The objective of preliminary statistical analysis in hydrology is to examine the independence and stationarity of the data series. Rao and Hamid (2000) identified four major analysis which is independence and stationarity test, homogeneity test, and the outliers and trends test that required in hydrology analysis.
The test for independence and stationarity can be conducted using Wald-Wolfowitz method (WW-Test. The equations of this test is shown below:
The homogeneity test was used to test the hypothesis of two samples data. The test is conducted using The Mann and Whitney method.
The outliers test is important to smooth the data fitting distribution. Low and high outliers will give different effects on the analysis. Grubbs and Beck test is used to detect outliers based on the assumption of normality.
The distribution test is conducted to understand the behavior of hydrology time series data.The Mann-Kendall test is widely used to detect trends in hydrologic data. The Wilcoxon inversion Test (Kanji, 1993) is used to test if two random samples have the same frequency distribution.
Data for Hydrology Analysis
The magnitude of data (i.e. daily, 1-day, monthly, annual) is important in hydrology and water resources analysis. In hydrology analysis those data were classified based on time base and event base data. The length of data also play important role in hydrology analysis. Based on WMO, 30 years of data is sufficient to quantify the changes in hydrology variables.
In climate change analysis, 30 years of high reliability daily precipitation is needed and for Malaysia condition data range from 1961 to 1990 is selected. Previous study shows that this range of data is good enough to investigate the current and future climate change.
Observed large-scale NCEP (National Centre for Environmental Prediction) reanalysis atmospheric variables for the same time period (1961 to 1990) have been used as predictors. The NCEP/NCAR Reanalysis produced a retroactive 51-year (1948–1998)record of global atmospheric fields derived from a Numerical Weather Prediction model kept unchanged over the analysis period and constrained by observations (Kalnay et al., 1996). NCEP daily global analyses data provided by the NCEP/NCAR internet site http://dss.ucar.edu/pub/reanalysis/.
The GCM simulations used for this study are from Hadley Centre 3rd generation (HadCM3) coupled oceanic-atmospheric general circulation model (Wilby et al., 2001). The Hadley circulation provides a useful framework for understanding the nature of large scale flow, the actual circulation in the tropics involves substantial zonal and regional variations (Manton and Bonell, 1995). This data is available at http:// www. cics. uvic.ca /scenarios / sdsm/ select. cgi. These experiments forced with changes in greenhouse gas (GHG) concentrations alone and those forced with greenhouse gas and sulphate aerosol changes. In terms of precipitation, control runs from HadCM3 transient simulations indicate approximately a 3% increase in global precipitation by the end of the 21st century (IPCC, 2001). The GCM data from 1961 to 2099 were extracted for 30-year time slices. For consistency description the scenarios data will be named as follow; the baseline period, 1961-1990 (current observed), 2010 to 2039 (the 2020s), 2040 to 2069 (the 2050s) and 2070 to 2099 (the 2080s). The justification for this division was based om a substantial change in rainfall. This point also coincide with a change justified by WMO. In climate change analysis, it is important that equal time segments are used for contrast and comparison purposes.
In climate change analysis, 30 years of high reliability daily precipitation is needed and for Malaysia condition data range from 1961 to 1990 is selected. Previous study shows that this range of data is good enough to investigate the current and future climate change.
Observed large-scale NCEP (National Centre for Environmental Prediction) reanalysis atmospheric variables for the same time period (1961 to 1990) have been used as predictors. The NCEP/NCAR Reanalysis produced a retroactive 51-year (1948–1998)record of global atmospheric fields derived from a Numerical Weather Prediction model kept unchanged over the analysis period and constrained by observations (Kalnay et al., 1996). NCEP daily global analyses data provided by the NCEP/NCAR internet site http://dss.ucar.edu/pub/reanalysis/.
The GCM simulations used for this study are from Hadley Centre 3rd generation (HadCM3) coupled oceanic-atmospheric general circulation model (Wilby et al., 2001). The Hadley circulation provides a useful framework for understanding the nature of large scale flow, the actual circulation in the tropics involves substantial zonal and regional variations (Manton and Bonell, 1995). This data is available at http:// www. cics. uvic.ca /scenarios / sdsm/ select. cgi. These experiments forced with changes in greenhouse gas (GHG) concentrations alone and those forced with greenhouse gas and sulphate aerosol changes. In terms of precipitation, control runs from HadCM3 transient simulations indicate approximately a 3% increase in global precipitation by the end of the 21st century (IPCC, 2001). The GCM data from 1961 to 2099 were extracted for 30-year time slices. For consistency description the scenarios data will be named as follow; the baseline period, 1961-1990 (current observed), 2010 to 2039 (the 2020s), 2040 to 2069 (the 2050s) and 2070 to 2099 (the 2080s). The justification for this division was based om a substantial change in rainfall. This point also coincide with a change justified by WMO. In climate change analysis, it is important that equal time segments are used for contrast and comparison purposes.
Statistical Downscaling Model
Statistical Downscaling Model (SDSM) developed by Wilby et al. (2002) widely used for testing the downscaling feasibility in the simulation of daily precipitation series.
