Andere Kunden interessierten sich auch für
![Regional Hydrological Response to Climate Change]( "Regional Hydrological Response to Climate Change")
Regional Hydrological Response to Climate Change39,99 €![Impacts of land use change on the hydrological cycle]( "Impacts of land use change on the hydrological cycle")
Impacts of land use change on the hydrological cycle26,99 €![Advances in Flow Modeling for Water Resources and Hydrological Engineering]( "Advances in Flow Modeling for Water Resources and Hydrological Engineering")
Advances in Flow Modeling for Water Resources and Hydrological Engineering114,99 €![Advances in Natural Hazards and Hydrological Risks: Meeting the Challenge]( "Advances in Natural Hazards and Hydrological Risks: Meeting the Challenge")
Advances in Natural Hazards and Hydrological Risks: Meeting the Challenge110,99 €![Hydrological Data Driven Modelling]( "Hydrological Data Driven Modelling")
Hydrological Data Driven Modelling76,99 €![Groundwater Hydrological Model Simulation]( "Groundwater Hydrological Model Simulation")
Groundwater Hydrological Model Simulation92,99 €![River Flood Risk Forecasting Using Hydrological and Hydraulic Models]( "River Flood Risk Forecasting Using Hydrological and Hydraulic Models")
River Flood Risk Forecasting Using Hydrological and Hydraulic Models40,99 €
HYPE (Hydrological Predictions for the Environment) model was applied for simulation of discharge and streamwater inorganic nitrogen (IN) concentration in two different meso scale catchments of the German lower mountain range, the Selke (463 km2) and Weida catchments (99 km2). DREAM(ZS) (DiffeRential Evolution Adaptive Metropolis algorithm) was combined with the HYPE model to implement parameter calibration and uncertainty analysis. Results showed that IN concentration and daily IN load had a proportional relationship with discharge, indicating that IN leaching is mainly controlled by runoff. The HYPE model was proved to be capable of capturing dynamics and balances of water and IN load with a Nash-Sutcliffe coefficient above 0.83. Multi-site calibration improved model performances at internal sites and decreased parameter posterior uncertainty ranges as well as prediction uncertainty. Compared with calibration using bi-weekly measurements, nitrogen-process parameters calibrated using daily averages of nitrate-N concentration observations produced better and more robust IN modelling performance, narrower posterior parameter uncertainty ranges and IN prediction uncertainty.