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Observation rainfall networks in developing countries like Nepal face many challenges (like availability and quality). Global Precipitation Products (GPPs) could be an alternative to Gauge-based Observed Rainfall (GOR) in hydro-meteorological studies. However, GPPs performance across the Himalayan regions still needs to be discovered and is influenced by several factors such as spatial and temporal resolutions, primary data sources, etc. We have comprehensively assessed the suitability of the latest GPPs using categorical and continuous variable performance metrics for the Gandak river basin in the Nepalese Himalayas. We then ranked GPPs for the first time using the Multicriteria Decision-Making technique. 11 out of 12 GPPs considered underestimated the annual rainfall in the basin. The performance of GPPs was also inconsistent for monthly/annual and daily timescales. At longer timescales, CHIRPS and IMERG_Final are better at representing the spatial and temporal pattern of the rainfall (spatial correlation of 0.78) and the least percentage bias (PBIAS

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98 °C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Critical applications of satellite data products include monitoring vegetation dynamics using vegetation indices such as Normalized Difference Vegetation Index (NDVI) and thermal characteristics such as Land Surface Temperature (LST) variations across different spatial and temporal scales. One of the major problems with passive remote sensing satellite data products is cloud and shadow cover that void the information from a specific image. The cloud cover problems further amplified during monsoon seasons as the formation of clouds are more frequent during these seasons. The present study proposes temporal aggregation of images and developing Harmonic Analysis of Time Series (HANTS) and Pixel-wise Multiple Linear Regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series of L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI data sets. The calculated root mean square (RMSE) values were ranging from 0.004 to 0.05 and 1.05°C to 3.11°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) calculated at province level show that most of the western provinces showed a healthy vegetation condition than other provinces in the northeast of Thailand.

The objective of the study was to find the significant impact of season on the outdoor gamma dose rate including statistical analysis. Outdoor gamma dose rates were studied for 52 locations of the Balod district, Chhattisgarh, India, in summer and winter. Statistical analysis confirmed that there was no significant difference in dose rate between summer and winter. Geology of the area was the most important factor, and meteorological parameters had less influence; consequently, in this investigation, the impact of these parameters were checked through linear regression, cluster, principal component and factor analysis.

Discrete groundwater level datasets are interpolated often using kriging group of models to produce a spatially continuous groundwater level map. There is always some level of uncertainty associated with different interpolation methods. Therefore, we developed a new trend function with the mean groundwater level as a drift variable in the regression kriging approach to predict the groundwater levels at the unvisited locations. Groundwater level data for 29 observation wells in Adyar River Basin were used to assess the performance of the developed regression kriging models. The cross-validation results shows that the proposed regression kriging method in the spatial domain outperforms other physical and kriging-based methods with R2 values of 0.96 and 0.98 during pre-monsoon and post-monsoon seasons, respectively.

The groundwater quality with respect to uranium and its seasonal variation in Kabirdham district, Chhattisgarh, India was assessed. The uranium content of the collected water samples of the study area varied from

This study has been carried out to determine the uranium concentration associated with physicochemical parameters and water quality index during pre-monsoon and post-monsoon of Bemetara district to correlate the quality of water for public health. The correlation matrix has been applied for the determination of the correlation value to find out the relationship of uranium with water quality parameters. The uranium levels in water samples range from 1.15 to 83.5 µg/L and 0.68 to 96.08 µg/L during pre-monsoon and post-monsoon, respectively. The uranium concentration of few samples exceeds the safe limit of 30 µg/L prescribed by World Health Organization 2011. A positive correlation of uranium concentration with total hardness and total dissolved solids during both monsoons has been observed. The lifetime cancer risk varied from 0.07 × 10−6 to 5.06 × 10−6 and 0.04 × 10−6 to 5.82 × 10−6 in pre-monsoon and post-monsoon period, respectively, which is lower than the maximum permissible limit (

The present study highlights uranium levels, associated health effects, and physicochemical properties of ground water samples collected from Rajnandgaon District of Chhattisgarh State, Central India. Uranium concentrations of ground water samples are found to exceed than 30 µg/L (United States Environmental Protection Agency and World Health Organisation recommended limit) at two villages in summer and at one village in winter while it is found higher than 60 µg/L (Atomic Energy Regulatory Board, Department of Atomic Energy, India recommended limit) at only one village in summer. Correlation analysis between uranium and physicochemical parameters suggest that Ca2+ ion and total alkalinity play an important role in uranium contamination of ground water.

