Environmental water demand assessment under climate change conditions

Measures taken to cope with the possible effects of climate change on water resources management are key for the successful adaptation to such change. This work assesses the environmental water demand of the Karkheh river in the reach comprising Karkheh dam to the Hoor-al-Azim wetland, Iran, under climate change during the period 2010–2059. The assessment of the environmental demand applies (1) representative concentration pathways (RCPs) and (2) downscaling methods. The first phase of this work projects temperature and rainfall in the period 2010–2059 under three RCPs and with two downscaling methods. Thus, six climatic scenarios are generated. The results showed that temperature and rainfall average would increase in the range of 1.7–5.2 and 1.9–9.2%, respectively. Subsequently, flows corresponding to the six different climatic scenarios are simulated with the unit hydrographs and component flows from rainfall, evaporation, and stream flow data (IHACRES) rainfall-runoff model and are input to the Karkheh reservoir. The simulation results indicated increases of 0.9–7.7% in the average flow under the six simulation scenarios during the period of analysis. The second phase of this paper’s methodology determines the monthly minimum environmental water demands of the Karkheh river associated with the six simulation scenarios using a hydrological method. The determined environmental demands are compared with historical ones. The results show that the temporal variation of monthly environmental demand would change under climate change conditions. Furthermore, some climatic scenarios project environmental water demand larger than and some of them project less than the baseline one.     

Statistical Modeling of Health Effects on Climate-Sensitive Variables and Assessment of Environmental Burden of Diseases Attributable to Climate Change in Nepal

An ecological time-series study is conducted to quantify health-effect coefficients associated with climate-sensitive variables namely temperature, rainfall, relative humidity, and wind speed and estimate environmental burden of diseases attributed to temperature as the main climatic variable together with climate change in Nepal. The study is based upon daily data of climate-sensitive variables and hospitalizations collected for 5 years between 2009 and 2014. Generalized linear model is used to estimate health-effect coefficients accounting distributed lag effects. Results show 3.08%, 10.14%, and 3.27% rise in water-borne, vector-borne, and renal disease hospitalizations, respectively, and 3.67% rise in water- and vector-borne disease deaths per 1 °C rise in average temperature. Similarly, 2.45% and 1.44% rise in heart disease hospitalization and all-cause mortality, respectively per 1 °C rise in absolute difference of average temperature with its overall average (20 °C). The computed attributable fractions are 0.3759, 0.6696, 0.2909, and 0.1024 for water-borne, vector-borne, renal, and heart disease hospitalizations, respectively, and 0.0607 and 0.4335 for all-cause mortality and disease-specific mortality of water- and vector-borne diseases, respectively. The percent change in attributable burdens due to climate change are found to be 4.32%, 4.64%, 7.20%, and ?2.29% for water-borne, vector-borne, renal, and heart disease hospitalizations, respectively, and ?1.39% and 6.55% for all-cause deaths and water-borne and vector-borne disease deaths, respectively. In conclusion, climate-sensitive variables have significant effects on many major health burdens in Nepal. In the context of changing climatic scenarios around the world including that of Nepal, such changes are bound to affect the health burden of Nepalese people.     

Predicting climate change effects on surface soil organic carbon of Louisiana,USA

This study aimed to assess the degree of potential temperature and precipitation change as predicted by the HadCM3 (Hadley Centre Coupled Model, version 3) climate model for Louisiana, and to investigate the effects of potential climate change on surface soil organic carbon (SOC) across Louisiana using the Rothamsted Carbon Model (RothC) and GIS techniques at the watershed scale. Climate data sets at a grid cell of 0.5°?×?0.5° for the entire state of Louisiana were collected from the HadCM3 model output for three climate change scenarios: B2, A2, and A1F1, that represent low, higher, and even higher greenhouse gas emissions, respectively. Geo-referenced datasets including USDA-NRCS Soil Geographic Database (STATSGO), USGS Land Cover Dataset (NLCD), and the Louisiana watershed boundary data were gathered for SOC calculation at the watershed scale. A soil carbon turnover model, RothC, was used to simulate monthly changes in SOC from 2001 to 2100 under the projected temperature and precipitation changes. The simulated SOC changes in 253 watersheds from three time periods, 2001–2010, 2041–2050, and 2091–2100, were tested for the influence of the land covers and emissions scenarios using SAS PROC GLIMMIX and PDMIX800 macro to separate Tukey-Kramer (p?p?p?p?相似文献   

