Macro-economic assessments of climate impacts lack an analysis of the distribution of daily rainfall, which can resolve both complex societal impact channels and anthropogenically forced changes
. ...Here, using a global panel of subnational economic output for 1,554 regions worldwide over the past 40 years, we show that economic growth rates are reduced by increases in the number of wet days and in extreme daily rainfall, in addition to responding nonlinearly to the total annual and to the standardized monthly deviations of rainfall. Furthermore, high-income nations and the services and manufacturing sectors are most strongly hindered by both measures of daily rainfall, complementing previous work that emphasized the beneficial effects of additional total annual rainfall in low-income, agriculturally dependent economies
. By assessing the distribution of rainfall at multiple timescales and the effects on different sectors, we uncover channels through which climatic conditions can affect the economy. These results suggest that anthropogenic intensification of daily rainfall extremes
will have negative global economic consequences that require further assessment by those who wish to evaluate the costs of anthropogenic climate change.
This study investigated the temporal rainfall pattern in order to facilitate rainfall design, which normally requires a good understanding of the temporal patterns of rainstorm events. The analysis ...employed a storm-event-based approach using the concept of inter-event time definition (IETD) and rainfall depth/duration/intensity thresholds. The 5-min rainfall data at three raingauge stations were analysed to determine representative quartiles of a design storm in a tropical city. The temporal characteristics of the design storm could be determined from the rainfall depth ratios of consecutive peak rainfalls for each interval of storm duration, and time to the first peak rainfall depending on each quartile's rainstorm events. The determination of the quantile distribution of tropical rainfall could help improve the representativeness of design rainfall and facilitate rainfall-runoff modelling for urban flood control in a tropical region.
This paper focuses on the spatio-statistical analysis of rainfall fluctuation, anomaly and trend in the Hindu Kush region using auto-regressive integrated moving averages (ARIMA) approach. In the ...study area, trend in rainfall has significant impact on fluctuations in river discharge, which ultimately led to floods and hydrological drought. In this study, rainfall has been used as a climatic parameter. For this study, average annual and mean monthly rainfall data for Dir, Timergara, Saidu, Chitral, Drosh, Malam Jabba and Kalam meteorological stations located in the study region were gathered from Regional Meteorological Center Peshawar. In the study area, the rainfall is mostly received during two prominent periods, i.e., summer rainfall from monsoon, whereas winter and spring rainfall from western depressions. In the study area, Malam Jabba has recorded the heavy mean annual rainfall (1647 mm) and is considered as the humid station followed by met station Dir with a 1362 mm mean annual rainfall. Similarly, Saidu met station received 1050 mm mean annual rainfall and Kalam 1038 mm, whereas Timergara, Drosh and Chitral recorded 796 mm, 568 mm and 458 mm, respectively. The temporal data regarding rainfall were calculated and simulated in Addinsoft Excel state 2014 by applying ARIMA statistical model for trend prediction, fluctuations and anomaly. The analysis indicates that in terms of rainfall, an increasing trend has been detected at Dir, Chitral, Saidu and Kalam meteorological stations, whereas a declining trend has been recorded at Timergara, Drosh and Malam Jabba meteorological stations. In terms of rainfall anomaly, the met station Dir has indicated comparatively high positive anomaly. Contrary to this, the met stations of Saidu and Drosh have experienced negative rainfall anomaly.
Heavy rainfall generated by landfalling tropical cyclones (TCs) can cause extreme flooding. A physics-based TC rainfall model (TCRM) has been developed and coupled with a TC climatology model to ...study TC rainfall climatology. In this study, we evaluate TCRM with rainfall observations made by satellite (of North Atlantic TCs from 1999 to 2018) and radar (of 36 U.S. landfalling TCs); we also examine the influence on the rainfall estimation of the key input to TCRM—the wind profile. We found that TCRM can simulate relatively well the rainfall from TCs that have a coherent and compact structure and limited interaction with other meteorological systems. The model can simulate the total rainfall from TCs well, although it often overestimates rainfall in the inner core of TCs, slightly underestimates rainfall in the outer regions, and renders a less asymmetric rainfall structure than the observations. It can capture rainfall distribution in coastal areas relatively well but may underestimate rainfall maximums in mountainous regions and has less capability to accurately simulate TC rainfall in higher latitudes. Also, it can capture the interannual variability of TC rainfall and averaged features of the time series of TC rainfall but cannot accurately reproduce the probability distribution of short-term (1 h) rainfall. Among the tested theoretical wind profile inputs to TCRM, a complete wind profile that accurately describes the wind structure in both the inner ascending and outer descending regions of the storm is found to perform the best in accurately generating various rainfall metrics.
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.
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
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.
In this study, three regional climate models (RCMs), CCLM5‐0‐15, RegCM4‐7 and REMO2015, from CORDEX‐CORE (AFR‐22) are evaluated in their ability to reproduce rainfall variability in Rwanda for the ...period 1981–2005. They are driven by three different global climate models (GCMs), namely MPI‐M‐MPI‐ESM‐LR, NCC‐NorESM1‐M and MOHC‐HadGEM2‐ES, and the European Centre for Medium‐Range Weather Forecasts Reanalysis (ECMWF‐ERAINT). Simulated rainfall is evaluated against observations from Rwanda Meteorology Agency to assess models' performance. A set of metrics are used to quantify discrepancies of models' simulations from observations. A possible association of El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) to rainfall over Rwanda is investigated. It is found that in general, all RCMs, their ensemble and multimodel ensemble means reproduce satisfactorily the spatial distribution of the mean seasonal rainfall (MSR), the mean rainfall annual cycle, and the interannual variability of the MSR for both March–April–May (MAM) and October–November–December (OND). However, significant biases in individual RCMs are observed with varying magnitude of bias in space. Observed MSR indicates a positive trend of 0.045 and 0.058 mm·day·year−1, respectively, for MAM and OND at 0.05 significance level, but almost all models indicate no significant trend (at 0.05 significance level). The seasonal correlations between observed rainfall anomalies and sea surface temperature (SST) anomalies indices across the tropical Pacific (Niño1+2 and Niño3.4) and Indian Oceans associated, respectively, with ENSO and IOD, although relatively weak, are reproduced by the three RCMs driven by ECMWF‐ERAINT and the multimodel ensemble means of ECMWF‐ERAINT and MPI‐M‐MPI‐ESM‐LR. Analysis of the Taylor diagram indicates that CCLM5‐0‐15_MPI‐M‐MPI‐ESM‐LR and the multimodel ensemble mean of MPI‐M‐MPI‐ESM‐LR outperform individual models. Overall, the evaluation finds reasonable model skill in representing seasonal rainfall climatology and variability, suggesting the potential use of CORDEX‐CORE (AFR‐22) RCMs for the assessment of future climate projections in Rwanda.
Observed mean daily seasonal rainfall and simulated by different CORDEX‐CORE (AFR‐22) RCMs driven by different GCMs and ECMWF‐ERANT, their ensembles and multi‐ensembles, over Rwanda for OND during the period 1981–2005.
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
PDF