This study proposes a methodological framework to evaluate and rank climate models based on extreme climate indices of precipitation and temperature for impact studies in seven sectors: Cryosphere, Energy, Forestry/GHGs, Health, AgricultureFood Security, Disaster Risk Reduction (flood and drought), and Water ResourcesHydrology. The ranking of the climate models is based on their performance in sector-relevant extreme climate indices. Extreme climate indices for observed and climate models' datasets for a historical period and overall performance statistics were used to create a payoff matrix. The payoff matrix then served as an input to a multi-criteria decision-making process to rank the climate models for each of the climate indices. The final sector-specific ranking was achieved by averaging the ranks obtained in the sector-relevant indices. The developed methodology is demonstrated with an application to the Songkhram River Basin (Thailand), a sub-basin of the Mekong. Eighteen CMIP6 GCMs are used for the proposed evaluation and ranking processes and four performance statistics were used. Weights to each of the four performance statistics were determined using the entropy method. Compromise programming was applied to rank the GCMs based on the distance technique. The results indicate that the six best performing models are different for different sectors, with the GFDL_CM4 model common in all the seven sectors considered in the study. KACE1_0_G, GFDL_ESM4, GFDL_CM4, MRI_ESM2_0, and ACCESS_ESM1_5 models are the five top (ranked 1 to 5 respectively) performing models for the Water ResourcesHydrology sector. The developed framework is generic and can be applied to any region or basin; at the same time, it can also provide researchers and policymakers with specific information on best-performing models for particular sectors.

Since precipitation and temperature are the major driving factors for the fragility of the Himalayan ecosystem and resources, it is crucial to understand the changes in temperature and precipitation under climate change scenarios to take appropriate adaptation measures. This work, therefore, examined the changes of precipitation and temperature under all Representative Concentration Pathway (RCP2.6, 4.5, 6.0, and 8.5) scenarios of climate change in the Sikkim Himalayan region of India. The datasets from two different global circulation models (GCMs) have been used to analyseSikkim’s;s daily precipitation and temperature for the near, mid, and far future. The linear scaling bias correction method (LCBCM) was employed to remove the bias because of a significant difference between the raw and observed monthly climate data for both GCMs.The predictions based on bias-corrected GCMs data under all RCP scenarios indicated that Tmax and Tmin are projected to increase in the near, mid and far futures. The projection of CSIRO_MK 3.6 model indicated that the increase in Tmax from near to far ranges from 1.0 to 1.5°C, 0.8 to 2.8°C, 0.4 to 2.3°C and 0.5 to 4.2°C under the four scenarios, respectively. Similarly, the projected to increase in Tmin from near to far ranges from 1.5 to 2.0°C, 1.1 to 3.5°C, 0.5 to 3.0°C and 0.8 to 4.5°C in RCP 2.6, 4.5, 6.0 and 8.5 scenarios, correspondingly. The results also showed that in climate change scenarios, the rate of precipitation is expected to increase, which could lead to the rise of snowmelt and flooding in the near future. This study is recommended to increase the number GCMs in future studies to reduce the uncertainty in future prediction and utilize the LCBC method for bias correction.

The impacts of flooding in our societies needed most cost-effective adaptation measures that must be selected by considering site condition. In this paper, flood mitigation measures in a micro catchment has been performed by coupling Hydrologic and Hydraulic model developed by Hydrologic Engineering Center-(HEC). The objective of this study is to suggest flood protection measures in Champa pooling station, Taga village of Chhattisgarh state in India. Quantification of peak flood discharge has been done using HEC-HMS (Hydrologic Modelling System) this model is coupled with HEC-RAS (River Analysis System) model to generate flood inundation map. Further resulted map is calibrated using field observation data. Based on inundation pattern and site suitability, different structural measures were adopted in the hydraulic model and simulated. Simulation results were verified for inundations. Several alternatives were checked and most feasible and economical mitigation method was prescribed for implementation to avoid flooding in future at study area.

