Prosečno oticanje sa malog neizučenog sliva korišćenjem rasterskih klimatoloških podataka

Abstract

U radu je sprovedena analiza prosečnog godišnjeg oticanja za Mali, neizučeni sliv Tulovske reke, u slivu Južne Morave. Primenjene su metoda Turka, metoda Langbajna i regionalni izrazi za dobijanje prosečnog oticaja. Kao ulazni podaci korišćene su srednje temperature, prosečne godišnje padavine na slivu i srednja nadmorska visina sliva. Proračuni su sprovedeni korišćenjem različitih klimatoloških ulaznih podataka, a rezultati su zatim upoređeni. Podaci o padavinama i temperaturi dobijeni su iz digitalnog repozitorijuma projekta CarpatClim u rasterskom formatu, prostorne rezolucije 10 km x 10 km. Korišćene su i vrednosti osmotrene na meteorološkim stanicama RHMZ. Razlike u vrednostima rasterskih i osmotrenih klimatoloških podataka pripisane su uticaju visinske razlike terena, koji rasterski podaci nisu bili u stanju da obuhvate. Analize su pokazale da procenjene vrednosti oticaja mogu da se razlikuju do 50% u zavisnosti od odabira metode i ulaznih podataka.

An analysis of mean annual runoff was conducted for a small ungauged catchment of the Tulovska River in the South Morava River Basin. Langbein's method, Turk's method and regional regression models were applied for obtaining the mean runoff. Long-term mean temperature, annual precipitation and the mean catchment elevation are used as input data. The estimations were conducted using various sources of climatological input data and the results were then compared. Precipitation and temperature data were derived, in a 10 km x 10 km resolution raster data format, from the digital data repository of the CarpatClim project. Point measurements of temperature and precipitation by the Republic Hydrometeorological Service of Serbia were also used as input data and compared with the raster data. The difference between the raster and point meteorological data was attributed to the effects of terrain elevation spatial variability, not captured in the raster data. The analyses showed that the predicted mean runoff can differ up to 50%, depending on the chosen method and the input data.