Flood hydrologic response is influenced by rainfall structure (i.e., variability in space and time). How this structure shapes flood frequency is unknown, and flood frequency analyses typically ...neglect or simplify potentially important aspects of rainfall variability. This study seeks to understand how rainfall structure impacts flood frequency. We use stochastic storm transposition combined with a 15‐year record of hourly, 4‐km2 radar rainfall to generate 10,000 synthetic extreme rain events. These events are resampled into four “scenarios” with differing spatial and temporal resolutions, which are used as input to a distributed hydrologic model. Analysis of variance is used to identify the proportions of total flood peak variability attributable to spatial and to temporal rainfall variability under two antecedent soil moisture conditions. We simulate peak discharges for recurrence intervals of 2 to 500 years for 1,343 subwatersheds ranging in size from 16 to 4,400 km2 in Turkey River in the Midwestern United States, which is situated in a typically humid continental climactic region. Antecedent soil moisture modulates the role of rainfall structure in simulated flood response, particularly for more frequent events and large watershed scales. Rainfall spatial structure is more important than temporal structure for drainage areas larger than approximately 2,000 km2 (approximately 200 km2) for wet (dry) initial soil conditions, especially when soils are dry, while the reverse is true for smaller subwatersheds. The results appear to be related to the differing propensities for surface and subsurface runoff production as a function of basin scale, event magnitude, and soil saturation. Our results suggest that hydrologic model‐based flood frequency analyses, and particularly efforts attempting to spanning a range of scales, must carefully consider rainfall structure.
Plain Language Summary
There is increasing interest in “derived flood frequency analysis”: the use of stochastically generated rainfall and high‐resolution distributed hydrologic models to understand current and future flood frequency. Potential issues surrounding rainfall structure, resolution, and accuracy in this context have received very little attention, however. Design storm methods, common in hydrologic engineering practice, use highly idealized assumptions regarding rainfall space‐time structure, and the consequences of these assumptions are poorly understood. This study seeks to better understand how flood frequency is affected by rainfall spatial and temporal structure, as well as how these effects are modulated by watershed initial conditions (i.e., antecedent soil moisture). The findings, which are summarized in the manuscript's , should be useful for future researchers and practitioners. We believe that this work constitutes a useful contribution in the effort to advance the derived flood frequency analysis.
Key Points
Framework for partitioning impacts of rainfall spatial and temporal variability on flood frequency
The impact of rainfall structure varies significantly with antecedent soil moisture, watershed scale, and event magnitude
Rainfall temporal variability is more important than spatial variability at small scales; the opposite is true at large scales
Southern West Africa (SWA) is characterised by a wide range of rainfall types, the relative importance of which have never been quantified on a regional level. Here, we use 16 years of ...three‐dimensional reflectivity data from the Tropical Rainfall Measuring Mission–Precipitation Radar (TRMM‐PR) to objectively distinguish between seven different rainfall types in three subregions of SWA.
Highly organized Mesoscale Convective System (MCS) events are the dominating rain‐bearing systems in SWA. They tend to occur in highly sheared environments as a result of mid‐level northeasterlies ahead of a cyclonic vortex. Their contribution to annual rainfall decreases from 71% in the Soudanian to 56% in the coastal zone. MCSs in SWA also propagate slower than their Sahelian counterparts and occur predominantly at the start of the first coastal rainy season. However, in terms of numbers, about 90% of rainfall systems are weakly organized classes, particularly small‐sized, highly reflective and moderately deep (40 dBZ at altitude <10 km) systems. Contrary to MCSs, less organized convection typically occurs during and after the passage of a cyclonic vortex within a regime of deep westerly anomalies, low wind shear and low to moderate CAPE (convective available potential energy), bearing some resemblance to what has been termed “monsoon” or “vortex rainfall”. Combining TRMM‐PR rainfall system identification with infrared‐based cloud tracking reveals that organized convection over SWA typically lasts for more than >9 h, whereas less intense rainfall types tend to be short‐lived, diurnal phenomena.
