Temporal Distribution Of Rainfall Research Articles

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Influence of Rainfall Patterns on Rainfall–Runoff Processes: Indices for the Quantification of Temporal Distribution of Rainfall

To understand the influence of rainfall patterns on rainfall–runoff processes, we propose two indices: skewnessPEAK (skewp), representing the relative timing of peak rainfall, and the normalized root-mean-square error peak (NRMSEp), which quantifies the concentration of rainfall near the peak. By analyzing approximately 25,000 rainfall scenarios, we examined the relationship between these indices and peak flood discharge in the rainfall–runoff process. The analysis revealed that peak flood discharge positively correlates with the NRMSEp, indicating that concentrated rainfall near the peak substantially increases discharge. Conversely, a negative correlation with skewp suggests that earlier peak rainfall reduces discharge. These insights were synthesized into a three-dimensional solution space providing a comprehensive framework for predicting how variations in rainfall distribution affect flood discharge. The findings underscore the importance of incorporating these indices into real-time flood forecasting models and urban flood risk management strategies.

Rainfall water collection and irrigation via stone bud and karren on karst rocky desertification slopes: Application and benefit analysis

Lack of surface water is a key factor leading to agricultural drought in karst regions and exacerbates the karst rocky desertification, which is characterized by high rate of exposed bedrock. With the characteristics of impermeability, runoff collection, and wide distribution, stone bud and karren (SBK), a unique karst landscape type composed of exposed bedrock and gullies, has a great potential to solve water shortage in agricultural irrigation, especially in the rain-rich karst regions of Guizhou Province, China. Using unmanned aerial vehicle images to identify SBK and measured rainfall–runoff data for validation, we applied SBK as a basic unit to harvest rainfall water on rocky desertification slopes and analyzed the benefits of rainfall water collection and irrigation via SBK. The results showed the following: (1) Karrens on rocky desertification slopes could be identified using a method coupling terrain opening difference and depression filling, and the simulated runoff on SBK could achieved the average NSE and R2 of 0.82 and 0.86, with RMSE and RSR less than 0.03 m3 and 0.51, respectively. (2) The exposed bedrock utilization rate and rainfall water collection coefficient of SBK were 0.62 and 0.55, respectively, such that the area ratio of exposed bedrock to the cultivated land meeting irrigation water demand reached 1:3.1, 1:3.7 and 1:7.0 under 90 %, 80 % and 50 % possibilities of irrigation. (3) Implementing SBK for rainfall water harvesting and irrigation could efficiently avoid crop failure in the karst regions where crops faced drought within only one week after rainfall, and was at an affordable cost to local farmers with limited environmental damage. The use of SBK to harvest rainfall water could also alleviate surface water shortages in karst regions caused by problems such as uneven temporal distribution of rainfall and engineering water shortages. Consequently, SBK demonstrate a strong application potential to alleviate agricultural drought in karst rocky desertification regions.

Effect of Anti-Transpiration and Potassium on Water Use Efficiency and Water Consumption under Water Stress Conditions to Climate Change Resistance

Climate change impact serious threats to global food security due to changes in water requirements resulting from the variation and instability of the spatial and temporal distribution of rainfall and the lack of water availability. Therefore, our research aims to use anti-transpiration agents that reduce plant water consumption, thus providing adequate amounts of water for agriculture in arid areas. A field experiment was conducted under water stress conditions during the winter seasons 2022-2023 and 2023-2024. The study included three levels of water stress, which are depletion of 40, 55 and 70% of the available water at a depth of 20 cm for the first three irrigations and a depth of 30 cm for the remaining irrigations and for the three treatments in succession. Anti-transpiration was added from different sources, which are kaolin, paraffin wax, silicon and potassium, in addition to the comparison treatment (spraying with water only). The results showed significant differences between the depletion treatments, as the 40% depletion treatment of the available water was significantly superior in most of the characteristics of the crop and its components, as the amount of increase in the grain yield was 62.24 and 64.22% for the two seasons in succession, compared to the 70% depletion treatment of the available water, which gave the lowest averages for all the studied characteristics. The irrigation treatment after depleting 40% of the available water was characterized by the best water use efficiency of 1.60 and 1.44 kg m-3 for the two seasons respectively, compared to the treatment of depleting 70% of the available water, which gave the lowest water use efficiency of 1.14 and 1.02 kg m-3 for the two seasons respectively. Spraying with anti-transpiration and potassium led to a significant increase in the yield and all its components compared to the comparison treatment (spraying with water only), which gave the lowest averages for all the studied traits, as spraying with potassium and kaolin gave a significant increase in most of the studied traits. Also, anti-transpiration and potassium, represented by potassium and kaolin, had a significant effect on water use efficiency, as the increase amount when spraying with potassium was 45.87 and 46.93% for the two seasons respectively compared to the comparison treatment (spraying with water only). As for the actual water consumption, we find an increase in the values ​​of water consumption and the number of irrigations in the comparison treatment (spraying with water only) compared to the anti-transpiration and potassium treatments, as the anti-transpiration and potassium provided water at a depth of 550, 414 and 405 m3 ha-1 for the first season and 615, 375 and 393 m3 ha-1 for the second season for the depletion treatments of 40, 55 and 70% of the available water, respectively, compared to the comparison treatment (spraying with water only). This increase is not small when applied to large areas and providing additional agricultural areas that can be invested. These findings suggest that targeted use of these treatments can effectively mitigate the effects of water stress, improving wheat production and resource efficiency in water-limited environments.

