This page has only limited features, please log in for full access.
An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources.
Ziauddin Safari; Sayed Rahimi; Kamal Ahmed; Ahmad Sharafati; Ghaith Ziarh; Shamsuddin Shahid; Tarmizi Ismail; Nadhir Al-Ansari; Eun-Sung Chung; Xiaojun Wang. Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing. Sustainability 2021, 13, 1549 .
AMA StyleZiauddin Safari, Sayed Rahimi, Kamal Ahmed, Ahmad Sharafati, Ghaith Ziarh, Shamsuddin Shahid, Tarmizi Ismail, Nadhir Al-Ansari, Eun-Sung Chung, Xiaojun Wang. Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing. Sustainability. 2021; 13 (3):1549.
Chicago/Turabian StyleZiauddin Safari; Sayed Rahimi; Kamal Ahmed; Ahmad Sharafati; Ghaith Ziarh; Shamsuddin Shahid; Tarmizi Ismail; Nadhir Al-Ansari; Eun-Sung Chung; Xiaojun Wang. 2021. "Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing." Sustainability 13, no. 3: 1549.
Climate change is supposed to alter not only the mean and variability but also the distribution of rainfall. Changes in rainfall distribution during the monsoon months (June to September) of Bangladesh are evaluated in this study using quantile regression. Monthly rainfall data for the period 1961–2014 recorded at 18 locations distributed over the country were used for this purpose. Distributional changes of monthly rainfall showed downward convergent lines are dominant in peak monsoon months of June, July and August at 28%, 50% and 28% stations, respectively, followed by horizontally divergent lines at 17% of stations during those months. The dominating category of last monsoon month (September) rainfall was found upward divergent lines at 50% stations. The results revealed a decrease in many rainfall quantiles from June to August and increase in September in most of the stations. The increasing trend lines of September rainfall quantiles were found to become more diverse with time, which indicates an increase in rainfall extremes and the possibility of more floods which are already very common in the last month of monsoon in Bangladesh. The decrease in lower quantiles of rainfall in most of the monsoon months may cause an increase in the probability of droughts in the country. The study provided more insight on monsoon rainfall changes and improved understanding of climate change impacts on monsoon rainfall regime which can help in planning climate change adaptations in Bangladesh.
Morteza Mohsenipour; Shamsuddin Shahid; Ghaith Falah Ziarh; Zaher Mundher Yaseen. Changes in monsoon rainfall distribution of Bangladesh using quantile regression model. Theoretical and Applied Climatology 2020, 142, 1329 -1342.
AMA StyleMorteza Mohsenipour, Shamsuddin Shahid, Ghaith Falah Ziarh, Zaher Mundher Yaseen. Changes in monsoon rainfall distribution of Bangladesh using quantile regression model. Theoretical and Applied Climatology. 2020; 142 (3-4):1329-1342.
Chicago/Turabian StyleMorteza Mohsenipour; Shamsuddin Shahid; Ghaith Falah Ziarh; Zaher Mundher Yaseen. 2020. "Changes in monsoon rainfall distribution of Bangladesh using quantile regression model." Theoretical and Applied Climatology 142, no. 3-4: 1329-1342.
Expansion of arid lands due to climate change, particularly in water stressed regions of the world can have severe implications on the economy and people’s livelihoods. The spatiotemporal trends in aridity, the shift of land from lower to higher arid classes and the effect of this shift on different land uses in Syria have been evaluated in this study for the period 1951–2010 using high-resolution monthly climate data of the Terrestrial Hydrology Research Group of Princeton University. The trends in rainfall, temperature and potential evapotranspiration were also evaluated to understand the causes of aridity shifts. The results revealed an expansion of aridity in Syria during 1951–1980 compared to 1981–2010. About 6.21% of semi-arid land was observed to shift to arid class and 5.91% dry-subhumid land to semi-arid land between the two periods. Analysis of results revealed that the decrease in rainfall is the major cause of increasing aridity in Syria. About 28.3% of agriculture land located in the north and the northwest was found to shift from humid to dry-subhumid or dry-subhumid to semi-arid. Analysis of results revealed that the shifting of drylands mostly occurred in the northern agricultural areas of Syria. The land productivity and irrigation needs can be severely affected by increasing aridity which may affect food security and the economy of the country.
Mohammad Rajab Houmsi; Mohammed Sanusi Shiru; Mohamed Salem Nashwan; Kamal Ahmed; Ghaith Falah Ziarh; Shamsuddin Shahid; Eun-Sung Chung; Sungkon Kim. Spatial Shift of Aridity and Its Impact on Land Use of Syria. Sustainability 2019, 11, 7047 .
AMA StyleMohammad Rajab Houmsi, Mohammed Sanusi Shiru, Mohamed Salem Nashwan, Kamal Ahmed, Ghaith Falah Ziarh, Shamsuddin Shahid, Eun-Sung Chung, Sungkon Kim. Spatial Shift of Aridity and Its Impact on Land Use of Syria. Sustainability. 2019; 11 (24):7047.
Chicago/Turabian StyleMohammad Rajab Houmsi; Mohammed Sanusi Shiru; Mohamed Salem Nashwan; Kamal Ahmed; Ghaith Falah Ziarh; Shamsuddin Shahid; Eun-Sung Chung; Sungkon Kim. 2019. "Spatial Shift of Aridity and Its Impact on Land Use of Syria." Sustainability 11, no. 24: 7047.
Reliable projection of climate is essential for climate change impact assessment and mitigation planning. General Circulation Models (GCMs) simulations are generally downscaled into much finer spatial resolution for climate change impact studies at local and regional scales. The objective of the present study is to use a two-stage bias correction approach for downscale and project future changes of daily average temperature. The approach was applied for downscaling and projection of daily average temperature of Senai meteorological station located in Johor Bahru, Malaysia using a GCM of Coupled Model Intercomparison Project Phase 5 (CMIP5) under four representative concentration pathways (RCP) scenarios. The two-stage bias correction method was based on correction in mean factor and variability inflation factor. The model performances were assessed using different statistical measures including mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), index of agreement (MD), Nash–Sutcliffe model efficiency (NSE) and coefficient of determination (R2). Results showed that the downscaling method could simulate historical daily average temperature at the station very well. The GCM projected an increase in daily average temperature by 1.4ºC, 2.2ºC, 2.5ºC, and 3.4ºC under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenarios, respectively in the end of this century. It is expected that the finding of the study would help in climate change impact assessment and adopting necessary adaptation measures.
Mohd Khairul Idlan Muhammad; Mohamad Rajab Houmsi; Ghaith Falah Ziarh; Muhammad Noor; Tarmizi Ismail; Sobri Harun. A two-stage bias correction approach for downscaling and projection of daily average temperature. European Journal of Climate Change 2019, 1 .
AMA StyleMohd Khairul Idlan Muhammad, Mohamad Rajab Houmsi, Ghaith Falah Ziarh, Muhammad Noor, Tarmizi Ismail, Sobri Harun. A two-stage bias correction approach for downscaling and projection of daily average temperature. European Journal of Climate Change. 2019; ():1.
Chicago/Turabian StyleMohd Khairul Idlan Muhammad; Mohamad Rajab Houmsi; Ghaith Falah Ziarh; Muhammad Noor; Tarmizi Ismail; Sobri Harun. 2019. "A two-stage bias correction approach for downscaling and projection of daily average temperature." European Journal of Climate Change , no. : 1.