The Middle East (ME) is characterized by its water shortage problem. This region with its arid climate is expected to be the most vulnerable in the world to the potential impacts of climate change. Iraq (located in ME...The Middle East (ME) is characterized by its water shortage problem. This region with its arid climate is expected to be the most vulnerable in the world to the potential impacts of climate change. Iraq (located in ME) is seriously experiencing water shortage problem. To overcome this problem rain water harvesting can be used. In this study the applicability of the long-term weather generator model in downscaling daily precipitation Central Iraq is used to project future changes of precipitation based on scenario of seven General Circulation Models (GCMs) outputs for the periods of 2011-2030, 2046-2065, and 2080-2099. The results indicated that December-February and September-November periods, based on the ensemble mean of seven GCMs, showed an increasing trend in the periods considered;however, a decreasing trend can be found in March, April, and May in the future.展开更多
The study evaluates the effect of climate change on temperature, which is one of the most important variables in water resources management and irrigation scheduling. Climate prediction is necessary in the agricultura...The study evaluates the effect of climate change on temperature, which is one of the most important variables in water resources management and irrigation scheduling. Climate prediction is necessary in the agricultural and hydrological analysis. This study proposed an approach to the application of the Long Ashton Research Station Weather Generator (LARS-WG) in Coupled Model Inter-comparison Project Phase 5 (CMIP5) under EC-Earth and MPI-ESM-MR. The first step is model calibration, where the observed dataset is analyzed statistically. In the second stage, the synthetic data and observed data are checked for Kolmogorov-Smirnov and the means and standard deviations. In order to evaluate the response of temperature under future warmer climate trends, the approach was assessed using data series. These parameters consisted of the minimum and maximum temperature at the Phitsanulok Meteorological Station (WMO Index 48378) and RCP4.5 climate change scenario for the base period as well as for 2021-2040 (the near future), 2041-2060 (the medium future) and 2061-2080 (the far future). The results of the numerical applications indicated that the linkage between the observed data spatially downscaled from LARS-WG simulations with the historical one of the locations during the baseline period had a very good accuracy. It was also found that the future climate change of temperature contributed to higher change. The mean of minimum temperature in the baseline year was 23.13<span style="white-space:nowrap;">°</span>C while the mean of minimum temperature in the projection period for 2021-2040, 2041-2060 and 2061-2080 is expected to be 24.09 (+4.18%), 24.49 (+5.94%) and 24.82 (+7.36%)<span style="white-space:nowrap;">°</span>C, and 24.12 (+4.32%), 24.82 (+7.36%) and 25.08 (+8.48%)<span style="white-space:nowrap;">°</span>C for the EC-Earth and MPI-ESM-MR, respectively. While, the mean of maximum temperature in the baseline year was 33.41<span style="white-space:nowrap;">°</span>C, the maximum temperatures are projected to increase at 34.47 (+3.19%), 34.88 (+4.43%) and 35.21 (+5.40%)<span style="white-space:nowrap;">°</span>C, and 34.53 (+3.36%), 35.19 (+5.34%) and 35.30 (+5.67%)<span style="white-space:nowrap;">°</span>C, respectively. Furthermore, the future local surface temperatures from the MPI-ESM-MR project tended to be higher than EC-Earth. In conclusion, the study results indicate that in coming three time periods, the minimum and maximum temperature increase is expected in Phitsanulok province, Thailand.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
Agriculture is the mainstay of Ethiopian economy. Developing country like Ethiopia suffers from climate change, due to their limited economic capability to build irrigation projects to combat the trouble. This study g...Agriculture is the mainstay of Ethiopian economy. Developing country like Ethiopia suffers from climate change, due to their limited economic capability to build irrigation projects to combat the trouble. This study generates climate change in rift valley basins of Ethiopia for three time periods (2020s, 2055s and 2090s) by using two emission scenarios: SRA1B and SRB1 for faster technological and environmental extreme respectively. First, outputs of 15 General Circulation Models (GCMs) under two emission scenarios (SRA1B and SRB1) are statistically downscaled by using LARS-WG software. Probability assessment of bounded range with known distributions is used to deal with the uncertainties of GCMs’ outputs. These GCMs outputs are weighted by considering the ability of each model to simulate historical records. The study result indicates that LARS-WG 5.5 version model is more uncertain to simulate future mean rainfall than generating maximum and minimum mean temperatures. GCMs weight difference for mean rainfall is 0.83 whereas weight difference for minimum and maximum mean temperatures is 0.09 among GCMs models. The study results indicate minimum and maximum temperatures absolute increase in the range of 0.34˚C to 0.58˚C, 0.94˚C to 1.8˚C and 1.42˚C to 3.2˚C and 0.32˚C to 0.56˚C, 0.91˚C to 1.8˚C and 1.34˚C to 3.04˚C respectively in the near-term (2020s), mid-term (2055s) and long-term (2090s) under both emission scenarios. The expected rainfall change percentage during these three time periods considering this GCMs weight difference into account ranges from -2.3% to 7%, 0.375% to 15.83% and 2.625% to 31.1% in the same three time periods. In conclusion, the study results indicate that in coming three time periods, maximum and minimum temperature and rainfall increase is expected in rift valley of basins of Ethiopia.展开更多
