scholarly journals Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1588 ◽  
Author(s):  
Zhu ◽  
Yang ◽  
Liu ◽  
Wen ◽  
Zhang ◽  
...  

Forecasting the potential hydrological response to future climate change is an effective way of assessing the adverse effects of future climate change on water resources. Data-driven models based on machine learning algorithms have great application prospects for hydrological response forecasting as they require less developmental time, minimal input, and are relatively simple compared to dynamic or physical models, especially for data scarce regions. In this study, we employed an ensemble of eight General Circulation Models (GCMs) and two artificial intelligence-based methods (Support Vector Regression, SVR, and Extreme Learning Machine, ELM) to establish the historical streamflow response to climate change and to forecast the future response under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 in a mountainous watershed in northwest China. We found that the artificial-intelligence-based SVR and ELM methods showed very good performances in the projection of future hydrological responses. The ensemble of GCM outputs derived very close historical hydrological hindcasts but had great uncertainty in future hydrological projections. Using the variables of GCM outputs as inputs to SVR can reduce intermediate downscaling links between variables and decrease the cumulative effect of bias in projecting future hydrological responses. Future precipitation in the study area will increase in the future under both scenarios, and this increasing trend is more significant under RCP 8.5 than under scenario 4.5. The results also indicate the streamflow change will be more sensitive to temperature (precipitation) under the RCP 8.5 (4.5) scenario. The findings and approach have important implications for hydrological response studies and the evaluation of impacts on localized regions similar to the mountainous watershed in this study.

Author(s):  
K. Lin ◽  
W. Zhai ◽  
S. Huang ◽  
Z. Liu

Abstract. The impact of future climate change on the runoff for the Dongjiang River basin, South China, has been investigated with the Soil and Water Assessment Tool (SWAT). First, the SWAT model was applied in the three sub-basins of the Dongjiang River basin, and calibrated for the period of 1970–1975, and validated for the period of 1976–1985. Then the hydrological response under climate change and land use scenario in the next 40 years (2011–2050) was studied. The future weather data was generated by using the weather generators of SWAT, based on the trend of the observed data series (1966–2005). The results showed that under the future climate change and LUCC scenario, the annual runoff of the three sub-basins all decreased. Its impacts on annual runoff were –6.87%, –6.54%, and –18.16% for the Shuntian, Lantang, and Yuecheng sub-basins respectively, compared with the baseline period 1966–2005. The results of this study could be a reference for regional water resources management since Dongjiang River provides crucial water supplies to Guangdong Province and the District of Hong Kong in China.


Geoforum ◽  
2019 ◽  
Vol 105 ◽  
pp. 158-167 ◽  
Author(s):  
Kristina Diprose ◽  
Chen Liu ◽  
Gill Valentine ◽  
Robert M. Vanderbeck ◽  
Katie McQuaid

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 712
Author(s):  
Innocent Mbokodo ◽  
Mary-Jane Bopape ◽  
Hector Chikoore ◽  
Francois Engelbrecht ◽  
Nthaduleni Nethengwe

Weather and climate extremes, such as heat waves (HWs), have become more frequent due to climate change, resulting in negative environmental and socioeconomic impacts in many regions of the world. The high vulnerability of South African society to the impacts of warm extreme temperatures makes the study of the effect of climate change on future HWs necessary across the country. We investigated the projected effect of climate change on future of South Africa with a focus on HWs using an ensemble of regional climate model downscalings obtained from the Conformal Cubic Atmospheric Model (CCAM) for the periods 2010–2039, 2040–2069, and 2070–2099, with 1983–2012 as the historical baseline. Simulations were performed under the Representative Concentration Pathway (RCP) 4.5 (moderate greenhouse gas (GHG) concentration) and 8.5 (high GHG concentration) greenhouse gas emission scenarios. We found that the 30-year period average maximum temperatures may rise by up to 6 °C across much of the interior of South Africa by 2070–2099 with respect to 1983–2012, under a high GHG concentration. Simulated HW thresholds for all ensemble members were similar and spatially consistent with observed HW thresholds. Under a high GHG concentration, short lasting HWs (average of 3–4 days) along the coastal areas are expected to increase in frequency in the future climate, however the coasts will continue to experience HWs of relatively shorter duration compared to the interior regions. HWs lasting for shorter duration are expected to be more frequent when compared to HWs of longer durations (over two weeks). The north-western part of South Africa is expected to have the most drastic increase in HWs occurrences across the country. Whilst the central interior is not projected to experience pronounced increases in HW frequency, HWs across this region are expected to last longer under future climate change. Consistent patterns of change are projected for HWs under moderate GHG concentrations, but the changes are smaller in amplitude. Increases in HW frequency and duration across South Africa may have significant impacts on human health, economic activities, and livelihoods in vulnerable communities.