PyMTRD: A Python package for calculating the metrics of temporal rainfall distribution

Temporal rainfall distribution facilitates the understanding of rainfall patterns at various time scales, extreme events, and corresponding water resources implications. Researchers have developed various metrics of temporal rainfall distribution but there exist no easy-to-use software packages for calculating these metrics. To address this gap, we developed the PyMTRD package, which can be conveniently used to calculate the metrics of temporal rainfall distribution and conduct rainfall pattern analysis. The metrics calculated in the package included rainfall intensity, rainfall frequency, consecutive dry days, Gini index, unranked Gini index, wet-day Gini index, precipitation concentration index, dimensionless seasonality index, and seasonality index. This paper documented our Python software development, which included the architecture design, the Application Programming Interfaces design and algorithms for calculating each metric, and also the point and global scale applications.

Evaluation of weather generator tools to estimate climate conditions in different agro ecological zones of North Shewa, Ethiopia

The information on climatic condition is difficult to obtain, expensive, and time-consuming so as to make timely decision on agricultural activities. As a scientific effort, this study was conducted to assess the temporal changes and trends of rainfall and temperature, to know the performance of weather generator (WG) tools in capturing the temporal and spatial distribution of rainfall, maximum temperature (Tmax) and minimum temperature (Tmin) and to evaluate the performance of WG in simulating the observed rainfall, Tmax and Tmin by using statistical methods. Mann–Kendall's trend analysis revealed that rainfall had non-significant (P < 0.05) decreasing trends, while Tmax and Tmin had an increasing significant (P < 0.05) trends at all stations. NASA POWER data followed by NewlocClim exactly capture the temporal changes of rainfall, Tmax, and Tmin in all stations except Debre Birhan and Mehal Meda. NewlocClim well captures rainfall at Alem ketema, while NASA well simulates rainfall at Debre Birhan and Majete stations. However Had-GEM2-ES, MRI-CGCM3, and CSIRO-Mk3.6.0 were not handling the spatial variability of observed rainfall at all stations. Similarly, some WGs showed moderate to good performance in capturing the spatial distributions of Tmax and Tmin. The smallest RMSE and CV, the highest R and d values were observed in NASA POWER and NewlocClim for rainfall, Tmax and Tmin. Therefore, NASA and NewlocClim are more accurate with goodness of fit to estimate rainfall, Tmax and Tmin in most of the stations to access weather data for ungagged stations for timely and reasonable decision-making on agriculture.

Enhancing Urban Flood Forecasting: Integrating Weather Forecasts and Hydrological Models

Precipitation data in urban hydrological models are derived from an ideal stormwater model, which has some uncertainties and limited prediction times. Therefore, to reliably forecast urban flooding, prolong prediction time periods, and better support associated research in urban flood forecasting, a combination of weather forecasts and urban hydrology is necessary. By applying comprehensive cloud microphysical schemes in the Weather Research and Forecasting (WRF) model to the predecessor torrential rainfall associated with Typhoon Khanun (2017), this study evaluated different configurations of atmospheric-hydrological simulations based on the WRF model and InfoWorks ICM. Results showed that the microphysics scheme could significantly affect spatial and temporal distributions of the simulated torrential rainfall. Generally, the combination of WRF and NSSL schemes produced better performance. Applying the NSSL scheme to the WRF model and combining it with the InfoWorks ICM system can reproduce torrential rainfall and urban flood formations.

