approach focusing on behavioural novelty, rewarding diverging, as opposed to pursuing static objectives. This is relevant for exploratory modelling and analysis, which focuses on exploration of model through open exploration or directed search. Novelty search as an open exploration strategy is being tested against proven methods such as latin hypercube sampling. Using existing evolutionary algorithms and a developed novelty function, the experiments focus on comparisson, impact of the number of functional evaluations and the impact of the goals of the evolutionary algorithm. Finally it can be concluded that novelty search finds novelties in the lake problem, which makes it a relevant search strategy, but not suited for indiviual exploration. That means that it would still be advised to use latin hypercube sampling for earlier exploration.Engineering and Policy Analysi
Determination and integration of appropriate spatial scales for river basin modelling
Get PDFAppropriate spatial scales of dominant variables are determined and integrated into an appropriate model scale. This is done in the context of the impact of climate change on flooding in the River Meuse in Western Europe. The objective is achieved by using observed elevation, soil type, land use type and daily precipitation data from several sources and employing different relationships between scales, variable statistics and outputs. The appropriate spatial scale of a key variable is assumed to be equal to a fraction of the spatial correlation length of that variable. This fraction was determined on the basis of relationships between statistics and scale and an accepted error in the estimation of the statistic of 10%. This procedure resulted in an appropriate spatial scale for precipitation of about 20 km in an earlier study. The application to river basin variables revealed appropriate spatial scales for elevation, soil and land use of respectively 0·1, 5·3 and 3·3 km. The appropriate model scale is determined by multiplying the appropriate variable scales with their associated weights. The weights are based on SCS curve number method relationships between the peak discharge and some specific parameters like slope and curve number. The values of these parameters are dependent on the scale of each key variable. The resulting appropriate model scale is about 10 km, implying 225-250 model cells in an appropriate model of the Meuse basin meant to assess the impact of climate change on river flooding. The usefulness of the appropriateness procedure is in its ability to assess the appropriate scales of the individual key variables before model construction and integrate them in a balanced way into an appropriate model scale. Another use of the procedure is that it provides a framework for decisions about the reduction or expansion of data networks and need
Modelling the effects of spatial and temporal resolution of rainfall and basin model on extreme river discharge
Get PDFImportant characteristics of an appropriate river basin model, intended to study the effect of climate change on basin response, are the spatial and temporal resolution of the model and the rainfall input. The effects of input and model resolution on extreme discharge of a large river basin are assessed to give some indication on appropriate resolutions. A simple stochastic rainfall model and a river basin model with uniform parameters and multiple rainfall input have been developed and applied to the River Meuse basin in northwestern Europe. The results show that the effect of model resolution on extreme river discharge is much greater than that of input resolution. The highest model resolution seems to be quite accurate in determining extreme discharge. Although the results should be interpreted with caution, they may give some indication of appropriate input and model resolutions for the determination of extreme discharge of a large river basin
Impact of climate change on river flooding assessed with different spatial model resolutions
Get PDFThe impact of climate change on flooding in the river Meuse is assessed on a daily basis using spatially and temporally changed climate patterns and a hydrological model with three different spatial resolutions. This is achieved by selecting a hydrological modelling framework and implementing appropriate model components, derived in an earlier study, into the selected framework (HBV). Additionally, two other spatial resolutions for the hydrological model are used to evaluate the sensitivity of the model results to spatial model resolution and to allow for a test of the model appropriateness procedure. Generations of a stochastic precipitation model under current and changed climate conditions have been used to assess the climate change impacts. The average and extreme discharge behaviour at the basin outlet is well reproduced by the three versions of the hydrological model in the calibration and validation, the results become somewhat better with increasing model resolution. The model results with synthetic precipitation under current climate conditions show a small overestimation of average discharge behaviour and a considerable underestimation of extreme discharge behaviour. The underestimation of extreme discharges is caused by the small-scale character of the observed precipitation input at the sub-basin scale. The general trend with climate change is a small decrease of the average discharge and a small increase of discharge variability and extreme discharges. The variability in extreme discharges for climate change conditions increases with respect to the simulations for current climate conditions. This variability results both from the stochasticity of the precipitation process and the differences between the climate models. The total uncertainty in river flooding with climate change (over 40%) is much larger than the change with respect to current climate conditions (less than 10%). However, climate changes are systematic changes rather than random changes and thus the large uncertainty range will be shifted to another level corresponding to the changed average situation
Extreme daily precipitation in Western Europe with climate change at appropriate spatial scales
Get PDFExtreme daily precipitation for the current and changed climate at appropriate spatial scales is assessed. This is done in the context of the impact of climate change on flooding in the river Meuse in Western Europe. The objective is achieved by determining and comparing extreme precipitation from stations, reanalysis projects, global climate models and regional climate models. An extreme value reduction methodology based on extreme precipitation correlation structure and surface area is used to deal with the transformation between different spatial scales. It appeared that return values are simulated quite well by the regional climate models and CSIRO9, but are underestimated by the reanalyses and overestimated by CGCM1 and HadCM3. The models simulated an average increase in extreme precipitation with climate change of about 18%, which is in the same range as the average model error and intermodel differences. The appropriate spatial scale for representing extreme precipitation was estimated at 20 km, when the bias permitted in the estimation of extreme precipitation is set at 10%. Downscaling modelled extreme precipitation to this appropriate scale results in considerable differences between reanalysis and GCM scale and appropriate scale return values. It is therefore obvious that return values at these appropriate scales should be used instead of at their original scale
Extreme precipitation, uncertainty and appropriate scales for assessment of climate change effect on river flooding
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