This novel approach stresses the relevance of mid‐level (wave) disturbances on the type and lifetime of convective systems and thereby their regionally, seasonally and diurnally varying contribution to rainfall amount. The present study suggests further investigations into the character of the disturbances as well as possible implications for operational forecasting and the understanding of rainfall variability in SWA.
Southern West Africa is influenced by a wide spectrum of rainfall systems that differ with regard to, amongst others, lifetime, horizontal extent and intensity, as illustrated in the image. Their relative importance to annual rainfall and the characteristic synoptic environments have not yet been quantified on a regional level. The present study reveals substantially different (thermo‐)dynamic controls at mid‐ and near‐surface levels, favouring different rainfall types, and suggests possible implications for operational forecasting of rainfall in this region.
Spatio‐temporal variability of contributions of stratiform and convective rainfall to Indian monsoon (June–September) rainfall have been investigated using hourly rainfall data of well distributed ...126 stations in India for the period 1969–2015. The criteria used for identifying stratiform rainfall are hourly rainfall ≤5 mm and spatial homogeneity. The study showed that Indian monsoon rainfall exhibits two distinctive features, viz. stratiform component dominating over peninsular (southern) India and convective component dominating over northern India. The diurnal variation shows domination of convective activity in the afternoon hours over the northern parts of India. Intra‐seasonal variability in the stratiform rainfall is the lowest over the West Coast stations through‐out the season. While it shows low values during the onset (June) and withdrawal (September) phases of monsoon and higher values during July–August in Peninsular India. Inter‐annual variations in the convective rainfall are larger than that of stratiform rainfall all over the country. Trend analysis indicates that both stratiform and convective rainfall are decreasing over the central parts of the country and increasing significantly along West Coast and western parts of the country. During excess monsoon year, convective rainfall activity is more than in deficit monsoon year, in Central India. The results brought out in the study will be useful as a proxy for understanding the spatial and temporal variability of the latent heating fields over India in the monsoon season and validation of model simulations of clouds and rainfall types.
Stratiform rainfall proportion is more in peninsular (southern) India, while convective component dominates over northern India. The diurnal variation shows domination of convective activity in the afternoon hours over the northern parts of India. Intra‐seasonal variability in the stratified rainfall is the lowest over the West Coast stations through‐out the season. Intra‐seasonal variation in the stratiform rainfall shows low values during the onset (June) and withdrawal (September) phases of monsoon and higher values during July–August in Peninsular India. Inter‐annual variations in the convective rainfall are larger than that of stratiform rainfall all over the country. Stratiform rainfall contributions are decreasing over central parts of the country. During excess monsoon year, convective rainfall activity shows substantial rise in Central India.
Understanding and quantifying long-term rainfall variability at regional scale is important for a country like India where economic growth is very much dependent on agricultural production which in ...turn is closely linked to rainfall distribution. Using machine learning techniques viz., cluster analysis (CA) and principal component analysis (PCA), the spatial and temporal rainfall patterns over the meteorological subdivisions in India are examined. Monthly rainfall data of 117 years (1901–2017) from India Meteorological Department over 36 meteorological subdivisions in India is used in this study. Using hierarchical clustering method, six homogeneous rainfall clusters were identified in India. Among the rainfall clusters, Group 1 had 30% dissimilarity with Groups 2, 3, and 4 while Group 5 and Group 6 are highly dissimilar (more than 90% dissimilarity) with the rest of the groups. Rainfall seasons in each group were further classified into dry, wet, and transition periods. The duration of dry period is smaller in group which consists of subdivisions from southern part of the country. The transition period between dry and wet period was found to be smaller for subdivisions in the coastal region. Both CA and PCA showed high rainfall variability in Groups 5 and 6, which comprise subdivisions from north east, Kerala, Konkan, and costal Karnataka and low rainfall variability in Groups 1 and 2 which comprise subdivisions from east, north, and central part of the country. Strong negative trend in annual and Indian summer monsoon rainfall is seen in northeast India and Kerala while positive trend is observed over costal Karnataka and Konkan region. The negative trend in post monsoon rainfall particularly over the peninsular and northeast India indicates weakening of northeast monsoon rainfall in the country.
