9 research outputs found
Accounting for seasonal land use dynamics to improve estimation of agricultural irrigation water withdrawals
No full textThe assessment of water withdrawals for irrigation is essential for managing water resources in cultivated tropical catchments. These water withdrawals vary seasonally, driven by wet and dry seasons. A land use map is one of the required inputs of hydrological models used to estimate water withdrawals in a catchment. However, land use maps provide typically static information and do not represent the hydrological seasons and related cropping seasons and practices throughout the year. Therefore, this study assesses the value of seasonal land use maps in the quantification of water withdrawals for a tropical cultivated catchment. We developed land use maps for the main seasons (long rains, dry, and short rains) for the semi-arid Kikuletwa catchment, Tanzania. Three Landsat 8 images from 2016 were used to develop seasonal land use land cover (LULC) maps: March (long rains), August (dry season), and October (short rains). Quantitative and qualitative observation data on cropping systems (reference points and questionnaires/surveys) were collected and used for the supervised classification algorithm. Land use classifications were done using 20 land use and land cover classes for the wet season image and 19 classes for the dry and short rain season images. Water withdrawals for irrigated agriculture were calculated using (1) the static land use map or (2) the three seasonal land use maps. Clear differences in land use can be seen between the dry and the other seasons and between rain-fed and irrigated areas. A difference in water withdrawals was observed when seasonal and static land use maps were used. The highest differences were obtained for irrigated mixed crops, with an estimation of 572 million m3/year when seasonal dynamic maps were used and only 90 million m3/year when a static map was used. This study concludes that detailed seasonal land use maps are essential for quantifying annual irrigation water use of catchment areas with distinct dry and wet seasonal dynamics.Water Resource
Efficient and Robust Sensitivity Analysis of Complex Environmental Models: An Application for Water Productivity simulations using SWAT Model
No full textComplex agro-environmental models have a large number of parameters which are problematic during calibration. A sensitivity analysis can help identify the most sensitive parameters which should be included in the calibration process. Depending on the number of parameters being analysed and the method used, the required number of simulations varies from moderate to very large. Consequently, the number of simulations, number of years run and size of the model affect the computation power and thus, computer time required. This often limits the number of parameters and/or number of simulations, hence the robustness of the analysis. A possible solution is to conduct an initial screening of parameters using a method that requires fewer simulations and therefore can include more parameters. Subsequently, a more robust method can be performed on the obtained fewer sensitive parameters using substantially more simulation runs. Additionally, cloud computing and parallelisation can be used to reduce the computation time taken. SWAT, a complex hydrological model, has a large number of parameters that influence Water Productivity (WP) estimations, defined as the ratio of production (calculated as biomass increment or agricultural crop yield) over water consumption (calculated as evapotranspiration). To aid the calibration process, a sensitivity analyses for WP variables at the basin scale was conducted investigating the possible benefits of using: a two-step method with an initial screening (LH-OAT) prior to running a more advanced quantitative method (SOBOL); parallelisation and cloud computing. Initial results indicate substantial time benefits using the two-step method including parallelisation and cloud computing
WetSpa-Urban: An Adapted Version of WetSpa-Python, A Suitable Tool for Detailed Runoff Calculation in Urban Areas
No full textA tool called WetSpa-Urban was developed to respond to the need for precise runoff estimations in an increasingly urbanized world. WetSpa-Urban links the catchment model WetSpa-Python to the urban drainage model Storm Water Management Model (SWMM). WetSpa-Python is an open-source, fully distributed, process-based model that accurately represents surface hydrological processes but does not simulate hydraulic structures. SWMM is a well-known open-source hydrodynamic tool that calculates pipe flow processes in an accurate manner while runoff is calculated conceptually. Merging these tools along with certain modifications, such as improving the efficiency of surface runoff calculation and simulating flow at the sub-catchment level, makes WetSpa-Urban suitable for event-based and continuous rainfall–runoff modeling for urban areas. WetSpa-Urban was applied to the Watermaelbeek catchment in Brussels, Belgium, which recently experienced rapid urbanization. The model efficiency was evaluated using different statistical methods, such as Nash–Sutcliffe efficiency and model bias. In addition, a statistical investigation, independent of time, was performed by applying the box-cox transformation to the observed and simulated values of the flow peaks. By speeding up the simulation of the hydrological processes, the performance of the surface runoff calculation increased by almost 130%. The evaluation of the simulated 10 minute flow versus the observed flow at the outlet of the catchment for 2015 reached a Nash–Sutcliffe efficiency of 0.86 and a bias equal to 0.06
