Characteristics Of Rainfall Events Research Articles

The Climatological Perspective of Remote Rainfall Events Triggered by the Northeasterly Wind and the Outer Circulation of Typhoon in Taiwan

AbstractThe climate characteristics of remote rainfall events in Taiwan from September to February over 41 years (1980–2020) are studied. These events are induced by the interaction between the northeasterly flow and the typhoon's outer circulation. Our findings reveal that rainfall in northeastern Taiwan becomes more prominent when tropical cyclones move to the remote rainfall‐prone area, located in the north Philippine area to the northern South China Sea, and when the background northeasterly wind speed exceeds 7 m s−1. Under these criteria, the confluence of the typhoon's outer circulation and the northeasterly flow creates a convergence area that enhances rainfall in northeastern Taiwan, increasing the occurrence of moderate to extreme rainfall (ER) events. This leads to an average enhancement in rainfall amount of 80–220 mm per day. Additionally, when typhoons are in the remote rainfall‐prone area, there is a greater than 20% chance for events with maximum rainfall over 200 mm day−1 to occur, particularly in the region of 20°–22°N, 116°−124°E, and north to Luzon Island. In this area, the occurrence rate can exceed more than a 45% chance. The highest risk of ER events occurs between 20°–22°N and 118°−120°E, with a probability of over 90%. Notably, the convergence area for the Taiwan cases does not necessarily coincide with the baroclinic forcing as that associated with remote rainfall events observed in Japan, Korea, and North America.

Effects of rainfall pattern classification methods on the probability estimation of typhoon-induced debris-flow occurrence

ABSTRACT The frequent occurrence of typhoons causes geological disasters, such as debris flow and landslide, by bringing extreme rainfall events. Due to the lack of data collection on extreme rainfall events caused by typhoons, the relationship between rainfall patterns and debris flow has not been deeply studied. Therefore, based on hourly rainfall data during typhoons in Wenzhou from 1980 to 2017, this study used a variety of methods to classify the rainfall events and analyze the characteristics of typhoon-induced rainfall events and their impacts on the probability of debris-flow occurrence. Three classification techniques, including dynamic time warping, K-Means cluster, and self-organizing maps, are applied with two ways to normalize rainfall records, including dimensionless rainfall density curves and dimensionless rainfall cumulation curves, for extracting rainfall patterns from recorded 1 h rainfall data. The rainfall patterns are then used for the estimation of typhoon-induced debris-flow occurrence probability. Results show that different methods present different rainfall patterns. The probability of debris flows varies with different patterns of rainfall events. The research results help deepen the understanding of typhoon rainfall events and debris-flow disaster prevention in the region and contribute to regional flood control and disaster reduction.

Responses of Mesoscale Convective System to Global Warming: A Study on the Henan 2021 Record‐Breaking Rainfall Event

AbstractThe limited capabilities of global climate models in simulating mesoscale convective systems (MCSs) restrict our understanding of how global warming impacts MCSs. This study uses a high‐resolution numerical model with large‐ensemble experiments to simulate MCSs during the record‐breaking extreme rainfall event in Henan Province, China, in July 2021. We compare the changes in the MCS's strength, size, and structure in a real‐world simulation (RW) and a 0.8°C colder simulation (analog to no‐anthropogenic‐warming‐world simulation, short for NAWW) to assess the response of MCSs to global warming. Our results show that the total rainfall from the MCS increased by 10.0% in RW compared to NAWW, with a 3.1% increase in area and a 6.7% increase in rainfall intensity. The development of MCS becomes more rapid in response to warming since the pre‐industrial era. The warmer and wetter climate results in higher convective available potential energy, and accelerates the MCS growth, but then the narrower low‐convective inhibition regions suppress the continuous growth of MCS. During the mature phase, the maximum hourly rainfall intensity (Pmax) can increase by up to 26.5%/K, while Pmax locations can either remain unchanged or shift depending on the interaction of flows and terrains. These results highlight varying responses of MCSs to global warming during its different stages and provide valuable insights into the changing characteristics of extreme rainfall events under global warming.

