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1

Seasonality shift and streamfow fow variability trends in central India

Kuriqi Alban, Ali Rawshan, Pham Quoc Bao, Montenegro Gambini Julio, Gupta Vivek, Malik Anurag, Linh Nguyen Thi Thuy, Joshi Yogesh, Anh Duong Tran, Nam Thai Van, Dong Xiaohua

Acta Geophysica

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2020

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Vol. 68, no. 5

1461--1475

EN

A better understanding of intra/inter-annual streamfow variability and trends enables more efective water resources planning and management for current and future needs. This paper investigates the variability and trends of streamfow data from fve stations (i.e. Ashti, Chindnar, Pathgudem, Polavaram, and Tekra) in Godavari river basin, India. The streamfow data were obtained from the Indian Central Water Commission and cover more than 30 years of mean daily records (i.e. 1972–2011). The streamfow data were statistically assessed using Gamma, Generalised Extreme Value and Normal distributions to under stand the probability distribution features of data at inter-annual time-scale. Quantifable changes in observed streamfow data were identifed by Sen’s slope method. Two other nonparametric, Mann–Kendall and Innovative Trend Analysis methods were also applied to validate fndings from Sen’s slope trend analysis. The mean fow discharge for each month (i.e. January to December), seasonal variation (i.e. Spring, Summer, Autumn, and Winter) as well as an annual mean, annual maximum and minimum fows were analysed for each station. The results show that three stations (i.e. Ashti, Tekra, and Polavaram) demonstrate an increasing trend, notably during Winter and Spring. In contrast, two other stations (i.e. Pathgudem, Chindnar) revealed a decreasing trend almost at all seasons. A signifcant decreasing trend was observed at all station over Summer and Autumn seasons. Notably, all stations showed a decreasing trend in maximum fows; remarkably, Tekra station revealed the highest decreasing magnitude. Signifcant decrease in minimum fows was observed in two stations only, Chindnar and Pathgudem. Findings resulted from this study might be useful for water managers and decision-makers to propose more sustainable water management recommendations and practices.

2

Multifractal description of streamfow and suspended sediment concentration data from Indian river basin

Adarsh S., Dharan Drisya S., Nandhu A. R., Vishnu B. Anand, Mohan Vysakh K., Wątorek M.

Acta Geophysica

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2020

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Vol. 68, no. 2

519--535

EN

This study investigated the multifractality of streamfow data of 192 stations located in 13 river basins in India using the multifractal detrended fuctuation analysis (MF-DFA). The streamfow datasets of diferent river basins displayed multifractality and long-term persistence with a mean exponent of 0.585. The streamfow records of Krishna basin displayed least persistence and that of Godavari basin displayed strongest multifractality and complexity. Subsequently, the streamfow-sediment links of fve major river basins were evaluated using the novel multifractal cross-correlation analysis (MFCCA) method of cross-correlation studies. The results showed that the joint persistence of streamfow and total suspended sediments (TSS) is approximately the mean of the persistence of individual series. The streamfow displayed higher persistence than TSS in 60% of the stations while in majority of stations of Godavari basin the trend was opposite. The annual cross-correlation is higher than seasonal cross-correlation in majority of stations but at these time scales strength of their association difers with river basin.

3

Improving ANN based streamfow estimation models for the Upper Indus Basin using satellite derived snow cover area

Hassan Muhammad, Hassan Ishtiaq

Acta Geophysica

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2020

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Vol. 68, no. 6

1791--1801

EN

The mountainous catchments often witness contrasting regimes and the limited available meteorological network creates uncertainty in both the hydrological data and developed models. To overcome this problem, remotely sensed data could be used in addition to on-ground observations for hydrological forecasting. The fusion of these two types of data gives a better picture and helps to generate adequate hydrological forecasting models. The study aims at the improvement of ANN-based streamfow estimation models by using an integrated data-set containing, the satellite-derived snow cover area (SCA) with on-ground fow observations. For this purpose, SCA of three sub catchments of Upper Indus Basin, namely Gilgit, Astore and Bunji coupled with their respective gauge discharges is used as model inputs. The weekly stream-fow models are developed for infows at Besham Qila located just upstream of Tarbela dam. The data-set for modeling is prepared through normal izing all variables by scaling between 0 and 1. A mathematical tool, Gamma test is applied to fuse the inputs, and a best input combination is selected on the basis of minimum gamma value. A feed forward neural network trained via two layer Broyden Fletcher Goldfarb Shanno algorithm is used for model development. The models are evaluated on the basis of set of performance indicators, namely, Nash–Sutclife Efciency, Root Mean Square Error, Variance and BIAS. A comparative assessment has also been made using these indicators for models developed, through data-set containing gauge discharges, only and the data-set fused with satellite-derived SCA. In particular, the current study concluded that the efciency of ANN-based streamfow estimation models developed for mountainous catchments could be improved by integrating the SCA with the gauge discharges.

4

Daily streamfow prediction using support vector machine artifcial fora (SVM AF) hybrid model

Dehghani Reza, Torabi Poudeh Hassan, Younesi Hojatolah, Shahinejad Babak

Acta Geophysica

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2020

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Vol. 68, no. 6

1763--1778

EN

Precise estimation of river fow in catchment areas has a signifcant role in managing water resources and, particularly, mak ing frm decisions during food and drought crises. In recent years, diferent procedures have been proposed for estimating river fow, among which hybrid artifcial intelligence models have garnered notable attention. This study proposes a hybrid method, so-called support vector machine–artifcial fora (SVM-AF), and compares the obtained results with outcomes of wavelet support vector machine models and Bayesian support vector machine. To estimate discharge value of the Dez river basin in the southwest of Iran, the statistical daily watering data recorded by hydrometric stations located at upstream of the dam over the years 2008–2018 were investigated. Four performance criteria of coefcient of determination (R2 ), rootmean-square error, mean absolute error, and Nash–Sutclife efciency were employed to evaluate and compare performances of the models. Comparison of the models based on the evaluation criteria and Taylor’s diagram showed that the proposed hybrid SVM-AF with the correlation coefcient R2 = 0.933–0.985, root-mean-square error RMSE = 0.008–0.088 m3 /s, mean absolute error MAE = 0.004–0.040 m3 /s, and Nash-Sutclife coefcient NS = 0.951–0.995 had the best performance in estimating daily fow of the river. The estimation results showed that the proposed hybrid SVM-AF model outperformed other models in efciently predicting fow and daily discharge.

5

Transformation of rivers streamflow of Belarus

Volchek A. A., Busko E. G., Tanas W., Szymanek M.

Journal of Research and Applications in Agricultural Engineering

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2014

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Vol. 59, nr 1

131--137

EN

An estimation of streamflow transformation in rivers of Belarus under present conditions influenced by natural fluctuations of flow and anthropogenic impacts, has been performed. On the whole, no sizeable changes in the annual streamflow have been found. At the time of spring floods, an average decrease in the maximum annual discharge in the territory of Belarus is 43%, while the increase in peak summer-autumn and winter yields are 27% and 36%, respectively.

PL

W artykule przeprowadzono ocenę transformacji wodnego spływu rzek Białorusi w warunkach współczesnych, spowodowanych naturalnymi wahaniami i antropogenicznymi czynnikami. Nie stwierdzono w ocenianym okresie znaczących zmian. Zmniejszenie maksymalnego spływu w okresie wiosennych wylewów średnio na Białorusi nie przekroczyło 43%. Zwiększenie minimalnych letnio-jesiennych i minimalnych zimowych poziomów wodnych wyniosło odpowiednio 27% i 36%.