scholarly journals Influence of Landscape Retention Capacity Upon Flood Processes in Jičínka River Basin

2014 ◽  
Vol 62 (1) ◽  
pp. 191-199
Author(s):  
František Pavlík ◽  
Miroslav Dumbrovský
Keyword(s):  
River Basin ◽  
Daily Precipitation ◽  
Original Method ◽  
Important Criterion ◽  
Retention Capacity ◽  
Flood Discharge ◽  
Disastrous Flood ◽  
Final Profile

In a survey of landscape retention capability results of measurements obtained during the disastrous flood in June 2009 were used. The original method based on the balance among the daily precipitation fallen on the basin with discharges in the final profile was used on the analogy with transformation of the flood discharge through a reservoir. Following basin retention are defined: dynamic Rd, static Rs including the underground retention Rug and evaporation E, and total Rt. Main principal criteria were the effective static retention of the basin Rsef and a coefficient of the effective static basin retention ρsef (3). The coefficient of reducing flood culmination λcul (4) was calculated, too. Also investigated factors having the most influence on a retention capacity of a basin are introduced. Summary of results are shown in the Tab. I. Values of the most important criterion quantities are marked in shadow colour. The results show, for example, that the found out coefficient ρsef is 0.52. It means that the soil (and slightly a vapour, too) in the basin caught 52% of volume of wave in the time of culmination discharge in a basin. Also some further interested findings are introduced in the results and conclusions.

scholarly journals Daily Precipitation Generation using a Hidden Markov Model with Correlated Emissions for the Potomac River Basin

PAMM ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Gerson C. Kroiz ◽  
Reetam Majumder ◽  
Matthias K. Gobbert ◽  
Nagaraj K. Neerchal ◽  
Kel Markert ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
River Basin ◽  
Daily Precipitation ◽  
Hidden Markov ◽  
Potomac River ◽  
Potomac River Basin

scholarly journals Spatiotemporal Variations of Extreme Precipitation and Study on Chaotic Characteristics in the Xijiang River Basin, China

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2106
Author(s):  
Xingchen Ding ◽  
Weihong Liao ◽  
Hao Wang ◽  
Xiaohui Lei ◽  
Wei Zhang ◽  
...  
Keyword(s):  
River Basin ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Research Area ◽  
Largest Lyapunov Exponent ◽  
The West ◽  
Intensity Index ◽  
Xijiang River ◽  
Extreme Precipitation Events ◽  
The Xijiang River

Climate change leads to the increase of frequency and intensity for extreme precipitation events, potentially threatening the development of our society. It is of great significance to study the spatiotemporal variation of precipitation for understanding cycle process of water and its response to global warming. This paper selects the Xijiang River basin, which locates on a low latitude and coastland, as the research area. The spatiotemporal distribution and homogeneity of precipitation are analyzed, and the spatial trend is studied using 12 extreme precipitation indices. Finally, chaotic characteristics are evaluated for daily precipitation. The results showed that the precipitation in the basin tended to be unevenly distributed. On wet days, precipitation in the middle and the west was more and more uniform. The proportion of tiny rain was the largest, between 33.5% and 41.3%. The proportion of violent rain was the smallest, between 0.1% and 4.7%. Duan had the highest frequency for violent rain, and the probability of disasters caused by extreme precipitation near the station was the highest. The simple daily intensity index (SDII) showed a significant increase in the middle and the northeast. PRCPTOT (annual total wet-day precipitation) showed a decreasing trend in the northwest. The average rates of variation for R95PTOT (precipitation on very wet days) and R99PTOT (precipitation on extremely wet days) were −0.01 mm/year and 0.06 mm/year, respectively. There might be a risk of drought on the west of the basin in the future. Precipitation in other locations was still relatively abundant. Daily precipitation showed high dimension and high chaotic characteristics. The MED (minimum embedding dimension) was between 11 and 30, and the MLE (largest Lyapunov exponent) was between 0.037 and 0.144.

Spatio-temporal dynamics of flood regulating ecosystem services in the Arno river basin, Italy

2020 ◽  
Author(s):  
Stefano Mori ◽  
Tommaso Pacetti ◽  
Luigia Brandimarte ◽  
Enrica Caporali
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
River Basin ◽  
Assessment Tool ◽  
Temporal Dynamics ◽  
Meteorological Data ◽  
Supply And Demand ◽  
Flood Management ◽  
Mitigation Measures ◽  
Retention Capacity

<p>Human activities can strongly influence the capacity of ecosystems to provide flood regulating ecosystem services (ES). Therefore, the effects of land use alteration, population migration and urbanization are key aspects to be considered when dealing with flood management. This study aims at analyzing the spatio‑temporal dynamics of flood regulating ES to support watershed management planning. The spatial explicit analysis of flood regulating ES is carried out with SWAT - Soil and Water Assessment Tool, using daily meteorological data between 2000 and 2014. Two indicators are elaborated in order to evaluate the retention capacity of each land use setting and to map the ES supply. Demand quantification is obtained from the information derived by the existing flood management plans (i.e. PAI-Piano per l’Assetto Idrogeologico and PGRA-Piano di Gestione del Rischio Alluvioni) which contain the identification and the perimeter of hydraulic hazard classes. Supply and demand data are then merged in order to obtain budget maps of flood regulating ES and their evolution from 1960 up to 2012 (1960, 1990, 2000 and 2012). The results show that both the demand and the supply of ecosystem services change during the time. With the increasing urbanization, the demand values have grown in the Arno floodplains, where residential, industrial and commercial zones are located. At the same time, land use changes (e.g. intensive agriculture) have caused negative effects on water regulation supply. This work shows the advantages of assessing flood regulating ES to improve flood regulation in the Arno river basin and provide a sound base of knowledge to identify floods prevention and mitigation measures.</p>

Analysis of Precipitation Variability using Memory Based Artificial Neural Networks

2019 ◽  
Vol 10 (1) ◽  
pp. 29-42
Author(s):  
Shyama Debbarma ◽  
Parthasarathi Choudhury ◽  
Parthajit Roy ◽  
Ram Kumar
Keyword(s):  
River Basin ◽  
Daily Precipitation ◽  
Data Sets ◽  
Precipitation Series ◽  
Adaptive Memory ◽  
Mean Error ◽  
Daily Data ◽  
Barak River ◽  
Ann Models ◽  
Statistical Criteria

This article analyzes the variability in precipitation of the Barak river basin using memory-based ANN models called Gamma Memory Neural Network(GMNN) and genetically optimized GMNN called GMNN-GA for precipitation downscaling precipitation. GMNN having adaptive memory depth is capable techniques in modeling time varying inputs with unknown input characteristics, while an integration of the model with GA can further improve its performances. NCEP reanalysis and HadCM3A2 (a) scenario data are used for downscaling and forecasting precipitation series for Barak river basin. Model performances are analyzed by using statistical criteria, RMSE and mean error and are compared with the standard SDSM model. Results obtained by using 24 years of daily data sets show that GMNN-GA is efficient in downscaling daily precipitation series with maximum daily annual mean error of 6.78%. The outcomes of the study demonstrate that execution of the GMNN-GA model is superior to the GMNN and similar with that of the standard SDSM.

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