7 research outputs found
Scenario approach for the seasonal forecast of Kharif flows from Upper Indus Basin
No full textAbstract. Snow and glacial melt runoff are the major sources of water contribution from the high mountainous terrain of Indus river upstream of the Tarbela reservoir. A reliable forecast of seasonal water availability for the Kharif cropping season (April–September) can pave the way towards the better water management and subsequently boost the agro-economy of Pakistan. The use of degree-day models in conjunction with the satellite based remote sensing data for the forecasting of seasonal snow and ice melt runoff has proved to be a suitable approach for the data scarce regions. In the present research, Snowmelt Runoff Model (SRM) has not only been enhanced by incorporating the “glacier (G)” component but also applied for the forecast of seasonal water availability from the Upper Indus Basin (UIB). Excel based SRM + G takes into account of separate degree-day factors for snow and ice melt processes. The UIB has been divided into Upper and Lower part because of the different climatic conditions in the Tibetan plateau. The application of seasonal scenario based approach proved to be very adequate for long term water availability forecast. The comparison between different models of operational seasonal forecasts for the UIB for the period in consideration show that SRM + G tends to slightly underestimate the flow volume on average by about 2 % with an overall mean absolute error MAE of 9.6 %, while the two other approaches overestimate the Kharif flow volume on average by about 6 %. More important, the standard deviation of SRM + G forecast errors is 5.7 % only, which is an important indicator for the forecasting skill.
</jats:p
Scenario approach for the seasonal forecast of Kharif flows from the Upper Indus Basin
No full textAbstract. Snow and glacial melt runoff are the major sources of water contribution from the high mountainous terrain of the Indus River upstream of the Tarbela reservoir. A reliable forecast of seasonal water availability for the Kharif cropping season (April–September) can pave the way towards better water management and a subsequent boost in the agro-economy of Pakistan. The use of degree-day models in conjunction with satellite-based remote-sensing data for the forecasting of seasonal snow and ice melt runoff has proved to be a suitable approach for data-scarce regions. In the present research, the Snowmelt Runoff Model (SRM) has not only been enhanced by incorporating the glacier (G) component but also applied for the forecast of seasonal water availability from the Upper Indus Basin (UIB). Excel-based SRM+G takes account of separate degree-day factors for snow and glacier melt processes. All-year simulation runs with SRM+G for the period 2003–2014 result in an average flow component distribution of 53, 21, and 26 % for snow, glacier, and rain, respectively. The UIB has been divided into Upper and Lower parts because of the different climatic conditions in the Tibetan Plateau. The scenario approach for seasonal forecasting, which like the Ensemble Streamflow Prediction method uses historic meteorology as model forcings, has proven to be adequate for long-term water availability forecasts. The accuracy of the forecast with a mean absolute percentage error (MAPE) of 9.5 % could be slightly improved compared to two existing operational forecasts for the UIB, and the bias could be reduced to −2.0 %. However, the association between forecasts and observations as well as the skill in predicting extreme conditions is rather weak for all three models, which motivates further research on the selection of a subset of ensemble members according to forecasted seasonal anomalies.
</jats:p
Comparison of two model calibration approaches and their influence on future projections under climate change in the Upper Indus Basin
No full textThis study performs a comparison of two model calibration/validation approaches and their influence on future hydrological projections under climate change by employing two climate scenarios (RCP2.6 and 8.5) projected by four global climate models. Two hydrological models (HMs), snowmelt runoff model + glaciers and variable infiltration capacity model coupled with a glacier model, were used to simulate streamflow in the highly snow and glacier melt–driven Upper Indus Basin. In the first (conventional) calibration approach, the models were calibrated only at the basin outlet, while in the second (enhanced) approach intermediate gauges, different climate conditions and glacier mass balance were considered. Using the conventional and enhanced calibration approaches, the monthly Nash-Sutcliffe Efficiency (NSE) for both HMs ranged from 0.71 to 0.93 and 0.79 to 0.90 in the calibration, while 0.57–0.92 and 0.54–0.83 in the validation periods, respectively. For the future impact assessment, comparison of differences based on the two calibration/validation methods at the annual scale (i.e. 2011–2099) shows small to moderate differences of up to 10%, whereas differences at the monthly scale reached up to 19% in the cold months (i.e. October–March) for the far future period. Comparison of sources of uncertainty using analysis of variance showed that the contribution of HM parameter uncertainty to the overall uncertainty is becoming very small by the end of the century using the enhanced approach. This indicates that enhanced approach could potentially help to reduce uncertainties in the hydrological projections when compared to the conventional calibration approach. © 2020, The Author(s)
Comparison of two model calibration approaches and their influence on future projections under climate change in the Upper Indus Basin
No full textThis study performs a comparison of two model calibration/validation approaches and their influence on future hydrological projections under climate change by employing two climate scenarios (RCP2.6 and 8.5) projected by four global climate models. Two hydrological models (HMs), snowmelt runoff model + glaciers and variable infiltration capacity model coupled with a glacier model, were used to simulate streamflow in the highly snow and glacier melt–driven Upper Indus Basin. In the first (conventional) calibration approach, the models were calibrated only at the basin outlet, while in the second (enhanced) approach intermediate gauges, different climate conditions and glacier mass balance were considered. Using the conventional and enhanced calibration approaches, the monthly Nash-Sutcliffe Efficiency (NSE) for both HMs ranged from 0.71 to 0.93 and 0.79 to 0.90 in the calibration, while 0.57–0.92 and 0.54–0.83 in the validation periods, respectively. For the future impact assessment, comparison of differences based on the two calibration/validation methods at the annual scale (i.e. 2011–2099) shows small to moderate differences of up to 10%, whereas differences at the monthly scale reached up to 19% in the cold months (i.e. October–March) for the far future period. Comparison of sources of uncertainty using analysis of variance showed that the contribution of HM parameter uncertainty to the overall uncertainty is becoming very small by the end of the century using the enhanced approach. This indicates that enhanced approach could potentially help to reduce uncertainties in the hydrological projections when compared to the conventional calibration approach.Technische Universität München (1025
The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study
No full textAim: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. Methods: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. Results: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. Conclusion: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease
SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study
No full textBackground Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population. The aim of this study was to inform vaccination prioritization by modelling the impact of vaccination on elective inpatient surgery. The study found that patients aged at least 70 years needing elective surgery should be prioritized alongside other high-risk groups during early vaccination programmes. Once vaccines are rolled out to younger populations, prioritizing surgical patients is advantageous
