Global efforts to upgrade water, drainage, and sanitation services are hampered by hydrometeorological data-scarcity plus uncertainty about climate change. Intensity–duration–frequency (IDF) tables ...are used routinely to design water infrastructure so offer an entry point for adapting engineering standards. This paper begins with a novel procedure for guiding downscaling predictor variable selection for heavy rainfall simulation using media reports of pluvial flooding. We then present a three-step workflow to: (1) spatially downscale daily rainfall from grid-to-point resolutions; (2) temporally scale from daily series to sub-daily extreme rainfalls and; (3) test methods of temporal scaling of extreme rainfalls
within
Regional Climate Model (RCM) simulations under changed climate conditions. Critically, we compare the methods of moments and of parameters for temporal scaling annual maximum series of daily rainfall into sub-daily extreme rainfalls, whilst accounting for rainfall intermittency. The methods are applied to Kampala, Uganda and Kisumu, Kenya using the Statistical Downscaling Model (SDSM), two RCM simulations covering East Africa (CP4 and P25), and in hybrid form (RCM-SDSM). We demonstrate that Gumbel parameters (and IDF tables) can be reliably scaled to durations of 3 h within observations and RCMs. Our hybrid RCM-SDSM scaling reduces errors in IDF estimates for the present climate when compared with direct RCM output. Credible parameter scaling relationships are also found within RCM simulations under changed climate conditions. We then discuss the practical aspects of applying such workflows to other city-regions.
Many applications in urban areas require high‐resolution rainfall measurements. Typical operational weather radars can provide rainfall intensities at 1‐km2 grid cells every 5 min. Opportunistic ...sensing with commercial microwave links yields path‐averaged rainfall intensities (typically 0.1–10 km) within urban areas. Additionally, large amounts of urban in situ rainfall measurements from amateur weather observers are obtainable in real‐time. The accuracy of these three techniques is evaluated for an urban study area of 20 × 20 km, taking into account their respective network layouts and sampling characteristics. We use two simulated rainfall events described in terms of drop size distributions on a 100‐m grid and with a temporal resolution of 30 s. Accurate radar rainfall estimation with the Z‐R relationship relies heavily on an appropriate choice of parameters, and a dual‐polarization strategy is more suitable for higher intensities. Under ideal measurement conditions, the weather station network is the most promising, with a Pearson correlation coefficient above 0.86 and a relative bias below 4% for 100‐m rainfall estimates at 5‐min resolution. Microwave link rainfall observations contain the largest error, shown by a consistently larger coefficient of variation. The accuracy of all techniques improves when considering rainfall at larger scales, especially by increasing time intervals, with the strongest improvements found for microwave links for which errors are largely caused by their temporal sampling. Sparser networks are examined, showing that the decline in measurement accuracy only becomes significant when the link and station network density are reduced to less than half their levels in Amsterdam.
Key Points
Assuming perfect measurement accuracy, the personal weather station network captures small‐scale rainfall dynamics best in Amsterdam
Measurement accuracy increases at larger temporal and spatial scales, most significantly for commercial microwave links
With current network layouts, similar accuracy is achieved by half the number of commercial microwave links and personal weather stations
The aim of this study was to investigate temporal variation in seasonal and annual rainfall trend over Ranchi district of Jharkhand, India for the period (1901–2014: 113 years). Mean monthly rainfall ...data series were used to determine the significance and magnitude of the trend using non-parametric Mann–Kendall and Sen’s slope estimator. The analysis showed a significant decreased in rainfall during annual, winter and southwest monsoon rainfall while increased in pre-monsoon and post-monsoon rainfall over the Ranchi district. A positive trend is detected in pre-monsoon and post-monsoon rainfall data series while annual, winter and southwest monsoon rainfall showed a negative trend. The maximum decrease in rainfall was found for monsoon (− 1.348 mm year
−1
) and minimum (− 0.098 mm year
−1
) during winter rainfall. The trend of post-monsoon rainfall was found upward (0.068 mm year
−1
). The positive and negative trends of annual and seasonal rainfall were found statistically non-significant except monsoon rainfall at 5% level of significance. Rainfall variability pattern was calculated using coefficient of variation CV, %. Post-monsoon rainfall showed the maximum value of CV (70.80%), whereas annual rainfall exhibited the minimum value of CV (17.09%), respectively. In general, high variation of CV was found which showed that the entire region is very vulnerable to droughts and floods.
