区域蒸散量(evapotranspiration)预测对精准灌溉预报与农田水分管理意义重大。该文利用川中丘陵区11个气象站点1961-2013年逐日气象资料,采用FAO-56 Penman-Monteith公式计算参考作物蒸散量(reference evapotranspiration,ET0),基于Hadl...区域蒸散量(evapotranspiration)预测对精准灌溉预报与农田水分管理意义重大。该文利用川中丘陵区11个气象站点1961-2013年逐日气象资料,采用FAO-56 Penman-Monteith公式计算参考作物蒸散量(reference evapotranspiration,ET0),基于Hadley Centre Coupled Model version 3(HadCM3)的输出和统计降尺度模型(statistical downscaling model,SDSM)分别对A2(高温室气体排放)、B2(低温室气体排放)情景下川中丘陵区2014-2099年ET0进行预测,并使用Mann-Kendall检验和反距离加权插值法对1961-2099年ET0的时空演变特征进行分析。结果表明:基准期(1961-2010年)川中丘陵区ET0整体呈现明显下降趋势,空间上呈现出东北部、西北部和东南部相对较大、中部相对较小的差异;与基准期相比,A2、B2情景下未来2020 s(2011-2040年)、2050 s(2041-2070年)和2080 s(2071-2099年)川中丘陵区ET_0月和年均值都呈增大趋势;A2情景下3个时期ET0将分别增加7.9%、10.9%和16.7%,B2情景下ET_0将分别增加7.1%、4.9%和12.8%;A2、B2情景下3个时期川中丘陵区ET_0空间分布均呈现西北部和南部较大、中部较小的空间差异,且3个时期的ET0相对变化率显示中部及其偏北、偏南区域ET_0增幅相对较大,北部和南部增幅相对较小。因此,未来川中丘陵区ET0的上升可能导致水资源短缺与季节性干旱进一步加剧。该研究可为川中丘陵区水资源优化管理和灌溉制度制定提供科学参考。展开更多
Climate change is described as the most universal and irreversible environmental problem facing the planet Earth. While climate change is already manifesting in Ethiopia through changes in temperature and rainfall, it...Climate change is described as the most universal and irreversible environmental problem facing the planet Earth. While climate change is already manifesting in Ethiopia through changes in temperature and rainfall, its magnitude is poorly studied at regional levels. The objective of this paper was to assess and quantify the magnitude of future changes of climate parameters using Statistical Downscaling Mode (SDSM) version 4.2 in Amhara Regional State which is located between 8°45‘N and 13°45‘N latitude and 35°46‘E and 40°25‘E longitude. Daily climate data (1979- 2008) of rainfall, maximum and minimum temperatures were collected from 10 observed meteorological stations (predictand). The stations were grouped and compared using clustering and Markov chain model, whereas the degree of climate change in the study area was estimated using the coupled HadCM3 general circulation model (GCM) with A2a emission scenarios (Predictors). Both maximum and minimum temperatures showed an increasing trend;the increase in mean maximum temperature ranges between 1.55°C and 6.07°C and that of the mean minimum temperature ranges from 0.11°C and 2.81°C. While the amount of annual rainfall and rainy days decreased in the study Regions in the 2080s. The negative changes in rainfall and temperature obtained from the HadCM3 model in the current study are alarming and suggest the need for further study with several GCM models to confirm the current results and develop adaptation options.展开更多
The Long Ashton Research Station Weather Generator (LARS-WG) is a stochastic weather generator used for the simulation of weather data at a single site under both current and future climate conditions using General Ci...The Long Ashton Research Station Weather Generator (LARS-WG) is a stochastic weather generator used for the simulation of weather data at a single site under both current and future climate conditions using General Circulation Models (GCM). It was calibrated using the baseline (1981-2010) and evaluated to determine its suitability in generating synthetic weather data for 2020 and 2055 according to the projections of HadCM3 and BCCR-BCM2 GCMs under SRB1 and SRA1B scenarios at Mount Makulu (Latitude: 15.550°S, Longitude: 28.250°E, Elevation: 1213 meter), Zambia. Three weather parameters—precipitation, minimum and maximum temperature were simulated using LARS-WG v5.5 for observed station and AgMERRA reanalysis data for Mount Makulu. Monthly means and variances of observed and generated daily precipitation, maximum temperature and minimum temperature were used to evaluate the suitability of LARS-WG. Other climatic conditions such as wet and dry spells, seasonal frost and heat spells distributions were also used to assess