Capturing intense rainfall bursts: a remote sensing perspective on temporal and spatial resolution effects

The study aims to analyze spatio-temporal variations in rainfall data over Indravati River basin, India. Three rainfall data sets, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), India Meteorological Department (IMD) grid, and IMD gauge were used. Data from 2001 to 2013 were analyzed for three time scales, namely, daily, monthly, and annual. Analysis showed good correlation between IMD gauge and TMPA grid rainfall at monthly time scale, poor correlation is observed at daily and annual time scales. Mann–Kendall (MK) trend test reveals a significant increasing trend of IMD gauge and IMD grid data, whereas TMPA rainfall is free from trends at the majority of stations for daily time scale. Nevertheless, both IMD grid as well as TMPA grid rainfall can be considered as a better representative of rainfall, since it is attained from observed rainfall data over the country. The Pettitt and standard normal homogeneity tests show that TMPA rainfall has a more non-homogeneous nature, whereas IMD grid rainfall and IMD gauge rainfall data are homogeneous. Overall, the trend and homogeneity analysis indicate that TMPA grid and IMD grid rainfall is in line with IMD gauge data, however IMD grid rainfall has the edge over TMPA grid data.

Long‐term variations in the frequencies of different rainfall events during summer monsoon season (June through September) are analysed over a period of 38‐years (1980–2017) using the simulations of weather research and forecasting (WRF) model evaluated against the India Meteorological Department (IMD). Further, we have divided the study period into two sub‐periods such as before (1980–1998), and after the mega ENSO event (1999–2017). Our analysis suggests the WRF exhibits good skill in capturing the increased trends in the frequency of heavy and very heavy (extreme) rainfall events during the recent epoch (1999–2017).We have assessed the skill of WRF in capturing the observed probability distributions of daily rain rates over different sub‐regions of India. Our results depict that the downscaled products of WRF captured well the extreme rainfall as observed in IMD, signifying that this dynamically downscaled model with continuous 36‐hr re‐initialization makes it suitable to simulate extreme rainfall events compared to light rainfall event. However, our analysis reports that there is a slight overestimation in WRF rainfall and fails in capturing the light rainfall events compared to IMD. The overestimation of rainfall by WRF possibly be due to increased pressure gradients and enhances the convection in the upper streams and associated rainfall. Our study emphasizes that the variability in summer monsoon rainfall during the recent period is due to the changes in the monsoon circulations, particularly monsoon low‐level jet that leads to alter the moisture budget in terms of moisture convergence and transport. Moreover, the variability in frequencies of rainfall events are analysed for strong La Niña/El Niño years and observed an increase in extremes over major part of India during strong La Niña except over parts of Central India and NEI where strong El Niño dominates; which is well captured by the WRF model.

Grazing intensity, characterized by high spatial heterogeneity, is a vital parameter to accurately depict human disturbance and its effects on grassland ecosystems. Grazing census data provide useful county-scale information; however, they do not accurately delineate spatial heterogeneity within counties, and a high-resolution dataset is urgently needed. Therefore, we built a methodological framework combining the cross-scale feature extraction method and a random forest model to spatialize census data after fully considering four features affecting grazing, and produced a high-resolution gridded grazing dataset on the Qinghai–Tibet Plateau in 1982–2015. The proposed method (R2 = 0.80) exhibited 35.59% higher accuracy than the traditional method. Our dataset were highly consistent with census data (R2 of spatial accuracy = 0.96, NSE of temporal accuracy = 0.96) and field data (R2 of spatial accuracy = 0.77). Compared with public datasets, our dataset featured a higher temporal resolution (1982–2015) and spatial resolution (over two times higher). Thus, it has the potential to elucidate the spatiotemporal variation in human activities and guide the sustainable management of grassland ecosystem.