The characteristics of raindrop size distributions (DSDs) and vertical structures of rainfall during the Asian summer monsoon season in East China are studied using measurements from a ground‐based ...two‐dimensional video disdrometer (2DVD) and a vertically pointing Micro Rain Radar (MRR). Based on rainfall intensity and vertical structure of radar reflectivity, the observed rainfall is classified into convective, stratiform, and shallow precipitation types. Among them, shallow precipitation has previously been ignored or treated as outliers due to limitations in traditional surface measurements. Using advanced instruments of 2DVD and MRR, the characteristics of shallow precipitation are quantified. Furthermore, summer rainfall in the study region is found to consist mainly of stratiform rain in terms of frequency of occurrence but is dominated by convective rain in terms of accumulated rainfall amount. Further separation of the summer season into time periods before, during, and after the Meiyu season reveals that intrasummer variation of DSDs is mainly due to changes in percentage occurrence of the three precipitation types, while the characteristics of each type remain largely unchanged throughout the summer. Overall, higher raindrop concentrations and smaller diameters are found compared to monsoon precipitation at other locations in Asia. Higher local aerosol concentration is speculated to be the cause. Finally, rainfall estimation relationships using polarimetric radar measurements are derived and discussed. These new relationships agree well with rain gauge measurements and are more accurate than traditional relations, especially at high and low rain rates.
Key Points
First report of 2DVD and MRR measurements in China during summer monsoon season
Structure and DSD of convective, stratiform, and shallow precipitation types
Intrasummer variation of DSD and radar rainfall estimation relation
Rainfall pattern (RP) and precipitation concentration (PC) are two critical indices for measuring rainfall. Detecting their changes under global warming helps to understand better the rainfall ...variability and the flooding formation. Using the Guangdong Province, China, as a study case, five criteria were used to determine the independent precipitation events for hourly precipitation data from 1967 to 2012. The RP and PC of the events were identified, and then, their spatiotemporal variability was investigated further. The results show that (a) the occurrence frequency during 46 years, average rainfall amount and duration of independent rainfall in the coastal areas were higher than in other regions. (b) The dominant RPs in Guangdong are unimodal (more than 70%), especially the pattern with early peak (mode I; 39.5%); however, the number of stations with mode I as dominant RP decreased over time, while those with mode III (the pattern with late peak) increased. (c) Precipitation concentration index can be used for measuring the concentration of independent rainfall events, and its fixed minimum inter‐event time has significant impacts on RP and PC. The PC of the events with early peak is higher, and the concentration in the west is generally higher than those in the east under different RPs.
The main contribution of this study is to discuss the spatiotemporal variability of rainfall pattern (RP) and concentration based on the event‐based rainstorm (hourly scale), especially the first usage of precipitation concentration index (PCI) to measure the concentration of each independent rainfall event. The key findings include the following: (a) the occurrence frequency during 46 years, average rainfall amount and duration of independent rainfall in the coastal areas were higher than in other regions. (b) The dominant RPs in Guangdong are unimodal (more than 70%), especially the pattern with early peak (mode I; 39.5%); however, the number of stations with mode I as dominant RP decreased over time, while those with mode III (the pattern with late peak) increased. (c) PCI can be used for measuring the concentration of independent rainfall events, and its fixed minimum inter‐event time (MIT) has significant impacts on RP and PC. The PC of the events with early peak is higher, and the concentration in the west is generally higher than those in the east under different RPs.