Screening of representative rainfall event series for long-term hydrological performance evaluation of grassed swales.

Evaluation of the hydrological performance of grassed swales usually needs long-term monitoring data. At present, suitable techniques for simulating the hydrological performance using limited monitoring data are not available. Therefore, current study aims to investigate the relationship between saturated hydraulic conductivity (Ks) fitting results and rainfall characteristics of various events series length. Data from a full-scale grassed swale (Enschede, the Netherlands) were utilized as long-term rainfall event series length (95 rainfall events) on the fitting outcomes. Short-term rainfall event series were extracted from these long-term series and used as input in fitting into a multivariate nonlinear model between Ks and its influencing rainfall indicators (antecedent dry days, temperature, rainfall, rainfall duration, total rainfall, and seasonal factor (spring, summer, autumn, and winter, herein refer as 1, 2, 3, and 4). Comparison of short-term and long-term rainfall event series fitting results allowed to obtain a representative short-term series that leads to similar results with those using long-term series. A cluster analysis was conducted based on the fitting results of the representative rainfall event series with their rainfall event characteristics using average values of influencing rainfall indicators. The seasonal index (average value of seasonal factors) was found to be the most representative short rainfall event series indicator. Furthermore, a Bayesian network was proposed in the current study to predict if a given short-term rainfall event series is representative. It was validated by a data series (58 rainfall events) from another full-scale grassed swale located in Utrecht, the Netherlands. Results revealed that it is quite promising and useful to evaluate the representativeness of short-term rainfall event series used for long-term hydrological performance evaluation of grassed swales.

Employing the generalized Pareto distribution to analyze extreme rainfall events on consecutive rainy days in Thailand's Chi watershed: implications for flood management

Abstract. Extreme rainfall events in the Chi watershed of northeastern Thailand have significant implications for the safe and economic design of engineered structures and effective reservoir management. This study investigates the characteristics of extreme rainfall events in the watershed and their implications for flood risk management. We apply extreme value theory to historical maximum cumulative rainfall data for consecutive rainy days from 1984 to 2022. The generalized Pareto distribution (GPD) was used to model the extreme rainfall data, with the parameters estimated using maximum likelihood estimation (MLE) and linear moment estimation (L-ME) methods based on specific conditions. The goodness-of-fit tests confirm the suitability of the GPD for the data, with p values exceeding 0.05. Our findings reveal that certain regions, notably Udon Thani, Chaiyaphum, Maha Sarakham, Tha Phra Agromet., Roi Et, and Sisaket provinces, show the highest return levels for consecutive 2 d (CONS-2) and 3 d (CONS-3) rainfall. These results underscore the heightened risk of flash flooding in these regions, even with short periods of continuous rainfall. Based on our findings, we developed 2D return level maps using the Q-geographic information system (Q-GIS) program, providing a visual tool to assist with flood risk management. The study offers valuable insights for designing effective flood management strategies and highlights the need for considering extreme rainfall events in water management and planning. Future research could extend our findings through spatial correlation analysis and the use of copula functions. Overall, this study emphasizes the importance of preparing for extreme rainfall events, particularly in the era of climate change, to mitigate potential flood-related damage.

Quantifying hydrological responses to monsoon‐controlled precipitation across the soil‐groundwater‐stream continuum with long‐term high‐frequency hydrometric monitoring