Establishing resilience-targeted prediction models of rainfall for transportation infrastructures for three demonstration regions in China

Rainstorm is one of the global meteorological disasters that threaten the safety of transportation infrastructure and the connectivity of transportation system. Aiming to support the resilience assessment of transportation infrastructure in three representative regions: Sichuan–Chongqing, Yangtze River Delta, and Beijing-Tianjin-Hebei-Shandong, rainfall data over 40 years in the three regions are collected, and the temporal distribution of rainfall are analyzed. Prediction equations of rainfall are established. For the purpose of this, the probabilistic density function (PDF) is assigned to the rainfall by fitting the frequency distribution histogram. Using the assigned PDF, the rainfall data are transformed into standard normal space where regression of prediction equations is performed and the prediction accuracy is tested. The results show that: (1) The frequency of rainfall in the three regions follows a lognormal distribution based on which the prediction equations of rainfall can be established in standard normal space. The error of regression shows no remarkable dependence on self-variables, and the significance analysis indicates that the equations proposed in this paper are plausible for predicting rainfalls for the three regions. (2) The Yangtze River Delta region has a higher risk of rainstorm disaster compared to the other two regions according to the frequency of rainfall and the return period of precipitation concentration. (3) Over the period of 1980–2021, the Sichuan–Chongqing region witnessed an increase in yearly rainfall but a decrease in rainstorm disasters, whereas the other two regions experienced a consistent rise in both metrics.

Temporal and spatial distribution of extreme rainfall from tropical storms in the Gulf of Mexico from 1979 to 2021

Temporal and spatial distribution of extreme rainfall from tropical storms in the Gulf of Mexico from 1979 to 2021

Role of rainfall temporal distribution on effective infiltration in the loess slope and prediction model

Rainfall is one of the main causes of landslides, particularly in the Loess Plateau. When rainfall occurs, rainfall thresholds can be used to predict whether it will cause landslides in monitoring and early-warning system. However, most rainfall thresholds are established based on constant rainfall processes. Therefore, this study takes the temporal distribution of rainfall into account in rainfall threshold studies and proposes four well-delineated temporal distribution types and an innovative three-step model. Rainfall thresholds are derived from 45 months of rainfall data and slope moisture data obtained through in-situ monitoring of the loess slope in Yan'an city, northwest China. Our analysis focuses on effective rainfall infiltration rather than shallow landslides as the monitoring index for thresholds. The results revealed a total of 189 rainfall events over the course of 45 months. Among these, 167 were classified as ineffective rainfall events with an infiltration depth of <10 cm, while 22 were categorized as effective rainfall events with an infiltration depth exceeding 10 cm, accounting for only 11.6% of the total. In this study, the temporal distribution of rainfall was classified into four types: transitory, constant, unimodal, and multimodal. Analysis of the monitoring data showed that transitory rainfall events were all effective, constant rainfall events were mostly effective (> 76%), unimodal rainfall events were 86.7% effective, and all multimodal rainfall events were effective, with the deepest infiltration depth. A three-step method was employed to establish thresholds for different types of rainfall. Firstly, cumulative rainfall exceeding 34 mm at a single time point was considered the most effective rainfall. Secondly, multimodal or constant rainfall with a constant value of Ac > 3.5 mm/3 h and a peak value of Ap > 9.5 mm/3 h was deemed the most effective rainfall. Lastly, a temperature difference Tr during rainfall below 7.7 °C was indicative of mostly ineffective rainfall. The model demonstrated an accuracy of 96%, as validated by existing monitoring data in the nearby loess area. This model has been demonstrated to be superior to previous model and is currently suitable for application in loess regions of China.

