scholarly journals Impacts of a regional multi-year insect defoliation event on seasonal runoff ratios and instantaneous streamflow characteristics

2020 ◽  
Author(s):  
Sarah Smith-Tripp ◽  
Alden Griffith ◽  
Valerie Pasquarella ◽  
Jaclyn Matthes
Keyword(s):  
Insect Defoliation ◽  
Seasonal Runoff

scholarly journals Projections of runoff in the Vistula and the Odra river basins with the help of the SWAT model

2017 ◽  
Vol 49 (2) ◽  
pp. 303-317 ◽  
Author(s):  
Mikołaj Piniewski ◽  
Mateusz Szcześniak ◽  
Shaochun Huang ◽  
Zbigniew W. Kundzewicz
Keyword(s):  
Assessment Tool ◽  
Statistical Significance ◽  
Swat Model ◽  
Climate Change Impacts ◽  
Odra River ◽  
Assess Climate Change ◽  
Rcp 8.5 ◽  
Model Set ◽  
Seasonal Runoff ◽  
Set Up

Abstract The objective of this paper is to assess climate change impacts on spatiotemporal changes in annual and seasonal runoff and its components in the basins of two large European rivers, the Vistula and the Odra, for future horizons. This study makes use of the Soil and Water Assessment Tool (SWAT) model, set up at high resolution, and driven by a multi-model ensemble (MME) of nine bias-corrected EURO-CORDEX simulations under two representative concentration pathways (RCPs), 4.5 and 8.5. This paper presents a wealth of illustrative material referring to the annual and seasonal runoff (R) in the reference period as well as projections for the future (MME mean change), with explicit illustration of the multi-model spread based on the agreement between models and statistical significance of change according to each model. Annual R increases are dominating, regardless of RCP and future horizon. The magnitude of the MME mean of spatially averaged increase varies between 15.8% (RCP 4.5, near future) and 41.6% (RCP 8.5, far future). The seasonal patterns show the highest increase in winter and the lowest in spring, whereas the spatial patterns show the highest increase in the inner, lowland part, and the lowest in the southern mountainous part of the basin.

scholarly journals Relative Contribution of the Xiaolangdi Dam to Runoff Changes in the Lower Yellow River

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 521
Author(s):  
Qinghe Zhao ◽  
Shengyan Ding ◽  
Xiaoyu Ji ◽  
Zhendong Hong ◽  
Mengwen Lu ◽  
...  
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Sustainable Management ◽  
Yellow River ◽  
Dam Construction ◽  
Regulated Rivers ◽  
Flood Season ◽  
Abrupt Changes ◽  
Runoff Changes ◽  
Seasonal Runoff

Human activities are increasingly recognized as having a critical influence on hydrological processes under the warming of the climate, particularly for dam-regulated rivers. To ensure the sustainable management of water resources, it is important to evaluate how dam construction may affect surface runoff. In this study, using Mann–Kendall tests, the double mass curve method, and the Budyko-based elasticity method, the effects of climate change and human activities on annual and seasonal runoff were quantified for the Yellow River basin from 1961–2018; additionally, effects on runoff were assessed after the construction of the Xiaolangdi Dam (XLD, started operation in 2001) on the Yellow River. Both annual and seasonal runoff decreased over time (p < 0.01), due to the combined effects of climate change and human activities. Abrupt changes in annual, flood season, and non-flood season runoff occurred in 1986, 1989, and 1986, respectively. However, no abrupt changes were seen after the construction of the XLD. Human activities accounted for much of the reduction in runoff, approximately 75–72% annually, 81–86% for the flood season, and 86–90% for the non-flood season. Climate change approximately accounted for the remainder: 18–25% (annually), 14–19% (flood season), and 10–14% (non-flood season). The XLD construction mitigated runoff increases induced by heightened precipitation and reduced potential evapotranspiration during the post-dam period; the XLD accounted for approximately 52% of the runoff reduction both annually and in the non-flood season, and accounted for approximately −32% of the runoff increase in the flood season. In conclusion, this study provides a basic understanding of how dam construction contributes to runoff changes in the context of climate change; this information will be beneficial for the sustainable management of water resources in regulated rivers.