The evaluation of groundwater quality is an imperative part of water security and plays a significant role in ensuring its sustainable development . The present paper reveals the quality of groundwater of Kanker district situated in Chhattisgarh state, India. Groundwater samples from different regions were collected to observe the seasonal variation in uranium concentration. The spatial distribution of major parameters and hydrochemical facies was described by Geographic Information System based maps, correlation coefficient , box plot, and piper diagrams. The uranium concentration ranges from 0.5 to 69.9 μg/L and 0.5–87.9 μg/L with the mean value of 4.8 μg/L and 6.8 μg/L in winter and summer season respectively. The uranium concentrations for the samples taken from one area in winter and two areas in summer have been found to be slightly higher than the permissible limit of 60 μg/L recommended by the Atomic Energy Regulatory Board. The seasonal variation of uranium in groundwater depends upon the rainfall recharge and precipitation conditions of the geological formation and the environment. In certain regions; it is observed that the total hardness, calcium, magnesium and total alkalinity (bicarbonate) were found higher than the acceptable limit prescribed by the Bureau of Indian Standards. Mixed type behaviors of tested samples were showed by the piper diagrams. Uranium exhibits a strong correlation with magnesium (r > 0.7) in both the seasons.

Increase in population and rapid urbanization are two main challenges to urban water management, especially for cities in developing countries like India. The assessment of changes in catchment surface runoff due to urbanization is critical for water resource planning and management. Uncertainty in rainfall and changing landuse pattern results from urbanization is difficult to correlate with present changing surface runoff conditions. This study has shown an approach to find a relation between these three by using a statistical term dynamic degree with application of the well-established tool, Curve Number (SCS-CN) method to observe surface runoff over the study area in conjunction with Geographic information system and remote sensing. This study assesses changes in runoff characteristics of Raipur catchment which has been delineated from a smallest sub-basin of Mahanadi River in Chhattisgarh, India. Pixel-based Runoff depth of each sub-catchment was estimated by incorporating digital elevation model, rainfall data, and Land Use/Land Cover (LULC) information for the years 1971–2015. Spatio-temporal variation in each sub-catchment has been computed and significant change in runoff has been observed. The motivation of this study aims to understand and to study the changing nature of urban growth pattern/sprawl, to quantify surface imperviousness and changes in surface runoff characteristics.

After becoming a capital city of Chhattisgarh state in 2001, the city Raipur in India has been rapidly growing in terms of population as well as areal expansion. With the process of urbanization and alteration of the natural drainage system, the existing stormwater drainage system became insufficient to drain the area, resulting in a higher risk of flooding. In this study, the effect of urbanization on the runoff generation and peak flow and their impact on the stormwater drainage system have been investigated. Digital land-use data from aerial photograph, satellite images and Differential Global Positioning System (DGPS) survey were used to detect and analyse the shift in land-uses since the evolution of Raipur city. For the same periods, total generated runoff and peak flow as well as flood-prone locations along the drainage network have been simulated. The results showed an increase in build-up area of 99.2, 22.0 and 0.6%, respectively. Agricultural areas increased by 210.8, 157.5 and 0.6%, respectively. Forests and tree covers almost completely disappeared. The higher share of build-up areas led to an increase in total runoff generation and peak flows. The maximum runoff increased by 87.92 × 106 L and maximum peak flows changed from 11.03 to 14.09 m3/s. The results show significant increase in runoff and peak discharge in the study area due to human intervention, since Raipur city started altering natural drainage system. Moreover, the model can be used for future flood forecasting in Raipur city, and the results from this study can be utilized to develop flood mitigation measured as best management practices in Raipur.