AbstractStream water, groundwater and soil water in the riparian zone are closely linked. Their responses to rainfall events controlled by monsoon climate are variable and intertwined, which are still not well known. To address this knowledge gap, we established a monitoring transect adjacent to a headwater stream in Huashan Catchment, eastern China, with typical monsoon climate. We monitored precipitation, stream stage, groundwater level and soil moisture content at intervals of maximum 30 min. We then conducted an event‐based analysis of rainfall event characteristics and diverse response metrics, and assessed their correlations and interrelationships through correlation and regression analysis. Our 2‐year monitoring results show that water level responses occurred in most rainfall events. They had smaller threshold of rainfall amount and timing but longer time to peak response. Stream responses exhibited smaller response magnitude and intensities than groundwater responses. Rainfall amount and event duration were the most critical driving factors for groundwater responses. Soil moisture responses varied with depth. Only large storms could propagate into topsoil and generate rapid responses. Middle soil moisture responses had more frequent response occurrence and more variable response magnitude, while deep soil moisture responses had smaller response magnitude, longer time to peak response and larger wetting front movement velocity. Attenuated initial response timing with depth identified preferential flow, reflecting heterogeneity in the soil profile. Monsoon‐controlled heavy rainfall improved hydrologic connectivity in the soil‐groundwater‐stream continuum (SGSC), mediating the influence of heterogeneity on soil moisture responses and potentially contributing more subsurface flow to catchment runoff. Overall, this study aimed to reveal the mechanism of hydrological responses to monsoon‐controlled precipitation across the SGSC.

Identification of Rainfall Events and Heavy Rainfall Events From Radar Measurements in Southeastern Australia

AbstractRadar data can be of significant utility in investigating characteristics of rainfall events that cannot be studied with rain gauges alone. The recent establishment of a long‐term, quality‐controlled data set covering most of the radars on the Australian continent enables a deeper characterization of rainfall, including heavy rainfall events. This study develops a methodology to identify and characterize rainfall events from radar data and tests its utility by applying it to the regions surrounding the major cities of Brisbane, Sydney, and Melbourne. The event characteristics studied include rainfall accumulation and intensity, event duration and spatial extent, and the contribution convective areas make to the overall event rainfall. Rainfall events in Brisbane and Sydney are found to be more intense, more convective, and smaller in extent whilst producing larger rainfall accumulations than Melbourne rainfall events. Rainfall event duration and total accumulated rainfall are strongly positively correlated, as are the overall event intensity and the intensity of convective rainfall. The events that produce the largest rainfall accumulations exhibit significant differences from the events that produce the highest rainfall intensities. Overall, the study demonstrates that long‐term radar data sets in Australia provide an invaluable and rich source to study rainfall characteristics in a variety of regions at a high spatial and temporal resolution.

Precipitation Scaling in Extreme Rainfall Events and the Implications for Future Indian Monsoon: Analysis of High‐Resolution Global Climate Model Simulations

AbstractThe increase in water holding capacity of the atmosphere with temperature, given by the Clausius‐Clapeyron (CC) relationship, describes the changes in extreme rainfall intensities at warmer atmospheric states. We study the characteristics of extreme rainfall events (EREs) during the Indian summer monsoon season with respect to thermodynamic changes and precipitation‐scaling over the Indian subcontinent and its homogeneous rainfall zones. We utilize outputs from a present‐day climate simulation and a time‐slice future climate change projection experiments of a high‐resolution global climate model. Large changes are seen for very EREs (vEREs) which suggests their sensitivity to warmer temperatures. In future, the altered radiative forcing will heat up the upper atmosphere, stabilize it and offset the effect of increasing humidity on precipitation intensity. Our analysis also suggests that more convective clouds and the interplay of increased moisture content and circulation will result in future changes in EREs.

The Environment and Precipitation Characteristics of Widespread Extreme Rainfall Events during the Akisame Season

The Environment and Precipitation Characteristics of Widespread Extreme Rainfall Events during the Akisame Season

The Kinematic and Microphysical Characteristics of Extremely Heavy Rainfall in Zhengzhou City on 20 July 2021 Observed with Dual-Polarization Radars and Disdrometers