Meat,Dairy and Climate Change: Assessing the Long-Term Mitigation Potential of Alternative Agri-Food Consumption Patterns in Canada

We use a newly developed model of the entire Canadian energy system (TIMES-Canada) to assess the climate change mitigation potential of different agri-food consumption patterns in Canada. For this, our model has been extended by disaggregating the agricultural demand sector into individual agri-food demands to allow for a more in-depth analysis. Besides a business-as-usual (baseline) scenario, we have constructed four different agri-food scenarios to assess the viability of reducing Canadian meat and dairy consumption in order to diminish Canada’s agricultural sector energy consumption and greenhouse gas (GHG) emissions. Our policy scenarios progressively restrict the consumption of different meat and dairy agricultural products until the year 2030. Our results suggest that the implementation of a meat and dairy consumption reduction policy would lead to a 10 to 40 % reduction in agricultural GHG emissions, depending on the severity of the scenario. This translates to a 1 to 3 % decrease in total Canadian GHG emissions by the year 2030. Besides these environmental benefits, health benefits associated with a reduction in meat and dairy consumption (as inferred from other studies) are presented as an additional source of motivation for implementing such a policy in Canada.     

Multi-model ensemble simulation and projection in the climate change in the Mekong River Basin. Part I: temperature

This paper evaluates the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating annual and decadal temperature in the Mekong River Basin from 1950 to 2005. By use of Bayesian multi-model averaging method, the future projection of temperature variation under different scenarios are also analyzed. The results show, the performances of climate model are more accurate in space than time, the model can catch the warming characteristics in the Mekong river Basin, but the accuracy of simulation is not good enough. Bayesian multi-model averaging method can improve the annual and decadal temperature simulation when compared to a single result. The projected temperature in Mekong River will increase by 0.88 °C/100 year, 2.15 °C/100 year and 4.96 °C/100 year for the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, over the twenty-first century. The findings will be beneficial for local people and policy-maker to formulate regional strategies against the potential menaces of warming scenarios.     

Land Use and Climate Change Impacts on the Hydrology of the Bago River Basin,Myanmar

Assessment of land use and climate change impacts on the hydrological cycle is important for basin scale water resources management. This study aims to investigate the potential impacts of land use and climate change on the hydrology of the Bago River Basin in Myanmar. Two scenarios from the representative concentration pathways (RCPs): RCP4.5 and RCP8.5 recommended by the Intergovernmental Panel on Climate Change, Fifth Assessment Report (IPCC AR5) were used to project the future climate of 2020s, 2050s, and 2080s. Six general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were selected to project the future climate in the basin. An increase of average temperature in the range of 0.7 to 1.5 °C and 0.9 to 2.7 °C was observed under RCP 4.5 and RCP 8.5, respectively, in future periods. Similarly, average annual precipitation shows a distinct increase in all three periods with the highest increase in 2050s. A well calibrated and validated Soil and Water Assessment Tool (SWAT) was used to simulate the land use and climate change impacts on future stream flows in the basin. It is observed that the impact of climate change on stream flow is higher than the land use change in the near future. The combined impacts of land use and climate change can increase the annual stream flow up to 68 % in the near future. The findings of this study would be beneficial to improve land and water management decisions and in formulating adaptation strategies to reduce the negative impacts, and harness the positive impacts of land use and climate change in the Bago River Basin.     