Rainfall is time concentric in nature. The spatial and temporal distribution of rainfall is changing over the Earth with recent anthropogenic warming. The study explores various characteristics of ...annual and seasonal concentration of rainfall across India using the precipitation concentration index (PCI) and its trends during the period of 1986–2015, based on a high‐resolution gauge‐based rainfall data (0.25 × 0.25°), obtained from the India Meteorological Department (IMD). An intercomparison is made with 11 other gridded rainfall datasets to infer whether these datasets can reasonably reproduce the spatiotemporal distribution of PCI and its trends in various homogenous rainfall regions of India or not. These datasets are categorized into gauge‐based (APHRODITE, GPCC, CPC), satellite‐derived (CHIRPS, PERSIANN‐CDR), and reanalysis (JRA‐55, MERRA‐2, NCEP‐2, PGF, ERA‐Interim, ERA‐5). On annual scale, about 8.52, 24.1, and 67.38% of area of India are under moderate, irregular, and strongly irregular rainfall distribution, respectively. Spatial variation of PCI in India is influenced by geographical factors such as latitude, longitude, and elevation. Significant increasing and decreasing trends in annual PCI have been observed in the northeast, eastern, and western coasts of India, respectively. Gridded data intercomparison suggests that the gauge‐based APHRODITE, GPCC, and satellite‐derived CHIRPS datasets better perform in capturing the temporal and spatial variation of PCI across India when compared to the IMD gridded dataset, whereas the ERA‐5 performs better among the reanalysis datasets. However, the rainfall datasets exhibited marked differences with the IMD while estimating the annual and seasonal trend and its magnitude across various regions of India. The JRA‐55 overestimated areas of positive trend and its magnitude on annual and seasonal scale. These findings have practical implications for hydroclimatic studies.
Rainfall is seasonally concentrated in India and exhibits moderate to highly irregular distribution. Rainfall is highly concentrated in arid and semi‐arid regions of NW India. Gauge‐based APHRODITE and GPCC data effectively capture the spatial pattern of annual and seasonal PCI in India compared to IMD gridded dataset while CHIRPS better performed than the PERSIANN‐CDR among satellite rainfall datasets and ERA‐5 and MERRA‐2 datasets among reanalysis datasets. Gridded rainfall products exhibited higher bias in observational data‐sparse regions. In the figure, annual and seasonal PCI over India (a) and its coefficient of variation (b) during the period of 1986–2015.
Abstract
Rainfall–runoff modeling is a complex nonlinear time-series problem in the field of hydrology. Various methods, such as physical-driven and data-driven models, have been developed to study ...the highly random rainfall–runoff process. In the past 2 years, with the advancement of computing hardware resources and algorithms, deep-learning methods, such as temporal convolutional network (TCN), have been shown to be good prospects in time-series prediction tasks. The aim of this study is to develop a prediction model based on TCN structure to simulate the hourly rainfall–runoff relationship. We use two datasets in the Jingle and Kuye watersheds to test the model under different network structures and compare with the other four models. The results show that the TCN model outperforms the Excess Infiltration and Excess Storage Model (EIESM), artificial neural network, and long short-term memory and improves the flood forecasting accuracy at different foreseeable periods. It is shown that the TCN has a faster convergence rate and is an effective method for hydrological forecasting.
The present study investigates long-term changes in the rainfall regime over the Sabarmati River Basin, Western India, during 1981–2020 using computational and spatial analysis tools. Daily gridded ...rainfall data from India Meteorological Department (IMD) at 0.25 × 0.25 spatial resolution was employed to determine changes in rainfall at annual, monthly, and seasonal scales and analyze changes in rainfall characteristics using different thresholds for dry/ wet days and prolonged spells over Western India. Mann–Kendall test, Sen slope estimation, and linear regression analysis indicate that annual and monsoon rainfall over the basin has increased while the rest of the seasons have shown a declining trend. However, none of the trends obtained was found to be statistically significant. Spatial analysis of rainfall trends for each decade between 1980 and 2020 revealed that certain parts of the basin had experienced a significant declining trend during 1991–2000. Monthly rainfall analysis indicates the presence of a unimodal distribution of rainfall and a shift in rainfall towards later monsoon months (August and September). It is also inferred that days with moderate rainfall have decreased while low and extreme rainfall events have increased over the basin. It is evident from the study that the rainfall regime is highly erratic, and the study is important in understanding the changes in the rainfall regime during the last 40 years. The study has significant implications for water resource management, agricultural planning, and mitigation of water-related disasters.
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