Accurate assessment of spatio-temporal variations of consumptive water use (CWU) in large irrigation schemes is crucial for several hydrological applications. This study is designed to evaluate the impact of climate change on CWU in the Lower Chenab Canal (LCC) irrigation scheme of the Indus basin irrigation system of Pakistan. A distributed hydrological model, the soil and water assessment tool (SWAT), was spatially calibrated (2005–2009) and validated (2010–2012) for monthly CWU. The estimated CWU using the SWAT model showed promising results (the coefficient of determination (R2) = 0.87 ± 0.06, Nash–Sutcliffe model efficiency (NSE) = 0.83 ± 0.06)) when compared with CWU determined by the Surface Energy Balance Algorithm (SEBAL) (R2 = 0.87 ± 0.06, NSE = 0.83 ± 0.06). Future evaluation, performed by considering the representative concentration pathways (RCP) 4.5 and 8.5 climate change scenarios, showed that changes in temperature and rainfall would significantly influence the CWU in the LCC scheme. Compared with the reference period, annual water consumption in the basin would increase overall by 7% and 11% at the end of 2020 with monthly variations of –40% to 60% and –17% to 80% under RCP 4.5 and RCP 8.5 climate change scenarios, respectively. The water managers in the region have to consider this fluctuating consumptive use in water allocation plans due to climate change for better management of available water resources.
The fine-scale insights of existing cropland trends and their nexus with agrometeorological parameters are of paramount importance in assessing future food security risks and analyzing adaptation options under climate change. This study has analyzed the seasonal cropland trends in the Indus River Plain (IRP), using multi-year remote sensing data. A combination of Sen’s slope estimator and Mann–Kendall test was used to quantify the existing cropland trends. A correlation analysis between enhanced vegetation index (EVI) and 9 agrometeorological parameters, derived from reanalysis and remote sensing data, was conducted to study the region’s cropland-climate nexus. The seasonal trend analysis revealed that more than 50% of cropland in IRP improved significantly from the year 2003 to 2018. The lower reaches of the IRP had the highest fraction of cropland, showing a significant decreasing trend during the study period. The nexus analysis showed a strong correlation of EVI with the evaporative stress index (ESI) during the water-stressed crop season. Simultaneously, it exhibited substantial nexus of EVI with actual evapotranspiration (AET) during high soil moisture crop season. Temperature and solar radiation had a negative linkage with EVI response. In contrast, a positive correlation of rainfall with EVI trends was spatially limited to the IRP’s upstream areas. The relative humidity had a spatially broad positive correlation with EVI compare to other direct climatic parameters. The study concluded that positive and sustainable growth in IRP croplands could be achieved through effective agriculture policies to address spatiotemporal AET anomalies.
Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan's Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R 2 ), Root Mean Square Error (RMSE, t/ ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat; 0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6-8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress.
Urethral polyps are one of the rare deformities of the urethra. In most cases, the urethral polyps would not be considered in the differential diagnosis process by a huge number of physicians, mainly owing to the rarity of documented cases in the medical literature and because of the wide variety of unspecified symptoms the urethral polyp might demonstrate. Urethral polyps are more common in males than in females, and they are usually diagnosed at an early age. Treatment options include transurethral resection, endoscopic suprapubic approach and open surgery. The disease prognosis is excellent as it does not usually recur after being completely removed and the risk of malignant transformation is very low. We are going to report a case of a 3-month-old boy who presented with bilateral vesicoureteral reflux and hydronephrosis, which revealed the presence of a large posterior urethral polyp.
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