scholarly journals Evaluation of a statistical downscaling procedure for the estimation of climate change impacts on droughts

2009 ◽  
Vol 9 (3) ◽  
pp. 879-894 ◽  
Author(s):  
L. Vasiliades ◽  
A. Loukas ◽  
G. Patsonas
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Standardized Precipitation Index ◽  
Circulation Model ◽  
Economic Losses ◽  
Monthly Precipitation ◽  
Statistical Downscaling Method ◽  
Downscaling Method

Abstract. Despite uncertainties in future climates, there is considerable evidence that there will be substantial impacts on the environment and human interests. Climate change will affect the hydrology of a region through changes in the timing, amount, and form of precipitation, evaporation and transpiration rates, and soil moisture, which in turn affect also the drought characteristics in a region. Droughts are long-term phenomena affecting large regions causing significant damages both in human lives and economic losses. The most widely used approach in regional climate impact studies is to combine the output of the General Circulation Models (GCMs) with an impact model. The outputs of Global Circulation Model CGCMa2 were applied for two socioeconomic scenarios, namely, SRES A2 and SRES B2 for the assessment of climate change impact on droughts. In this study, a statistical downscaling method has been applied for monthly precipitation. The methodology is based on multiple regression of GCM predictant variables with observed precipitation developed in an earlier paper (Loukas et al., 2008) and the application of a stochastic timeseries model for precipitation residuals simulation (white noise). The methodology was developed for historical period (1960–1990) and validated against observed monthly precipitation for period 1990–2002 in Lake Karla watershed, Thessaly, Greece. The validation indicated the accuracy of the methodology and the uncertainties propagated by the downscaling procedure in the estimation of a meteorological drought index the Standardized Precipitation Index (SPI) at multiple timescales. Subsequently, monthly precipitation and SPI were estimated for two future periods 2020–2050 and 2070–2100. The results of the present study indicate the accuracy, reliability and uncertainty of the statistical downscaling method for the assessment of climate change on hydrological, agricultural and water resources droughts. Results show that climate change will have a major impact on droughts but the uncertainty introduced is quite large and is increasing as SPI timescale increases. Larger timescales of SPI, which, are used to monitor hydrological and water resources droughts, are more sensitive to climate change than smaller timescales, which, are used to monitor meteorological and agricultural droughts. Future drought predictions should be handled with caution and their uncertainty should always be evaluated as results demonstrate.

Assessing Climate Change Impact on Water Resources in the Tone River Basin, Japan, Using Super-High-Resolution Atmospheric Model Output

2009 ◽  
Vol 4 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Kaoru Takara ◽  
◽  
Sunmin Kim ◽  
Yasuto Tachikawa ◽  
Eiichi Nakakita ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Water Resources ◽  
River Basin ◽  
General Circulation ◽  
Meteorological Data ◽  
Standardized Precipitation Index ◽  
Circulation Model ◽  
Japan Meteorological Agency ◽  
Orographic Rainfall

We examined the potential impact of climate change on Tokyo metropolitan water resources in the Tone River basin using output from a super-high-resolution global atmospheric general circulation model, AGCM20, having 20-km spatial resolution and 1-hour temporal resolution. AGCM20 is run on the Earth Simulator and being developed under the Japanese government’s Kakushin21 program. AGCM20 has an advantage in simulating orographic rainfall and frontal rain bands, so we used its output to analyze Tone River basin water resources. The basin covers 16,840 km2and the main channel is 322 km long from its source to the Pacific Ocean. AGCM20 outputs hourly precipitation and daily variables such as snowfall, rainfall, snowmelt, evaporation, and transpiration for a present period, 1979-1998, and a projected period, 2075-2094. A comparison of these two periods showed that snow-related variables will decrease and all others will increase. Based on a comparison of ordered daily precipitation curves (ODPC) between AGCM20 and the Automated Meteorological Data Acquisition System (AMeDAS), a high-resolution Japan Meteorological Agency (JMA) surface observation network, we corrected AGCM20 precipitation data bias, and calculated the standardized precipitation index (SPI) drought indicator. The SPI for less than 6 months does not show noticeable variations under climate change, but the yearly SPI predicts more frequent dry conditions, indicating increased future vulnerability to subtle droughts.