Storm‐induced landslides are a common hazard, but the link between their spatial pattern and rainfall properties is poorly understood, mostly because hillslope stability is modulated by ...under‐constrained, spatially variable topographic, hydrological, and mechanical properties. Here, we use a long‐term rainfall data set from the Japanese radar network to discuss why the landslide pattern caused by a major typhoon poorly correlates with the event rainfall but agrees with the event rainfall normalized by the 10‐year return period rainfall amount, that is, a rainfall anomaly. This may be explained if the variability in hillslope properties has coevolved with the recent climate and can be accounted for with such normalization. Further, rock types seem to respond to rainfall anomalies at various timescales, favoring specific landslide geometries, and suggesting various hydrological properties in these zones. The computation of rainfall anomalies for multiple timescales may pave the way toward operational landslide forecasts in case of large storms.
Plain Language Summary
Landslides caused by heavy rainfall frequently cause substantial loss of life and property. However, the location of landslides across a landscape depends on both the rainfall amount and various local properties of the landscape (e.g., soil thickness and strength) that are difficult to measure. Here, we use 26 years of weather‐radar measurements to show that the landslides caused by a large typhoon in Japan are poorly explained by the rainfall amount during the typhoon but much better by the rainfall anomaly, which is the amount of rainfall normalized by the rainfall amount occurring during extreme rainfall. We also find that landsliding seems driven by short, intense bursts of rainfall in regions underlain by some rock types while elsewhere rainfall accumulated over 2 days matters most. To replace total rainfall by rainfall anomaly and to consider rainfall accumulated over various time periods may fundamentally change landslide susceptibility scenarios and may allow quantitative forecasts of landslide patterns caused by large storms, based on weather forecasts and rainfall archives.
Key Points
Patterns of storm‐induced landslides tracks the rainfall anomaly (relative to a 10‐year‐return rainfall) better than absolute rainfall
Quantitative prediction of the spatial pattern of storm‐induced landsliding is primarily achieved with slope and rainfall anomaly maps
Rock types with different hydrological properties may modulate the temporal scale over which rainfall accumulation cause landsliding
The El Niño‐Southern Oscillation (ENSO) is considered an important driver of rainfall variability in Australia, amongst many other global locations. Despite knowledge of the expected modulation of ...seasonal rainfall by ENSO, there is no consistently used method to quantify the role that specific ENSO events play in driving the observed anomalous rainfall. In this manuscript we adapt the Fraction of Attributable Risk (FAR) method, commonly used to identify the anthropogenic impact on a particular event, to quantify the impact of ENSO on the occurrence of monthly rainfall anomalies. We also explicitly calculate the ENSO induced change in risk and the FAR for all observed spring rainfall rates for our eastern Australian regions. A prominent role for ENSO in driving the large spring 2022 rainfall anomalies is identified. Though we choose to focus on ENSOs impact on rainfall in various Eastern Australian regions, the results are applicable to other climate modes, regions and climatic variables.
Plain Language Summary
The El Niño‐Southern Oscillation (ENSO) is considered an important driver of rainfall variability in Australia, amongst many other global locations. Despite understanding how ENSO is expected to alter rainfall, we do not currently quantify the role ENSO played in driving a observed rainfall anomaly in any given season. In this manuscript we adapt a method that is commonly used to identify the anthropogenic impact on a particular event; and instead, we quantify the impact of ENSO on the occurrence of monthly rainfall anomalies. We then calculate the ENSO‐induced change in risk for all observed spring rainfall rates for our selected eastern Australian regions. A prominent role for ENSO in driving large rainfall anomalies of spring 2022 is also identified. Though we choose to focus on ENSOs impact on rainfall in various eastern Australian regions in this study, the results are applicable to other climate modes, regions and climatic variables.
