Water is a very vital needed substance in order to maintain the important activities of humans. However, contaminated water can transmit diseases such as typhoid, dysentery, diarrhea, cholera and polio. Pakistan is a highly affected country due to the scarcity of safe and healthy water sources. The current study mainly focuses to determine water quality in the selected area. For this purpose, an integrated surface water quality index (SWQI) based on 18 hydrochemical parameters is employed. SWQI substantially correlates with pH, EC, SO₄, HCO₃ and heavy metals. Therefore, these parameters are utilized in the calculation of SWQI. The results of SWQI show that about 69.23% of samples are ‘very good quality’. Moreover, 30.77% of the total samples are of poor quality and are identified as unsuitable for drinking. Further, Ordinary kriging (OK) and Universal kriging (UK) are used to predict the SWQI at unobserved locations and map the SWQI in the study area to explore the spatial distribution. The prediction performance of the two kriging methods is assessed through cross-validation in terms of root mean square prediction error (RMSPE). It is concluded that the prediction performance of the UK is better than OK in terms of RMSPE.

Drought is one of the natural hazards with potentially significant impacts on society, economy, and other natural resources over the globe. However, the understanding of drought characteristics and its persistence can significantly help to reduce the potential impacts of drought. Moreover, the knowledge about the spatiotemporal pattern of seasonal drought frequency and drought persistence is important for water resource management, agricultural development, energy consumption, and crop yields. Therefore, the present study is employed to examine the seasonal drought frequency and drought persistence in the region. In this regard, the standardized precipitation index (SPI) at the three‐month time scale was used to determine meteorological drought. Furthermore, the logistic regression model is used to calculate the odds and probability of drought persistence from one season to the next for the selected stations by identifying the spatial pattern of seasonal drought frequency and persistence. The potential of the current analysis is validated on six selected stations of the northern area of Pakistan. The outcomes related to the current analysis provide the basis for taking more considerations on early warning systems and help to make the valuable decision for water resource management and agriculture sectors in Pakistan.

Tellus A: Dynamic Meteorology and Oceanography is an open access journal focusing on all aspects of atmospheric dynamics related to Earth science processes. Tellus A, along with its sister journal Tellus B: Chemical and Physical Meteorology, are international, peer-reviewed journals of the International Meteorological Institute in Stockholm, an independent not-for-profit body integrated into the Department of Meteorology at the Faculty of Sciences of Stockholm University, Sweden. The two journals serve an international community of researchers, policymakers, managers, media and the general public. Together they promote the exchange of knowledge about meteorology from across a range of scientific sub-disciplines. Topics covered in Tellus A include:dynamic meteorology | physical oceanography | data assimilation techniques | numerical weather prediction | climate dynamics | climate modelling and observation. Types of papers accepted include original research papers, review articles, brief research notes, Letters to the Editor, and special issues and conference proceedings (from time to time). The journal operates a single-blind peer-review policy. All published articles are made freely and permanently available online through gold open access publication with a CC BY license. Read the Guidelines for Authors for more information on how to submit your manuscript for review.

This research aims to find the best model for predicting the Standardized Precipitation Index (SPI) and the Standardized Precipitation and Evapotranspiration Index (SPEI) in the future. The study estimates SPI and SPEI at different time scales, ranging from 1 to 48 months. To predict drought, Random Forest (RF) models are used based on lag times of 1–12 months for the estimated drought indices (SPI and SPEI). Accuracy and error metrics like Nash–Sutcliffe efficiency (NSE), root‐mean‐square error (RMSE), producer accuracy (PA), user accuracy (UA), and Choen’s kappa are used to assess the models. The NSE values for the SPI at varying time scales (1, 3, 6, 9, 12, and 48 months) indicate that Bahawalpur, Rawalpindi, Murree, and Sargodha stations have the highest NSE values of 0.1148, 0.5868, 0.8302, 0.9196, 0.9516, 0.9801, and 0.9845, respectively. Similarly, the RMSE values for SPI at these time scales show that Bahawalpur, Rawalpindi, Murree, and Sargodha stations have the lowest RMSE values of 0.6187, 0.6094, 0.4091, 0.2865, 0.2275, 0.1594, and 0.1106, respectively. The NSE and variance explained for SPI and SPEI at a 1‐month time scale were found to be poor, but they improved as the time scale increased. On the other hand, the RMSE values for SPI and SPEI at a 1‐month time scale were found to be high but decreased with longer time scales. The stations that exhibit the highest values of the NSE for the SPEI at various time scales (1, 3, 6, 9, 12, and 48 months) are Rawalpindi, Jhelum, Murree, Mianwali, Rawalpindi, and Sargodha, respectively. These stations have NSE values of 0.0784, 0.6074, 0.8353, 0.9225, 0.9542, 0.9760, and 0.9896, respectively. Similarly, the stations with the lowest RMSE values for SPEI at these time scales are Sargodha, Murree, Murree, Murree, Murree, and Sargodha, with RMSE values of 1.002, 0.5909, 0.3993, 0.2626, 0.2132, 0.1546, and 0.0941, respectively. The analysis reveals a distinct pattern indicating that stations situated at higher elevations exhibit a more pronounced correlation between the SPI and SPEI indices in comparison to stations at lower elevations. Notably, Murree, Jhelum, Sialkot, and Rawalpindi demonstrate a statistically significant and strong correlation between the SPI and SPEI. Overall, the results show that SPEI is a better drought index for classifying and monitoring meteorological drought in stations with lower elevations. However, in stations with higher elevations, the selected indices provide similar information, but with some differences.

The study mainly focuses on heavy metals’ quantification, investigation of physicochemical characteristics, source apportionments, human health risk assessment, and connectivity between the water of alpine glacial lakes and rivers. In this regard, twenty-six water samples are taken from sixteen lakes and two lakes-fed rivers. The physiochemical parameters, particularly heavy metals are quantified using atomic absorption spectrophotometer. The Pearson correlations are used to find out the relationship between various heavy metals and physicochemical parameters. The multivariate methods, Principal component analysis (PCA), and Hierarchal cluster analysis (HCA) are conducted for contamination source apportionment. In the findings of the current analysis, the mean concentration of Lead (Pb 0.51), Cadmium (Cd 0.04), Nickel (Ni 0.06), Chromium (Cr 0.08), Iron (Fe 0.06), and Manganese (Mn 0.09) is the most common heavy metal contaminants found in both lakes and river water. Copper (Cu 0.00) and Arsenic (As 0.001) concentrations are lower in both lakes and rivers. Other tested physicochemical characteristics, such as pH, Hardness, Chemical oxygen demands (COD), total dissolved solids (TDS), dissolved oxygen (DO) chloride (Cl^-), sulfate (SO₄), nitrate (NO₃), and bicarbonates (HCO₃) are within the maximum permissible limits of World Health Organization’s (WHO).