the performance of the model. The results showed that these variables were modeled with good accuracy and LARS-WG could be used with high confidence to reproduce the current and future climate scenarios. Mount Makulu did not experience any seasonal frost. The average temperatures for the baseline (Observed station data: 1981-2010 and AgMERRA reanalysis: 1981-2010) were 21.33°C and 22.21°C, respectively. Using the observed station data, the average temperature under SRB1 (2020), SRA1B (2020), SRB1 (2055), SRA1B (2055) would be 21.90°C, 21.94°C, 22.83°C and 23.18°C, respectively. Under the AgMERRA reanalysis, the average temperatures would be 22.75°C (SRB1: 2020), 22.80°C (SRA1B: 2020), 23.69°C (SRB1: 2055) and 24.05°C (SRA1B: 2055). The HadCM3 and BCM2 GCMs ensemble mean showed that the number of days with precipitation would increase while the mean precipitation amount in 2020s and 2050s under SRA1B would reduce by 6.19% to 6.65%. Precipitation would increase under SRB1 (Observed), SRA1B, and SRB1 (AgMERRA) from 0.31% to 5.2% in 2020s and 2055s, respectively.展开更多
In the present study SDSM downscaling model was used as a tool for downscaling weather data statistically in upper Godavari river basin. Two Global Climate Models (GCMs), CGCM3 and HadCM3, have been used to project fu...In the present study SDSM downscaling model was used as a tool for downscaling weather data statistically in upper Godavari river basin. Two Global Climate Models (GCMs), CGCM3 and HadCM3, have been used to project future maximum temperature (Tmax), minimum temperature (Tmin) and precipitation. The predictor variables are extracted from: 1) the National Centre for Environmental Prediction (NCEP) reanalysis dataset for the period 1961-2003, 2) the simulations from the third-generation Hadlycentre Coupled Climate Model (HadCM3) and Coupled Global Climate Model (CGCM3) variability and changes in Tmax, Tmin and precipitation under scenarios A1B and A2 of CGCM3 model and A2 and B2 of HadCM3 model have been presented for future periods: 2020s, 2050s and 2080s. The scatter-plots and cross-correlations are used for verifying the reliability of the simulation. Maximum temperature increases in future for almost all the scenarios for both GCMs. Also downscaled future precipitation shows increasing trends for all scenarios.展开更多
This study centers on applying the statistical downscaling technique to the daily minimum and maximum temperatures of Port Harcourt from the period 1985-2014. To select the period of calibration, the wilby and wigley ...This study centers on applying the statistical downscaling technique to the daily minimum and maximum temperatures of Port Harcourt from the period 1985-2014. To select the period of calibration, the wilby and wigley assumption of 2014 was adopted. However, the Bruckner circle assumption was adopted in selecting the normal under review. Secondary data of minimum and maximum temperatures for Port Harcourt were collected from the archive of Nigerian meteorological agency (NIMET). The grid cell of the HadCM3 corresponding to the Port Harcourt meteorological station was selected from the HadCM3 website to generate the largescale predictors. Data for temperature was there after normalized for the period of calibration. To analyze data, ANOVA and Paired t tests were used. Result showed that, the model was significant at p °C from 1960-2080, while for B2 the increase will be 0.18°C for same period. For minimum temperature, the ANOVA also showed a difference of 0.21°C and 0.11°C for A2 and B2 respectively. The paired t test statistics showed that these variations in temperatures for both maximum and minimum at A2 and B2 scenarios are significant at p < 0.05. Reforestation, afforestation, carbon sequestration are strongly advocated.展开更多
