Precipitation Estimates Research Articles

Exploring patterns in precipitation intensity–duration–area–frequency relationships using weather radar data

Abstract. Accurate estimations of extreme precipitation return levels are critical for many hydrological applications. Extreme precipitation is highly variable in both space and time; therefore, to better understand and manage the related risks, knowledge of their probability at different spatial–temporal scales is crucial. We employ a novel non-asymptotic framework to estimate extreme return levels (up to 100 years) at multiple spatial–temporal scales from weather radar precipitation estimates. The approach reduces uncertainties and enables the use of relatively short archives typical of weather radar data (12 years in this case). We focus on the eastern Mediterranean, an area of high interest due to its sharp climatic gradient, containing Mediterranean, semi-arid, and arid areas across a few tens of kilometres, and its susceptibility to flash flood. At-site intensity–duration–area–frequency relations are derived from radar precipitation data at various scales (10 min–24 h, 0.25–500 km2) across the study area, using ellipses of varying axes and orientations to account for the spatial component of storms. We evaluate our analysis using daily rain gauge data over areas for which sufficiently dense gauge networks are available. We show that extreme return levels derived from radar precipitation data for 24 h and 100 km2 are generally comparable to those derived from averaging daily rain gauge data over a similar areal scale. We then analyse differences in multi-scale extreme precipitation over coastal, mountainous, and desert regions. Our study reveals that the power-law scaling relationship between precipitation and duration (simple scaling) weakens for increasing area sizes. This finding has implications for temporal downscaling. Additionally, precipitation intensity varies significantly for different area sizes at short durations but becomes more similar at long durations, suggesting that, in the region, areal reduction factors may not be necessary for computing return levels over long durations. Furthermore, the reverse orographic effect, which causes decreased precipitation for hourly and sub-hourly durations, diminishes for larger areas. Finally, we discuss the effects of orography and coastline proximity on extreme precipitation intensity over different spatial–temporal scales.

A combined extended triple collocation and cumulative distribution function merging framework for improved daily precipitation estimates over mainland China

Accurate monitoring of daily precipitation is essential for many applications, such as simulation and prediction of watershed water-related disasters. However, precipitation data from satellite and gauge-based precipitation products face challenges in capturing true observations because of their large uncertainties. To improve daily precipitation estimates, this study proposed a new merging framework, ETCCCDF, that combines an extended triple collocation (ETCC) with maximized correlation and cumulative distribution function (CDF) matching with reduced bias. The ETCCCDF framework was applied to systematically integrate three independent precipitation products from SM2RAIN-ASCAT satellite soil moisture estimator, IMERG Early Run satellite retrieval, and CPC unified gauge-based analysis. A new merged precipitation product, with a fine spatiotemporal resolution of 0.25°/1 d, was developed and then thoroughly evaluated using 2304 stations over mainland China from 2007 to 2014. The results showed that the ETCCCDF framework significantly improved the precipitation representation in terms of the correlation, bias, and absolute difference, and it increased modified Kling-Gupta efficiency value relative to any of the parent precipitation products, demonstrating the effectiveness of the proposed merging framework. In addition, the ETCCCDF framework is more robust than the ETCC, triple collocation (TC) merging, and combined strategy of TC with CDF, in which CDF matching successfully corrects the precipitation biases in collocation fusions. The accuracy of the merged products in heavy rainfall estimation during Typhoon Morakot in 2009 and Typhoon Fitow in 2013 was further evaluated. The results confirmed that the ETCCCDF-merged product performed satisfactorily for heavy rainfall events and presented superior statistical scores. This study proves that the ETCCCDF framework is a promising solution for obtaining high-quality precipitation data, particularly for limited-gauge areas.

Evaluation of input variable selection methods in artificial neural networks for estimating missing daily precipitation

ABSTRACT This study evaluates different input variable selection (IVS) methods for precise daily precipitation estimation using artificial neural network (ANN) models. The effectiveness of the models is measured using the average Nash-Sutcliffe efficiency (NSE) from 50 runs per model. Initially, a multiple linear regression and a base model with all 16 parameters resulted in test NSE scores of 0.390 and 0.656, respectively. Subsequently, the self-organizing map (SOM), analytic hierarchy process, K-nearest neighbour, and K-means methods were used to select input variables of different types. The SOM method achieved its best performance with a test NSE of 0.692 when four variables were selected, while the other methods reached their peak performance with the same NSE of 0.718 when two similar variables were selected. This highlights the importance of IVS methods in using ANN models to overcome overfitting and achieve an efficient model for precise precipitation estimation.

