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The study analysed the changes in the rainfall, extreme indices and their future projections over Rajasthan state based on observed gridded datasets (1976–2005) and simulated climate models. The climate projections from two global circulation models (HadCM3 and GFCM21) are used in statistical downscaling tool LARS-WG5 (Long Ashton Research Station-Weather Generator) to generate future precipitation. Further, the changes in precipitation pattern are investigated for the baseline period and the future periods based on seven extreme precipitation indices. Three future periods are used for the analysis i.e., early century period 2011–2040 (2025s), a mid-century period of 2041–2070 (2055s) and a late-century period of 2071–2100 (2085s). The study area is classified in three regions based on elevation range i.e., region 1 (< 250 m), region 2 (251–350 m) and region 3 (350–1700 m). Based on results, it is observed that there is a possible decrease in monsoon precipitation at many grid points for all the three future periods. The maximum decrease in rainfall (−142 mm) is observed in Banswara for the period 2041–2070, while the maximum increase (37 mm) is found in Alwar along with Churu 1 and Ganganagar during the period 2071–2100. Consecutive dry days (CDD) is predicted to increase in the west and south-west direction, while it shows decrease values in eastern and central part of the study area with the maximum value in Ajmer district. The pattern in PRCPTOT revealed maximum negative change (− 90 mm) in southern parts, and maximum positive change in the northern regions (62.2 mm) in Churu 1. Further, R20 and RX5day are projected to decrease in all three regions in future with several magnitudes. For RX1day, a maximum positive change is observed in eastern parts (Jhalawar, Sawai Madhopur) and negative changes in the southern part of the study area. In case of R95p index, both positive and negative changes are observed. Similarly, the SDII indicates a positive change in 2011–2040 and negative changes for the remaining two future periods. Finally, SDII shows maximum positive changes in the south and southeastern regions (Jhalawar, Chittaurgarh) and positive changes in various parts with spatial and temporal changes. The results will help water resources planner to understand the change pattern in various precipitation indices in water scarce state of India.

Compound extreme events, such as flash drought, have received wide attention in recent decades due to their far-reaching effects on the ecosystem. Thus, a new concept of flash drought has begun to spread globally in the scientific community, and it is continuously being developed. This study offers for the first time an overview of the global trends in flash drought research from 2000 to 2021. The analysis was based on the Scopus database in order to investigate the publication trends, contributions, collaborations, and challenges on a global scale. Furthermore, collaboration analysis was performed to detect collaboration networks within the flash drought research field. A total of 76 studies were published during the study period. The research output grew exponentially with an average growth rate of 30% per year. The challenging issues in the field of flash drought research are the search for appropriate definition of flash drought, the development of effective early warning systems and the scarcity of high-resolution data. By presenting the details of the evolution of this new conceptualization in drought research, our study highlights the main pathways of scientific progress and stimulates future research.

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydrometeorological datasets. This study addresses the performance analysis of reference evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state-of-the-art Hamon’s and Penman-Monteith’s methods were utilized for the ETo estimation in the Northern India. The performance indices such as bias, root mean square error (RMSE), and correlation (r) were calculated, which showed the values 0.242, 0.422, and 0.959 for NCEP data (without downscaling) and 0.230, 0.402, and 0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with bias, RMSE, and correlation values of 0.154, 0.348, and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less computationally expensive and easily available than performing WRF simulations.

Accurate estimation of precipitation at the global scale is of utmost importance. Even though satellite and reanalysis products are capable of providing high-spatiotemporal-resolution estimations at the global level, they are associated with significant uncertainties that vary with regional characteristics and scales. The uncertainties among precipitation estimates, in general, are much higher at the sub-daily scale compared to daily, monthly, and annual scales. Therefore, evaluating these sub-daily estimations is of specific importance. In this context, this study explores the diurnal cycle of precipitation using all the currently available space-borne and reanalysis-based precipitation products with at least hourly resolution at the quasi-global scale (60° N–60° S), i.e. Integrated Multi-satellitE Retrievals for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Center Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and ECMWF Reanalysis v5 (ERA5). The diurnal variability of precipitation is estimated using three parameters, namely, precipitation amount, frequency, and intensity, all remapped at a common resolution of 0.25° and 1 h. All the estimates represent the spatiotemporal variation across the globe well. Nevertheless, considerable uncertainties exist in the estimates regarding the peak precipitation hour, as well as the diurnal mean precipitation amount, frequency, and intensity. In terms of diurnal mean precipitation, PERSIANN shows the lowest estimates compared to the other datasets, with the largest difference observed over the ocean rather than over land. As for diurnal frequency, ERA5 exhibits the highest disparity among the estimates, with a frequency twice as high as that of the other estimates. Furthermore, ERA5 shows an early diurnal peak and highest variability compared to the other datasets. Among the satellite estimates, IMERG, GSMaP, and CMORPH exhibit a similar pattern, with a late-afternoon peak over land and an early-morning peak over the ocean. Overall, it emphasizes the need to integrate diverse datasets and exercise caution when relying solely on individual precipitation products to ensure a thorough understanding and precise analysis of global precipitation patterns.