A study on the detection and future projection of climate change in the city of Rio de Janeiro is here presented, based on the analysis of indices of temperature and precipitation extremes. The aim of this study is to...A study on the detection and future projection of climate change in the city of Rio de Janeiro is here presented, based on the analysis of indices of temperature and precipitation extremes. The aim of this study is to provide information on observed and projected extremes in support of studies on impacts and vulnerability assessments required for adaptation strategies to climate change. Observational data from INMET’s weather stations and projections from INPE’s Eta- HadCM3 regional model are used. The observational analyses indicate that rainfall amount associated with heavy rain events is increasing in recent years in the forest region of Rio de Janeiro. An increase in both the frequency of occurrence and in the rainfall amount associated with heavy precipitation are projected until the end of the 21st Century, as are longer dry periods and shorter wet seasons. In regards to temperature, a warming trend is noted (both in past observations and future projections), with higher maximum air temperature and extremes. The average change in annual maximum (minimum) air temperatures may range between 2℃and 5℃(2℃and 4℃) above the current weather values in the late 21st Century. The warm (cold) days and nights are becoming more (less) frequent each year, and for the future climate (2100) it has been projected that about 40% to 70% of the days and 55% to 85% of the nights will be hot. Additionally, it can be foreseen that there will be no longer cold days and nights.展开更多
文摘区域蒸散量(evapotranspiration)预测对精准灌溉预报与农田水分管理意义重大。该文利用川中丘陵区11个气象站点1961-2013年逐日气象资料,采用FAO-56 Penman-Monteith公式计算参考作物蒸散量(reference evapotranspiration,ET0),基于Hadley Centre Coupled Model version 3(HadCM3)的输出和统计降尺度模型(statistical downscaling model,SDSM)分别对A2(高温室气体排放)、B2(低温室气体排放)情景下川中丘陵区2014-2099年ET0进行预测,并使用Mann-Kendall检验和反距离加权插值法对1961-2099年ET0的时空演变特征进行分析。结果表明:基准期(1961-2010年)川中丘陵区ET0整体呈现明显下降趋势,空间上呈现出东北部、西北部和东南部相对较大、中部相对较小的差异;与基准期相比,A2、B2情景下未来2020 s(2011-2040年)、2050 s(2041-2070年)和2080 s(2071-2099年)川中丘陵区ET_0月和年均值都呈增大趋势;A2情景下3个时期ET0将分别增加7.9%、10.9%和16.7%,B2情景下ET_0将分别增加7.1%、4.9%和12.8%;A2、B2情景下3个时期川中丘陵区ET_0空间分布均呈现西北部和南部较大、中部较小的空间差异,且3个时期的ET0相对变化率显示中部及其偏北、偏南区域ET_0增幅相对较大,北部和南部增幅相对较小。因此,未来川中丘陵区ET0的上升可能导致水资源短缺与季节性干旱进一步加剧。该研究可为川中丘陵区水资源优化管理和灌溉制度制定提供科学参考。
文摘Climate change is described as the most universal and irreversible environmental problem facing the planet Earth. While climate change is already manifesting in Ethiopia through changes in temperature and rainfall, its magnitude is poorly studied at regional levels. The objective of this paper was to assess and quantify the magnitude of future changes of climate parameters using Statistical Downscaling Mode (SDSM) version 4.2 in Amhara Regional State which is located between 8°45‘N and 13°45‘N latitude and 35°46‘E and 40°25‘E longitude. Daily climate data (1979- 2008) of rainfall, maximum and minimum temperatures were collected from 10 observed meteorological stations (predictand). The stations were grouped and compared using clustering and Markov chain model, whereas the degree of climate change in the study area was estimated using the coupled HadCM3 general circulation model (GCM) with A2a emission scenarios (Predictors). Both maximum and minimum temperatures showed an increasing trend;the increase in mean maximum temperature ranges between 1.55°C and 6.07°C and that of the mean minimum temperature ranges from 0.11°C and 2.81°C. While the amount of annual rainfall and rainy days decreased in the study Regions in the 2080s. The negative changes in rainfall and temperature obtained from the HadCM3 model in the current study are alarming and suggest the need for further study with several GCM models to confirm the current results and develop adaptation options.