Koreksi Bias Data Hujan Satelit Persiann Sebagai Alternatif Pos Stasiun Hujan dengan Pendekatan Quantile Mapping di Sub Das Brantas Hulu

The rainfall data is usually obtained from meteorological stations, which many sill observed manually in Indonesia and can lead to an observation error. Remote sensing rainfall data observed by satellite offer interesting alternatives to overcome the limitation of existing meteorological stations in Indonesia. It offers accuracy, spatial coverage, timeliness and cost effectiveness. However it contains bias if compared to land based station. This research will evaluate rainfall data observed in PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) in Brantas Hulu Watershed. The rainfall data of 13 years from 12 stations is evaluated to obtain area averaged rainfall using Thiessen Polygon method. Three datasets from PERSIANN, PERSIANN CCS and PERSIANN CDR are compared statistically with the area averaged area for daily, monthly and yearly rainfall. Bias correction is performed using Quantile Mapping to match the cumulative distribution curve from the area averaged rainfall. Verification of bias correction is performed using 2 years data from 2019 to 2020. Finally, the extreme rainfall analysis is performed to test the corrected data for design rainfall calculations. The correlation of daily data shows poor agreement for all three datasets with correlation coefficient below 0,40. The monthly and yearly data gives better correlation with coefficient above 0,80. It shows that for time series event, the satellite data gives poor correlation with the area averaged rainfall. However, quantile mapping correction gives consistent cumulative distribution agreement for correction and model verification. The verification analysis shows an increase in coefficient correlation from 0.387 to 0.43 (daily) and 0.887 to 0.915 (monthly). In addition, there is a decrease in normalized root mean square error (NRMSE) from 31.03% to 16.91% (daily) and 46.03% to 14.03% (monthly). A decrease in normalized mean absolute error (NMAE) occurred from 16.12% to 9.74% (daily) and 31.67% to 10.07% (monthly). Beside that, the results of extreme rainfall analysis produce values that are very close to the rain station. So it is found that the PERSIANN CCS quantile mapping regression model is the best in improving data quality.

Potential of Gridded Precipitation Estimate Products for Hydrological Modelling: The Case of Small Watersheds in Côte d'Ivoire

Rainfall information measured on the ground is not sufficiently representative the watershed scale in Côte d'Ivoire due to a low density of the rain gauge network. Many watersheds are ungauged or present a high rate of missing data on flow record since 1990s. The use of gridded precipitation estimate products is an alternative to remedy this lack of information. Therefore, the aim of this study was to evaluate the performance of CHIRPS, GPCC, MSWEP, PERSIANN-CDR, PERSIANN-CCS-CDR, TAMSAT, WFDE5-CRU, and WFDE5-CRU-GPCC precipitation estimates for hydrological modelling in Côte d'Ivoire over the period from 1984 to 2004. A continuous-based flow simulation was made for 14 small watersheds using GR4J lumped model. The methodology was based on a global analysis to identify the products that gave the best scores in both calibration and validation over the selected watersheds. KGE2 was the major metric considered along with NSE, VE, PBIAS, and ME. The performance of these gridded precipitation estimate products was then evaluated according to the different climatic zones of Côte d'Ivoire. The results showed that WFDE5-CRU-GPCC performed well in all climatic zones with KGE2 > 0.6 for at least the half watersheds for each climatic zone. CHIRPS and PERSIANN-CDR exhibited the best performance in the transitional tropical and mountainous zones (KGE2mean > 0,7), whereas WFDE5-CRU was good, mainly in the mountainous zone with KGE2 > 0,7 for 2 watersheds out of 3.

Evaluation of GPM IMERG Early, Late, and Final Run in Representing Extreme Rainfall Indices in Southwestern Iran

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Final Run (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001–2020. The Asfezari gridded precipitation data, which are developed using a dense of ground-based observation, were utilized as the reference dataset. The findings indicate that IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggests that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region.