Distribution,sources, and toxicity assessment of polycyclic aromatic hydrocarbons in surface soils of a heavy industrial city,Liuzhou, China

As a heavy industrial city, Liuzhou has been facing a serious pollution problem. It is necessary to take steps to control and prevent environmental pollution wherever possible. Surface soil samples were collected from four communities in Liuzhou City, to determine the concentrations, distributions, sources, and toxicity potential of polycyclic aromatic hydrocarbons (PAHs) present. The mean concentrations of total PAHs in the surface soil are 756.43 ng/g for the heavy industrial area, 605.06 ng/g for the industrial area, 481.24 ng/g for the commercial–cum–residential area, and 49.93 ng/g for the rural area. Both the isomer ratio and principal component analyses for the PAHs prove that these pollutants originate mainly from coal, diesel, gasoline, and natural gas combustion. The pollution hierarchies and toxic equivalency factor of BaP prove that the city is subject to heavy pollution caused by industry, transportation, and daily human activities.     

Monitoring the changes in impervious surface ratio and urban heat island intensity between 1987 and 2011 in Szeged,Hungary

Landsat time series data make it possible to continuously map and examine urban land cover changes and effects on urban environments. The objectives of this study are (1) to map and analyse an impervious surface and its changes within a census district and (2) to monitor the effects of increasing impervious surface ratios on population and environment. We used satellite images from 1987, 2003 and 2011 to map the impervious surface ratio in the census district of Szeged, Hungary through normalized spectral mixture analysis. Significant increases were detected from 1987 to 2011 in industrial areas (5.7–9.1%) and inner residential areas (2.5–4.8%), whereas decreases were observed in the city centre and housing estates due to vegetation growth. Urban heat island (UHI) values were derived from the impervious surface fraction map to analyse the impact of urban land cover changes. In 2011, the average value in the industrial area was 1.76 °C, whereas that in the inner residential area was 1.35–1.69 °C. In the city centre zones and housing estates, values ranging from 1.4 to 1.5 °C and from 1.29 to 1.5 °C, respectively, were observed. Our study reveals that long-term land cover changes can be derived at the district level from Landsat images and that their effects can be identified and analysed, providing important information for city planners and policy makers.     

Modelling nutrient loads to Sydney estuary (Australia)

Sydney estuary (Australia) catchment is substantially urbanised (80%) and small (480 km²) with a large population (2.5 million) and is therefore highly sensitive to anthropogenic influence. The Model for Urban Stormwater Improvement Conceptualisation used to model nutrient export to the estuary determined an average annual load of 475 t total nitrogen, 63.5 t total phosphorus and 343,000 t total suspended solids. Model verification included intense, short-term water sampling and analysis undertaken in the current project and use of published data spanning 10 years. Under high-rainfall conditions (>50 mm day^???1), the estuary becomes stratified and nutrients are either removed from the estuary directly in a plume or indirectly by advective/dispersive remobilisation. The majority of the nutrient load is delivered during moderate rainfall (5–50 mm day^???1) conditions and accumulates close to discharge points and remains in the estuary. To significantly reduce nutrient load, management strategies should aim to minimise low and moderate rainfall pollutant loads.     

Health risk assessment of mercury and arsenic associated with consumption of fish from the Persian Gulf

Concentrations of mercury and arsenic in fish from the Persian Gulf were determined by graphite furnace atomic absorption spectrometry. Concentrations of the metals in muscle samples were 0.049–0.402 μg g^?1 for mercury and 0.168–0.479 μg g^?1 for arsenic, with means of 0.133 and 0.312 μg g^?1, respectively. The maximum daily consumption rate (grams per day) and meal consumption limit (meals per month) was calculated to estimate health risks associated with fish consumption. According to the results, the maximum allowable consumption rate varies between 8–56 and 15–96 g/day base on mercury and arsenic content, respectively. The results of this study indicate that the concentration of mercury and arsenic is well below the maximum permissible levels for mercury (0.5 μg g^?1) and arsenic (6 μg g^?1) according to international standards.     