2018 ◽  
Vol 22 (1) ◽  
pp. 305-316 ◽  
Author(s):  
Qianqian Zhou ◽  
Guoyong Leng ◽  
Maoyi Huang

Abstract. As China becomes increasingly urbanised, flooding has become a regular occurrence in its major cities. Assessing the effects of future climate change on urban flood volumes is crucial to informing better management of such disasters given the severity of the devastating impacts of flooding (e.g. the 2016 flooding events across China). Although recent studies have investigated the impacts of future climate change on urban flooding, the effects of both climate change mitigation and adaptation have rarely been accounted for together in a consistent framework. In this study, we assess the benefits of mitigating climate change by reducing greenhouse gas (GHG) emissions and locally adapting to climate change by modifying drainage systems to reduce urban flooding under various climate change scenarios through a case study conducted in northern China. The urban drainage model – Storm Water Management Model – was used to simulate urban flood volumes using current and two adapted drainage systems (i.e. pipe enlargement and low-impact development, LID), driven by bias-corrected meteorological forcing from five general circulation models in the Coupled Model Intercomparison Project Phase 5 archive. Results indicate that urban flood volume is projected to increase by 52 % over 2020–2040 compared to the volume in 1971–2000 under the business-as-usual scenario (i.e. Representative Concentration Pathway (RCP) 8.5). The magnitudes of urban flood volumes are found to increase nonlinearly with changes in precipitation intensity. On average, the projected flood volume under RCP 2.6 is 13 % less than that under RCP 8.5, demonstrating the benefits of global-scale climate change mitigation efforts in reducing local urban flood volumes. Comparison of reduced flood volumes between climate change mitigation and local adaptation (by improving drainage systems) scenarios suggests that local adaptation is more effective than climate change mitigation in reducing future flood volumes. This has broad implications for the research community relative to drainage system design and modelling in a changing environment. This study highlights the importance of accounting for local adaptation when coping with future urban floods.


2016 ◽  
Vol 48 (5) ◽  
pp. 1327-1342 ◽  
Author(s):  
Spyridon Paparrizos ◽  
Andreas Matzarakis

Assessment of future variations of streamflow is essential for research regarding climate and climate change. This study is focused on three agricultural areas widespread in Greece and aims to assess the future response of annual and seasonal streamflow and its impacts on the hydrological regime, in combination with other fundamental aspects of the hydrological cycle in areas with different climate classification. ArcSWAT ArcGIS extension was used to simulate the future responses of streamflow. Future meteorological data were obtained from various regional climate models, and analysed for the periods 2021–2050 and 2071–2100. In all the examined areas, streamflow is expected to be reduced. Areas characterized by continental climate will face minor reductions by the mid-century that will become very intense by the end and thus these areas will become more resistant to future changes. Autumn season will face the strongest reductions. Areas characterized by Mediterranean conditions will be very vulnerable in terms of future climate change and winter runoff will face the most significant decreases. Reduced precipitation is the main reason for decreased streamflow. High values of actual evapotranspiration by the end of the century will act as an inhibitor towards reduced runoff and partly counterbalance the water losses.


2021 ◽  
Author(s):  
Xiaohong Chen ◽  
Haoyu Jin ◽  
Pan Wu ◽  
Wenjun Xia ◽  
Ruida Zhong ◽  
...  

Abstract The source region of the Yangtze River (SRYR) is located in the hinterland of the Tibetan Plateau (TP). The natural environment is hash, and the hydrological and meteorological stations are less distributed, making the observed data are relatively scarce. In order to overcome the impact of lack of data, the China Meteorological Forcing Dataset (CMFD) was used to correct the meteorological data, to make the data more closer to the real distribution on the SRYR surface. This paper used the Soil and Water Assessment Tool (SWAT) to verify interpolation effect. Since the SRYR is an important water resource protection area, have a great significance to study the hydrological response under future climate change. The Back Propagation (BP) neural network algorithm was used to integrate data extracted from the six Global Climate Models (GCMs), and then the SWAT model was used to predict runoff changes in the future status. The results show that the CMFD data set has a high precision in the SRYR, and can be used for meteorological data correction. After the meteorological data correction, the Nash-Sutcliffe efficiency increased from 0.64 to 0.70. Under the future climate change, the runoff in the SRYR shows a decreasing trend, and the distribution of runoff during the year changes greatly. This reflects the amount of water resources in the SRYR will be decreased, which will brings challenges to water resources management in the SRYR.


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.


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