Development of a multidecadal land reanalysis over High Mountain Asia

Anthropogenic and climatic changes affect the water and energy cycles in High Mountain Asia (HMA), home to over two billion people and the largest reservoirs of freshwater outside the polar zone. Despite their significant importance for water management, consistent and reliable estimates of water storage and fluxes over the region are lacking because of the high uncertainties associated with the estimates of atmospheric conditions and human management. Here, we relied on multivariate data assimilation (MVDA) to provide estimates of energy and water storage and fluxes that reflect the processes occurring in the region such as greening and irrigation-driven groundwater depletion. We developed and employed an ensemble precipitation estimate by blending different precipitation products thereby reducing the uncertainties and inconsistencies associated with precipitation in HMA. Then, we assimilated five variables that capture the changes in hydrology in response to climate change and anthropogenic activities. Overall, our results have shown that MVDA has allowed a better representation of the land surface processes including greening and irrigation-driven groundwater depletion in HMA.

Precipitation dynamics and its interactions with possible drivers over global highlands

The climate and ecosystem in the highland regions are fragile and vulnerable to climate change. This study used ERA5, CRU, and CHIRPS at 0.5° × 0.5° resolution to assess spatiotemporal precipitation trends over global highlands from 1981 to 2021. The Standardized anomaly index (SAI) is used to evaluate the comparative anomalies between the datasets, while the Mann-Kendall and Sen Slope estimator tests the precipitation trends. Empirical orthogonal function (EOF), pixel-wise correlation, and detrended cross-correlation (DCCA) were employed to investigate the dominant precipitation patterns and their relationships with regional climatic impacts, while the wavelet analysis determines ocean-atmospheric factors in the time-frequency domain. The SAI correlation strongly relates to CRU-CHIRPS for precipitation estimates in North America (NA) R2 = 0.84, Europe R2 = 0.71, and South America (SA), whereas CRU-ERA5 showed better results in the Asian highlands R2 = 0.29, highlighting regional differences in the precipitation dynamics. For precipitation, EOF1 showed a positive variance in most highland regions, but a negative variance prevailed in the Tibetan Plateau (TP), whereas EOF1–2 explained ∼32%, 33%, and 30% variance globally for ERA, CRU, and CHRIPS, respectively. A strong positive correlation is observed between precipitation and mean temperature (TMP) in North America (NA) and the European Alps. However, a significant negative correlation prevailed in the TP and some parts of NA, indicating that these factors can significantly influence the precipitation dynamics in the highlands. Additionally, Detrended Cross-Correlation Analysis (DCCA) has verified that there are positive correlations between precipitation and key climatic factors such as total column water vapor (TCWV), potential evaporation (PEV), and soil moisture (SM). In addition, the influence of ocean-atmospheric (interannual and decadal) coherence patterns originating from the Pacific and Atlantic Oceans indicates a significant impact on precipitation variability, especially in the Asian highlands. This study can contribute to a better understanding of mechanisms and serve as a reference for forecasting precipitation dynamics to develop corresponding strategies in global highlands.

Quantile mapping technique for enhancing satellite-derived precipitation data in hydrological modelling: a case study of the Lam River Basin, Vietnam

ABSTRACT Accurate precipitation is crucial for hydrological modelling in sparse gauge regions like the Lam River Basin (LRB) in Vietnam. Gridded precipitation data from satellite and numerical models offer significant advantages in such areas. However, satellite precipitation estimates (SPEs) are subject to uncertainties, especially in high variable of topography and precipitation. This study focuses on enhancing the accuracy of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Prediction Center morphing technique (CMORPH) using the Quantile Mapping (QM) technique, aligning the cumulative distribution functions of the observed precipitation data with those of the SPEs, and assessing the impact on hydrological predictions. The study highlights that the post-correction IMERG precipitation using QM performs better than other data sets, enhancing the hydrological model's performance for the LRB at different temporal scales. Nash–Sutcliffe efficiency values increased from 0.60 to 0.77, surpassing the original IMERG's 0.52 to 0.74, and correlation coefficients improved from 0.79 to 0.89 (compared with the previous 0.75–0.86) for hydrological modelling. Additionally, Percent Bias (PBIAS) decreased from approximately −1.66 to −2.21% (contrasting with the initial −20.22 and 4.6%) with corrected SPEs. These findings have implications for water resource management and disaster risk reduction initiatives in Vietnam and other countries.