Monitoring and validating the temporal dynamics of interday streamflow from two upland head micro-watersheds with different vegetative conditions during dry periods of the growing season in the Bohemian Massif,Czech Republic

At present, dynamic land use, climate change, and growing needs for fresh water are increasing the demand on the ecosystem effects of forest vegetation. Mountainous areas are at the forefront of scientific interest in European forest ecology and forest hydrology. Although uplands cover a significant area of the Czech Republic and other countries and are often covered with forest formations, they do not receive an appropriate amount of attention. Therefore, two experimental upland head micro-watersheds in the Bohemian Massif were selected for study because they display similar natural conditions, but different vegetative conditions (forest versus meadow). During the 2011 growing season, short-term streamflow measurements were carried out at the discharge profiles of both catchments and were evaluated in relation to climatic data (rainfall and temperature). The basic premise was that the streamflow in a forested catchment must exhibit different temporal dynamics compared to that in treeless areas and that these differences can be attributed to the effects of woody vegetation. These conclusions were drawn from measurements performed during dry periods lasting several days. A decreasing streamflow trend during the day part of the day (0900–1900 hours) was observed in both localities. The decrease reached approx. 44 % of the initial morning streamflow (0.24 dm³ s^?1 day^?1) in the treeless catchment and approx. 20 % (0.19 dm³ s^?1 day^?1) in the forested catchment. At night (1900–0900 hours), the streamflow in the forested catchment increased back to its initial level, whereas the streamflow in the treeless catchment stagnated or slowly decreased. We attribute these differences to the ecosystem effects of woody vegetation and its capacity to control water loss during the day. This type of vegetation can also function as a water source for the hydrographic network during the night.     

Modeling of Ecological Footprint and Climate Change Impacts on Food Security of the Hill Tracts of Chittagong in Bangladesh

This paper presents an integrated and dynamic model for the management of the uplands of the Hill Tracts of Chittagong to predict food security and environmental loading for gradual transition of shifting agriculture land into horticulture crops and teak plantation, and crop land into tobacco cultivation. Food security status for gradual transmission of shifting agriculture land into horticulture crops and teak plantation, and crop land into tobacco cultivation is the best option for food security, but this causes the highest environmental loading resulting from tobacco cultivation. Considering both food security and environmental degradation in terms of ecological footprint, the best option is gradual transition of shifting agriculture land into horticulture crops which provides moderate increase in the food security with a relatively lower environmental degradation in terms of ecological footprint. Crop growth model InfoCrop was used to predict the climate change impacts on rice and maize production in the uplands of the Hill Tracts of Chittagong. Climate change impacts on the yields of rice and maize of three treatments of temperature, carbon dioxide and rainfall change (+0 °C, +0 ppm and +0 % rainfall), (+2 °C, +50 ppm and 20 % rainfall) and (+2 °C, +100 ppm and 30 % rainfall) were assessed. The yield of rice decreases for treatment 2, but it increases for treatment 3. The yield of maize increases for treatments 2 and 3 since maize is a C₄ plant. There is almost no change in food security at upazila (sub-district) level for the historical climate change scenario, but there is small change in the food security at upazila levels for IPCC climate change scenario.     

Seasonal variation in the acute effects of ozone on premature mortality among elderly Japanese

We conducted a multicity time-series study using monitoring data to assess seasonal patterns of short-term ozone–mortality association among elderly aged 65 years and over in Japan. Daily exposure to ambient ozone was computed using hourly measurements of photochemical oxidants available at multiple monitoring stations in each city. Effects of ozone on daily all-cause non-accidental, cardiovascular, and respiratory mortality were estimated using distributed lag linear models, controlling for confounding by temporal, day of the week, temperature, and flu epidemics. City-level effect estimates were combined using inverse variance meta-analysis. In spring and autumn, a 10-ppbv increase of daily maximum 8-h average ozone concentration in the previous 3 days was associated with 0.69 % (95 % confidence interval (CI): 0.27–1.10), 1.07 % (0.34–1.82), and 1.77 % (0.78–2.77) increases in daily all-cause, cardiovascular, and respiratory mortality, respectively. Forward displacement of respiratory mortality was large during the cold season despite lower ozone concentration. Results were generally independent of fine particulate matter and nitrogen dioxide. Findings suggest significant mortality effects of short-term ozone exposure among the elderly during the moderate season. Those with underlying respiratory diseases were susceptible, even during winter.     

