Jordan is very vulnerable to drought because of its location in the arid to semi-arid part of the Middle East. Droughts coupled with water scarcity are becoming a serious threat to the economic growth, social cohesion...Jordan is very vulnerable to drought because of its location in the arid to semi-arid part of the Middle East. Droughts coupled with water scarcity are becoming a serious threat to the economic growth, social cohesion and political stability. Rainfall time series from four rain stations covering the Jordan River Basin were analyzed for drought characterization and forecasting using standardized precipitation index (SPI), Markov chain and autoregressive integrated moving average (ARIMA) model. The 7-year moving average of Amman data showed a decreasing trend while data from the other three stations were stable or showed an increasing trend. The frequency analysis indicated 2-year return period for near zero SPI values while the return period for moderate drought was 7 years. Successive droughts had occurred at least three times during the past 40 years. Severe droughts are expected once every 20 - 25 year period at all rain stations. The extreme droughts were rare events with return periods between 80 and 115 years. There are equal occurrence probabilities for drought and wet conditions in any given year, irrespective, of the condition in the previous year. The results showed that ARIMA model was successful in predicting the overall statistics with a given period at annual scales. The overall number of predicted/observed droughts during the validation periods were 2/2 severe droughts for Amman station and, 0/1, 1/1, 0/1 extreme droughts for Amman, Irbid and Mafraq stations, respectively. In addition, the ARIMA model also predicted 3 out of 4 actual moderate droughts for Amman and Mafraq stations. It was concluded that early warning of developing droughts can be deduced form the monthly Markov transitional probabilities. ARIMA models can be used as a forecasting tool of the future drought trends. Using the first and second order Markov probabilities can complement the ARIMA predictions.展开更多
Water is at the core of sustainable development and is critical for socio-economic development, healthy ecosystems and for human survival. This research study has been carried out in Nakuru County, a tropical region i...Water is at the core of sustainable development and is critical for socio-economic development, healthy ecosystems and for human survival. This research study has been carried out in Nakuru County, a tropical region in the Rift Valley of Kenya, bounded between latitude 0.28°N and 1.16°S, and longitude 36.27°E and 36.55°E. The objective of the study has been to use GIS and remote sensing in assessment of water scarcity using Land use Land cover area changes, standard precipitation index and crop yields. Landsat satellite images for the year 1985, 1995, 2005 and 2015 were used. Classification was done using maximum likelihood algorithm while classification accuracy assessment entailed the use of confusion matrix method and ground truth data. Post classification change detection results gave percentage cropland areas as 21% in 1985, 29% in 1995, 53% in 2005 and also 53% in 2015. Eleven (11) ground rainfall stations and TRMM satellite rainfall data from 1985 to 2015 has been used to show meteorological drought. Validation of rainfall data done using correlation coefficient (R2) and root mean square (RMS) methods showed that ground rainfall data and TRMM data correlate. Modelling of 3 months SPI for each of the three seasons (MAM, JJA and OND) has been done using interpolation distance weighted method (IDW). 3 months SPI time scales curves gave October 1987 May 1993, and July 2004 as water scarce and dry seasons and were categorized as either Normal, moderately dry, severely dry and extremely dry. Crop yield trends curves showed crop yield decrease in this identified water scarce and dry years. Conclusion reached is that crop yields is not dependent on size of land ploughed only but mostly on rainfall quantities. Therefore, the findings of this research can be used as drought monitoring tools.展开更多
文摘Jordan is very vulnerable to drought because of its location in the arid to semi-arid part of the Middle East. Droughts coupled with water scarcity are becoming a serious threat to the economic growth, social cohesion and political stability. Rainfall time series from four rain stations covering the Jordan River Basin were analyzed for drought characterization and forecasting using standardized precipitation index (SPI), Markov chain and autoregressive integrated moving average (ARIMA) model. The 7-year moving average of Amman data showed a decreasing trend while data from the other three stations were stable or showed an increasing trend. The frequency analysis indicated 2-year return period for near zero SPI values while the return period for moderate drought was 7 years. Successive droughts had occurred at least three times during the past 40 years. Severe droughts are expected once every 20 - 25 year period at all rain stations. The extreme droughts were rare events with return periods between 80 and 115 years. There are equal occurrence probabilities for drought and wet conditions in any given year, irrespective, of the condition in the previous year. The results showed that ARIMA model was successful in predicting the overall statistics with a given period at annual scales. The overall number of predicted/observed droughts during the validation periods were 2/2 severe droughts for Amman station and, 0/1, 1/1, 0/1 extreme droughts for Amman, Irbid and Mafraq stations, respectively. In addition, the ARIMA model also predicted 3 out of 4 actual moderate droughts for Amman and Mafraq stations. It was concluded that early warning of developing droughts can be deduced form the monthly Markov transitional probabilities. ARIMA models can be used as a forecasting tool of the future drought trends. Using the first and second order Markov probabilities can complement the ARIMA predictions.
文摘Water is at the core of sustainable development and is critical for socio-economic development, healthy ecosystems and for human survival. This research study has been carried out in Nakuru County, a tropical region in the Rift Valley of Kenya, bounded between latitude 0.28°N and 1.16°S, and longitude 36.27°E and 36.55°E. The objective of the study has been to use GIS and remote sensing in assessment of water scarcity using Land use Land cover area changes, standard precipitation index and crop yields. Landsat satellite images for the year 1985, 1995, 2005 and 2015 were used. Classification was done using maximum likelihood algorithm while classification accuracy assessment entailed the use of confusion matrix method and ground truth data. Post classification change detection results gave percentage cropland areas as 21% in 1985, 29% in 1995, 53% in 2005 and also 53% in 2015. Eleven (11) ground rainfall stations and TRMM satellite rainfall data from 1985 to 2015 has been used to show meteorological drought. Validation of rainfall data done using correlation coefficient (R2) and root mean square (RMS) methods showed that ground rainfall data and TRMM data correlate. Modelling of 3 months SPI for each of the three seasons (MAM, JJA and OND) has been done using interpolation distance weighted method (IDW). 3 months SPI time scales curves gave October 1987 May 1993, and July 2004 as water scarce and dry seasons and were categorized as either Normal, moderately dry, severely dry and extremely dry. Crop yield trends curves showed crop yield decrease in this identified water scarce and dry years. Conclusion reached is that crop yields is not dependent on size of land ploughed only but mostly on rainfall quantities. Therefore, the findings of this research can be used as drought monitoring tools.