文摘The Long Ashton Research Station Weather Generator (LARS-WG) is a stochastic weather generator used for the simulation of weather data at a single site under both current and future climate conditions using General Circulation Models (GCM). It was calibrated using the baseline (1981-2010) and evaluated to determine its suitability in generating synthetic weather data for 2020 and 2055 according to the projections of HadCM3 and BCCR-BCM2 GCMs under SRB1 and SRA1B scenarios at Mount Makulu (Latitude: 15.550°S, Longitude: 28.250°E, Elevation: 1213 meter), Zambia. Three weather parameters—precipitation, minimum and maximum temperature were simulated using LARS-WG v5.5 for observed station and AgMERRA reanalysis data for Mount Makulu. Monthly means and variances of observed and generated daily precipitation, maximum temperature and minimum temperature were used to evaluate the suitability of LARS-WG. Other climatic conditions such as wet and dry spells, seasonal frost and heat spells distributions were also used to assess the performance of the model. The results showed that these variables were modeled with good accuracy and LARS-WG could be used with high confidence to reproduce the current and future climate scenarios. Mount Makulu did not experience any seasonal frost. The average temperatures for the baseline (Observed station data: 1981-2010 and AgMERRA reanalysis: 1981-2010) were 21.33°C and 22.21°C, respectively. Using the observed station data, the average temperature under SRB1 (2020), SRA1B (2020), SRB1 (2055), SRA1B (2055) would be 21.90°C, 21.94°C, 22.83°C and 23.18°C, respectively. Under the AgMERRA reanalysis, the average temperatures would be 22.75°C (SRB1: 2020), 22.80°C (SRA1B: 2020), 23.69°C (SRB1: 2055) and 24.05°C (SRA1B: 2055). The HadCM3 and BCM2 GCMs ensemble mean showed that the number of days with precipitation would increase while the mean precipitation amount in 2020s and 2050s under SRA1B would reduce by 6.19% to 6.65%. Precipitation would increase under SRB1 (Observed), SRA1B, and SRB1 (AgMERRA) from 0.31% to 5.2% in 2020s and 2055s, respectively.
文摘In the present study SDSM downscaling model was used as a tool for downscaling weather data statistically in upper Godavari river basin. Two Global Climate Models (GCMs), CGCM3 and HadCM3, have been used to project future maximum temperature (Tmax), minimum temperature (Tmin) and precipitation. The predictor variables are extracted from: 1) the National Centre for Environmental Prediction (NCEP) reanalysis dataset for the period 1961-2003, 2) the simulations from the third-generation Hadlycentre Coupled Climate Model (HadCM3) and Coupled Global Climate Model (CGCM3) variability and changes in Tmax, Tmin and precipitation under scenarios A1B and A2 of CGCM3 model and A2 and B2 of HadCM3 model have been presented for future periods: 2020s, 2050s and 2080s. The scatter-plots and cross-correlations are used for verifying the reliability of the simulation. Maximum temperature increases in future for almost all the scenarios for both GCMs. Also downscaled future precipitation shows increasing trends for all scenarios.
文摘This study centers on applying the statistical downscaling technique to the daily minimum and maximum temperatures of Port Harcourt from the period 1985-2014. To select the period of calibration, the wilby and wigley assumption of 2014 was adopted. However, the Bruckner circle assumption was adopted in selecting the normal under review. Secondary data of minimum and maximum temperatures for Port Harcourt were collected from the archive of Nigerian meteorological agency (NIMET). The grid cell of the HadCM3 corresponding to the Port Harcourt meteorological station was selected from the HadCM3 website to generate the largescale predictors. Data for temperature was there after normalized for the period of calibration. To analyze data, ANOVA and Paired t tests were used. Result showed that, the model was significant at p °C from 1960-2080, while for B2 the increase will be 0.18°C for same period. For minimum temperature, the ANOVA also showed a difference of 0.21°C and 0.11°C for A2 and B2 respectively. The paired t test statistics showed that these variations in temperatures for both maximum and minimum at A2 and B2 scenarios are significant at p < 0.05. Reforestation, afforestation, carbon sequestration are strongly advocated.
文摘A study on the detection and future projection of climate change in the city of Rio de Janeiro is here presented, based on the analysis of indices of temperature and precipitation extremes. The aim of this study is to provide information on observed and projected extremes in support of studies on impacts and vulnerability assessments required for adaptation strategies to climate change. Observational data from INMET’s weather stations and projections from INPE’s Eta- HadCM3 regional model are used. The observational analyses indicate that rainfall amount associated with heavy rain events is increasing in recent years in the forest region of Rio de Janeiro. An increase in both the frequency of occurrence and in the rainfall amount associated with heavy precipitation are projected until the end of the 21st Century, as are longer dry periods and shorter wet seasons. In regards to temperature, a warming trend is noted (both in past observations and future projections), with higher maximum air temperature and extremes. The average change in annual maximum (minimum) air temperatures may range between 2℃and 5℃(2℃and 4℃) above the current weather values in the late 21st Century. The warm (cold) days and nights are becoming more (less) frequent each year, and for the future climate (2100) it has been projected that about 40% to 70% of the days and 55% to 85% of the nights will be hot. Additionally, it can be foreseen that there will be no longer cold days and nights.