This study presents an extended version of a single site daily weather generator after Richardson. The model is driven by daily precipitation series derived by a first-order two-state Markov chain and considers the an...This study presents an extended version of a single site daily weather generator after Richardson. The model is driven by daily precipitation series derived by a first-order two-state Markov chain and considers the annual cycle of each meteorological variable. The evaluation of its performance was done by deploying its synthetic time series into the physical based hydrological model BROOK90. The weather generator was applied and tested for data from the Anchor Station at the Tharandt Forest, Germany. Additionally its results were compared to the output of another weather generator with spell-length approach for the precipitation series (LARS-WG). The comparison was distinguished into a meteoro-logical and a hydrological part in terms of extremes, monthly and annual sums and averages. Extreme events could be preserved adequately by both models. Nevertheless a general underestimation of rare events was observed. Natural correlations between vapour pressure and minimum temperature could be conserved as well as annual cycles of the hydro-logical and meteorological regime. But the simulated spectrums of extremes, especially, of precipitation and temperature, are more limited than the observed spectrums. While LARS-WG already finds application in practice, the results show that the data derived from the presented weather generator is as useful and reliable as those from the established model for the simulation of the water balance.展开更多
This study described the development and validation of an artificial neural network （ANN） for the purpose of analyzing the effects of climate change on nonpoint source （NPS） pollutant loads from agricultural small...This study described the development and validation of an artificial neural network （ANN） for the purpose of analyzing the effects of climate change on nonpoint source （NPS） pollutant loads from agricultural small watershed. The runoff discharge was estimated using ANN algorithm. The performance of ANN model was examined using observed data from study watershed. The simulation results agreed well with observed values during calibration and validation periods. NPS pollutant loads were calculated from load-discharge relationship driven by long-term monitoring data. LARS-WG （Long Ashton Research Station-Weather Generator） model was used to generate rainfall data. The calibrated ANN model and load-discharge relationship with the generated data from LARS-WG were applied to analyze the effects of climate change on NPS pollutant loads from the agricultural small watershed. The results showed that the ANN model provided valuable approach in estimating future runoff discharge, and the NPS pollutant loads.展开更多
文摘The Middle East (ME) is characterized by its water shortage problem. This region with its arid climate is expected to be the most vulnerable in the world to the potential impacts of climate change. Iraq (located in ME) is seriously experiencing water shortage problem. To overcome this problem rain water harvesting can be used. In this study the applicability of the long-term weather generator model in downscaling daily precipitation Central Iraq is used to project future changes of precipitation based on scenario of seven General Circulation Models (GCMs) outputs for the periods of 2011-2030, 2046-2065, and 2080-2099. The results indicated that December-February and September-November periods, based on the ensemble mean of seven GCMs, showed an increasing trend in the periods considered;however, a decreasing trend can be found in March, April, and May in the future.
文摘The study evaluates the effect of climate change on temperature, which is one of the most important variables in water resources management and irrigation scheduling. Climate prediction is necessary in the agricultural and hydrological analysis. This study proposed an approach to the application of the Long Ashton Research Station Weather Generator (LARS-WG) in Coupled Model Inter-comparison Project Phase 5 (CMIP5) under EC-Earth and MPI-ESM-MR. The first step is model calibration, where the observed dataset is analyzed statistically. In the second stage, the synthetic data and observed data are checked for Kolmogorov-Smirnov and the means and standard deviations. In order to evaluate the response of temperature under future warmer climate trends, the approach was assessed using data series. These parameters consisted of the minimum and maximum temperature at the Phitsanulok Meteorological Station (WMO Index 48378) and RCP4.5 climate change scenario for the base period as well as for 2021-2040 (the near future), 2041-2060 (the medium future) and 2061-2080 (the far future). The results of the numerical applications indicated that the linkage between the observed data spatially downscaled from LARS-WG simulations with the historical one of the locations during the baseline period had a very good accuracy. It was also found that the future climate change of temperature contributed to higher change. The mean of minimum temperature in the baseline year was 23.13<span style="white-space:nowrap;">°</span>C while the mean of minimum temperature in the projection period for 2021-2040, 2041-2060 and 2061-2080 is expected to be 24.09 (+4.18%), 24.49 (+5.94%) and 24.82 (+7.36%)<span style="white-space:nowrap;">°</span>C, and 24.12 (+4.32%), 24.82 (+7.36%) and 25.08 (+8.48%)<span style="white-space:nowrap;">°</span>C for the EC-Earth and MPI-ESM-MR, respectively. While, the mean of maximum temperature in the baseline year was 33.41<span style="white-space:nowrap;">°</span>C, the maximum temperatures are projected to increase at 34.47 (+3.19%), 34.88 (+4.43%) and 35.21 (+5.40%)<span style="white-space:nowrap;">°</span>C, and 34.53 (+3.36%), 35.19 (+5.34%) and 35.30 (+5.67%)<span style="white-space:nowrap;">°</span>C, respectively. Furthermore, the future local surface temperatures from the MPI-ESM-MR project tended to be higher than EC-Earth. In conclusion, the study results indicate that in coming three time periods, the minimum and maximum temperature increase is expected in Phitsanulok province, Thailand.