To understand the characteristics of severe floods under global climate change, we created a design hyetograph for a 100-year return period. This incorporates a modified ranking method using the top 10 extreme rainfall events for present, near-future, and far-future periods. The rainfall data sets were projected with a general circulation model with high spatial and temporal resolution and used with a flood model to simulate the higher discharge peaks for the top 10 events of each term in a local watershed. The conventional-like ranking method, in which only a dimensionless shape is considered for the creation of a design hyetograph for a temporal distribution of rainfall, likely results in overestimates of discharge peaks because, even with a lower peak of rainfall intensity and a smaller amount of cumulative rainfall, the distribution shape is the only the factor for the design hyetograph. However, the modified ranking method, which considers amounts of cumulative rainfalls, provides a discharge peak from the design hyetograph less affected by a smaller cumulative rainfall depth for extreme rainfall. Furthermore, the effects of global climate change indicate that future discharge peaks will increase by up to three times of those of Present-term peaks, which may result in difficult flood control for the downstream river reaches.

Particularly the Liguria region in Northern Italy is highly affected by soil erosion processes. This study was conducted in the Portofino promontory in eastern Liguria, to predict potential annual soil loss using the Revised Universal Soil Loss Equation (RUSLE). Moreover, we evaluate the relative accuracy of the predictions at detailed scale, using high resolution spatial information for model calibration. The RUSLE factors were calculated for the study area based on terrain survey data and rain gauge measurements. The results were plotted on a 1:10,000 scale soil erosion map and subsequently compared with the European soil loss estimation method (RUSLE2015) developed by the European Joined Research Centre. This study shows that the RUSLE2015 model can be applied in a typical Mediterranean environment such as the Portofino promontory. However, the accuracy of the single factors we calculated using high resolution data sets might improve the results substantially and thus, also model efficiency.

The Awash basin of Ethiopia experiences frequent climate extremes-related disasters. Climate change is contributing to frequent flooding in different parts of the basin. This study explores the drivers of extreme rainfall, the multi-causality and consequences of flooding, governance, and policy implications using a combination of interdisciplinary approaches. The multi-dimensional perspective includes analysis of hydroclimatic variables at the basin level including global drivers, flood characterization, and understanding of affected communities at different parts of the basin through examining the experiences of different water and land users. The study covered urban and rural areas, small-scale agricultural, and pastoral or agropastoral catchments. To obtain diversified perspectives consultation with various basin stakeholders was conducted. By considering the 2020 extreme wet season the study aims to contribute to future management practices that might adapt to extremes and associated floods. The results show that recent rainfall extreme during the summer of 2020 occurred in unusual parts of the basin. Compared to the 1981-2010 baseline the lower part of the basin had a rainfall anomaly of more than 50%. Moreover, antecedent rainfall conditions during April-June contributed to saturating the soils as the months before July were wetter than the base period on average by 62%. The soil moisture content conditions were wetter than average from 10 to 40% in these antecedent months. Unusual rainfall in terms of location, magnitude, and timing is the major cause of flooding in the cases of 2020.  First, the western part of the lower basin received higher rainfall than normal earlier in the season. Then, in the later part of the season, the upper basin received high rainfall that increased the amount of water in upstream rivers which contributed to the massive flooding in the lower basin. This characterizes the 2020 flood occurrences by early onset and delayed recession. The extreme rainfall collided with weak La Nina and positive Western Indian Ocean as global drivers. There are other contributing factors that exacerbate the cause and impacts of flooding on communities. This includes challenges in river morphology, flood forecasting, reservoir management, and differences between private investors and local vulnerable communities in managing extreme cases. For instance, the Awash River broke off its normal course during 2020 extreme rainfall. Uncontrolled water diversion by farmers for irrigation created new water pathways. Low quality of engineering structures e.g., dikes failed to prevent extreme floods. Land use changes, such as urbanization and deforestation increased erosion and blocked drainage ways. Lack of coordination among institutions, weak collective action and governance aspects are exacerbating factors of climate extreme impacts on vulnerable communities. A holistic approach to solving the devastating impact of climate extremes provides better understanding of the multi-causality and multi-dimensionality of water-related risks, to support implementation of adaptive management and coordination approaches in monitoring human-physical systems interactions across sectors.

