Precipitation Changes in Hokkaido and Future Water Resources in Its Main Rivers

Impacts of Climate and Land-Use Change on Blue and Green Water: A Case Study of the Upper Ganjiang River Basin, China

Water ◽

10.3390/w12102661 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2661

Author(s):

Yongfen Zhang ◽

Chongjun Tang ◽

Aizhong Ye ◽

Taihui Zheng ◽

Xiaofei Nie ◽

...

Keyword(s):

Land Use ◽

Resource Management ◽

Water Resources ◽

Land Use Change ◽

River Basin ◽

Water Resource ◽

Water Resource Management ◽

Green Water ◽

Precipitation Changes ◽

Ganjiang River Basin

Quantitatively figuring out the effects of climate and land-use change on water resources and their components is essential for water resource management. This study investigates the effects of climate and land-use change on blue and green water and their components in the upper Ganjiang River basin from the 1980s to the 2010s by comparing the simulated changes in blue and green water resources by using a Soil and Water Assessment Tool (SWAT) model forced by five climate and land-use scenarios. The results suggest that the blue water flow (BWF) decreased by 86.03 mm year−1, while green water flow (GWF) and green water storage (GWS) increased by 8.61 mm year−1 and 12.51 mm year−1, respectively. The spatial distribution of blue and green water was impacted by climate, wind direction, topography, and elevation. Climate change was the main factor affecting blue and green water resources in the basin; land-use change had strong effects only locally. Precipitation changes significantly amplified the BWF changes. The proportion of surface runoff in BWF was positively correlated with precipitation changes; lateral flow showed the opposite tendency. Higher temperatures resulted in increased GWF and decreased BWF, both of which were most sensitive to temperature increases up to 1 °C. All agricultural land and forestland conversion scenarios resulted in decreased BWF and increased GWF in the watershed. GWS was less affected by climate and land-use change than GWF and BWF, and the trends in GWS were not significant. The study provides a reference for blue and green water resource management in humid areas.

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Disentangling spatiotemporal characteristics of six different unregulated streamflow stations in India

10.5194/egusphere-egu21-7362 ◽

2021 ◽

Author(s):

Tarun Pant ◽

Pavan kumar Yeditha ◽

Ankit Agarwal ◽

Maheswaran Rathinasamy

Keyword(s):

Wavelet Transform ◽

Water Resources ◽

Phase Relationship ◽

Wavelet Coherence ◽

Detection Methods ◽

Climate Indices ◽

Almost All ◽

Precipitation Changes ◽

The Impact ◽

Cross Wavelet

<p>With the increasing stress on water resources for a developing country like India, it is very much pertinent to study how the water resources are varying with time and investigate the dominant streamflow patterns for carrying effective planning and management activities. In this study, we attempt to investigate the spatiotemporal characterization of streamflow of six unregulated catchments in India and also quantify the impact of precipitation changes and four climate indices, namely, Ni&#241;o 3.4, IOD, PDO and NAO on streamflow. Initial analysis of streamflow and precipitation was carried out using Mann Kendall and step change detection methods. Temporal variability of streamflow and its association with precipitation and climate indices was unraveled using continuous wavelet transform and Wavelet coherence respectively. Cross-wavelet transform was also used to capture the coherent relationships and phase relationships between streamflow and climate indices. The results of the study reveal an in-phase relationship between precipitation and streamflow. The analysis also considers that streamflow is mostly affected by Ni&#241;o 3.4 and PDO indices. Based on the results of this work, better understanding of interrelationship between the streamflow and precipitation was well captured using Wavelet coherence when compared to Cross wavelet. It was observed that almost all basins had showed the effect of changes in precipitation on streamflow. Based on these observations, it is clear that WTC can be used for understanding interrelationship between variable when compared to XWT and gives better insights regarding the interrelationship</p>

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