Integrated assessment of extreme climate and landuse change impact on sediment yield in a mountainous transboundary watershed of India and Pakistan

Assessment of climate and land use changes impact including extreme events on the sediment yield is vital for water and power stressed countries. Mangla Reservoir is the second-largest reservoir in Pakistan, and its capacity is being reduced due to rapid sedimentation and will be threatened under climate and land use changes. This paper discusses the consequences of climate and land use change on sediment yield at Mangla Dam using General Circulation Models(GCMs), Land Change Modeler(LCM), Soil and Water Assessment Tool(SWAT) model after calibration and validation.Results show that over the historical period temperature is observed to increase by 0.10 o C/decade and forest cover is observed to reduce to the level of only 16% in 2007. Nevertheless, owing to the forest conservation policy, the forest cover raised back to 27% in 2012. Anticipated land use maps by using LCM of 2025, 2050 and 2100 showed that the forest cover will be 33%, 39.2%, and, 53.7%, respectively. All seven GCMs projected the increase in temperature and five GCMs projected an increase in precipitation,however, two GCMs projected a decrease in precipitation. Owing to climate change, land use change and combined impact of climate and land use change on annual sediment yield(2011-2100) may vary from-42.9% to 39.4%, 0% to-27.3% and,-73%to 39.4%, respectively. Under climate change scenarios projected sediment yield is mainly linked with extreme events and is expected to increase with the increase in extreme events. Under land use change scenarios projected sediment yield is mainly linked with the forest cover and is expected to decrease with the increase in forest cover. The results of this study are beneficial for planners, watershed managers and policymakers to mitigate the impacts of climate and land use changes to enhance reservoir life by reducing the sediment yield.

Implications of future climate change on crop and irrigation water requirements in a semi-arid river basin using CMIP6 GCMs

Agriculture faces risks due to increasing stress from climate change,particularly in semi-arid regions.Lack of understanding of crop water requirement(CWR)and irrigation water requirement(IWR)in a changing climate may result in crop failure and socioeconomic problems that can become detrimental to agriculture-based economies in emerging nations worldwide.Previous research in CWR and IWR has largely focused on large river basins and scenarios from the Coupled Model Intercomparison Project Phase 3(CMIP3)and Coupled Model Intercomparison Project Phase 5(CMIP5)to account for the impacts of climate change on crops.Smaller basins,however,are more susceptible to regional climate change,with more significant impacts on crops.This study estimates CWRs and IWRs for five crops(sugarcane,wheat,cotton,sorghum,and soybean)in the Pravara River Basin(area of 6537 km^(2))of India using outputs from the most recent Coupled Model Intercomparison Project Phase 6(CMIP6)General Circulation Models(GCMs)under Shared Socio-economic Pathway(SSP)245 and SSP585 scenarios.An increase in mean annual rainfall is projected under both scenarios in the 2050s and 2080s using ten selected CMIP6 GCMs.CWRs for all crops may decline in almost all of the CMIP6 GCMs in the 2050s and 2080s(with the exceptions of ACCESS-CM-2 and ACCESS-ESM-1.5)under SSP245 and SSP585 scenarios.The availability of increasing soil moisture in the root zone due to increasing rainfall and a decrease in the projected maximum temperature may be responsible for this decline in CWR.Similarly,except for soybean and cotton,the projected IWRs for all other three crops under SSP245 and SSP585 scenarios show a decrease or a small increase in the 2050s and 2080s in most CMIP6 GCMs.These findings are important for agricultural researchers and water resource managers to implement long-term crop planning techniques and to reduce the negative impacts of climate change and associated rainfall variability to avert crop failure and agricultural losses.

Evaluate the Possibility of Cracking Pipe on Water Supply Network under the Age of Pipe

Minimizing water loss in water supply networks is one of the objectives for protecting water resources. Currently, the large amount of water loss is mainly due to leakage of the pipeline network. The leaking of pipe can be caused by incorrect construction, impacted by external forces, or corroded pipe material and aging. Therefore, to control and predict the cracking area on pipe, it is necessary to collect data about pipe conditions, approve the solution of technology improvement and define the ability of pipe capacity from setting up to the first preparing time. This paper will demonstrate how to evaluate corrosion pipe under the age of pipe and the impact level of internal pressure pipe at different times, and will put forward solution of effective leaking management on water supply network.

Evaluation of MODIS-Landsat and AVHRR-Landsat NDVI data fusion using a single pair base reference image:a case study in a tropical upstream catchment on Java,Indonesia

Image blending is one of the alternative methods to fill temporal gaps in the monitoring of historical vegetation properties using continuous NDVI derived from Landsat 5 TM/7 ETM+and 8 OLI images.Frequent cloud occurrence in the tropical upstream catchment limits the use of image blending methods that allow to employment of a single pair base reference.This study aims to evaluate two image blending methods with nine input data configurations to select the most applicable one.Scatter plots and statistical indices such as ME,RMSE,model efficiency and structure similarity showed FSDAF outperforms STARFM in generating both synthetic Landsat 8 OLI NDVI and Landsat 5 TM/7 ETM+NDVI when employing unsupervised and supervised classification images,respectively,where both were applied along with MODIS NDVI 250 m v.005.When generating synthetic Landsat 5 TM/7 ETM+NDVI using AVHRR NDVI,both algorithms performed similarly.However,when considering the temporal over spatial variance ratio between base reference and predictor images,both algorithms performed almost similar when the value close to minimum.This study shows that selection of image blending algorithm with use single pair base reference image should consider input data configuration and temporal over spatial variance ratio.

An efficient built-up land expansion model using a modified U-Net

This paper introduces an improved convolutional neural network based on the conventional U-Net for simulating built-up land expansion.The proposed method hires a pixel-wise semantic segmentation approach considering the spatial drivers affecting urbanization as data cubes.Independent variables including altitude,slope,and distance from barren,crop,greenery,roads,and urban areas for 1998,2008,and 2018 were considered as covariates for the simulation of built-up land expansion in Tehran and Karaj regions in Iran.The proposed method was compared with the random forest(RF)algorithm as the baseline model.Evaluation using the area under the total operating characteristic indicated the superiority of our modified U-Net(0.87)over the RF(0.82)algorithm.Furthermore,evaluation using the percent correct metric indicated that our proposed model is capable of learning neighborhood effects effectively leading to simulate built-up land expansion accurately,independent from applying a cellular automata(CA)model.Therefore,the modified U-Net independent from the CA which can consider the neighborhood effects is recommended for the simulation of built-up land expansion precisely.