Determination of the reliable estimate of risk associated with hydrometeorological extremes over a region requires discerning information on spatial variability of the associated at-site statistics/parameters. Extreme rainfall at finer spatio-temporal resolution allows for improved analysis of spatial variability, as local-scale statistical similarities (LSS) and heterogeneities are disclosed. The knowledge of LSS facilitates the use of information on regional spatial variability (in lieu of complex at-site spatial variability) for risk analysis. In addition, it is established in literature that geographical features influence the occurrence of extreme rainfall over an area. For a subcontinent with complex non-uniform patterns of geographical features, the regional spatial variability may be influenced by the geographic composition. To quantify this regional spatial variability, statistically homogenous regions need to be deciphered. Most studies on the regionalization of sub-daily extreme rainfall (SDER) are limited to a smaller spatial extent, and none was focused on a subcontinent. Furthermore, there are no prior studies focused on the analysis of regional spatial variability of SDER. To study the role of geography in modulation of the regional spatial variability of mesoscale SDER, the present study proposes a framework. It involves (i) dividing the study area into subareas based on geographical features, as they are deemed to influence the occurrence of extreme rainfall, (ii) the delineation of each subarea into statistically homogenous SDER regions using a novel regionalization technique, (iii) quantification of the regional spatial variability of SDER in each subarea using the delineated regions and a proposed novel index, and (iv) identifying the role of geographic features in modulating the regional spatial variability. The efficacy of the proposed framework is demonstrated by application to Indian subcontinent (66.5-100o E, 6.5-38.5o N) considering 0.12o resolution SDER data corresponding to different durations (1,2,3,6 and 12-hour) for the period 1981-2020. The data were prepared by bias correcting the 0.12o resolution NCMRWF IMDAA hourly gridded rainfall (at 20,717 grids) to be consistent with the widely used 0.25o resolution IMD (India Meteorological Department) daily rainfall. The Indian subcontinent is divided into seven subareas based on geographic features. On application of the framework, it has been found that the regional spatial variability of SDER in a subarea is regulated by its geography and that of its neighbouring subareas. Insights are obtained on the effect of factors such as orography and coastal width on regional spatial variability of SDER. The study is of significance as the knowledge discerned on potential covariates/attributes has wide applications including identification of similar extreme rainfall sites for regional frequency analysis for extreme rainfall and risk assessment of consequent floods at ungauged/sparsely gauged hotspots such as water control (e.g., dams, barrages, levees) and conveyance infrastructure (culverts) in river basins under various climate change scenarios. The inherent physio-geographic features of the catchment may not be enough to analyze the similarity with neighbouring catchments. The boundary conditions around the catchment also plays a role.

Cloud feedback mechanisms remain a major source of uncertainty in weather prediction, particularly over monsoon dominated regions like India where rainfall exhibits strong spatial and temporal variability. This study analyzed Indian Meteorological Department (IMD) daily gridded rainfall, and European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) reanalysis datasets for the monsoon months June-September (JJAS) during 2014–2023, to examine how cloud microphysical properties and processes modulate extreme heavy rainfall across the three monsoon convective zones of India - Western Ghats, Central India, and North-East India. Rainfall categorization, cloud hydrometeor profiles, and cloud microphysical process estimated through Tao’s algorithm were used to investigate the vertical profiles of cloud liquid, ice, and rainwater content, and their linkages with dynamical and thermodynamical conditions. The present work shows pronounced spatial heterogeneity in cloud microphysical properties and processes, with distinct variation in magnitude and vertical distribution, indicating region-specific cloud microphysical characteristics and processes. These differences translate into region-specific microphysical process rates (rain condensation, evaporation, and deposition) which directly influence the formation and intensity of Extreme Heavy rainfall (EH). Deeper convection over North-East region is shown to result in stronger condensation and EH. However, over Western Ghats a higher amount of lower-level liquid water content is found to drive EH. Furthermore, over Central India the high ice water content found in the upper troposphere during EH events is found to form due to aggregation rather than vapor deposition. Such contrasting cloud structures explain the uneven spatial distribution of extreme rainfall across India. This study underscores that spatial heterogeneity in cloud microphysics is fundamental to understanding extreme heavy rainfall variability and strengthening climate resilience in monsoon-affected regions.

