Urban hydrological responses to climate change and urbanization in cold climates

Author(s):  
Xuan Pang ◽  
Yundong Gu ◽  
Samuli Launiainen ◽  
Mingfu Guan
Urban Climate ◽  
2012 ◽  
Vol 1 ◽  
pp. 40-54 ◽  
Author(s):  
Goro Mouri ◽  
Seirou Shinoda ◽  
Valentin Golosov ◽  
Michiharu Shiiba ◽  
Tomoharu Hori ◽  
...  

2011 ◽  
Vol 17 (12) ◽  
pp. 3736-3746 ◽  
Author(s):  
Guoyi Zhou ◽  
Xiaohua Wei ◽  
Yiping Wu ◽  
Shuguang Liu ◽  
Yuhui Huang ◽  
...  

2019 ◽  
Vol 5 (2) ◽  
pp. 83-85 ◽  
Author(s):  
Simon Stewart ◽  
Trine T Moholdt ◽  
Louise M Burrell ◽  
Karen Sliwa ◽  
Ana O Mocumbi ◽  
...  

Climate change is a major contributor to annual winter peaks in cardiovascular events across the globe. However, given the paradoxical observation that cardiovascular seasonality is observed in relatively mild as well as cold climates, global warming may not be as positive for the syndrome of heart failure (HF) as some predict. In this article, we present our Model of Seasonal Flexibility to explain the spectrum of individual responses to climatic conditions. We have identified distinctive phenotypes of resilience and vulnerability to explain why winter peaks in HF occur. Moreover, we identify how better identification of climatic vulnerability and the use of multifaceted interventions focusing on modifiable bio-behavioural factors may improve HF outcomes.


2020 ◽  
Vol 3 (1) ◽  
pp. 481-498
Author(s):  
G. Sireesha Naidu ◽  
M. Pratik ◽  
S. Rehana

Abstract Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression was used to develop the precipitation and temperature projections based on MIROC GCM outputs with the RCP 4.5 scenario. The proposed modelling framework has been demonstrated on a semi-arid river basin of peninsular India, Krishna River Basin (KRB). The basin outlet runoff was predicted to decrease (13.26%) for future scenarios under climate change due to an increase in temperature (0.6 °C), compared to a precipitation increase (13.12%), resulting in an overall reduction in water availability over KRB.


2011 ◽  
Vol 8 (4) ◽  
pp. 7595-7620 ◽  
Author(s):  
J. Jarsjö ◽  
S. M. Asokan ◽  
C. Prieto ◽  
A. Bring ◽  
G. Destouni

Abstract. This paper quantifies and conditions expected hydrological responses in the Aral Sea Drainage Basin (ASDB; occupying 1.3 % of the earth's land surface), Central Asia, to multi-model projections of climate change in the region from 20 general circulation models (GCMs). The aim is to investigate how uncertainties of future climate change interact with the effects of historic human re-distributions of water for land irrigation to influence future water fluxes and water resources. So far, historic irrigation changes have greatly amplified water losses by evapotranspiration (ET) in the ASDB, whereas the 20th century climate change has not much affected the regional net water loss to the atmosphere. Projected future climate change (for the period 2010–2039) however is here calculated to considerably increase the net water loss to the atmosphere. Furthermore, the ET response strength to any future temperature change will be further increased by maintained (or increased) irrigation practices. With such irrigation practices, the river runoff is likely to decrease to near-total depletion, with risk for cascading ecological regime shifts in aquatic ecosystems downstream of irrigated land areas. Without irrigation, the agricultural areas of the principal Syr Darya river basin could sustain a 50 % higher temperature increase (of 2.3 °C instead of the projected 1.5 °C until 2010–2039) before yielding the same consumptive ET increase and associated R decrease as with the present irrigation practices.


2012 ◽  
Vol 16 (5) ◽  
pp. 1335-1347 ◽  
Author(s):  
J. Jarsjö ◽  
S. M. Asokan ◽  
C. Prieto ◽  
A. Bring ◽  
G. Destouni

Abstract. This paper quantifies and conditions expected hydrological responses in the Aral Sea Drainage Basin (ASDB; occupying 1.3% of the earth's land surface), Central Asia, to multi-model projections of climate change in the region from 20 general circulation models (GCMs). The aim is to investigate how uncertainties of future climate change interact with the effects of historic human re-distributions of water for land irrigation to influence future water fluxes and water resources. So far, historic irrigation changes have greatly amplified water losses by evapotranspiration (ET) in the ASDB, whereas 20th century climate change has not much affected the regional net water loss to the atmosphere. Results show that errors in temperature (T) and precipitation (P) from single GCMs have large influence on projected change trends (for the period 2010–2039) of river runoff (R), even though the ASDB is spatially well resolved by current GCMs. By contrast, observed biases in GCM ensemble mean results have relatively small influence on projected R change trends. Ensemble mean results show that projected future climate change will considerably increase the net water loss to the atmosphere. Furthermore, the ET response strength to any future T change will be further increased by maintained (or increased) irrigation practices, which shows how climate change and water use change can interact in modifying ET (and R). With maintained irrigation practices, R is likely to decrease to near-total depletion, with risk for cascading ecological regime shifts in aquatic ecosystems downstream of irrigated land areas. Without irrigation, the agricultural areas of the principal Syr Darya river basin could sustain a 50% higher T increase (of 2.3 °C instead of the projected 1.5 °C until 2010–2039) before yielding the same consumptive ET increase and associated R decrease as with the present irrigation practices.


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