Comparisons of Machine Learning Methods of Statistical Downscaling Method: Case Studies of Daily Climate Anomalies in Thailand

2021 ◽  
Author(s):  
Kanawut Chattrairat ◽  
Waranyu Wongseree ◽  
Adisorn Leelasantitham
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Performance Criteria ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Support Vector ◽  
Seasonal Temperature ◽  
Statistical Downscaling Method ◽  
Downscaling Method

The climate change which is essential for daily life and especially agriculture has been forecasted by global climate models (GCMs) in the past few years. Statistical downscaling method (SD) has been used to improve the GCMs and enables the projection of local climate. Many pieces of research have studied climate change in case of individually seasonal temperature and precipitation for simulation; however, regional difference has not been included in the calculation. In this research, four fundamental SDs, linear regression (LR), Gaussian process (GP), support vector machine (SVM) and deep learning (DL), are studied for daily maximum temperature (TMAX), daily minimum temperature (TMIN), and precipitation (PRCP) based on the statistical relationship between the larger-scale climate predictors and predictands in Thailand. Additionally, the data sets of climate variables from over 45 weather stations overall in Thailand are used to calculate in this calculation. The statistical analysis of two performance criteria (correlation and root mean square error (RMSE)) shows that the DL provides the best performance for simulation. The TMAX and TMIN were calculated and gave a similar trend for all models. PRCP results found that in the North and South are adequate and poor performance due to high and low precipitation, respectively. We illustrate that DL is one of the suitable models for the climate change problem.

Statistical downscaling of general circulation model outputs to precipitation, evaporation and temperature using a key station approach

2016 ◽  
Vol 7 (4) ◽  
pp. 683-707
Author(s):  
D. A. Sachindra ◽  
F. Huang ◽  
A. Barton ◽  
B. J. C. Perera
Keyword(s):  
General Circulation Model ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Emission Scenario ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Maximum Temperature ◽  
Monthly Precipitation ◽  
Regression Equations ◽  
Highly Correlated

Using a key station approach, statistical downscaling of monthly general circulation model outputs to monthly precipitation, evaporation, minimum temperature and maximum temperature at 17 observation stations located in Victoria, Australia was performed. Using the observations of each predictand, over the period 1950–2010, correlations among all stations were computed. For each predictand, the station which showed the highest number of correlations above 0.80 with other stations was selected as the first key station. The stations that were highly correlated with that key station were considered as the member stations of the first cluster. By employing this same procedure on the remaining stations, the next key station was found. This procedure was performed until all stations were segregated into clusters. Thereafter, using the observations of each predictand, regression equations (inter-station regression relationships) were developed between the key stations and the member stations for each calendar month. The downscaling models at the key stations were developed using reanalysis data as inputs to them. The outputs of HadCM3 pertaining to A2 emission scenario were introduced to these downscaling models to produce projections of the predictands over the period 2000–2099. Then the outputs of these downscaling models were introduced to the inter-station regression relationships to produce projections of predictands at all member stations.

scholarly journals Future Changes in Drought Characteristics under Extreme Climate Change over South Korea

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Joo-Heon Lee ◽  
Hyun-Han Kwon ◽  
Ho-Won Jang ◽  
Tae-Woong Kim
Keyword(s):  
Climate Change ◽  
South Korea ◽  
General Circulation ◽  
Standardized Precipitation Index ◽  
Low Frequency ◽  
Circulation Model ◽  
Significance Test ◽  
Precipitation Index ◽  
Intergovernmental Panel ◽  
The Future

This study attempts to analyze several drought features in South Korea from various perspectives using a three-month standard precipitation index. In particular, this study aims to evaluate changes in spatial distribution in terms of frequency and severity of droughts in the future due to climate change, using IPCC (intergovernmental panel on climate change) GCM (general circulation model) simulations. First, the Mann-Kendall method was adopted to identify drought trends at the five major watersheds. The simulated temporal evolution of SPI (standardized precipitation index) during the winter showed significant drying trends in most parts of the watersheds, while the simulated SPI during the spring showed a somewhat different feature in the GCMs. Second, this study explored the low-frequency patterns associated with drought by comparing global wavelet power, with significance test. Future spectra decreased in the fractional variance attributed to a reduction in the interannual band from 2 to 8 years. Finally, the changes in the frequency and the severity under climate change were evaluated through the drought spell analyses. Overall features of drought conditions in the future showed a tendency to increase (about 6%) in frequency and severity of droughts during the dry season (i.e., from October to May) under climate change.

scholarly journals Coupling a Markov Chain and Support Vector Machine for At-Site Downscaling of Daily Precipitation