Rainfall forecasting in arid regions in response to climate change using ARIMA and remote sensing

Climate changes in increasing at alarming rates which caused outward changes in the hydrological cycle parameters such as temperature and precipitation. The spatial and temporal distribution of rainfall has changed all over the world. Rainfall forecasting is considered an important tool that could help decisionmakers for managing water resources. Syria is facing several drought events that may affect its water resources. Due to the lack of available rainfall data from land stations in Syria after the civil war started in 2010 till now, one of the aims of this study is to depend on rainfall data from satellite images after correcting these data using the available land stations data. This study also aims to assess rainfall trends in Syria due to climate change and forecasting rainfall. The rainfall data were collected from 71 land stations from 1991 to 2009. Satellite images from Climate Data Record (CDR) were downloaded from 1983 to 2020 and ArcMap was used to extract rainfall data at locations of land stations. The extracted rainfall data from satellite images were corrected by land stations data from 1991 to 2009. Then Statistical Package for the Social Sciences (SPSS) program with Auto-Regressive Integrated Moving Average (ARIMA) models are used to fit and forecasting rainfall in Syria. The rainfall trends at the studied stations were analyzed for 40 years (1983–2022) and a forecast of one year is conducted. The results showed decreasing trends at all the stations that lead to a decrease the quantity of water received every year which could affect the water resources in Syria and agriculture and food security will be also affected. The results are consistent with Intergovernmental Panel on Climate Change (IPCC) reports that concluded a decrease in rainfall at southern and eastern Mediterranean regions. The results of the current study could help in the management of water resources in Syria and forecasting using ARIMA is recommended for rainfall predictions.

Rainfall prediction for data scares areas using metrological satellites in the case of the Lake Tana sub-basin, Ethiopia

Abstract In African nations with complex topographies, alternative rainfall estimation methods such as satellites are crucial. This study is aimed at predicting the spatial and temporal distribution of rainfall in the Lake Tana sub-basin from 1990 to 2020. A satellite-based rainfall estimate of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) was used with the same spanning period (1990–2020). The validation process employs point-to-pixel analysis, comparing CHIRPS estimates with observed data at specific gauge stations. The findings showed that CHIRPS had well-estimated rainfall incidence in the highland areas and significantly overestimated it in the lowland areas. The Mann–Kendall trends for January, June, and August indicate decreasing trends, while the Bega and spring seasons show notable declines. Regression analysis reveals a non-significant decrease in annual rainfall with the highest rainfall in the summer and relatively dry winters. In addition, the coefficient of variation value of 26.37% suggests a moderate level of variability around the mean annual rainfall. In conclusion, the CHIRPS satellite exhibited varied performance across the Tana Sub-basin, with site-specific discrepancies and notable inaccuracies at certain stations. The study underscores the importance of considering local factors and topography in satellite-based rainfall assessments, providing valuable insights for agricultural planning in the region.

Impacts of rainfall on peat fire during the dry season and wet dry season on degraded tropical peatland in South Sumatra, Indonesia

Peat fires on cleared and drained tropical peatlands continue to cause tremendous damage to their ecological functions, contribute to global warming, and reduce in health of local communities, where the fire incidents in 2019 and 2020 correlated with weather conditions. The research aims to determine and clarify the correlation between rainfall dynamics and the occurrence and severity of peat fires in degraded tropical peatlands in South Sumatra, Indonesia. The research was conducted by recording rainfall Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) data, surface and peat fuel, and peat fire area primer data from 2019 to 2020. The results show that two types of weather correlate with the size of the burned area in the Ogan Komering Ilir (OKI) regency. Temporal rainfall distribution, quantity, and duration a determining factor for the transition of surface fires to peat fires, especially in the role they play in the hydrology and moisture conditions of peat and surface fuels. This study will present the effect of rainfall in the dry and wet dry seasons on peatland fires and the extent of peat fires that occurred in two years.

Optimal allocation of soil and water resources in agriculture under the total regional evapotranspiration indicator: A case study in Guangping, China

