Estimating the quantitate contribution of climate and land use change is necessary for planning water resources.Tarbela catchment in Pakistan was selected for this study.The Mann Kendall and Pettit test has been used for trend analysis of hydro climatic variables.Original climate elasticity method and improved empirical model of precipitation have been used.The results of trend analysis showed that precipitation and runoff trends decreased and potential evaporation trends increased.The annual rainfall and runoff presented a change point around the years 1999 and 1994 respectively.According to change point analysis, the runoff series was divided into two parts.The period before change point has been regarded as the pre-change period and the period after change point has been regarded as the post change period.According to the original climate elasticity method, the relative contribution of climate change and land use change has been computed as 39.3% and 60.7% respectively.Similarly the improved empirical model of precipitation showed relative contributions of climate change as 41.7% and the same for land use change as 58.3%.To validate the land use change contribution we prepared land use maps.It can be concluded that land use and climate change are responsible for runoff change in Tarbela catchment, and both methods performed well and results are in agreement.
Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the “dynamic clustered Bayesian averaging (DCBA)” approach with those utilizing the “regional principal component analysis (RPCA)” under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan’s diverse climatic zones—glacial, humid, arid, and hyper-arid—employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil’s U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil’s U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil’s U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs.
The present study is to understand how climatic variables such as precipitation and temperature vary over time and how those changes affect stream flow in the Jhelum River basin in Pakistan under different emission scenarios A2 and B2. The simulation results of HadCM3 were employed to create potential climate change scenarios with the Statistically Downscale Model (SDSM). The calibrated model Soil and Water Assessment Tool (SWAT) was used to forecast imminent stream flow to develop a proposed future climate change scenario. Results indicated that cooling patterns were identified in the north portion of the study area whereas warming patterns were detected in the south portion. The projected mean annual maximum temperature (Tmax) of 2020’s 2050’s and 2080’s would be 0.3 oC, 0.8 oC, and 0.99 oC, respectively, under the A2 scenario. The changes in mean annual minimum temperature (Tmin) were also observed as it would be 0.4 oC, 0.7 oC, and 1.4 oC during 2020’s (2021-2040), 2050’s (2041-2070) and 2080’s (2071-2100), respectively. Similarly, it was observed that average annual rainfall would rise by 14%, 10%, and 20% during the 2020s, 2050s, and 2080s, respectively, in the Mangla basin. The results showed an increase in annual stream flows of 100% (1545 m3/sec), with increases in the winter and autumn seasons of up to 409% and 211%, respectively, and a drop in the spring and summer seasons of up to 29% and 25%, respectively, in the 2080’s compared to baseline. Water managers should consider the current trends and variability brought on by climate change to improve water management where water is scarce.
Abstract Real-time, low-cost, and wireless mechanical vibration monitoring is necessary for industrial applications to track the operation status of equipment, environmental applications to proactively predict natural disasters, as well as day-to-day applications such as vital sign monitoring. Despite this urgent need, existing solutions, such as laser vibrometers, commercial Wi-Fi devices, and cameras, lack wide practical deployment due to their limited sensitivity and functionality. In this work, we propose and verify that a fully passive, metamaterial-based vibration processing device attached to the vibrating surface can improve the sensitivity of wireless vibration measurement methods by more than 10 times at designated frequencies. Additionally, the device realizes an analog real-time vibration filtering/labeling effect, and the device also provides a platform for surface editing, which adds more functionalities to the current non-contact sensing systems. Finally, the working frequency of the device is widely adjustable over orders of magnitudes, broadening its applicability to different applications.
Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a framework for selecting GCMs and detecting future climate change trends after regionalizing the Indus river sub-basins in three basic steps: (1) regionalization of large river basins, based on spatial climate homogeneities, for four seasons using different machine learning algorithms and daily gridded precipitation data for 1975–2004; (2) selection of GCMs in each homogeneous climate region based on performance to simulate past climate and its temporal distribution pattern; (3) detecting future precipitation change trends using projected data (2006–2099) from the selected model for two future scenarios. The comprehensive framework, subject to some limitations and assumptions, provides divisional boundaries for the climatic zones in the study area, suitable GCMs for climate change impact projections for adaptation studies and spatially mapped precipitation change trend projections for four seasons. Thus, the importance of machine learning techniques for different types of analyses and managing long-term data is highlighted.
Duplex stainless steel having attractive combination of austenitic and ferritic properties is being used in industry such as petrochemical, pulp and paper mills. In this study, the corrosion and stress corrosion behavior of duplex stainless steel in 3.5% sodium chloride environment was investigated by weight loss measurements, electrochemical DC testing and slow strain rate test (SSRT). Weight loss data showed no significant corrosion after 1700 hours. Electrochemical polarization test in 3.5% NaCl solution exhibited a uniform corrosion rate of 0.008 mpy (calculated using Tafel analysis) showing passivity in the range of 735-950 mV. A comparison of the slow strain rate test in 3.5% NaCl solution with air shows almost a similar stress strain curve for duplex stainless steel. In comparison, the stress strain curves for 0.15% carbon steel show a loss of about 25% tensile elongation for the same comparison. The excellent corrosion and especially resistance to localized corrosion (pitting) is responsible for no loss of ductility in duplex stainless steel.
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