Rainfall data are important inputs for effective planning, designing and operation of water resources projects. A well-designed optimal rain-gauge network is being difficult task for hydrologist, as comprises rainfall values. Rain-gauge network provides rainfall measurement with high degree of accuracy at specific location, but most cases, the rain-gauge is too sparely distributed to capture accurate high spatial and temporal rainfall data. Therefore, a number of guidelines for setting up rain-gauge network from India Meteorological Department (IMD) and World Meteorological Organization (WMO), still the question is being arisen, where to effectively deploy new or change the current position of RG network. A new rain-gauge network set-up over different part is challenging job for hydrologist and meteorologist. As per the IMD and WMO guidelines, area with high elevation has a greater number of rain-gauge as compared with plain area. Thus, the study demonstrates the designing of the rain-gauge network in different locations and estimating the rainfall with desire accuracy. The results obtained optimal rain-gauge network design through combined use of added and redundant station is equally applicable to any other catchment area. The study represents the guidelines and rainfall estimation utilizing different methods and techniques in a case-to-case basis with respect to basins of Odisha which will help in better flow computation and flood forecasting.

The topography significantly influences local climate precipitation and the intensity of precipitation events, yet the specific differences in its elevational effects require further understanding. This study focuses on precipitation in Zhengzhou City, Henan Province, utilizing hourly data and a topographic elevation precipitation increment model to assess the impact of topography on local climate precipitation and extreme heavy rainfall events. The results indicate that the daily precipitation attributed to topographic elevation in Zhengzhou in July was 0.21 mm, accounting for 4.9% of the total precipitation. In the extreme heavy rainfall event on 20 July 2021 (“7.20” event), the precipitation due to topographic elevation reaches 48.7 mm, constituting 15.8% of the total precipitation. Additionally, numerical simulations using the Weather Research and Forecasting (WRF) model for the 20–21 July 2021 rainfall event in Zhengzhou show that the WRF model effectively reproduces the spatiotemporal characteristics of the precipitation process. The simulated topographic elevation precipitation intensity is 49.8 mm/day, accounting for 16.6% of daily precipitation, closely resembling observational data. Sensitivity experiments further reveal that reducing the heights of the Taihang Mountains and Funiu Mountains weakens the low-level easterly winds around Zhengzhou. Consequently, as the center of the heavy rainfall shifts northward or westward, the intensity of topographic elevation-induced precipitation decreases to 7.3 mm/day and 12.9 mm/day.

Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in Nash-Sutcliffe Efficiency Coefficient (NSC) values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.

Reliable rainfall estimates are essential for understanding extreme rainfall patterns and supporting climate adaptation in vulnerable areas. In Ethiopia’s Jemma sub-basin, limited surface weather observations challenge accurate characterization of rainfall extremes, necessitating the use of satellite and reanalysis products. We evaluated five rainfall datasets, CHIRPS v2.0, TAMSAT v3.1, ERA5, MERRA v2, and MSWEP v2.8, for their ability to replicate daily rainfall, detect rainfall events, and represent rainfall indices across sub-tropical and temperate agro-ecological zones (AEZs). A point to pixel evaluation approach was used, and statistical metrics including continuous metrics such as root mean square error (RMSE), percent bias (PBIAS), Kling Gupta efficiency (KGE), and correlation coefficient (R), and categorical metrics such as probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and frequency bias index (FBI) were used to assess performance and assign ranks using a comprehensive rating index (CRI). Results show that MSWEP v2.8 is most reliable for daily rainfall across both AEZs, while CHIRPS v2.0 excels in capturing extreme rainfall in the sub-tropical AEZ. MSWEP v2.8 also performs well in the temperate AEZ but shows limitations for indices like R20mm in sub-tropical AEZ. ERA5 and MERRA v2 exhibit higher biases, lower resolution, and weaker correlations. These findings highlight the importance of selecting rainfall products based on indices and regional conditions. MSWEP v2.8 and CHIRPS v2.0 are recommended for flood and drought forecasting, water management, and agricultural planning. Integrating multiple datasets with local observations and establishing additional meteorological stations can further enhance rainfall assessments and modeling.