A novel error decomposition and fusion framework for daily precipitation estimation based on near-real-time satellite precipitation product and gauge observations

Near-real-time satellite precipitation products (SPPs) possess inherent application prospects in the hydro-meteorological field due to their convenient acquisition and accessibility. Integrating gauge-based measurements with near-real-time SPPs is an effective approach for achieving precise spatial precipitation estimates at daily scale. This study proposed a newly developed error decomposition and fusion framework, named EDGWR, which integrates error decomposition and geographically weighted regression (GWR). The final merged product, denoted as IMERG-EDGWR, was obtained from the near-real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-E) product. IMERG-EDGWR was compared with the raw IMERG-E, near-real-time IMERG-L, post-real-time IMERG-F, global multi-source merged precipitation data (MSWEP), interpolated results (IDW and OK), and direct application of GWR outcomes (IMERG-GWR), utilizing the daily ground measurements from 12 meteorological stations located in the Yellow River source region (YRSR). The evaluation results from 2014 to 2018 revealed that IMERG-EDGWR exhibited significant enhancements over the raw IMERG-E, surpassed the research-level IMERG-F, and generally outperformed IDW, OK, MSWEP, and IMERG-GWR. Notably, IMERG-EDGWR enhances the detection of heavy precipitation events, refining estimates of both magnitude and frequency for precipitation over 25 mm. During the winter season, IMERG-EDGWR produced the most accurate precipitation estimates with notable improvement of precipitation detection capabilities. An experiment reducing input data by excluding gauge observations from the GPCC dataset tested the robustness of the EDGWR algorithm, confirming its superiority even with diminished input data. The merging framework proposed in this study constitutes an efficacious and implementable solution to enhance the accuracy of near-real-time SPPs and is expected to be implemented in different regions in future research.

Modelación de mapa de inundaciones durante el Huracán Alex: simulación usando precipitación multisensorial en la ciudad de Monterrey, México

Alex Hurricane was one of the most intense tropical cyclones in the North Atlantic, that caused fatalities and loses in the Northeast of Mexico due to the flash floods. Flood hazard mapping is a vital tool to assess inundation areas, which can be simulated using hydraulic and hydrologic models. This study describes the modelling of a flood event during Alex Hurricane in the Santa Catarina River Watershed, Northeast of Mexico, applying HEC-HMS and two dimensional (2D) HEC-RAS models forced with Multi Radar Multi Sensor - Quantitative Precipitation Estimation (MRMS-QPE). A HEC-HMS model was developed forced by (MRMS-QPE) as input to simulate discharges along the Santa Catarina River. The simulated discharges were introduced as border conditions along the mainstream of the Santa Catarina River inside a HEC-RAS 2D model to simulate a flood map along the mainstream of the Santa Catrina River. The observed against the simulated peak discharges achieved a R-squared of 0.97 and a Nash - Sutcliffe coefficient of 0.97. The observed against the simulated accumulated discharges achieved a R-squared of 0.99 and a Nash - Sutcliffe coefficient of 1.0. The observed against the simulated stages achieved a R-squared of 0.74 and, a Nash - Sutcliffe coefficient of 0.68. The use of HEC-HMS and HEC-RAS 2D models coupled with MRMS-QPE. shows that these models are user friendly to setup, the model has stability and the capacity to simulate flood maps along the whole mainstream of the Santa Catarina River with good results.

Has IMERG_V07 Improved the Precision of Precipitation Retrieval in Mainland China Compared to IMERG_V06?

Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users’ understanding of error characteristics and sources in IMERG.

Deep learning-based bias correction of ISMR simulated by GCM

Simulated data of atmospheric variables like precipitation is very important for climate science research, especially for understanding future scenarios. Such data is generated by global or regional climate models (GCM/RCM), for both past and future periods under different initial conditions. Unfortunately, these models frequently display biases in simulated precipitation data compared to ground observations, due to their inability to incorporate the entire physics of the process accurately. It is imperative to mitigate such biases using contemporary techniques to make the simulated data a valuable end product. Our aim in this research is to correct seasonal (from June to September) Indian Summer Monsoon Rainfall (ISMR) data as simulated by the Climate Forecast System (CFS) over the Indian subcontinent, with the Global Precipitation Climatology Project (GPCP) data serving as the ground truth reference. This period is important as more than one-third of India's annual rainfall occurs during these months. This study presents a novel Deep Learning (DL) based architecture known as the Convolutional Neural Network for Bias Correction (CNNBC) that aims to calibrate the model-simulated data with past observations, to address these biases while preserving the statistical properties and spatial correlations relationships among grid points and enhancing the spatial mean of precipitation estimates. To evaluate the effectiveness of our proposed method, we compare its performance to that of three other statistical and DL techniques: quantile mapping (QM), quantile delta mapping (QDM), and the SRDRN model. The comparative analysis demonstrates that the CNNBC model outperforms the other in terms of our task.

