Long term variations of river temperature and the influence of air temperature and river discharge: case study of Kupa River watershed in Croatia

被引:21
|
作者
Zhu, Senlin [1 ]
Bonacci, Ognjen [2 ]
Oskoru, Dijana [3 ]
Hadzima-Nyarko, Marijana [4 ]
Wu, Shiqiang [1 ]
机构
[1] Nanjing Hydraul Res Inst, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210029, Jiangsu, Peoples R China
[2] Univ Split, Fac Civil Engn & Architecture, Matice Hrvatske 15, Split 21000, Croatia
[3] Meteorol & Hydrol Serv, Gric 3, Zagreb 10000, Croatia
[4] Josip Juraj Strossmayer Univ Osijek, Fac Civil Engn & Architecture Osijek, Vladimira Preloga 3, Osijek 31000, Croatia
基金
中国博士后科学基金; 国家重点研发计划;
关键词
Climate change; Machine learning models; River water temperature; CLIMATE-CHANGE; VARIABILITY; REGIME; RESERVOIR; IMPACTS; DANUBE;
D O I
10.2478/johh-2019-0019
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
The bio-chemical and physical characteristics of a river are directly affected by water temperature, which therefore affects the overall health of aquatic ecosystems. In this study, long term variations of river water temperatures (RWT) in Kupa River watershed, Croatia were investigated. It is shown that the RWT in the studied river stations increased about 0.0232-0.0796 degrees C per year, which are comparable with long term observations reported for rivers in other regions, indicating an apparent warming trend. RWT rises during the past 20 years have not been constant for different periods of the year, and the contrasts between stations regarding RWT increases vary seasonally. Additionally, multilayer perceptron neural network models (MLPNN) and adaptive neuro-fuzzy inference systems (ANFIS) models were implemented to simulate daily RWT, using air temperature (T-a), flow discharge (Q) and the day of year (DOY) as predictors. Results showed that compared to the individual variable alone with T-a as input, combining T-a and Q in the MLPNN and ANFIS models explained temporal variations of daily RWT more accurately. The best accuracy was achieved when the three inputs (T-a, Q and the DOY) were included as predictors. Modeling results indicate that the developed models can well reproduce the seasonal dynamics of RWT in each river, and the models may be used for future projections of RWT by coupling with regional climate models.
引用
收藏
页码:305 / 313
页数:9
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