Analysis of spatial and temporal changes of long-term precipitation and extreme precipitation distribution at a local scale is very important for the prevention and mitigation of water-related disasters. In the present study, we have analyzed the long-term trend of 116 years (1901–2016) of precipitation and distribution of extreme precipitation index over the Kosi River Basin (KRB), which is one of the frequent flooding rivers of India, using the 0.25° × 0.25° resolution gridded precipitation datasets obtained from the Indian Meteorological Department (IMD), India. The non-parametric Mann–Kendall trend test together with Sen’s slope estimator was employed to determine the trend and the magnitude of the trend of the precipitation time series. The annual and monsoon seasons revealed decreasing trends with Sen’s slope values of −1.88 and −0.408, respectively. For the extreme indices viz. R10 and R20 days, a decreasing trend from the northeastern to the southwest part of the basin can be observed, whereas, in the case of highest one-day precipitation (RX1 day), no clear trend was found. The information provided through this study can be useful for policymakers and may play an important role in flood management, runoff, and understanding related to the hydrological process of the basin. This will contribute to a better understanding of the potential risk of changing rainfall patterns, especially the extreme rainfall events due to climatic variations.

Accurate precipitation estimation at high spatial and temporal resolutions is essential for hydrological and meteorological applications, especially in regions experiencing water resource degradation. This study presents a robust non-parametric framework for disaggregating coarse-resolution satellite precipitation data to finer scales, using a hybrid model that integrates Extreme Gradient Boosting (XGBoost) with multivariate spatio-temporal fuzzy clustering. Eight clusters were delineated based on Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation and Shuttle Radar Topography Mission (SRTM) elevation data, with one representative station per cluster used for training and validation, and an additional 19 stations employed solely for independent validation. We downscaled 255 months (June 2000–September 2021) of IMERG precipitation data from 11 to 1 km spatial resolution across the Czech Republic. The disaggregated precipitation demonstrated marked accuracy improvements when evaluated against observed station data, with R 2 values ranging from 0.63 to 0.85, RMSE between 17.43 mm and 32.41 mm, NSE from 0.39 to 0.82, and KGE spanning 0.67 to 0.86-indicating a significant reduction in the bias inherent in the original IMERG data. The proposed methodology achieved (1) enhanced agreement between disaggregated and observed monthly precipitation, (2) significant improvement in IMERG data accuracy at finer scales, and (3) demonstrated operational potential in regions with sparse ground-based observations. This approach offers a promising solution for generating reliable, high-resolution precipitation datasets in data-scarce environments, with broad applicability in global hydrological and meteorological modelling.

The water cycle in Czechia has been observed to be changing in recent years, with precipitation and evapotranspiration rates exhibiting a trend of acceleration. However, the spatial patterns of such changes remain poorly understood due to the heterogeneous network of ground observations. This study relied on multiple state-of-the-art reanalyses and hydrological modeling. Herein, we propose a novel method for benchmarking hydroclimatic data fusion based on water cycle budget closure. We ranked water cycle budget closure of 96 different combinations for precipitation, evapotranspiration, and runoff using CRU TS v4.06, E-OBS, ERA5-Land, mHM, NCEP/NCAR R1, PREC/L, and TerraClimate. Then, we used the best-ranked data to describe changes in the water cycle in Czechia over the last 60 years. We determined that Czechia is undergoing water cycle acceleration, evinced by increased atmospheric water fluxes. However, the increase in annual total precipitation is not as pronounced nor as consistent as evapotranspiration, resulting in an overall decrease in the runoff. Furthermore, non-parametric bootstrapping revealed that only evapotranspiration changes are statistically significant at the annual scale. At higher frequencies, we identified significant spatial heterogeneity when assessing the water cycle budget at a seasonal scale. Interestingly, the most significant temporal changes in Czechia occur during spring, while the spatial pattern of the change in median values stems from summer changes in the water cycle, which are the seasons within the months with statistically significant changes.

In-depth understanding of malaria transmission dynamics in a region can be assessed by identifying the vector populations of infected mosquitoes, Anopheles spp. (Diptera: Culicidae), and by quantifying the infectiousness extent. In this study of malaria transmission dynamics relating to vector incrimination in three districts of Odisha, India – hyperendemic Kalahandi, mesoendemic Bargarh, and hypoendemic Cuttack – we examined how quality and quantity of plasmodial infection rates vary among mosquito species and their organs and among districts. The minimum infection rate of Plasmodium falciparum sporozoite for Anopheles culicifacies was highest in Kalahandi and nil in Cuttack. However, for A. annularis, the rate was highest in Cuttack, followed by Kalahandi and Bargarh. In Kalahandi, the gland-positive rate was higher for A. culicifacies, but in Cuttack, it was higher for A. annularis. To quantify plasmodium infection in salivary glands and guts of anopheline vectors, quantitative polymerase chain reaction was performed. We observed higher parasite density in glands than in guts of vector mosquitoes in the three districts. The findings demonstrate that plasmodium-infected vectors preferentially bind sporozoites to salivary glands in the three study areas. The results improve understanding of infection status within malaria vectors and how parasite population density may affect transmissibility, which will, in turn, provide baseline evidence to develop intervention measures.