Rainfall erosivity is a critical parameter for soil degradation assessment. It is especially important in areas prone to soil erosion due to frequent or intense rainfall events. Rainfall erosivity is typically quantified through the EI30 index, which combines the rainfall's kinetic energy (E) with its maximum 30-minute intensity (I30). Rainfall data with high temporal resolution (e.g., 5-minute) are often limited in availability, particularly across large spatial scales. Gridded rainfall products usually provide half-hourly or hourly data and are frequently used despite the potential biases they introduce into erosivity calculations. In Austria, the introduction of INCA (Integrated Nowcasting through Comprehensive Analysis) has provided an opportunity to enhance rainfall erosivity studies. INCA offers rainfall data at a 15-minute temporal and 1-kilometer spatial resolution. In our study, INCA’s accuracy and limitations in capturing erosive rainfall events, especially extreme events, are carefully evaluated against high-resolution in-situ rainfall station data. Understanding the degree of underestimation or overestimation in EI30 calculations is crucial for applying INCA data in erosion simulations and according to soil conservation strategies. This study focuses on the Main Agricultural Production Zones (MAPZ) in Austria. The study evaluates the performance of INCA in calculating erosivity compared to a dense network of 5-minute resolution rainfall stations. It also investigates the occurrence probability of extreme erosive events using a probabilistic approach, providing insights into long-term erosion risks and regional differences in erosivity patterns. Eventually, the study examines the impact of temporal resolution on erosivity estimates, assessing biases introduced by coarser resolutions (15- and 30-minute) for both mean and extreme rainfall events. Results indicate that INCA overestimates 8.1% of the total event number but underestimates 3.1% of the total rainfall erosivity, with the largest under- and overestimation in east of Austria. On the other hand, the mean annual maximum EI30 was underestimated by 13.6%, and the south showed the most considerable underestimation. It was found that INCA can detect highly erosive events occurring in the in-situ datasets. The underestimation of EI30 sources from the temporally smoothened peak I30 rather than the E of the rainfall events. Long-term extreme EI30 were analyzed using the Generalized Extreme Value (GEV) distribution, revealing that EI30 values increase with longer return periods (e.g., 50 years) and that the southern region exhibits the largest EI30 values, indicating a greater risk of extreme erosive events. On the other hand,  INCA may emphasize more recent, potentially intense rainfall trends, leading to larger return levels. The impact assessment by coarser temporal resolutions on EI30 confirms that the underestimation substantially increases with lower temporal resolution, primarily due to I30 rather than E. Eventually, a 15-minute temporal resolution dataset may lead to acceptable underestimations across our investigated MAPZ; underestimations ranged from 2.8–8.5% in event numbers and 1.0–10.0% in total rainfall erosivity at 15-minute resolution and from 10.2–33.7% and 5.2–26.6%, respectively, at a 30-minute resolution. The results of this study highlight the potential value of INCA data as a practical source for rainfall erosivity assessments, particularly in regions with limited high-resolution rainfall measurements.

This work investigates the usage of both rain gauge measurements and radar data of a 500-year event that happened in Innsbruck in July 2016. The occurring intensities varied strongly, e.g. they could be classified into a return period of 500 and 25 years within a distance of 2 km. The underlying research question is, whether a change of model performance can be expected due to a better representation of the spatial variability of this specific rain event. This further leads to the question, whether a spatially accurate representation of rain events is still decisive for model performance when modelling such intense rainfalls. For high return periods, other impacts may gain importance instead. Performance assessment is done by comparing simulated water levels to in-sewer measurements. Results show, that measured peak intensities and runoff volumes are significantly higher for radar measurements than for gauged precipitation which leads to a significant overestimation of water levels. Consequently, radar rainfall input did not improve the model performance. A reason could be found in the missing radar image at peak time, different temporal resolutions of the data sets, in the accuracy of the measuring methods during such heavy rainfall intensities or even in the underlying ideas of the initial model establishment.

Efficient disaster response planning is critical for minimizing human loss and economic impact during natural disasters such as hurricanes, earthquakes, and floods. However, inherent uncertainties—such as disaster intensity, affected population distribution, infrastructure damage, and environmental factors—complicate effective response strategies. This paper explores advanced stochastic programming methodologies as powerful mathematical tools for managing these uncertainties in disaster logistics and resource optimization. We synthesize and extend recent approaches, including multi-stage adaptive frameworks for hurricane response, two-stage optimization for evacuation routing, integrated facility location and casualty management in earthquake scenarios, and risk-based reservoir management during flooding events. The proposed unified stochastic optimization framework leverages rigorous mathematical modeling and operations research principles to enhance decision-making adaptability, optimize resource allocation, and improve overall emergency response effectiveness. Computational insights, real-world applicability, and practical recommendations are discussed, providing clear pathways for implementing these methods into actionable disaster preparedness and response policies