Comprehensive inter-comparison of INSAT multispectral rainfall algorithm estimates and TMPA 3B42-RT V7 estimates across different climate regions of India during southwest monsoon period

The main objective of this study is to validate and inter-compare two Near-Real-Time Satellite Rainfall Estimates (NRT-SREs): INSAT Multispectral Rainfall Algorithm (IMSRA, simple blended product) and TMPA 3B42-RT V7 (3B42-RT, multisatellite product) across India. This study aims to provide some insight into the error characteristics of both the NRT-SREs to the algorithm developers and end users by inter-comparing the daily rainfall estimates during the southwest monsoon period of 2010–2013. This study utilizes various volumetric statistics and categorical statistics to understand and evaluate the performance of NRT-SREs in terms of both spatial and volumetric error characteristics (hit, miss, and false error) at different rainfall thresholds across different Köppen–Geiger climate regions of India using the gridded gauge data provided by Indian Meteorological Department as reference dataset. A detailed statistical evaluation shows that the 3B42-RT performs comparatively better than the IMSRA across India. The results indicate that both IMSRA and 3B42-RT have a general tendency of overestimating the low rainfall rates (0–2.5 mm/day) and underestimating the high rainfall rates (>?35.5 mm/day). At lower threshold values (0 and 2.5 mm/day), it is found that the miss error is dominant in IMSRA, whereas the false error is dominant in 3B42-RT. As the threshold increases (7.5 and 35.5 mm/day), both the miss and false errors increase in both SREs. Additionally, the spatial analysis of the results clearly indicate that the performance of the tested NRT-SREs is not uniform across different climatic regions, an important aspect to be considered for development/improvement of the tested NRT-SRE algorithms.     

Erosivity, surface runoff, and soil erosion estimation using GIS-coupled runoff–erosion model in the Mamuaba catchment, Brazil

This study evaluates erosivity, surface runoff generation, and soil erosion rates for Mamuaba catchment, sub-catchment of Gramame River basin (Brazil) by using the ArcView Soil and Water Assessment Tool (AvSWAT) model. Calibration and validation of the model was performed on monthly basis, and it could simulate surface runoff and soil erosion to a good level of accuracy. Daily rainfall data between 1969 and 1989 from six rain gauges were used, and the monthly rainfall erosivity of each station was computed for all the studied years. In order to evaluate the calibration and validation of the model, monthly runoff data between January 1978 and April 1982 from one runoff gauge were used as well. The estimated soil loss rates were also realistic when compared to what can be observed in the field and to results from previous studies around of catchment. The long-term average soil loss was estimated at 9.4 t ha^?1 year^?1; most of the area of the catchment (60 %) was predicted to suffer from a low- to moderate-erosion risk (<6 t ha^?1 year^?1) and, in 20 % of the catchment, the soil erosion was estimated to exceed >?12 t ha^?1 year^?1. Expectedly, estimated soil loss was significantly correlated with measured rainfall and simulated surface runoff. Based on the estimated soil loss rates, the catchment was divided into four priority categories (low, moderate, high and very high) for conservation intervention. The study demonstrates that the AvSWAT model provides a useful tool for soil erosion assessment from catchments and facilitates the planning for a sustainable land management in northeastern Brazil.     