The Economics of Agricultural Water Productivity in the Blue Nile
No full textAgricultural Water Productivity (AWP) is often simplified as the ‘crop per drop’, described as the output in terms of yield or biomass per unit of water input. This purely physical measure of Water Productivity (WP) using only a single factor input (water) does not consider: (1) the level of other inputs used in conjunction with water, (2) the opportunity cost of water as an input, (3) the value of the output and (4) the costs of production for different crops. This study presents a justification and methodology for incorporating these factors into WP indicators. The application of these extensions to WP in a data poor region is demonstrated for the Blue Nile using the new Water Accounting Plus (WA+) framework. Particular attention is made to the use of globally applicable, remotely sensed data sources for estimation of physical and socioeconomic variables. An advantage of using the WA+ framework in calculating WP is that it provides basin water information partitioned into ‘blue’ and ‘green’ water, land use categories, consumptive and non-consumptive and beneficial and non-beneficial water as well as information on return flows. Extending single factor measures of WP should result in more theoretically justified yet practical WP results, making this a more comparable measure from basin to basin and sub-basin to sub-basin
Towards Open Resources Using Services (TORUS)
No full textL'obiettivo del progetto TORUS è quello di creare sinergie tra le scienze ambientali e il cloud computing per il processing di big-Data, per la preparazione di futuri programmi di formazione, e di progetti di ricerca in collaborazione con aziende private. In particolare l'attività è rivolta al trasferimento delle competenze tecnologiche nel campo del cloud computing e del processing di Big-Data acquisiti nell'ambito della fisica sperimentale alle scienze ambientali
A groundwater level-based filtering to improve the accuracy of locating agricultural tile drain and ditch networks
No full textRemote sensing (RS) using satellites circling around the Earth has great potential for monitoring surface processes with reduced cost and greater access. This study uses three approaches to identify possible drainage unit locations: existing benchmark techniques and a novel complementary approach based on groundwater table depth. The study area comprises a site in Ontario, Canada, and the Kleine Nete catchment, Belgium. First, a change detection method based on the interpretation of RS imagery is used to retrieve soil moisture differences. Based on the retrieved soil moisture differences, it is possible to distinguish between drained and undrained fields. Secondly, the decision tree classification (DTC) method based on filtering pixels corresponding to agricultural fields with a slowly draining soil class along with a gentle slope was applied to identify drainage units. Finally, a novel filtering technique based on groundwater table depth is applied as a complementary identification tool to the former approach.The remote sensing method resulted in 87.8% accuracy in the first study area, while the decision tree classification achieved 96.7% accuracy. Although the RS approach was not successful in following the ditch network, the DTC was able to indicate ditch networks with up to 58% accuracy. However, the additional filtering using groundwater level measurements increased the drainage unit identification accuracy in the first study area (corresponds to finding an additional 19.4 km2 area of drains). A final quantitative assessment for the second study area revealed a close follow-up of the ditch network to the shallow groundwater table maps. In general, it can be concluded that both the remote sensing and the DTC method have tremendous potential to identify drainage units, although with limitations in particular cases, such as low accuracy. Moreover, it can be advised that a local visit to the study area is required to investigate what type of drainage system is used. Next, the novel use of groundwater level-based filtering further improves the drainage identification procedure. Finally, combining several data and techniques allows for accurately identifying drainage units, which is ultimately useful for the sustainable management of drained water from agricultural fields
Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy
No full textBackground:
A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets.
Methods:
Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall’s tau for dichotomous variables, or Jonckheere–Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis.
Results:
A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p < 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p < 0.001).
Conclusion:
We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty
The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy
No full textBackground: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p < 0.001), with the proportions of operations lasting > 90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care.</p