There is a mismatch between crop water demand, the spatial and temporal distribution of natural rainfall, surface water sources, and arable land resources in the North China Plain. This has been compensated for by massive groundwater exploitation, and as a result, ground water levels have continuously declined and groundwater landing funnels have formed in some areas. Consequently, there is an urgent need for the rational allocation of soil and water resources. In this study, regional soil types and the corresponding crops in Guangping County, Hebei Province, China, alongside the regional annual average total water consumption were used to determine reasonable allocations for crop planting structures, with an aim of maximizing the water utilization efficiency and economic benefits while also achieving a regional groundwater dynamic equilibrium. Daily meteorological data from 1973 to 2021, including precipitation, temperature, sunshine hours, wind speed, and humidity, as well as crop data, including irrigation systems and crop growth status, were collected. Water consumption models for eight soil types and seven typical crops forming 56 calculation partitions were established to obtain annual average water consumption values for the different crops. The maximum economic benefit and water-use efficiency were then used as the objective functions and total water consumption as the control condition for the non-dominated sorted genetic algorithm-Ⅱ (NSGA-II), to optimize the allocation of soil and water resources in the region; the adjusted quantities for the different crops cultivated under different soil types in the study area were obtained. The areas growing sweet potato and vegetable-fruits were found to increase by 0.5255 km2 and 4.4452 km2, respectively, when compared to the pre-optimization period, whereas the areas with winter wheat-summer maize, millet, peanut, cotton, and summer maize decreased by 1.3481 km2, 0.1812 km2, 0.7316 km2, 2.0896 km2, and 0.62 km2, respectively. The economic benefit increased from 810.37 to 853.64 million yuan, while the water-use efficiency increased from 1.5426 kg/m3 to 1.5859 kg/m3. The results provide a basis for soil and water resource management in the region and a technical reference for similar areas to improve agricultural water-use efficiency and economic benefits.

Simulation of Gauged and Ungauged Streamflow of Coastal Catchments across Australia

Australia is a unique continent, surrounded by the ocean, and the majority of its catchments flow to the coast. Some of these catchments are gauged and others are ungauged. There are 405 gauged catchments covering 2,549,000 km2 across the coastal regions of 12 drainage divisions in Australia, whereas there are 771 catchments conceptualised as ungauged covering additional 835,000 km2. The spatial and temporal distribution of mean annual rainfall and potential evaporation (PET) vary significantly from one drainage division to another. We developed a continuous daily streamflow time series of all gauged and ungauged catchments from 1993 onwards. We applied the daily GR4J lumped conceptual model to these catchments. The performance of gauged catchments was analysed through (i) visual inspection of daily hydrographs, flow duration curves, and daily scatter plots; and (ii) performance metrics, including NSE and PBias. Based on the NSE and PBias, performance ratings of 80% and 96% of the models, respectively, were found to be ‘good’. There was no relationship found between the catchment area and the model performance. The ungauged catchments were divided into four categories based on distance from potential donor catchments, where observed data are available for GR4J model calibration, and Köppen climate zone. The total ungauged catchments represent 24.7% of the total drainage division areas. The streamflow from ungauged catchments was estimated using the GR4J model based on the parameters of their donor catchments. Overall, runoff ratios from ungauged catchments were found to be higher compared to their donor-gauged catchments, likely driven by their higher rainfall and less PET. This tendency was particularly evident in two drainage divisions—the Carpentaria Coast (CC) and the Tanami–Timor Sea Coast (TTS)—where ungauged areas comprised 51% and 43%, respectively. The mean gauged annual streamflow varied significantly across drainage divisions—230 gigalitres (GL) from the South Australian Gulf (SAG) to 146,150 GL in TTS. The streamflow from all ungauged catchments was estimated at 232,200 GL per year. Overall, the average streamflow from all drainage divisions, including gauged and ungauged areas, across the coastal regions of Australia was estimated at 419,950 GL per year. This nationwide estimate of streamflow dataset could potentially enhance our understanding of coastal processes and lead to improvements in marine modelling systems and tools.

Equações universais para as distribuições de chuva dos métodos SCS e Huff

ABSTRACT The temporal distribution of design rainfall hyetographs can be obtained through various procedures. One such method involves using temporal distribution curves to disaggregate a rainfall value of longer duration into chronological values within shorter time intervals. This is demonstrated by SCS distribution curves for durations of 6 and 24 hours and the well-known Huff curves. Another approach involves deriving temporal distribution using design hyetograph calculation methods, such as the Chicago and alternating block methods, based on Intensity-Duration-Frequency (IDF) equations. This paper aims to combine these approaches and develop universal equations capable of accurately fitting smooth rainfall time distribution curves. The advantage is the replacement of tables, like SCS and Huff, with analytical equations, enhancing usability in spreadsheets. Furthermore, these universal equations can determine the temporal rainfall distribution using an IDF equation or fitting a specified design hyetograph like Euler type II. Four numerical examples are provided.