2017 ◽  
Vol 18 (9) ◽  
pp. 2385-2406 ◽  
Author(s):  
Yu-Kun Hou ◽  
Hua Chen ◽  
Chong-Yu Xu ◽  
Jie Chen ◽  
Sheng-Lian Guo
Keyword(s):  
Climate Change ◽  
Markov Chain ◽  
Statistical Downscaling ◽  
Daily Precipitation ◽  
Global Climate Models ◽  
Support Vector ◽  
Statistical Downscaling Method ◽  
Calibration And Validation ◽  
Downscaling Method ◽  
Calibration Methods

Abstract Statistical downscaling is useful for managing scale and resolution problems in outputs from global climate models (GCMs) for climate change impact studies. To improve downscaling of precipitation occurrence, this study proposes a revised regression-based statistical downscaling method that couples a support vector classifier (SVC) and first-order two-state Markov chain to generate the occurrence and a support vector regression (SVR) to simulate the amount. The proposed method is compared to the Statistical Downscaling Model (SDSM) for reproducing the temporal and quantitative distribution of observed precipitation using 10 meteorological indicators. Two types of calibration and validation methods were compared. The first method used sequential split sampling of calibration and validation periods, while the second used odd years for calibration and even years for validation. The proposed coupled approach outperformed the other methods in downscaling daily precipitation in all study periods using both calibration methods. Using odd years for calibration and even years for validation can reduce the influence of possible climate change–induced nonstationary data series. The study shows that it is necessary to combine different types of precipitation state classifiers with a method of regression or distribution to improve the performance of traditional statistical downscaling. These methods were applied to simulate future precipitation change from 2031 to 2100 with the CMIP5 climate variables. The results indicated increasing tendencies in both mean and maximum future precipitation predicted using all the downscaling methods evaluated. However, the proposed method is an at-site statistical downscaling method, and therefore this method will need to be modified for extension into a multisite domain.

scholarly journals Assessing the climate change impact on hydrological response in the Gorganrood River Basin, Iran

2017 ◽  
Vol 9 (3) ◽  
pp. 421-433 ◽  
Author(s):  
Hamed Rouhani ◽  
Marayam Sadat Jafarzadeh
Keyword(s):  
Climate Change ◽  
River Basin ◽  
General Circulation ◽  
Assessment Tool ◽  
Circulation Model ◽  
Low Flow ◽  
Future Climate Change ◽  
Monthly Precipitation ◽  
Climate Change Scenarios ◽  
Annual Scale

Abstract A general circulation model (GCM) and hydrological model SWAT (Soil and Water Assessment Tool) under forcing from A1B, B1, and A2 emission scenarios by 2030 were used to assess the implications of climate change on water balance of the Gorganrood River Basin (GRB). The results of MPEH5C models and multi-scenarios indicated that monthly precipitation generally decreases while temperature increases in various parts of the basin with the magnitude of the changes in terms of different stations and scenarios. Accordingly, seasonal ET will decrease throughout the GRB over the 2020s in all seasons except in summer, where a slight increase is projected for A1B and A2 scenarios. At annual scale, average quick flow and average low flow under the B1, A1B, and A2 scenarios are projected to decrease by 7.3 to 12.0% from the historical levels. Over the ensembles of climate change scenarios, the simulations project average autumn total flow declines of ∼10% and an overall range of 6.9 to 13.2%. In summer, the components of flow at the studied basin are expected to increase under A2 and A1B scenarios but will slightly decrease under B1 scenario. The study result addresses a likelihood of inevitable future climate change.

scholarly journals Investigating the Impact of Climate Change on Future Runoff of River Satluj

2016 ◽  
Vol 8 (1) ◽  
pp. 10-21
Author(s):  
Narayan P Gautam ◽  
Manohar Arora ◽  
N.K. Goel ◽  
A.R.S. Kumar
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
General Circulation ◽  
Circulation Model ◽  
Climate Change Scenarios ◽  
Impact Of Climate Change ◽  
Basin Scale ◽  
The Future ◽  
Future Climatic Condition ◽  
The Impact

Climate change has been emerging as one of the challenges in the global environment. Information of predicted climatic changes in basin scale is highly useful to know the future climatic condition in the basin that ultimately becomes helpful to carry out planning and management of the water resources available in the basin. Climatic scenario is a plausible and often simplified representation of the future climate, based on an internally consistent set of climatological relationships that has been constructed for explicit use in investigating the potential consequences of anthropogenic climate change. This study based on statistical downscaling, provide good example focusing on predicting the rainfall and runoff patterns, using the coarse general circulation model (GCM) outputs. The outputs of the GCMs are utilized to study the impact of climate change on water resources. The present study has been taken up to identify the climate change scenarios for Satluj river basin, India.Journal of Hydrology and Meteorology, Vol. 8(1) p.10-21

scholarly journals Impact of climate change on drought in the Daklak province

2014 ◽  
Vol 17 (3) ◽  
pp. 5-11
Author(s):  
Khoi Nguyen Dao ◽  
Quang Nguyen Xuan Chau
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Standardized Precipitation Index ◽  
Circulation Model ◽  
Meteorological Drought ◽  
Impact Of Climate Change ◽  
The Future ◽  
Delta Change ◽  
The Standardized Precipitation Index ◽  
The Impact