Key Points
We adapt the commonly used Fraction of Attributable Risk method to attribute rainfall variability to the El Niño‐Southern Oscillation
We present the ENSO induced change in risk and the FAR for all observed spring rainfall rates for three eastern Australian regions
The increased spring 2022 East Australian rainfall was >5 times more likely, and largely attributed to the La Niña conditions present
We analyse long‐term (1900–2017) rainfall data in the southern part of the winter rainfall region of southern Africa to understand the spatial patterns of recent and long‐term trends and ...contextualize the 2015–2017 rainfall anomalies which led to the so‐called “Day Zero” drought in Cape Town. Our analyses reveal cohesive spatial patterns and seasonal differences in rainfall trends across a range of timescales. These suggest that rainfall is subject to regional driving mechanisms, predominantly manifested at the 20–50 year timescale, but the influence of these mechanisms is modified by subregional and seasonally specific processes, frequently resulting in trends of different magnitudes and even sign. Trend patterns are consistent with multidecadal‐scale quasi‐periodicity, with only the most recent phase (post‐1981 drying) corresponding to the expected regional response to hemispheric processes linked to anthropogenic climate change. The spatial and seasonal patterns of drying observed since 1981 alone do not explain the pattern of 2015–2017 drought anomalies, although they share a strong autumn and weak mid‐winter signal. These results have implications to the interpretation of drought in the context of observed rainfall trends. Furthermore, we identify directions for improvement of the conceptual understanding of drivers of rainfall variability and the role of anthropogenic climate change in the winter rainfall region of South Africa.
We reveal robust but divergent spatial and seasonal patterns in rainfall trends in the Cape Town region. These reflect influence of common regional forcing modified by subregional and seasonally specific processes. Trends are embedded within a multidecadal quasiperiodicity, but in recent period are consistent with hemispheric forcing reflecting anthropogenic climate change. Results have implications for interpretation of rainfall trends in the context of drought, and to understanding of climate drivers of rainfall variability this and similar regions.
This study describes a new high‐resolution (0.25°×0.25° latitude/longitude) gridded daily rainfall dataset (K‐Hidra version 2020) developed from rainfall records of 389 gauge stations irregularly ...distributed across the Korean peninsula. The observational datasets are often composed of incomplete time series covering different temporal periods with numerous missing values, informing that infilling process is essential. Three supportive but separated evaluation frameworks, which have been paid less attention to, are explicitly addressed to identify the proper infilling model in the study area. After a gridded rainfall dataset is created with infilled dataset, it is validated by comparing the gridded products obtained by observations. Furthermore, K‐Hidra is compared with other gridded rainfall estimates including Climate Prediction Center (CPC), Global Precipitation Climatology Project (GPCP), Asian Precipitation Highly‐Resolved Observational Data Integration Towards Evaluation (APHRODITE), and Tropical Rainfall Measuring Missing (TRMM) Multi‐satellite Precipitation Analysis (TMPA). Our results suggest that the elastic net model algorithm is the most effective in dealing with missing values in the study area. Results also demonstrate that the infilling process is vital to properly produce the reconstructed series although its effects substantially vary based on the size of the available data as well as the spatial and temporal distribution of the observed precipitation. Furthermore, K‐Hidra reveals that the Korean peninsula have experienced significant changes in precipitation‐related characteristics such as annual daily maximum precipitation and total annual dry days, likely leading to frequent hydrologic extremes. Lastly, K‐Hidra is compared with other estimates, CPC has a small bias and high correlations with the new dataset while precipitation variabilities are acceptably represented by all other gridded estimates. From the results demonstrated in this study, when officially released, K‐Hidra is expected to be useful for climate impact and rainfall variability analyses over the Korean peninsula.
Locations of rainfall stations and 0.25° × 0.25° grid for K‐Hidra. Here, three different datasets (ASOS, AWS, and NKO) are also denoted.