INFLUENCE OF LARGE-SCALE ATMOSPHERIC PROCESSES ON SEASONAL RUNOFF OF LARGE SIBERIAN RIVERS

2021 ◽  
pp. 36-47
Author(s):  
L. N. VASILEVSKAYA ◽  
◽  
I. A. LISINA ◽  
D. N. VASILEVSKII ◽  
◽  
...  
Keyword(s):  
Atmospheric Circulation ◽  
River Runoff ◽  
Large Scale ◽  
Annual Runoff ◽  
Spring Flood ◽  
Atmospheric Circulation Indices ◽  
Rate Of Increase ◽  
Circulation Indices ◽  
Seasonal Runoff ◽  
Kolyma River

Based on daily runoff volumes of four large Siberian rivers (the Ob, Yenisei, Lena, and Kolyma) for 1936-2018, the regime and changes in the total annual and seasonal runoff are analyzed. High synchronous and asynchronous correlations between monthly river runoff and atmospheric circulation indices of hemispheric and regional scales are revealed. In recent decades, the total annual runoff and its variations have increased (the rate of increase is most pronounced for the Kolyma River). A change in water content within a year is heterogeneous: weak positive trends are characteristic of the spring flood runoff and the summer-autumn period, and a significant increase occurred in the winter months. High correlations with a 1-8-month shift made it possible to identify the most informative regions, the atmospheric circulation over which makes a certain contribution to the variance of river runoff.

scholarly journals Hydrological scenarios of future seasonal runoff distribution in Central Slovakia

2008 ◽  
Vol 4 ◽  
pp. 012022
Author(s):  
K Hlavcová ◽  
J Szolgay ◽  
S Kohnová ◽  
G Bálint
Keyword(s):  
Seasonal Runoff

scholarly journals Utilization of Satellite Snow-Cover Observations for Seasonal Streamflow Estimates in the Western Himalayas

1983 ◽  
Vol 14 (5) ◽  
pp. 257-266 ◽  
Author(s):  
B. Dey ◽  
D. C. Goswami ◽  
A. Rango
Keyword(s):  
Regression Model ◽  
Snow Cover ◽  
Multiple Regression ◽  
Snowmelt Runoff ◽  
Indus River ◽  
Snow Covered Area ◽  
Covered Area ◽  
Measured Flow ◽  
Seasonal Runoff ◽  
Kabul River

The results presented in this study indicate the possibility of seasonal runoff prediction when satellite-derived basin snow-cover data are related to point source river discharge data for a number of years. NOAA-VHRR satellite images have been used to delineate the areal extent of snow cover for early April over the Indus and Kabul River basins in Pakistan. Simple photo-interpretation techniques, using a zoom transfer scope, were employed in transferring satellite snow-cover boundaries onto base map overlays. A linear regression model with April 1 through July 31 seasonal runoff (1974-1979) as a function of early April snow cover explains 73% and 82% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. The correlation between seasonal runoff and snow cover is significant at the 97% level for the Indus River and at the 99% level for the Kabul River. Combining Rango et al.'s (1977) data for 1969-73 with the above period, the April snow cover explains 60% and 90% of the variance, respectively, of the measured flow in the Indus and Kabul Rivers. In an attempt to improve the Indus relationship, a multiple regression model, with April 1 through July 31, 1969-79, seasonal runoff in the Indus River as a function of early April snow-covered area of the basin and concurrent runoff in the adjoining Kabul River, explains 79% of the variability in flow. Moreover, a significant reduction (27%) in the standard error of estimate results from using the multi-variate model. For each year of the study period, 1969-79, a separate multiple regression equation is developed dropping the data for the year in question from the data-base and using those for the rest of the years. The snow cover area and concurrent runoff data are then used to estimate the snowmelt runoff for that particular year.The difference between the estimated and observed dircharge values averaged over the 11 year study period is 10%. Satellite derived snow-covered area is the best available input for snowmelt-runoff estimation in remote, data sparse basins like the Indus and Kabul Rivers. The study has operational relevance to water resource planning and management in the Himalayan region.

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