Characteristics of rainfall distribution induced by tropical cyclones using GSMaP data over the Vietnam region

ABSTRACT Tropical cyclones (TCs) contribute significantly to rainfall along Vietnam's coast, yet their complex precipitation structures remain poorly resolved, hindering forecast skill. This study analyzes TC rainfall distributions over the Vietnam East Sea from 2000 to 2020. The Global Satellite Mapping of Precipitation (GSMaP) product provides precipitation estimates with 0.1° resolution at hourly intervals, enabling detailed structural characterization. Rainfall features are analyzed across TC intensities, motion vectors, landfall locations, and interactions with cold surge (CS) air masses. Results show that total coverage differences are less significant than the intensity variations in narrow inner core rainbands. Asymmetric rainfall distributions concentrate in the front-right quadrant but shift after landfall. Northern Vietnam observes higher TC frequencies, but southern regions experience heavier extreme rains. Additionally, CS intrusions substantially intensify eyewall convection and redirect TC precipitation. These structural sensitivities visible in GSMaP observations elucidate the dynamics modulating TC rainfall. Characterizing multi-scale interactions and precipitation processes aids in forecasting and impact assessment for these high-risk storms with complex regional behavior.

Global-scale evaluation of precipitation datasets for hydrological modelling

Abstract. Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

Improving CMIP6 Atmospheric River Precipitation Estimation by Cycle‐Consistent Generative Adversarial Networks

AbstractGiven the important role of Atmospheric River precipitation (ARP) in the global hydrological cycle, accurate representation of ARP is significant. However, general circulation models (GCMs) demonstrate bias in simulating ARP. The target of this study is to quantify the performance of ARP intensity/frequency for CMIP6 simulations, and further to improve ARP estimation of CMIP6 using Cycle‐Consistent Generative Adversarial Networks (CycleGAN) with highlighting the more accurate ARP features under the global warming background. The findings of this study are as follows: (a) although ARP intensity/frequency in reserved‐optimal CMIP6 overall reproduces the observation, it is still underestimated at the stronger Atmospheric river (AR) scales, particularly for the AR highly active mid‐latitude regions. (b) The CycleGAN‐based bias correction approach markedly diminishes the bias of the CMIP6 simulations within most of the AR scales among both global and the four AR highly active regions. Moreover, the performance of the ARP in AR highly active regions is significant improvement, which is mainly due to the reduction of the bias at the strongest scale. (c) Relative to reference period (1986–2005), ARP intensity/frequency at the strongest scale increase notably under 3°C warming level, with an average value of 373.3% in intensity and 415.9% in frequency for global and the four key regions before correction, and the value is 451.9% and 492.5% after bias correction. The results illustrate that CycleGAN can effectively improve the ARP simulations of GCMs, and an early warning implies that future strong extreme ARP should potentially surpass the current expected.

Pre-processing satellite rainfall products improves hydrological simulations with machine learning

ABSTRACT A new pre-processing methodology for gridded Satellite Precipitation Products (SPPs) is developed to improve the performance of Machine Learning (ML) algorithms for runoff prediction. The developed approach was applied to capture the rainfall patterns, and to select relevant input data. This approach was tested using the FeedForward Neural Network (FFNN) and the Extreme Learning Machine (ELM) given their flexibility and ability in hydrological modelling. The methodology was tested in a semiarid transboundary watershed located in North Africa (Algeria, Tunisia) with the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPMIMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. The results demonstrate the effectiveness of the proposed approach using all employed SPPs. In terms of Nash-Sutcliffe efficiency, the suggested pre-processing technique improved the prediction ability of FFNN by 13%, and of ELM by 15%, which highlights how pre-processing techniques significantly enhance ML models with SPP data.

