Quantifying the relative contributions of different factors to runoff change is helpful for basin management, especially in the context of climate change and anthropogenic activities. The effect of snow change on runoff is seldom evaluated. We attribute the runoff change in the Heihe Upstream Basin (HUB), an alpine basin in China, using two approaches: a snowmelt-based water balance model and the Budyko framework. Results from these approaches show good consistency. Precipitation accounts for 58% of the increasing runoff. The contribution of land-cover change seems unremarkable for the HUB as a whole, where land-cover change has a major effect on runoff in each sub-basin, but its positive effect on increasing runoff in sub-basins 1 and 3 is offset by the negative effect in sub-basin 2. Snow change plays an essential role in each sub-basin, with a contribution rate of around 30%. The impact of potential evapotranspiration is almost negligible.EDITOR D. KoutsoyiannisASSOCIATE EDITOR S. Huang
Abstract Merged multisatellite precipitation datasets (MMPDs) combine the advantages of individual satellite precipitation products (SPPs), have a tendency to reduce uncertainties, and provide higher potentials to hydrological applications. This study applied a dynamic clustered Bayesian model averaging (DCBA) algorithm to merge four SPPs across Pakistan. The DCBA algorithm produced dynamic weights to different SPPs varying both spatially and temporally to accommodate the spatiotemporal differences of SPP performances. The MMPD is developed at daily temporal scale from 2000 to 2015 with spatial resolution of 0.25° using extensively evaluated SPPs and a global atmospheric reanalysis–precipitation dataset: Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Climate Prediction Center morphing technique (CMORPH), and ERA-Interim. The DCBA algorithm is evaluated across four distinct climate regions of Pakistan over 102 ground precipitation gauges (GPGs). DCBA forecasting outperformed all four SPPs with average Theil’s U of 0.49, 0.38, 0.37, and 0.36 in glacial, humid, arid, and hyperarid regions, respectively. The average mean bias error (MBE), mean error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and standard deviation (SD) of DCBA over all of Pakistan are 0.54, 1.40, 4.94, 0.77, and 5.17 mm day −1 , respectively. Seasonal evaluation revealed a dependency of DCBA performance on precipitation magnitude/intensity and elevation. Relatively poor DCBA performance is observed in premonsoon/monsoon seasons and at high/mild elevated regions. Average improvements of DCBA in comparison with TMPA are 59.56% (MBE), 49.37% (MAE), 45.89% (RMSE), 19.48% (CC), 46.7% (SD), and 18.66% (Theil’s U ). Furthermore, DCBA efficiently captured extreme precipitation trends (premonsoon/monsoon seasons).
This study evaluates the spatial and temporal performance of the Climate Hazard Group InfraRed Precipitation Satellite (CHIRPS) against Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42/3B43 v. 7 and Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG V06), from 2000 to 2013. Several statistical metrics were used to assess the performance of CHIRPS over the Indus Basin, and its hydrological utility is also assessed using the Soil and Water Assessment Tool (SWAT). The Gilgit and Soan basins were selected for hydrological modelling. The results demonstrate the spatial and temporal dependency of CHIRPS, i.e. better performance was observed in the Lower Indus Basin (LIB) while poor performance was observed in the Upper Indus Basin (UIB). The hydrological assessment of CHIRPS revealed poor performance (overestimation of streamflow) across the Gilgit Basin during both calibration and validation periods. Satisfactory to good performance was obtained across the Soan Basin.
Abstract Recently, tuning oxygen ionic transport in film through tensile stress has generated much interest. Herein, composite samarium neodymium codoped ceria (SNDC) thin film electrolytes are fabricated with aligned yttria‐stabilized zirconia (YSZ) nanowires to increase the oxygen conductivity by introducing tensile strain. The morphologies and oxygen‐ion conductivities are studied according to the diameter of YSZ nanowires in the composite electrolytes. The composite SNDC film electrolytes with YSZ nanowires exhibit great long‐term chemical stability with excellent conductivity at intermediate temperature, which is more than twice times higher than that of SNDC film. By using Raman spectrum and sin 2 ψ‐methodology, it is found that this conductivity enhancement originates from the bending surface of the film on the nanowires that can produce tensile strain paralleled to the film surface.
Inter alia, inter-annual and spatial variability of climate, particularly rainfall, shall trigger frequent floods and droughts in Pakistan. Subsequently, a higher proportion of the country’s population will be exposed to water-related challenges. This study analyzes and projects the long-term spatio-temporal changes in precipitation using the data from 2005 to 2099 across two large river basins of Pakistan. The plausible precipitation data to detect the projected trends seems inevitable to study the future water resources in the region. For, policy decisions taken in the wake of such studies can be instrumental in mitigating climate change impacts and shape water management strategies. Outputs of the Coupled Model Intercomparison Project 5 (CMIP5) climate models for the two forcing scenarios of RCP 4.5 and RCP 8.5 have been used for the synthesis of projected precipitation data. The projected precipitation data have been synthesized in three steps (1) dividing the area in different climate zones based on the similar precipitation statistics (2) selection of climate models in each climate zone in a way to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity in a baseline period of 1971–2004 and the projected period of 2005–2099 and (3) combining the selected model’s data in mean and median combinations. The future precipitation trends were detected and quantified, for the set of four scenarios. The spatial distribution of the precipitation trends was mapped for better understanding. All the scenarios produced consistent increasing or decreasing trends. Significant declining trends were projected in the warm wet season at 0.05% significance level and the increasing trends were projected in cold dry, cold wet and warm dry seasons. Framework developed to project climate change trends during the study can be replicated for any other area. The study therefore can be of interest for researchers working on climate impact modeling.
Yttria stabilized zirconia thermal barrier coating (TBC) along with CoNiCrAlY bondcoat was deposited using air plasma spray on Inconel-X750 superalloy. The coated samples were exposed at 9500C in a mixture of Na 2 SO 4 and V 2 O 5 . The exposed specimens were investigated using XRD and SEM. The formation of spinel and perovskite structures was revealed at the interface of topcoat and the bondcoat. XRD analyses of the samples confirmed phase transformation of the tetragonal zirconia into monoclinic zirconia and yttrium vanadate.
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