Estimating productivity of water at different spatial scales using simulation modeling

Water resources / Productivity / Simulation models / Water scarcity / Water supply / Water balance / Performance indexes / Indicators / River basins / Cropping systems / Crop yield / Cotton / Hydrology / Economic analysis

Comparing estimates of actual evapotranspiration from satellites, hydrological models, and field data: a case study from Western Turkey

Evapotranspiration / Estimation / Remote sensing / Satellite surveys / Field tests / Measurement / Productivity / Crops / Water requirements / Water balance / Irrigation management / River basins / Hydrology / Models / Turkey / Gediz River

Integrated basin modeling

Simulation models / Irrigation management / Water balance / Groundwater / River basins / Hydrology / Flow / Evapotranspiration / Precipitation / Soils / Turkey / Gediz Basin

Using datasets from the Internet for hydrological modeling: an example from the Kntnk Menderes Basin, Turkey

River basin development / Water resources / Data collection / Models / Hydrology / Land classification / Water management / Water scarcity / Water allocation / Stream flow / Water demand / Turkey / Kntnk Menderes Basin

Modeling scenarios for water allocation in the Gediz Basin, Turkey

Water management / Water allocation / Models / River basin development / Hydrology / Decision making / Environmental effects / Water use efficiency / Climate / Irrigation water / Irrigated farming / Stream flow / Surface water / Salt water intrusion / Turkey / Gediz Basin

Uncertainty in climate change projections of discharge for the Mekong River Basin

The Mekong River Basin is a key regional resource in Southeast Asia for sectors that include agriculture, fisheries and electricity production. Here we explore the potential impacts of climate change on freshwater resources within the river basin. We quantify uncertainty in these projections associated with GCM structure and climate sensitivity, as well as from hydrological model parameter specification. This is achieved by running pattern-scaled GCM scenarios through a semi-distributed hydrological model (SLURP) of the basin. Pattern-scaling allows investigation of specific thresholds of global climate change including the postulated 2 degrees C threshold of "dangerous" climate change. Impacts of a 2 degrees C rise in global mean temperature are investigated using seven different GCMs, providing an implicit analysis of uncertainty associated with GCM structure. Analysis of progressive changes in global mean temperature from 0.5 to 6 degrees C above the 1961-1990 baseline (using the HadCM3 GCM) reveals a relatively small but non-linear response of annual river discharge to increasing global mean temperature, ranging from a 5.4% decrease to 4.5% increase. Changes in mean monthly river discharge are greater (from -16% to +55%, with greatest decreases in July and August, greatest increases in May and June) and result from complex and contrasting intra-basin changes in precipitation, evaporation and snow storage/melt. Whilst overall results are highly GCM dependent (in both direction and magnitude), this uncertainty is primarily driven by differences in GCM projections of future precipitation. In contrast, there is strong consistency between GCMs in terms of both increased potential evapotranspiration and a shift to an earlier and less substantial snowmelt season. Indeed, in the upper Mekong (Lancang sub-basin), the temperature-related signal in discharge is strong enough to overwhelm the precipitation-related uncertainty in the direction of change in discharge, with scenarios from all GCMs leading to increased river flow from April-June and decreased flow from July-August

The Diffusions Coefficient of Hemoglobin in Pure Water Solution

Carbon monoxide hemoglobin prepared from beef blood by the method of Marshall and Welker (J. Am. Chem. Soc., 35, 820. 1913) was allowed to diffuse from a water solution of constant concentration into pure pater contained in a thin flat cell of optical glass

Innovations in Simulation: Experiences with Cloud-based Simulation Experimentation

The amount of simulation experimentation that can be performed in a project can be restricted by time, especially if a model takes a long time to simulate and many replications are required. Cloud Computing presents an attractive proposition to speeding up, or extending, simulation experimentation as computing resources can be hired on demand rather than having to invest in costly infrastructure. However, it is not common practice for simulation users to take advantage of this and, arguably, rather than speeding up simulation experimentation users tend to make compromises by using unnecessary model simplification techniques. This may be due to a lack of awareness of what Cloud Computing can offer. Based on several years’ experience of innovation in this area, this article presents our experiences in developing Cloud Computing applications for simulation experimentation and discusses what future innovations might be created for the widespread benefit of our simulation community

Identification of bacterial pathogens in sudden unexpected death in infancy and childhood using 16S rRNA gene sequencing

Background Sudden unexpected death in infancy (SUDI) is the most common cause of post-neonatal death in the developed world. Following an extensive investigation, the cause of ~40% of deaths remains unknown. It is hypothesized that a proportion of deaths are due to an infection that remains undetected due to limitations in routine techniques. This study aimed to apply 16S rRNA gene sequencing to post-mortem (PM) tissues collected from cases of SUDI, as well as those from the childhood equivalent (collectively known as sudden unexpected death in infancy and childhood or SUDIC), to investigate whether this molecular approach could help identify potential infection-causing bacteria to enhance the diagnosis of infection. Methods In this study, 16S rRNA gene sequencing was applied to de-identified frozen post-mortem (PM) tissues from the diagnostic archive of Great Ormond Street Hospital. The cases were grouped depending on the cause of death: (i) explained non-infectious, (ii) infectious, and (iii) unknown. Results and conclusions In the cases of known bacterial infection, the likely causative pathogen was identified in 3/5 cases using bacterial culture at PM compared to 5/5 cases using 16S rRNA gene sequencing. Where a bacterial infection was identified at routine investigation, the same organism was identified by 16S rRNA gene sequencing. Using these findings, we defined criteria based on sequencing reads and alpha diversity to identify PM tissues with likely infection. Using these criteria, 4/20 (20%) cases of unexplained SUDIC were identified which may be due to bacterial infection that was previously undetected. This study demonstrates the potential feasibility and effectiveness of 16S rRNA gene sequencing in PM tissue investigation to improve the diagnosis of infection, potentially reducing the number of unexplained deaths and improving the understanding of the mechanisms involved

Industry Simulation Gateway on a Scalable Cloud

Large scale simulation experimentation typically requires significant computational resources due to an excessive number of simulation runs and replications to be performed. The traditional approach to provide such computational power, both in academic research and industry/business applications, was to use computing clusters or desktop grid resources. However, such resources not only require upfront capital investment but also lack the flexibility and scalability that is required to serve a variable number of clients/users efficiently. This paper presents how SakerGrid, a commercial desktop grid based simulation platform and its associated science gateway have been extended towards a scalable cloud computing solution. The integration of SakerGrid with the MiCADO automated deployment and autoscaling framework supports the execution of multiple simulation experiments by dynamically allocating virtual machines in the cloud in order to complete the experiment by a user-defined deadline