Downscaling Method
In climate change analysis, problems occur when dealing with the scale differences between global (or regional) climate models and local hydrologic models. The resolution of available GCM output is typically between 200 and 1000 km, for many impacts models, however, information is required on regional scale (i.e. 100km) and/or local scale (i.e. 10km). One potential solution to this problem is to downscale the output from the GCM to a local scale.
It is noted that downscaling should prove to be a useful tool for assessments involving multiple air issues, including climate change, because of the physical links between these issues and large-scale atmospheric flow. Downscaling schemes can be grouped into two broad categories; dynamical and statistical downscaling schemes. Dynamical downscaling (DD) employs a regional climate model (RCM). These models are driven by boundary conditions derived from observations and from the output of larger-scale global atmospheric models over a limited spatial domain. Wilby et al. (2002) found that DD methods are computationally costly and still need to downscale the output in order to use it in impact models.
The second method involves the development of statistical relationships between observed large scale variables of observed climate and local variables such as site-specific temperature and/or precipitation. These relationships are assumed to remain constant under a changed climate, and are applied to predict future local climate form the future large scale conditions simulated by a GCM (Wilby and Wigley, 1997). Statistical downscaling (SD) techniques are commonly based on methods from linear or non linear regression analysis, stochastic processes, artificial neural networks, etc. Each methodology has its unique strengths for reproducing specific statistical features of the fields. Notably, SD methods are computationally
inexpensive and do not required detailed information about physical processes.
It is noted that downscaling should prove to be a useful tool for assessments involving multiple air issues, including climate change, because of the physical links between these issues and large-scale atmospheric flow. Downscaling schemes can be grouped into two broad categories; dynamical and statistical downscaling schemes. Dynamical downscaling (DD) employs a regional climate model (RCM). These models are driven by boundary conditions derived from observations and from the output of larger-scale global atmospheric models over a limited spatial domain. Wilby et al. (2002) found that DD methods are computationally costly and still need to downscale the output in order to use it in impact models.
The second method involves the development of statistical relationships between observed large scale variables of observed climate and local variables such as site-specific temperature and/or precipitation. These relationships are assumed to remain constant under a changed climate, and are applied to predict future local climate form the future large scale conditions simulated by a GCM (Wilby and Wigley, 1997). Statistical downscaling (SD) techniques are commonly based on methods from linear or non linear regression analysis, stochastic processes, artificial neural networks, etc. Each methodology has its unique strengths for reproducing specific statistical features of the fields. Notably, SD methods are computationally
inexpensive and do not required detailed information about physical processes.
Rainfall Variability due to Climate Change
As for the cause of climate change, climate variability and parameter are often described and quantified by general circulation models (GCMs).The idea behind the evaluation of different climate change scenarios generated by the GCMs was to provide a wide range of scenarios for the next century built upon various detailed assumptions about non-climate factors such as global driving variables and emissions, other environmental factors and regional socio-economic factor which correspond to energy production and emission schemes. The most comprehensive emissions scenarios currently available are those from the IPCC Special Report on Emissions Scenarios (SRES), which detail emissions of carbon dioxide (CO 2 ), as well as other atmospheric constituents not accounted for by previous emissions scenarios (IPCC, 2000).
Rainfall is often governed by synoptic atmospheric patterns. Therefore, downscaling methods is widely used to evaluate the relationship between atmospheric patterns and rainfall data.The downscaling methods is used to evaluate local effects of a global climate change.
Rainfall is often governed by synoptic atmospheric patterns. Therefore, downscaling methods is widely used to evaluate the relationship between atmospheric patterns and rainfall data.The downscaling methods is used to evaluate local effects of a global climate change.
Malaysia Water Resources
Malaysia receives an average annual precipitation of about 3000 mm. The total average annual flow per year for Malaysia rivers is estimated as 580 km³/year.The total runoff for Malaysia is an average from the 150 river basins systems around the country and contribute 98% of the total national water use. The total annual groundwater resources are assessed as 64 km³. The World Resources Institute had estimated that in 2007 the annual renewable water supply of Malaysia to be approximately 22,100 m3/person/year, a fall of about 2% from 22,484 m3/person/year in 2006.
In recent times, several studies show that climate change is likely to impact significantly upon water resources availability. Since Malaysia lies entirely in the equatorial zone, the climate is governed by the yearly alternation of the northeast and southwest monsoons. Therefore, change in climate will affect frequency of flood and drought episodes in this region.
In recent times, several studies show that climate change is likely to impact significantly upon water resources availability. Since Malaysia lies entirely in the equatorial zone, the climate is governed by the yearly alternation of the northeast and southwest monsoons. Therefore, change in climate will affect frequency of flood and drought episodes in this region.
Water Resources Conservation
Water Resources (Vodnye resursy) was build to present materials on the assessment of water resources, integrated water-resource use, water quality, and environmental protection. The blogs covers many areas of research in water resources, including prediction of variations in continental water resources and regime; hydrophysical and hydrodynamic processes; environmental aspects of water quality and protection;water-resource development; experimental, analysis and modeling methods of hydrology and water resource.
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