In this study, we utilized dual-polarization weather radar and disdrometer data to investigate the kinematic and microphysical characteristics of an extreme heavy rainfall event that occurred on 20 July 2021, in Zhengzhou. The results are as follows: FY-2G satellite images showed that extremely heavy rainfall mainly occurred during the merging period of medium- and small-scale convective cloud clusters. The merging of these cloud clusters enhanced the rainfall intensity. The refined three-dimensional wind field, as retrieved by the multi-Doppler radar, revealed a prominent mesoscale vortex and convergence structure at the extreme rainfall stage. This led to echo stagnation, resulting in localized extreme heavy rainfall. We explored the formation mechanism of the notable ZDR arc feature of dual-polarization variables during this phase. It was revealed that during the record-breaking hourly rainfall event in Zhengzhou (20 July 2021, 16:00–17:00 Beijing Time), the warm rain process dominated. Effective collision–coalescence processes, producing a high concentration of medium- to large-sized raindrops, significantly contributed to heavy rainfall at the surface. From an observational perspective, it was revealed that raindrops exhibited significant collision interactions during their descent. Moreover, a conceptual model for the kinematic and microphysical characteristics of this extreme rainfall event was established, aiming to provide technical support for monitoring and early warning of similar extreme rainfall events.

Permeable pavement hydraulic optimization by using an analytical-probabilistic model

Abstract On-source storage controls are a sustainable solution for stormwater management in a scenario of continuous urban area growth. Structures that manage storage volumes through infiltration include extra environmental benefits, such as groundwater recharge, evapotranspiration, and pollutant load removal. Permeable pavement systems are among these controls and can easily integrate into dense urban areas, resulting in paved surfaces contributing to stormwater management. The shift toward on-source strategies is encouraged through regulations, policies, incentives, and awareness campaigns, which are substantially increasing their dissemination. Optimizing the design of on-source storage controls with infiltration, such as permeable pavement systems, through robust methodologies can reduce reservoir depth, reducing environmental impact and costs without impact on reliability. The analytical-probabilistic (AP) method using derived probability distribution theory from rainfall event characteristics and the mathematical description of hydrologic processes within the permeable pavement systems provides an analytical equation that can be used as a design tool, proving robustness analogous with continuous simulations. Results obtained with the AP method were compared with traditional event-based methodologies and continuous simulation, assessing the reliability of the proposed method in optimizing permeable pavement systems' reservoir depth.

Analysis of Extreme Rainfall Characteristics in 2022 and Projection of Extreme Rainfall Based on Climate Change Scenarios

In this study, we analyzed the characteristics of the heavy rainfall events that occurred in Seoul in 2022 and compared them with the projections of the representative concentration pathway (RCP). The analysis results indicated that climate change is ongoing. In this era of climate crisis, based on the shared socioeconomic pathway (SSP) data, we projected the 20-year frequency rainfall for South Korea at intervals of 1 day/24 h. Our results indicate that the maximum rainfall (with a 24 h duration) will increase by ~18% in the second half of the 21st century, compared to the current maximum rainfall. Finally, we projected the intensity–duration–frequency (IDF) curve for the infrastructure design of Seoul. According to the projected IDF curve, across all durations, the rainfall intensity was the strongest in the early 21st century, indicating that at present, we are in the midst of a climate crisis. Thus, it is important to develop and implement effective urban and river flood management measures to mitigate the current effects of climate change. Notably, our study can serve as a reference for future studies on climate change and help policymakers and decision-makers develop relevant policies and mitigation strategies related to the effects of climate change.

Spatiotemporal distribution of precipitation and its characteristics under tropical atmospheric systems of Brazil: Insights from a large sub‐hourly database

