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This study is based on both the recent and the predicted twenty first century climatic and hydrological changes over the mountainous Upper Indus Basin (UIB), which are influenced by snow and glacier melting. Conformal-Cubic Atmospheric Model (CCAM) data for the periods 1976-2005, 2006-2035, 2041-2070, and 2071-2100 with RCP4.5 and RCP8.5; and Regional Climate Model (RegCM) data for the periods of 2041-2050 and 2071-2080 with RCP8.5 are used for climatic projection and, after bias correction, the same data are used as an input to the University of British Columbia (UBC) hydrological model for river flow projections. The projections of all of the future periods were compared with the results of 1976-2005 and with each other. Projections of future changes show a consistent increase in air temperature and precipitation. However, temperature and precipitation increase is relatively slow during 2071-2100 in contrast with 2041-2070. Northern parts are more likely to experience an increase in precipitation and temperature in comparison to the southern parts. A higher increase in temperature is projected during spring and winter over southern parts and during summer over northern parts. Moreover, the increase in minimum temperature is larger in both scenarios for all future periods. Future river flow is projected by both models to increase in the twenty first century (CCAM and RegCM) in both scenarios. However, the rate of increase is larger during the first half while it is relatively small in the second half of the twenty first century in RCP4.5. The possible reason for high river flow during the first half of the twenty first century is the large increase in temperature, which may cause faster melting of snow, while in the last half of the century there is a decreasing trend in river flow, precipitation, and temperature (2071-2100) in comparison to 2041-2070 for RCP4.5. Generally, for all future periods, the percentage of increased river flow is larger in winter than in summer, while quantitatively large river flow was projected, particularly during the summer monsoon. Due to high river flow and increase in precipitation in UIB, water availability is likely to be increased in the twenty first century and this may sustain water demands.

This study investigates the projections of precipitation and temperature at the local scale in the Upper Indus Basin (UIB) in Pakistan using six Regional Climate Models (RCMs) from CORDEX under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). For twenty-four stations spread across the study area, the Long Ashton Research Station Weather Generator, version six (LARS-WG6), was used to downscale the daily data from the six different RCMs for maximum temperature (T max ), minimum temperature (T min ), and precipitation (pr) at a spatial resolution of 0.44°. Investigations were made to predict changes in mean annual values of T max , T min , and precipitation during two future periods, i.e., the mid-century (2041–2070) and end-century (2071–2100). The model results from statistical and graphical comparison validated that the LARS-WG6 can simulate the temperature and the precipitation in the UIB. Each of the six RCMs and their ensemble revealed a continuously increased temperature projection in the basin; nevertheless, there is variation in projected magnitude across RCMs and between RCPs. The rise in average T max and T min was more significant under RCP 8.5 than RCP 4.5, possibly due to unmitigated greenhouse gas emissions (GHGs). The precipitation projections follow the non-uniform trend, i.e., not all RCMs agree on whether the precipitation will increase or decrease in the basin, and no orderly variations were detected during any future periods under any RCP. However, an overall increase in precipitation is projected by the ensemble of RCMs.

Comprehensive precipitation data is essential for hydrological, agricultural, and climatological studies. Yet, gaps and sparse rain gauge distribution pose challenges, requiring imputation algorithms to fill data gaps. The aim of this research is to evaluate the performance of several approaches for estimating incomplete precipitation data in the Upper Indus Basin (UIB). Eight various imputation approaches were used on sparsely gauged mountainous UIB on a monthly time series of twenty-four meteorological observatories. Following that, the estimation approaches were evaluated using a rank-based approach comprising four different statistical indicators. The results indicate that multiple linear regression is the best-performing strategy for most of the stations regardless of season or orography, followed by the arithmetic average method and inverse distance weighing method.

