<p>This study investigates the performance of short term daily hydrological forecasts utilizing Global Forecast System (GFS) and Hydrologiska Byrans Vattenbalansavdelning (HBV) model over a major sub-region (~25000 km<sup>2</sup>) located in Euphrates River Basin, Turkey. The test basin, over which the forecast algorithm is implemented, is home to five (four operational and another almost-complete) dams with 3 three more planned; all eight located within last 300 km reach of 730 km-long Murat River. The algorithm is aimed to operate through a user-friendly and reliable commercially available forecast interface for decision makers working on fields such as energy production scheme optimization and flood mitigation. In the development of this forecast strategy, the main basin was divided into multiple subbasins that are bordered with corresponding facilities and each subbasin is independently modelled & calibrated by HBV model and meteorological records using available stream gauge and weather station data collected in the region. The Global Forecast System (GFS) that provides 16-day meteorological forecast is then applied to model as the input for hydrological predictions. By implementing and clustering daily operation records of inflow and outflow data received directly from related regional SCADA system, volumetric hydrograph corrections are applied on the model output as a final corrective filter to maximize the temporal performance over the 16-day forecast period. As the system has been in use for nearly 20 months (as of January 2020), our results have shown that a calibrated data cluster performance nearing 0.98 has been reached in correlation and 0.90 in Nash-Sutcliffe index:&#160; cluster-independent average weekly inspected forecast performance of almost 0.87 in correlation and 0.85 in Nash-Sutcliffe index has been obtained.</p>
Short-term differences in drug prices after implementation of the national essential medicines system: A case study in rural Jiangxi Province, China