Evaluating the urban climate of a typically tropical city of northeastern Brazil

This study attempted to assess a bioclimate index and the occurrence of an urban heat island in the city of Campina Grande, northeastern Brazil, using data taken from mobile measurements and Automatic Weather Stations (AWS). The climate data were obtained during two representative months, one for the dry season (November 2005) and one for the rainy season (June 2006) at seven points in an urban area. Ten-minute air temperatures recorded by an AWS installed in urban areas were compared to those from a similar station located in a suburban area to assess the urban heat island (UHI). The data were collected using a 23X data logger (Campbell Scientific, Inc.) programmed for collecting data every second. The thermal discomfort level was analyzed by Thom’s discomfort index (DI), and an analysis of variance was applied for assessing if there was any statistically significant difference at the 1% and 5% significance level of thermal comfort among points. Mann–Kendall statistical test was used for identifying possibly significant trends in a time series for air temperature, relative humidity, and class A pan evaporation for the city of Campina Grande. The present study found UHI intensities of 1.48°C and ??0.7°C for the months taken as representative of the dry and rainy seasons, respectively. Summer in the city has partially comfortable conditions while the winter is fully comfortable. There are significant changes in DI hourly values between seasons. Only during the rainy season did all points of the city have a comfortable condition until 8:19 h, at which time they become partially comfortable for the rest of the day. Results indicated that there was a 1.5°C increase in air temperature and a 7.2% reduction in relative humidity throughout the analyzed time series. The DI also showed a statistically significant increasing trend (Mann–Kendall test, p?<?0.01) for the dry and rainy seasons and annual period of approximately 1°C in the last 41 years in the city of Campina Grande.     

Projected Years Lost due to Disabilities (YLDs) for bacillary dysentery related to increased temperature in temperate and subtropical cities of China

The impact of climate change on enteric infection has been a concern in recent years. This study aims to project disability burdens of bacillary dysentery (BD) associated with increasing temperature in different climatic zones in China. Years Lost due to Disabilities (YLDs) were used as the measure of burden of bacillary dysentery in this study. A temperate city in northern China and a subtropical city in southern China were selected as the study areas. The quantitative relationship between temperature and the number of cases in each city was base on the regression models developed in our previous studies. YLDs for bacillary dysentery in 2000 were used as the baseline data. Projection of YLDs for bacillary dysentery in 2020 and 2050 under future temperature scenarios were conducted. Demographic changes over the next 20 to 50 years in study cities were considered in the projections. Under the temperature scenarios alone, the YLDs for bacillary dysentery may increase by up to 80% by 2020 and 174% by 2050 in the temperate city and up to 75% increase in the YLDs by 2020 and a 147% increase by 2050 in the tropical city. Considering potential changes in both temperature and population size and structure, if other factors remain constant, compared with the YLDs observed in 2000, the YLDs for bacillary dysentery may double by 2020 and triple by 2050 in both the temperate and subtropical cities in China. The temperature-related health burden of enteric infection in China may greatly increase in the future if there is no effective intervention. Relevant public health strategies should be developed at an earlier stage to prevent and reduce the impact of infectious disease associated with climate change.     

Singular Spectrum Analytic (SSA) Decomposition and Reconstruction of Flowering: Signatures of Climatic Impacts

An empirical approach for the decomposition and reconstruction of long-term flowering records of eight eucalypt species is presented. Results from singular spectrum analysis (SSA) allow for characterisation of the dynamic and complex flowering patterns in response to temperature and rainfall throughout the year. SSA identified trend, annual, biennial and other sub-components of flowering. The ability to discriminate flowering and climate relationships is demonstrated based on SSA (cross-)correlation analysis. SSA also identified the cyclical influence of temperature and rainfall on peak flowering. For each species, there is, on average, 6 months of the annual cycle when temperature positively influences flowering and 6 months when the influence of temperature is negative. For all species, rainfall exerts a negative influence when temperature is positive. Investigation of short-term and long-term lags of climate on flowering provided best-case climatic scenarios for each species’ flowering; e.g. more intense peak flowering is likely in Eucalyptus leucoxylon when cool, wet conditions coincide with peak flowering and is further enhanced if the preceding autumn and winter were warm and dry, and the previous spring and summer cool and wet. Three clear species groupings, according to similar SSA (cross-)correlation signatures, were identified: (1) E. leucoxylon and E. tricarpa; (2) E. camaldulensis, E. melliodora and E. polyanthemos and (3) E. goniocalyx, E. microcarpa and E. macrorhyncha. Lastly, change point years for flowering based on SSA sub-components in four of the species seem to align with years of major shift in global ENSO signal (1951/1957/1958) as indicated by the extended multivariate ENSO index.     