The main objective of this study was to evaluate the impact of climate change on the meteorological drought in the Daklak province. In this study, the meteorological drought was calculated using the Standardized Precipitation Index (SPI).From this result, two scensrios fot the precipitation VA1B and B1 were downscaled, from the outputs of 4 GCMs (General Circulation Model): CGCM3.1 (T63), CM2.0, CM2.1, and HadCM3 using the simple downscaling method (delta change method). The impacts of climate change on the droughts were assessed by comparing the present (1980- 2009) and the future droughts (2010-2039, 2040-2069, and 2070-2099).Results of the study suggested that the future temperature would increase by 0.9-2.8ºC and the future precipitation would decrease by 0.4-4.7% for both A1B and B1 scenarios. Under the future climate scenarios, the frequency and severity of extreme drought would increase. The results obtained in this study could be useful for planning and managing water resources at this region.

scholarly journals Challenges in water resources of Lagos mega city of Nigeria in the context of climate change

2019 ◽  
Vol 11 (4) ◽  
pp. 1067-1083 ◽  
Author(s):  
Mohammed Sanusi Shiru ◽  
Shamsuddin Shahid ◽  
Suleiman Shiru ◽  
Eun Sung Chung ◽  
Noraliani Alias ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
General Circulation ◽  
Salt Water ◽  
Circulation Model ◽  
Environmental Crisis ◽  
Salt Water Intrusion ◽  
Climate Projections ◽  
Environmental Challenges ◽  
Groundwater Overdraft

Abstract This study assesses the water resources and environmental challenges of Lagos mega city, Nigeria, in the context of climate change. Being a commercial hub, the Lagos population has grown rapidly causing an insurmountable water and environmental crisis. In this study, a combined field observation, sample analysis, and interviews were used to assess water challenges. Observed climate, general circulation model (GCM) projections and groundwater data were used to assess water challenges due to climate change. The study revealed that unavailability of sufficient water supply provision in Lagos has overwhelmingly compelled the population to depend on groundwater, which has eventually caused groundwater overdraft. Salt water intrusion and subsidence has occurred due to groundwater overexploitation. High concentrations of heavy metals were observed in wells around a landfill. Climate projections showed a decrease in rainfall of up to 140 mm and an increase in temperature of up to 8 °C. Groundwater storage is projected to decrease after the mid-century due to climate change. Sea level rise will continue until the end of the century. As the water and environmental challenges of Lagos are broad and the changing characteristics of the climate are expected to intensify these as projected, tackling these challenges requires a holistic approach from an integrated water resources management perspective.

scholarly journals Comparison of different statistical downscaling models and future projection of areal mean precipitation of a river basin under climate change effect

2021 ◽  
Author(s):  
A. Guven ◽  
A. Pala
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Gene Expression Programming ◽  
Circulation Model ◽  
Support Vector ◽  
Group Method ◽  
Monthly Precipitation ◽  
Weather Factors

Abstract Investigation of the hydrological impacts of climate change at the local scale requires the use of a statistical downscaling technique. In order to use the output of a Global Circulation Model (GCM) model, downscaling technique is used. In this study, statistical downscaling of monthly areal mean precipitation of Göksun River basin in Turkey was carried out using the Group Method of Data Handling (GMDH), Support Vector Machines (SVM) and Gene-expression Programming (GEP) techniques. Large-scale weather factors are used for a basin with monthly areal mean precipitation (PM) record from 1971 to 2000 for training and testing periods. The R2-value for precipitation in SVM, GEP and GMDH models are 0.62, 0.59, and 0.6 respectively, for testing periods. The results showed that SVM has the best model performance than the other proposed downscaling models, however, AIC values showed the GEP model has the lowest AIC value. The simulated results for CGCM3 A1B and A2 scenarios show a similarity in their average precipitation prediction. Generally, both scenarios anticipate a decrease in the average monthly precipitation during the simulated periods. Therefore, the results of future projections show that the mean precipitation might decrease during the period of 2021–2100.

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