The aim of present work was to evaluate the effects of titanium dioxide nanoparticles (TiO2 NPs) on rice's growth (Oryza sativa L.) and nutrient availability under different soil textures. Greenhouse experiment was carried out with three soil textures (sandy loam, silt loam and silty clay loam) and two concentrations of TiO2 NPs (500, 750 mg kg-1). Control (without TiO2 NPs) was also maintained for the comparison. Growth parameters including chlorophyll content, root/shoot length, fresh/dry biomass and nutrients' uptake including calcium (Ca), copper (Cu), iron (Fe), magnesium (Mg), phosphorous (P), potassium (K) and zinc (Zn) were determined. The results revealed that application of 500 mg kg-1 TiO2 NPs in silty clay loam soil increased the chlorophyll content (3.3-folds), root length (49%), shoot length (31%), root and shoot biomass (41% & 39%, respectively) as compared to other soil textures. The maximum plant growth was observed in silty clay loam > silt loam > sandy loam. Concentration of Cu, Fe, P and Zn in shoot was increased by 8 - , 2.3 - , 0.4 - , 0.05 -folds in silty clay loam upon 500 mg kg-1 TiO2 NPs application as compared to the control. Backward selection method to model the parameters (nutrients in soil) for the response variables (root/shoot length and biomass) showed that Ca, Fe, P are the main nutrients responsible for the increase in plant length and biomass. Overall, the growth of rice was better in silty clay loam at 500 mg kg-1 of TiO2 NPs.

Biased distribution of hydro-climate stations in high elevations are major obstacles for reliable appraisal of the hydro-climatic regime of the Chitral Basin located in the extreme north of Pakistan. We modeled this regime in the ARC-SWAT hydrological model forced with the latest gridded reanalysis ERA5 Land dataset, bias-corrected against a good quality reference dataset. The performance of the gridded dataset was cross-validated by comparing the model flow simulation against the observed flows. The ERA5 Land overall provided reasonably good estimates. The calibrated model on the daily time scale was able to provide excellent values of the employed statistical measures (NSE, KGE, PBIAS, RMSE and MAE). For a future climate change analysis, climate series was devised using two future projection scenarios (RCP4.5 and RCP8.5) using the best performing GCM (MIROC5_rlilp1) out of five investigated GCMs. The results of the climate change analysis reveal increment in the average temperature up to +3.73 °C and +5.62 °C for RCP4.5 and RCP8.5, respectively, while the analysis of precipitation suggests an annual decrease up to −16% and −35% against RCP4.5 and RCP8.5, respectively, by the end of century. A future simulated flow analysis showed an increment of +0.25 % and decrease of −6.82% for RCP4.5 and RCP8.5, respectively. Further analysis of climate suggests seasonal deflections especially in precipitation and flow regimes. A notable climb in flow quantities was observed during spring season (MAM) in spite of the major reduction in precipitation amounts for that season. This implicitly supports a high rate of glacial/snow melt especially in the spring season during that period. Frequent droughts and floods are also projected by examining flow durations at each interval of the 21st century.

Rainfall–runoff modelling has been at the essence of research in hydrology for a long time. Every modern technique found its way to uncover the dynamics of rainfall–runoff relation for different basins of the world. Different techniques of machine learning have been extensively applied to understand this hydrological phenomenon. However, the literature is still scarce in cases of extensive research work on the comparison of streamline machine learning (ML) techniques and impact of wavelet pre-processing on their performance. Therefore, this study compares the performance of single decision tree (SDT), tree boost (TB), decision tree forest (DTF), multilayer perceptron (MLP), and gene expression programming (GEP) in rainfall–runoff modelling of the Soan River basin, Pakistan. Additionally, the impact of wavelet pre-processing through maximal overlap discrete wavelet transformation (MODWT) on the model performance has been assessed. Through a comprehensive comparative analysis of 110 model settings, we concluded that the MODWT-based DTF model has yielded higher Nash–Sutcliffe efficiency (NSE) of 0.90 at lag order (Lo4). The coefficient of determination for the model was also highest among all the models while least root mean square error (RMSE) value of 23.79 m³/s was also produced by MODWT-DTF at Lo4. The study also draws inter-technique comparison of the model performance as well as intra-technique differentiation of modelling accuracy.

Levee breaches are some of the most common hazards in the world and cause the loss of lives, livelihoods, and property destruction. During the 2010 flood in Pakistan, the most devastating breach occurred at Tori Levee on the right bank of the Indus River, downstream of the Guddu Barrage, which caused residual floods in northern Sindh and the adjoining regions of the Balochistan province. In this study, 2D unsteady flow modeling performed for Tori Levee breach computed residual flood inundation by coupling a HEC-RAS (Hydrological Engineering Centre—River Analysis System) 2D hydraulic model with remote sensing and Geographic Information System techniques. The model performance was judged by comparing the observed and simulated water levels (stage) during peak flow at seven different gauging stations located within the Indus River reach and daily flood extents and multi-day composites. The quantitative values for the calibration and validation of the HEC-RAS model showed good performance with a range of difference from 0.13 to −0.54 m between the simulated and observed water levels (stage), 84% match for the maximum flood inundation area, and 73.2% for the measure of fit. The overall averages of these values for the daily flood comparison were 57.12 and 75%, respectively. Furthermore, the simulated maximum flow passed through the Tori Levee breach, which was found to be 4994.47 cumecs (about 15% of peak flow) with a head water stage of 71.56 m. By using the simulated flows through the Tori Levee breach, the flood risk maps for the 2010 flood identified hazard zones according to the flood characteristics (depth, velocity, depth times velocity, arrival time, and duration). All the flood risk maps concluded the fact that the active flood plain was uninhabitable under flood conditions.