Spatio-temporal footprints of urbanisation in Surat, the Diamond City of India (1990–2009)

Urbanisation is a ubiquitous phenomenon with greater prominence in developing nations. Urban expansion involves land conversions from vegetated moisture-rich to impervious moisture-deficient land surfaces. The urban land transformations alter biophysical parameters in a mode that promotes development of heat islands and degrades environmental health. This study elaborates relationships among various environmental variables using remote sensing dataset to study spatio-temporal footprint of urbanisation in Surat city. Landsat Thematic Mapper satellite data were used in conjugation with geo-spatial techniques to study urbanisation and correlation among various satellite-derived biophysical parameters, [Normalised Difference Vegetation Index, Normalised Difference Built-up Index, Normalised Difference Water Index, Normalised Difference Bareness Index, Modified NDWI and land surface temperature (LST)]. Land use land cover was prepared using hierarchical decision tree classification with an accuracy of 90.4 % (kappa?=?0.88) for 1990 and 85 % (kappa?=?0.81) for 2009. It was found that the city has expanded over 42.75 km² within a decade, and these changes resulted in elevated surface temperatures. For example, transformation from vegetation to built-up has resulted in 5.5?±?2.6 °C increase in land surface temperature, vegetation to fallow 6.7?±?3 °C, fallow to built-up is 3.5?±?2.9 °C and built-up to dense built-up is 5.3?±?2.8 °C. Directional profiling for LST was done to study spatial patterns of LST in and around Surat city. Emergence of two new LST peaks for 2009 was observed in N–S and NE–SW profiles.     

Generating Synthetic Rainfall on Various Timescales—Daily, Monthly and Yearly

The main objective of this paper is to present a model for generating synthetic rainfall totals on various timescales to be applicable for a variety of uses. Many large-scale ecological and water resource models require daily, monthly and yearly rainfall data as input to the model. As historical data provides only one realisation, synthetic generated rainfall totals are needed to assess the impact of rainfall variability on water resources systems (Srikanthan, In: MODSIM2005, Melbourne, Dec. 2005, pp. 1915–1921, 2005). Thus, our preferred model should simulate rainfall for yearly, monthly and daily periods. We believe that, for water supply issues, no higher resolution is needed, although higher resolution would be useful in models designed to measure the risk of local flooding. The critical factors are daily, monthly and yearly totals and daily, monthly and yearly variation. A model for generating yearly totals will be described using traditional time series methods. This model, along with a similarly constructed daily generation model by Piantadosi et al. (A New Model for Correlated Daily Rainfall, 2008), will be cascaded to start with a synthetic yearly total, then generate a synthetic sequence of monthly totals (through selection from a large number of realisations) that match the yearly total, and subsequently perform a similar operation for sequences of daily totals to match the required monthly totals. We present a new model for the generation of synthetic monthly rainfall data, which we demonstrate for Parafield in Adelaide, South Australia. The rainfall for each month of the year is modelled as a non-negative random variable from a mixed distribution with either a zero outcome or a strictly positive outcome. We use maximum likelihood to find parameters for both the probability of a zero outcome and the gamma distribution that best matches the observed probability density for the strictly positive outcomes. We describe a new model that generates correlated monthly rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers. Our model preserves the marginal monthly distributions and, hence, also preserves the monthly and yearly means. We show that, for Parafield, the correlation between rainfall totals for successive months is not significant, and so, it is reasonable to assume independence. This is, however, not true for daily rainfall. We describe a new model that generates correlated daily rainfall totals using a diagonal band copula with a single parameter to generate lag-1 correlated random numbers. This is an extended version of a paper presented at the 17th Biennial Congress on Modelling and Simulation, Christchurch, New Zealand, December 2007.