The analysis of fire hotspot distribution in Kalimantan and its relation- ship with ENSO phases

Kalimantan experiences fire hazards almost every year, which threaten the largest tropical forest in South- east Asia. Climatic conditions, such as increasing surface temperature and decreasing rainfall, become important es- pecially when El Nino Southern Oscillation (ENSO) occurs. Studies on fire are commonly conducted based on the climatic condition such as the dry or wet season, but those which focused on analysis of fire occurrences with the specific ENSO phases are still limited. This study aims to identify the spatial and temporal distribution of rainfall, land surface temperature, and soil moisture and analyses the distribution of hotspots in Kalimantan from 2014 to 2020 dur- ing different ENSO phases. The data used are Moderate Resolution Imaging Spectroradiometer (MODIS) for hotspot analysis, Global Precipitation Measurement (GPM) for rainfall analysis, MODIS Land Surface Temperature (LST) for surface temperature analysis and Soil Moisture Active Passive (SMAP) for soil moisture analysis. The methods used were descriptive and spatial analyses based on each ENSO phase, which were then combined to analyse the temporal and spatial distribution of fire, rainfall, LST and soil moisture. The temporal distribution shows a positive relationship between ENSO, rainfall, LST, soil moisture and hotspots with a confidence level of 90% in the dry months of August– October. Fire occurred in most parts of West and Central Kalimantan, associated with low elevation, organic soil types and agricultural peatland. The average trend of increasing hotspots is 17.4% in the El Nino phase and decreasing hot- spots by 84.7% in the La Nina phase during August–October in Kalimantan.

Evaluating the response and adaptation of urban stormwater systems to changed rainfall with the CMIP6 projections

Climate change is altering urban rainfall characteristics, leading to extreme urban stormwater and, particularly, more frequent flooding. Due to the uncertainty of climate change, the responses of urban drainage systems to climate change are becoming more complicated. This complexity makes it difficult for decision makers to assess whether urban infrastructure is sufficiently resilient to cope with flood risks. In this study, the Xiao Zhai area, a high-density urban area of China, was used as an example. A quantitative method for assessing these risks and the resilience of urban drainage systems to future urban stormwater was developed. First, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6), the variation and uncertainty of future rainfall in the study area were analysed. A high-fidelity hydro-hydraulic model was developed to analyse the influence of climate change on future urban stormwater. Finally, the relationship between urban flood risk and the resilience of urban drainage systems was evaluated. The results show that the temporal distribution of future rainfall from 2023 to 2100 is relatively uniform. However, the number of heavy rainfall events increases significantly during this period. The flood risk caused by future rainfall was one level higher than the historical flood risk. For example, the flood risk caused by future 5a rainfall is equal to the flood risk from historical 10a rainfall. The correlations between the spatial distributions of flood risk and resilience are 0.49–0.63. Urban drainage systems urgently need to be improved and refined in areas with flood risk and low resilience to become more resilient to climate change. Rational planning of grey-green rainwater facilities in flood risk and low resilience areas can improve the rainwater system's resilience to 0.67–0.95 for climate change.

Quantitative rainfall analysis of the 2021 mid-July flood event in Belgium

Abstract. The exceptional flood of July 2021 in central Europe impacted Belgium severely. As rainfall was the triggering factor of this event, this study aims to characterize rainfall amounts in Belgium from 13 to 16 July 2021 based on two types of observational data. First, observations recorded by high-quality rain gauges operated by weather and hydrological services in Belgium have been compiled and quality checked. Second, a radar-based rainfall product has been improved to provide a reliable estimation of quantitative precipitation at high spatial and temporal resolutions over Belgium. Several analyses of these data are performed here to describe the spatial and temporal distribution of rainfall during the event. These analyses indicate that the rainfall accumulations during the event reached unprecedented levels over large areas. Accumulations over durations from 1 to 3 d significantly exceeded the 200-year return level in several places, with up to 90 % of exceedance over the 200-year return level for 2 and 3 d values locally in the Vesdre Basin. Such a record-breaking event needs to be documented as much as possible, and available observational data must be shared with the scientific community for further studies in hydrology, in urban planning and, more generally, in all multi-disciplinary studies aiming to identify and understand factors leading to such disaster. The corresponding rainfall data are therefore provided freely in a supplement (Journée et al., 2023; Goudenhoofdt et al., 2023).

Comparison between statistical and dynamical downscaling of rainfall over the Gwadar‐Ormara basin, Pakistan

AbstractThis paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study.