Long-term trend analysis in climate variables and agricultural adaptation strategies to climate change in the Senegal River Basin

2016 ◽  
Vol 37 (6) ◽  
pp. 2873-2888 ◽  
Author(s):  
Koffi Djaman ◽  
Alpha B. Balde ◽  
Daran R. Rudnick ◽  
Ousmane Ndiaye ◽  
Suat Irmak
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
River Basin ◽  
Climate Variables ◽  
Term Trend ◽  
Agricultural Adaptation ◽  
Senegal River ◽  
Senegal River Basin ◽  
Long Term Trend

scholarly journals Long Term Trend Analysis of Rainfall, Rainy Days and Drought for Sindh River Basin, Madhya Pradesh, India

2020 ◽  
Vol 9 (12) ◽  
pp. 2738-2749
Author(s):  
P. S. Pawar ◽  
Umakant Rawat ◽  
Ankit Yadav ◽  
Aniket Rajput ◽  
Devendra Vasht ◽  
...  
Keyword(s):  
Trend Analysis ◽  
River Basin ◽  
Madhya Pradesh ◽  
Term Trend ◽  
Long Term Trend ◽  
Rainy Days

scholarly journals Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Gizachew Kabite Wedajo ◽  
Misgana K. Muleta ◽  
Berhan Gessesse ◽  
Sifan A. Koriche
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
River Basin ◽  
Climate Change Adaptation ◽  
Spatial Relationship ◽  
Local Scale ◽  
Climate Variables ◽  
Climatic Zones ◽  
Vegetation Greenness ◽  
Climate Change Adaptation Strategies

Abstract Background Understanding spatiotemporal climate and vegetation changes and their nexus is key for designing climate change adaptation strategies at a local scale. However, such a study is lacking in many basins of Ethiopia. The objectives of this study were (i) to analyze temperature, rainfall and vegetation greenness trends and (ii) determine the spatial relationship of climate variables and vegetation greenness, characterized using Normalized Difference in Vegetation Index (NDVI), for the Dhidhessa River Basin (DRB). Quality checked high spatial resolution satellite datasets were used for the study. Mann–Kendall test and Sen’s slope method were used for the trend analysis. The spatial relationship between climate change and NDVI was analyzed using geographically weighted regression (GWR) technique. Results According to the study, past and future climate trend analysis generally showed wetting and warming for the DRB where the degree of trends varies for the different time and spatial scales. A seasonal shift in rainfall was also observed for the basin. These findings informed that there will be a negative impact on rain-fed agriculture and water availability in the basin. Besides, NDVI trends analysis generally showed greening for most climatic zones for the annual and main rainy season timescales. However, no NDVI trends were observed in all timescales for cool sub-humid, tepid humid and warm humid climatic zones. The increasing NDVI trends could be attributed to agroforestry practices but do not necessarily indicate improved forest coverage for the basin. The change in NDVI was positively correlated to rainfall (r2 = 0.62) and negatively correlated to the minimum (r2 = 0.58) and maximum (r2 = 0.45) temperature. The study revealed a strong interaction between the climate variables and vegetation greenness for the basin that further influences the biophysical processes of the land surface like the hydrologic responses of a basin. Conclusion The study concluded that the trend in climate and vegetation greenness varies spatiotemporally for the DRB. Besides, the climate change and its strong relationship with vegetation greenness observed in this study will further affect the biophysical and environmental processes in the study area; mostly negatively on agricultural and water resource sectors. Thus, this study provides helpful information to device climate change adaptation strategies at a local scale.

scholarly journals Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Niloufar Nouri ◽  
Naresh Devineni ◽  
Valerie Were ◽  
Reza Khanbilvardi
Keyword(s):  
Population Density ◽  
Large Scale ◽  
Southern Oscillation ◽  
The United States ◽  
Anthropogenic Factors ◽  
Climate Variables ◽  
Term Trend ◽  
Cyclical Variability ◽  
Long Term Trend

AbstractThe annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.

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