scholarly journals IMPLICATION OF UNPLANNED URBANIZATION ON RIVER BASINS IN SRI LANKA WITH REFERENCE TO THE UPPER MAHAWELI CATCHMENT AREA

Author(s):  
J.M.S. Jayaweera
2021 ◽  
Vol 8 (1) ◽  
Author(s):  
A. Onuchin ◽  
Т. Burenina ◽  
А. Shvidenko ◽  
D. Prysov ◽  
A. Musokhranova

Abstract Background Assessment of the reasons for the ambiguous influence of forests on the structure of the water balance is the subject of heated debate among forest hydrologists. Influencing the components of total evaporation, forest vegetation makes a significant contribution to the process of runoff formation, but this process has specific features in different geographical zones. The issues of the influence of forest vegetation on river runoff in the zonal aspect have not been sufficiently studied. Results Based on the analysis of the dependence of river runoff on forest cover, using the example of nine catchments located in the forest-tundra, northern and middle taiga of Northern Eurasia, it is shown that the share of forest cover in the total catchment area (percentage of forest cover, FCP) has different effects on runoff formation. Numerical experiments with the developed empirical models have shown that an increase in forest cover in the catchment area in northern latitudes contributes to an increase in runoff, while in the southern direction (in the middle taiga) extensive woody cover of catchments “works” to reduce runoff. The effectiveness of geographical zonality in regards to the influence of forests on runoff is more pronounced in the forest-tundra zone than in the zones of northern and middle taiga. Conclusion The study of this problem allowed us to analyze various aspects of the hydrological role of forests, and to show that forest ecosystems, depending on environmental conditions and the spatial distribution of forest cover, can transform water regimes in different ways. Despite the fact that the process of river runoff formation is controlled by many factors, such as temperature conditions, precipitation regime, geomorphology and the presence of permafrost, the models obtained allow us to reveal general trends in the dependence of the annual river runoff on the percentage of forest cover, at the level of catchments. The results obtained are consistent with the concept of geographic determinism, which explains the contradictions that exist in assessing the hydrological role of forests in various geographical and climatic conditions. The results of the study may serve as the basis for regulation of the forest cover of northern Eurasian river basins in order to obtain the desired hydrological effect depending on environmental and economic conditions.


2015 ◽  
Vol 17 (3) ◽  
pp. 594-606 ◽  

<div> <p>The impact of climate change on water resources through increased evaporation combined with regional changes in precipitation characteristics has the potential to affect mean runoff, frequency and intensity of floods and droughts, soil moisture and water supply for irrigation and hydroelectric power generation. The Ganga-Brahmaputra-Meghna (GBM) system is the largest in India with a catchment area of about 110Mha, which is more than 43% of the cumulative catchment area of all the major rivers in the country. The river Damodar is an important sub catchment of GBM basin and its three tributaries- the Bokaro, the Konar and the Barakar form one important tributary of the Bhagirathi-Hughli (a tributary of Ganga) in its lower reaches. The present study is an attempt to assess the impacts of climate change on water resources of the four important Eastern River Basins namely Damodar, Subarnarekha, Mahanadi and Ajoy, which have immense importance in industrial and agricultural scenarios in eastern India. A distributed hydrological model (HEC-HMS) has been used on the four river basins using HadRM2 daily weather data for the period from 2041 to 2060 to predict the impact of climate change on water resources of these river systems.&nbsp;</p> </div> <p>&nbsp;</p>


Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 685 ◽  
Author(s):  
Peng-Fei Han ◽  
Xu-Sheng Wang ◽  
Li Wan ◽  
Xiao-Wei Jiang ◽  
Fu-Sheng Hu

The groundwater divide within a plane has long been delineated as a water table ridge composed of the local top points of a water table. This definition has not been examined well for river basins. We developed a fundamental model of a two-dimensional unsaturated–saturated flow in a profile between two rivers. The exact groundwater divide can be identified from the boundary between two local flow systems and compared with the top of a water table. It is closer to the river of a higher water level than the top of a water table. The catchment area would be overestimated (up to ~50%) for a high river and underestimated (up to ~15%) for a low river by using the top of the water table. Furthermore, a pass-through flow from one river to another would be developed below two local flow systems when the groundwater divide is significantly close to a high river.


2017 ◽  
Vol 42 (8) ◽  
pp. 981-999 ◽  
Author(s):  
L. Muthuwatta ◽  
H. P. T. W. Perera ◽  
N. Eriyagama ◽  
K. B. N. Upamali Surangika ◽  
W. W. Premachandra

2020 ◽  
Vol 16 (3) ◽  
pp. 358
Author(s):  
Beny Harjadi ◽  
Inkorena G. S. Sukartono ◽  
Etty Hesthiati

Watersheds (DAS), which are river basins flowing in one outlet and limited by hills and mountains, often occur in land degradation or erosion. Erosion that occurs is said to be safe if it does not exceed the tolerable erosion or erosion tolerance limits or often called T-values. With regard to problems on sloping land in a watershed, the purpose of this study is to calculate the value of T-values or erosion tolerance limits. Calculation of T-values using the Hammer method (1981), namely by observing the factors of soil depth and effective depth of roots, and resources life of 300 or 400 years. T-value results are classified into 4 classes, namely (tons/ha/year) : (1) very low (<5), (2) low (5-25), (3) high (25-50), and (4) very high (> 50). The research location in the Tulis watershed is 12,750 ha in DTW (Reservoir Catchment Area) Mrica Banjarnegara. From the calculation of the T-value obtained results: very low 0.03% (3.8 ha), low 2.46% (313.7 ha), high 49.31% (6,287 ha), and very high 48.2% (6,145.5 ha).


2021 ◽  
Author(s):  
Meeth Nimasha Lande Bandara ◽  
H M Ranjith Premasiri ◽  
B H Sudantha
Keyword(s):  

2016 ◽  
Vol 6 ◽  
pp. 339-343 ◽  
Author(s):  
W.D.T.M. Gunawardhana ◽  
J.M.C.K. Jayawardhana ◽  
E.P.N. Udayakumara

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