At the present stage, the field of detecting vegetable pests and diseases is in dire need of the integration of computer vision technologies. However, the deployment of efficient and lightweight object-detection models on edge devices in vegetable cultivation environments is a key issue. To address the limitations of current target-detection models, we propose a novel lightweight object-detection model based on YOLOv8n while maintaining high accuracy. In this paper, (1) we propose a new neck structure, Focus Multi-scale Feature Diffusion Interaction (FMDI), and inject it into the YOLOv8n architecture, which performs multi-scale fusion across hierarchical features and improves the accuracy of pest target detection. (2) We propose a new efficient Multi-core Focused Network (MFN) for extracting features of different scales and capturing local contextual information, which optimizes the processing power of feature information. (3) We incorporate the novel and efficient Universal Inverted Bottleneck (UIB) block to replace the original bottleneck block, which effectively simplifies the structure of the block and achieves the lightweight model. Finally, the performance of YOLO-FMDI is evaluated through a large number of ablation and comparison experiments. Notably, compared with the original YOLOv8n, our model reduces the parameters, GFLOPs, and model size by 18.2%, 6.1%, and 15.9%, respectively, improving the mean average precision (mAP50) by 1.2%. These findings emphasize the excellent performance of our proposed model for tomato pest and disease detection, which provides a lightweight and high-precision solution for vegetable cultivation applications.

Assessment of future water availability from the Himalayan watersheds of Indus Basin (Jhelum, Kabul and upper Indus basin—UIB) is a growing concern for safeguarding the sustainable socioeconomic wellbeing downstream. This requires, before all, robust climate change information from the present-day state-of-the-art climate models. However, the robustness of climate change projections highly depends upon the fidelity of climate modeling experiments. Hence, this study assesses the fidelity of seven dynamically refined (0.44\\[^{\\circ }\\]) experiments, performed under the framework of the coordinated regional climate downscaling experiment for South Asia (CX-SA), and additionally, their six coarse-resolution driving datasets participating in the coupled model intercomparison project phase 5 (CMIP5). We assess fidelity in terms of reproducibility of the observed climatology of temperature and precipitation, and the seasonality of the latter for the historical period (1971–2005). Based on the model fidelity results, we further assess the robustness or uncertainty of the far future climate (2061–2095), as projected under the extreme-end warming scenario of the representative concentration pathway (RCP) 8.5. Our results show that the CX-SA and their driving CMIP5 experiments consistently feature low fidelity in terms of the chosen skill metrics, suggesting substantial cold (6–10 \\[^{\\circ }\\]C) and wet (up to 80%) biases and underestimation of observed precipitation seasonality. Surprisingly, the CX-SA are unable to outperform their driving datasets. Further, the biases of CX-SA and of their driving CMIP5 datasets are higher in magnitude than their projected changes under RCP8.5—and hence under less extreme RCPs—by the end of 21st century, indicating uncertain future climates for the Indus Basin watersheds. Higher inter-dataset disagreements of both CMIP5 and CX-SA for their simulated historical precipitation and for its projected changes reinforce uncertain future wet/dry conditions whereas the CMIP5 projected warming is less robust owing to higher historical period uncertainty. Interestingly, a better agreement among those CX-SA experiments that have been obtained through downscaling different CMIP5 experiments with the same regional climate model (RCM) indicates the RCMs’ ability of modulating the influence of lateral boundary conditions over a large domain. These findings, instead of suggesting the usual skill-based identification of ’reasonable’ global or regional low fidelity experiments, rather emphasize on a paradigm shift towards improving their fidelity by exploiting the potential of meso-to-local scale climate models—preferably of those that can solely resolve global-to-local scale climatic processes—in terms of microphysics, resolution and explicitly resolved convections. Additionally, an extensive monitoring of the nival regime within the Himalayan watersheds will reduce the observational uncertainty, allowing for a more robust fidelity assessment of the climate modeling experiments.

Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radial M5 tree (RM5Tree) model to accurately estimate sediment yields using daily inputs of the snow cover fraction, air temperature, evapotranspiration and effective rainfall, in addition to the flow, in the Gilgit River, Upper Indus Basin (UIB) tributary, Pakistan. The results of the RM5Tree model were compared with support vector regression (SVR), artificial neural network (ANN), multivariate adaptive regression spline (MARS), M5Tree, sediment rating curve (SRC) and response surface method (RSM) models. The resulting accuracy of the models was assessed using Pearson’s correlation coefficient (R2), the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE). The prediction accuracy of the RM5Tree model during the testing period was superior to the ANN, MARS, SVR, M5Tree, RSM and SRC models with the R2, RMSE and MAPE being 0.72, 0.51 tons/day and 11.99%, respectively. The RM5Tree model predicted suspended sediment peaks better, with 84.10% relative accuracy, in comparison to the MARS, ANN, SVR, M5Tree, RSM and SRC models, with 80.62, 77.86, 81.90, 80.20, 74.58 and 62.49% relative accuracies, respectively.