Evaluating flood hazards in data-sparse coastal lowlands: highlighting the Ayeyarwady Delta (Myanmar)

Coastal lowlands and river deltas worldwide are increasingly exposed to coastal, pluvial and fluvial flooding as well as relative sea-level rise (RSLR). However, information about both single and multiple flood-type hazards, their potential impact and the characteristics of areas, population and assets at risk is often still limited as high-quality data either does not exist or is not accessible. This often constitutes a main barrier for generating sound assessments, especially for scientific and public communities in the so-called Global South. We provide a standardised, integrative approach for the first-order assessment of these single and multiple flood-type hazards and show how this can be conducted for data-sparse, hardly accessible and inaccessible coastal lowlands such as the Ayeyarwady Delta in Myanmar by using only open accessible and freely available datasets of satellite imagery, global precipitation estimates, satellite-based river discharge measurements, elevation, land use, and population data. More than 70% of the delta, mainly used for agriculture, and about 40% of its present population are prone to flooding due to either monsoon precipitation and runoff, storm surge, and RSLR, or their combination, jeopardising food security and economic development in the region. The approach allows for the integration and combination of various datasets, combined in a highly flexible workflow that performs at low computational capacities, supporting the evaluation of flood-prone areas on regional and local scale for data-sparse coastal lowlands worldwide. It thereby allows to attribute different types of flood hazards, complements concepts of vulnerability and risk, and supports risk-informed decision making and development of effective multi-flooding adaptation strategies.

Raindrop size distributions of summer monsoon rainfall observed over Eastern India

The present study aims to quantify the raindrop size distributions (RSDs) of Indian summer monsoon (ISM) rainfall over a tropical eastern Indian station, Rourkela using five years (2018–2021) of Thies disdrometer measurements. The RSD measurements of ISM rainfall at Rourkela are segregated into active and break spells using the standardized rainfall anomalies over the study area. The RSDs characteristics of active and break spells are analyzed and their empirical relations, which are appropriate for precipitation estimation algorithms, are also reported. A significant annual and monthly variations in RSDs is noticed between the active and break spells of the ISM rainfall over the study region. The probability distribution function (PDF) RSDs parameters (rainfall rate, liquid water content, mass-weighted mean diameter, and normalized intercept parameter) demonstrated clear distinctions between active and break spells. The active spells showed higher rainfall amounts, large mass-weighted mean diameter, and smaller normalized intercept parameters over the break spells. A further segregation of active and break spells into stratiform and convective types revealed more large size drops in convective (stratiform) precipitations of active (break) spells. Occurrence of higher rainfall amounts and large size raindrops in the active spells over the break spells can be attributed to the presence of abundant moisture and convective activity in active spells. Furthermore, the radar reflectivity-rainfall rate (Z–R), shape and slope (μ–Λ), and mass-weighted mean diameter-rainfall rate (Dm-R) relations established for the active and break spells could aid in improving the precipitation estimations of over the study region.

Assessing drought in Turkish basins through satellite observations

AbstractDrought occurs when there is a sustained decrease in rainfall over an extended period, impacting the socio‐cultural and environmental aspects of humans and other living beings. The geographic distribution and timing of droughts play a crucial role in drought management and mitigation strategies. Identifying and predicting the onset of droughts in specific regions, especially in watershed areas, is a primary concern in the field of hydrology. This study focuses on how the spatiotemporal patterns of drought are developing in Turkish Basins using detailed data on Terrestrial Water Storage (TWS), precipitation, and temperature at the pixel level. GRACE (Gravity Recovery and Climate Experiment), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and WorldClim (World Climate) data sets are employed to assess long‐term changes of drought on a basin‐scale. Spatial analyses are conducted in a Geographic Information System (GIS) environment for the derivation of basinal monthly mean, minimum, and maximum statistics of TWS, precipitation, and temperature anomalies within Turkish Basins. Time series analyses are implemented to investigate the temporal evolution of droughts in these basins, for the basinal monthly mean, minimum, and maximum statistics obtained. The Mann–Kendall trend test and Pettitt change point detection tests are used to assess the statistical significance of the calculated trends and to expose the existence of any change point therein, respectively. The findings of the study indicate that Turkiye faces a significant risk of drought development in nearly all its basins, particularly after 2016. The GRACE dataset provides realistic insights into the temporal behaviour of hydrological droughts. PERSIANN is effective in identifying years with extreme meteorological conditions, and the standardized precipitation index (SPI) shows similar effectiveness, while they are ineffective in exposing significant trends due to the nature of the precipitation data. WorldClim data proves insufficient for modelling the temporal behaviour of droughts in these basins.