AbstractThe study of rainfall properties at various spatiotemporal scales is deeply important for understanding a large range of socio‐environmental processes and variables (e.g., water resources, agriculture, socio‐ecosystemic services, natural risk assessment). However, such studies on rainfall characteristics, especially at sub‐hourly resolutions, are scarce in large areas of South America due to the lack of a high‐resolution temporal database. The Brazilian National Centre for Monitoring and Early Warning of Natural Disasters (CEMADEN) has gradually implemented, starting in 2013, a sub‐hourly monitoring network composed of approximately 3500 automated rain gauges distributed across Brazil, enabling access to new hydrological studies in this vast tropical country featuring a range of biomes. This study analysed the characteristics of rainfall events for the whole of Brazil on sub‐daily and sub‐hourly timescales, using 7 years of data (from 2014 to 2021) provided by CEMADEN. Rainfall events were defined by the minimum inter‐event time (MIT) and the minimum depth (1 mm). Seven MITs (30, 60, 120, 180, 360, 720, and 1440 min) were considered to evaluate the behaviour of rainfall event characteristics and their relationships. The Gaussian mixture model method was applied to identify regions with similar rainfall patterns according to the MIT. Six groups with homogeneous characteristics were identified, evidencing the climatic diversity of Brazil. The results show that the MIT strongly influences precipitation properties (especially the dry time and the number of events). The highest number of events occurred in the North‐east Coast region, which exceeded 200 events per year (MIT < 60 min), while the lowest number of events was observed in the Semiarid region, which reached only 38 events per year with an MIT of 1440 min. Moreover, the events with the highest rainfall intensities were found in the Central region. The results found in this study provide a better understanding of precipitation and its characteristics in Brazil, highlighting the climatological diversity of this country.

Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

This study aims to derive and evaluate new empirical rainfall thresholds as the basis for landslide early warning in Progo Catchment, Indonesia, using high-resolution rainfall datasets. Although attempts have been made to determine such thresholds for regions in Indonesia, they used coarse-resolution data and fixed rainfall duration that might not reflect the characteristics of rainfall events that induced the landslides. Therefore, we evaluated gauge-adjusted global satellite mapping of precipitation (GSMaP-GNRT) and bias-corrected climate prediction center morphing method (CMORPH-CRT) hourly rainfall estimates against measurements at rainfall stations. Based on this evaluation, a minimum rainfall of 0.2 mm/h was used to identify rain events, in addition to a minimum of 24 h of consecutive no-rain to separate two rainfall events. Rainfall thresholds were determined at various levels of non-exceedance probability, using accumulated and duration of rainfall events corresponding to 213 landslide occurrences from 2012 to 2021 compiled in this study. Receiver operating characteristics (ROC) analysis showed that thresholds based on rainfall station data, GSMaP-GNRT, and CMORPH-CRT resulted in area under ROC curve values of 0.72, 0.73, and 0.64, respectively. This result indicates that the performance of high-resolution satellite-derived data is comparable to that of ground observations in the Progo Catchment. However, GSMaP-GNRT outperformed CMORPH-CRT in discriminating the occurrence/non-occurrence of landslide-triggering rainfall events. For early warning purposes, the rainfall threshold is selected based on the probability exlevel at which the threshold maximizes the true skill score, i.e., at 10% if based on station data, or at 20% if based on GSMaP-GNRT.

Development of statistical downscaling methods for the assessment of rainfall characteristics under climate change scenarios

Abstract The objective of this research was to develop a statistical downscaling approach in the Phetchaburi River Basin, Thailand, consisting of two main processes: predictor selection and relationship construction between predictors and local rainfall. Super predictor (SP) and stepwise regression (SR) were employed with principal component analysis (PCA) and the statistical downscaling model (SDSM) was applied later. Four statistical models were finally used: M1 (SP–PCA), M2 (SP–SDSM), M3 (SR–PCA), and M4 (SR–SDSM) with 26 large circulation indices generated by CanESM2 (CMIP5). Finally, the characteristics of extreme rainfall events were observed under three climate change scenarios during three different periods (2020–2040, 2041–2070, and 2071–2100). The results revealed that rainfall and geostrophic airflow velocity were the best predictors for rainfall downscaling, followed by divergence, meridional velocity, and relative humidity. Three objective functions (R2, NSE, and RMSE) were applied to evaluate model performance. The M4 model presented the highest performance while M1 showed the lowest skill. The average annual rainfall specifically increased compared with the historical rainfall for RCP2.6, RCP4.5, and RCP8.5 scenarios in future periods. Very to extremely wet years determined by the standardized anomaly index (SAI) occurred more often in the far-future while severe to extremely dry years frequently occurred in the mid- and far-future.