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Agriculture is the mainstay of Ethiopian economy. Developing country like Ethiopia suffers from climate change, due to their limited economic capability to build irrigation projects to combat the trouble. This study generates climate change in rift valley basins of Ethiopia for three time periods (2020s, 2055s and 2090s) by using two emission scenarios: SRA1B and SRB1 for faster technological and environmental extreme respectively. First, outputs of 15 General Circulation Models (GCMs) under two emission scenarios (SRA1B and SRB1) are statistically downscaled by using LARS-WG software. Probability assessment of bounded range with known distributions is used to deal with the uncertainties of GCMs’ outputs. These GCMs outputs are weighted by considering the ability of each model to simulate historical records. The study result indicates that LARS-WG 5.5 version model is more uncertain to simulate future mean rainfall than generating maximum and minimum mean temperatures. GCMs weight difference for mean rainfall is 0.83 whereas weight difference for minimum and maximum mean temperatures is 0.09 among GCMs models. The study results indicate minimum and maximum temperatures absolute increase in the range of 0.34˚C to 0.58˚C, 0.94˚C to 1.8˚C and 1.42˚C to 3.2˚C and 0.32˚C to 0.56˚C, 0.91˚C to 1.8˚C and 1.34˚C to 3.04˚C respectively in the near-term (2020s), mid-term (2055s) and long-term (2090s) under both emission scenarios. The expected rainfall change percentage during these three time periods considering this GCMs weight difference into account ranges from -2.3% to 7%, 0.375% to 15.83% and 2.625% to 31.1% in the same three time periods. In conclusion, the study results indicate that in coming three time periods, maximum and minimum temperature and rainfall increase is expected in rift valley of basins of Ethiopia.
基金supported by the German Academic Exchange Service(DAAD).
文摘This study presents an extended version of a single site daily weather generator after Richardson. The model is driven by daily precipitation series derived by a first-order two-state Markov chain and considers the annual cycle of each meteorological variable. The evaluation of its performance was done by deploying its synthetic time series into the physical based hydrological model BROOK90. The weather generator was applied and tested for data from the Anchor Station at the Tharandt Forest, Germany. Additionally its results were compared to the output of another weather generator with spell-length approach for the precipitation series (LARS-WG). The comparison was distinguished into a meteoro-logical and a hydrological part in terms of extremes, monthly and annual sums and averages. Extreme events could be preserved adequately by both models. Nevertheless a general underestimation of rare events was observed. Natural correlations between vapour pressure and minimum temperature could be conserved as well as annual cycles of the hydro-logical and meteorological regime. But the simulated spectrums of extremes, especially, of precipitation and temperature, are more limited than the observed spectrums. While LARS-WG already finds application in practice, the results show that the data derived from the presented weather generator is as useful and reliable as those from the established model for the simulation of the water balance.
基金supported by a grant (code number 4-5-3) from Sustainable Water Resources Research Center of 21st Century Frontier Research Program (50%)and Han River Basin Environmental Office and Han River Environment Research Center, Ministry of Environment(50%)
文摘This study described the development and validation of an artificial neural network （ANN） for the purpose of analyzing the effects of climate change on nonpoint source （NPS） pollutant loads from agricultural small watershed. The runoff discharge was estimated using ANN algorithm. The performance of ANN model was examined using observed data from study watershed. The simulation results agreed well with observed values during calibration and validation periods. NPS pollutant loads were calculated from load-discharge relationship driven by long-term monitoring data. LARS-WG （Long Ashton Research Station-Weather Generator） model was used to generate rainfall data. The calibrated ANN model and load-discharge relationship with the generated data from LARS-WG were applied to analyze the effects of climate change on NPS pollutant loads from the agricultural small watershed. The results showed that the ANN model provided valuable approach in estimating future runoff discharge, and the NPS pollutant loads.