In complex environments, strawberry disease segmentation models face challenges, such as segmentation difficulties, excessive parameters, and high computational loads, making it difficult for these models to run effectively on devices with limited computational resources. To address the need for efficient running on low-power devices while ensuring effective disease segmentation in complex scenarios, this paper proposes BHI-YOLO, a lightweight instance segmentation model based on YOLOv8n-seg. First, the Universal Inverted Bottleneck (UIB) module is integrated into the backbone network and merged with the C2f module to create the C2f_UIB module; this approach reduces the parameter count while expanding the receptive field. Second, the HS-FPN is introduced to further reduce the parameter count and enhance the model’s ability to fuse features across different levels. Finally, by integrating the Inverted Residual Mobile Block (iRMB) with EMA to design the iRMA, the model is capable of efficiently combining global information to enhance local information. The experimental results demonstrate that the enhanced instance segmentation model for strawberry diseases achieved a mean average precision (mAP@50) of 93%. Compared to YOLOv8, which saw a 2.3% increase in mask mAP, the improved model reduced parameters by 47%, GFLOPs by 20%, and model size by 44.1%, achieving a relatively excellent lightweight effect. This study combines lightweight architecture with enhanced feature fusion, making the model more suitable for deployment on mobile devices, and provides a reference guide for strawberry disease segmentation applications in agricultural environments.

Bacteria are essential in the maintenance and sustainment of marine environments (e.g., benthic systems), playing a key role in marine food webs and nutrient cycling. These microorganisms can live associated as epiphytic or endophytic populations with superior organisms with valuable ecological functions, e.g., seagrasses. Here, we isolated, identified, sequenced, and exposed two strains of the same species (i.e., identified as Cobetia sp.) from two different marine environments to different nutrient regimes using batch cultures: (1) Cobetia sp. UIB 001 from the endemic Mediterranean seagrass Posidonia oceanica and (2) Cobetia sp. 4BUA from the endemic Humboldt Current System(HCS) seagrass Heterozostera chilensis. From our physiological studies, both strains behaved as bacteria capable to cope with different nutrient and pH regimes, i.e., N, P, and Fe combined with different pH levels, both in long-term (12 days (d)) and short-term studies (4 d/96 h (h)). We showed that the isolated strains were sensitive to the N source (inorganic and organic) at low and high concentrations and low pH levels. Low availability of phosphorus (P) and Fe had a negative independent effect on growth, especially in the long-term studies. The strain UIB 001 showed a better adaptation to low nutrient concentrations, being a potential N₂-fixer, reaching higher growth rates (μ) than the HCS strain. P-acquisition mechanisms were deeply investigated at the enzymatic (i.e., alkaline phosphatase activity, APA) and structural level (e.g., alkaline phosphatase D, PhoD). Finally, these results were complemented with the study of biochemical markers, i.e., reactive oxygen species (ROS). In short, we present how ecological niches (i.e., MS and HCS) might determine, select, and modify the genomic and phenotypic features of the same bacterial species (i.e., Cobetia spp.) found in different marine environments, pointing to a direct correlation between adaptability and oligotrophy of seawater.

The current study applied a new approach for the interpolation and regionalization of observed precipitation series to a smaller spatial scale (0.125° by 0.125° grid) across the Upper Indus Basin (UIB), with appropriate adjustments for the orographic effect and changes in glacier storage. The approach is evaluated and validated through reverse hydrology, and is guided by observed flows and the available knowledge base. More specifically, the generated corrected precipitation data is validated by means of SWAT-modelled responses of the observed flows to the different input precipitation series (original and corrected ones). The results show that the SWAT-simulated flows using the corrected, regionalized precipitation series as input are much more in line with the observed flows than those using the uncorrected observed precipitation input for which significant underestimations are obtained.