Extreme rainfall events over the Okavango river basin, southern Africa

Characteristics of extreme rainfall events in the highly biodiverse Okavango River Basin (ORB) in west central southern Africa are not well understood. Hence, this study analyses the frequencies, intensities, spatial distributions, variability and trends of extreme rainfall events for the main rainy season, January–April (JFMA). The events are accumulated over 1-day (DP1) and 3-days (DP3). To identify these events, a method that accounts for both event intensity and spatial extent is used. On average, DP1 (DP3) events contribute ∼10% (∼17%) rainfall totals but in some years their contributions exceed 30%. Tropical-extratropical cloud bands are responsible for most of the events with tropical lows also important. The considerable interannual variability in extreme events appears related to El Niño-Southern Oscillation (ENSO) and the Botswana High.Although ENSO influences the extreme events as well as rainfall totals more generally over southern Africa, by far the wettest season over the iconic Okavango Delta region, in Ngamiland, Botswana, occurred during neutral JFMA 2017. These heavy rains resulted from a deeper Angola Low, weaker mid-level Botswana High which favoured convection in the region together with anomalous westerly moisture fluxes from the tropical South East Atlantic and enhanced low level moisture convergence over most of the region during January-early March. A dry period from mid-March was broken by the second most intense rainfall event on April 22nd, resulting from a cut-off low.Significant increasing trends were found in DP1 frequencies, as well as in rain-days and rain totals over many areas. These trends have important implications for water and agricultural management, and wildlife conservation in the highly biodiverse ORB.

Observed Changes in Rainfall and Characteristics of Extreme Events in Côte d’Ivoire (West Africa)

This study evaluates how the characteristics of daily rainfall and extreme events in Côte d’Ivoire changed during 1961–2015 using the rain gauge observation network of the National Meteorological Service (SODEXAM). The results indicate that the northern and southern parts of Cote d’Ivoire experienced a change from a wet to a dry period, with cut-offs in 1982 and 1983, respectively. In the northern part, this dry period was marked by a decrease in rainfall intensity, the length of wet spells, and the contribution of heavy and extreme rainfall, as well as an increase in the number of rainy days and a decrease in the length of dry spells. Over the southern part, this dry period was marked by an increase in the maximum length of dry spells associated with an increase in the maximum 1-day and 5-day precipitation events. The western part of Côte d’Ivoire experienced a late cut-off from the wet to dry period in 2000; the dry period was associated with a decrease in the number of rainy days, rainfall intensities, and maximum length of wet spells. Changes in the central part of Cote d’Ivoire presented high variability, and trends were less marked, even though a cut-off from a wet to dry period was detected in 1991. This study shows that Côte d’Ivoire, which is located in a subhumid and humid region and has an economy dependent on agriculture (especially cash crops, which comprise 60% of the GDP), is experiencing dry spells that are increasing in frequency and length. Combined with deforestation to increase production, this situation could lead to desertification and compromise the sustainable development goals of the country. The contribution of heavy rainfall was found to increase during the last 15 years, increasing the overall risk of floods, especially in urban areas where city authorities and populations are not prepared, thereby threatening infrastructure and human security.

Investigating the interplay of the hydrometeorological and seasonal forest vegetation role in regulating the nitrate flushing in a small torrential catchment