In the Bay of Bengal (BoB) area, landfalling Tropical Cyclones (TCs) often produce heavy rainfall that results in coastal flooding and causes enormous loss of life and property. However, the rainfall ...contribution of TCs in this area has not yet been systematically investigated. To fulfil this objective, firstly, this paper used TC best track data from the Indian Meteorological Department (IMD) to analyze TC activity in this area from 1998 to 2016 (January–December). It showed that on average there were 2.47 TCs per year generated in BoB. In 1998, 1999, 2000, 2005, 2008, 2009, 2010, 2013, and 2016 there were 3 or more TCs; while in 2001, 2004, 2011, 2012, and 2015, there was only 1 TC. On a monthly basis, the maximum TC activity was in May, October, and November, and the lowest TC activity was from January to April and in July. Rainfall data from the Tropical Rainfall Measurement Mission (TRMM) were used to estimate TC rainfall contribution (i.e., how much TC contributed to the total rainfall) on an interannual and monthly scale. The result showed that TCs accounted for around 8% of total overland rainfall during 1998–2016, and with a minimum of 1% in 2011 and a maximum of 34% in 1999. On the monthly basis, TCs’ limited rainfall contribution overland was found from January to April and in July (less than 14%), whereas the maximum TC rainfall contribution overland was in November and December (16%), May (15%), and October (14%). The probability density functions showed that, in a stronger TC, heavier rainfall accounted for more percentages. However, there was little correlation between TC rainfall contribution and TC intensity, because the TC rainfall contribution was also influenced by the TC rainfall area and frequency, and as well the occurrence of other rainfall systems.
Abstract
In this study, we used hourly observations to investigate the cooling effect of summer rainfall on surface air temperature (Ta) in a subtropical area, Guangdong province, South China. Data ...were categorized step-by-step by rainfall system (convection, monsoon, and typhoon), daily rainfall amount, and relative humidity (RH) level. Moreover, the average hourly Ta variation due to solar radiation was removed from all observations before statistical analysis. The results showed that the linear relationship between hourly Ta variation and rainfall intensity did not exist. However, the cooling effect of rainfall on Ta variation was dominant. In addition, convective rainfall does cause a greater temperature drop than the other two rainfall systems. After further partitioning all samples by RH level preceding the rainfall, the relationship between hourly Ta variation and rainfall intensity became distinctive. When RH was below 70%, rainfall-induced cooling became more substantial and scaled linearly with event intensity, but when RH exceeded 70%, the rainfall cooling effect was generally restrained by the RH increase. A strong correlation between hourly Ta variation and RH level preceding the rainfall suggests the importance of RH on the rainfall cooling effect.
In the present paper, the variabilities and long-term trends of summer monsoon rainfall for different intensity bins (dry, low, moderate, high, very high, and extreme) are studied for five ...homogeneous regions, namely Northeast India (NEI), Northcentral India (NCI), Northwest India (NWI), Westcentral India (WCI), and Peninsular India (PI) for 118 years (1901–2018). The study was carried out based on gridded rainfall data from the India Meteorological Department (IMD). The rainfall characteristics such as number of rainy days, percentage contribution, and periodicity of rainfall intensity classes are analysed and found to be different in different homogeneous regions. The long-term trend (1901–2018) of total rainfall showed a significant increasing trend (19.9 mm decade
−1
) in NEI and significant decreasing trends in NCI (9.6 mm decade
−1
) and PI (4.9 mm decade
−1
). Analysis on rainfall intensity indicates a significant increasing trend for high, very high, and extreme classes in NEI, a significant increasing trend for dry, and a decreasing trend for moderate and very high classes over NCI and PI. From correlation analysis among the homogeneous regions, it is found that the rain events in different intensity classes show different relationships, which indicate the regional heterogeneity in rainfall characteristics. It is also important to note that an increase in rainfall contribution from very high and extreme classes was found over NEI, NWI, and WCI in the multidecadal period of 1991–2018, while NCI showed a decrease during this period; however, in NCI, a drastic increase for these intensity bins is distinct during the 1961–1990 multidecadal period. In addition to the trends and variabilities, we also explored spatial heterogeneity of different rainfall intensity categories, and found remarkable differences from one homogeneous region to another.
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