Forest vegetation is a very topical research subject as part of nature-based solutions for flood protection, soil erosion, water quality improvement, etc. However, limited capabilities of such measures are rarely investigated. Therefore, in this paper, study on the interplay of the hydrometeorological and seasonal forest vegetation role in regulating the nitrate‑nitrogen (NO3-N) flushing from a forested, torrential catchment is presented. For the 43 identified rainfall events it was found that there are no statistically significant seasonal differences in NO3-N concentrations; however, during baseflow conditions such differences were noted. The rainfall events were described by 17 hydro-meteorological and vegetation variables to investigate similarities between the events from the NO3-N export point of view using clustering methods. Additionally, the relationship between explanatory and dependent variables, i.e. NO3-N concentration and export variables, was modelled. In the models, the first four principal components were used as explanatory variables after the reduction of the initial number of variables. It was found that phenological phases as indicators of the forest vegetation seasonal activity are generally not able to considerably influence the NO3-N concentration and flushing dynamics. The results indicate that during baseflow conditions, the influence of the forest vegetation in the torrential catchment becomes predominant. During rainfall events, the role of vegetation is blurred and might be generally considered as insignificant. The characteristics of rainfall events apparently exceed the limits of a forest's ability to control NO3-N flushing. Given the pronounced impact of rainfall intensity on NO3-N flushing, the challenge for the future will be to mitigate the potential negative effects of climate change related impacts, especially through measures able to reduce the intensity of rainfall-runoff formation. The seasonal role of vegetation in diminishing the intensity of nutrient flushing in natural torrential catchments might be relatively constrained and probably also overwhelmed by the hydrometeorological conditions.

Updating the intensity-duration-frequency curves in major Canadian cities under changing climate using CMIP5 and CMIP6 model projections

Characteristics of extreme rainfall events are often presented using the Intensity-Duration-Frequency (IDF) curves. As Canada gets warmer, the credibility of using historical IDF curves for flood risk analysis and design of various urban infrastructure becomes questionable. This study aims at updating and intercomparing the IDF curves in 30 Canadian cities using the simulations of Climate Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathways 2–4.5 and 5–8.5 and their predecessor CMIP5 under Representative Concentration Pathways 4.5 and 8.5. Quantile-Quantile Downscaling is utilized to estimate extreme rainfall values at fine spatiotemporal scales. Overall, more frequent storms with an average increase of around 30%-40% in their intensity are projected in most cities based on both CMIP5 and CMIP6 models during 2071–2100. For instance, the fast-growing cities of Vancouver, Toronto, and Montreal could expect around 4–11 times more frequent 5-min storms with a 100-year return period during 2071–2100. Moreover, although IDF curves based on CMIP5 and CMIP6 individual models diverge, the expected IDF curves based on their multi-model ensemble mean are somewhat similar if models with a high climate sensitivity based on CMIP6 are excluded from the analysis. This study underlines the need to update IDF curves in Canadian cities.

Source availability and hydrological connectivity determined nitrate-discharge relationships during rainfall events in karst catchment as revealed by high-frequency nitrate sensing

Karst terrain seasonal monsoonal rainfall is often associated with high concentrations of nitrate-N in streams draining agricultural land. Such high concentrations can pose problems for environmental and human health. However, the relationship between rainfall events that mobilize nitrate and resulting nitrate export remains poorly understood in karst terrain. To better understand the processes that drive nitrate dynamics during rainfall events, the characteristics of individual rainfall events were analyzed using sensor technology. Thirty-eight rainfall events were separated from the high-frequency dataset spanning 19 months at a karst spring site. The results revealed that nitrate-discharge (N-Q) hysteresis in 79% of rainfall events showed anticlockwise hysteresis loop patterns, indicating nitrate export from long distances within short event periods. Karstic hydrological connectivity and source availability were considered two major determining factors of N-Q hysteresis. Gradual increase in hydrological connectivity during intensive rainfall period accelerated nitrate transportation by karst aquifer systems. Four principal components (PCs, including antecedent conditions PC1&3 and rainfall characteristics PC2&4 explained 82% of the cumulative variance contribution to the rainfall events. Multiple linear regression of four PCs explained more than 50% of the variation of nitrate loading and amplitude during rainfall events, but poorly described nitrate concentrations and hydro-chemistry parameters, which may be influenced by other factors, e.g., nitrate transformation, fertilization time and water-rock interaction. Although variation of N concentration during event flow is evident, accounting for antecedent conditions and rainfall factors can help to predict rainfall event N loading during rainfall events. Pollution of the karstic catchment occurred by a flush of nitrate input following rainfall events; antecedent and rainfall conditions are therefore important factors to consider for the water quality management. Reducing source availability during the wet season may facilitate to reduction of nitrogen loading in similar karst areas.