This paper presents a detailed spatiotemporal analysis of the rainfall variability, seasonality, and the extreme characteristics of Tehri catchment located in the lower Himalayan region in India. To ...this end, the daily rainfall data is extracted from 22 grids for 117 years (1901–2017) from the high-resolution (0.25° × 0.25°) gridded observation dataset. Monthly rainfall distribution is evaluated using precipitation concentration index (
PCI
) and seasonality index. The extreme rainfall indices, viz., maximum 1-day rainfall (Rx1Day), maximum 5-day rainfall (Rx5Day), number of rainy days (NxRainy), total precipitation in rainy days (PRCPTOT), number of heavy rainfall events (NxHeavy), maximum consecutive wet days (CWD), and simple daily intensity index (SDII) are computed for each year considering the thresholds suggested by India Meteorological Department (IMD). The Mann–Whitney-Pettitt test when applied to the annual rainfall time series revealed the year 1958 to be the statistically significant change point. The non-parametric modified Mann–Kendall and Sen’s slope tests are employed to detect the trend in monthly, seasonal, annual rainfall time series, extreme precipitation indices, and seasonality indices for both the pre- and post-1958 periods. The annual rainfall over the grids mostly possessed higher negative trends during 1959–2017 than those during 1901–1958, mainly due to the decreasing trends in post-monsoon and winter seasons. Compared to 1901–1958, NxRainy, CWD, and PRCPTOT exhibited a remarkable decreasing trend whereas NxHeavy, Rx1Day, Rx5Day, and SDII exhibited higher positive trends during 1959–2017, indicating intensification of precipitation. The precipitation over the catchment has been more concentrated in the latter epochs of monsoon season and annual rainfall and it is also evident from the increasing trends of the seasonality indices. There is no such study dealing comprehensively with identification of extreme characteristics, seasonality/concentration characteristics, and various categorical trends of precipitation in a Himalayan region reported in literature. This study will be useful in understanding the decreasing trend of precipitation volume coupled with increasing intensity and concentration and it is quite critical for a Himalayan catchment.
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
Abstract Modelling hydrological process in the critical zone not only contributes to a better understanding of interactions across different Earth surface spheres but also holds significant practical ...implications for water resource management and disaster prevention. Rainfall‐runoff simulation in critical zones is particularly challenging due to the amalgamation of temporal and spatial complexity, rainfall variability, and data limitations. As a pivotal input variable of hydrological models, accurate estimation of areal rainfall is critical to successful runoff simulation. However, most estimation methods ignore temporal information, thereby increasing uncertainty in rainfall estimation and constraining the precision of rainfall‐runoff simulation. In this study, the matrix decomposition‐based estimation method (F‐SVD), which considers the spatial and temporal correlation of the rainfall process is employed to estimate areal rainfall. The superiority of the method in producing two‐dimensional rainfall information is evaluated through its application in runoff simulation with the Xin'anjiang model. The simulation results of selected flood events in the Jianxi basin in southeast China, spanning from 2009 to 2019, are compared with those of two widely used rainfall estimation methods, namely Arithmetical Mean (AM) and Thiessen Polygons (TP). The results show that (1) F‐SVD not only produces the highest Pearson correlation coefficient between rainfall and runoff series but also reduces the number of flood events with abnormal rainfall‐runoff relationships; (2) the Xin'anjiang model based on F‐SVD achieves the highest Nash‐Sutcliffe efficiency and lowest Relative Error, and performs best in simulating peak flow and its occurrence time as compared to AM and TP. This study contributes to a finer characterization of watershed rainfall distribution, enhancing the accuracy and sharpness of runoff simulation. It provides reliable data support for critical zone research and offers a scientific foundation for rationally allocating and managing water resources.
Climate change significantly influences characteristics of rainfall events including rainfall depth, rainfall duration, inter‐event time and temporal patterns that directly affect water resources ...management, flood defence and hydraulic structure design. In this study, a framework is proposed to analyse daily‐scale rainfall event characteristics based on global climate model (GCM) simulations. This framework includes bias correction of raw GCM‐simulated rainfall series, selection of good‐performing bias‐corrected GCMs based on the mean absolute percentage error (MAPE) and evaluation of selected GCMs' skills in simulating rainfall event characteristics and finally assessment of changes in rainfall event characteristics in the future. In this study, 17 GCMs, four representative concentration pathways (i.e., RCP2.6, RCP4.5, RCP6.0 and RCP8.5) and two future periods (i.e., 2041–2070 and 2071–2100) are considered. After bias correction of the GCMs using the monthly‐scale double gamma distribution, 9 out of 17 GCMs with MAPE values smaller than 20% in the historical period 1971–2000 are selected. In general, these selected GCMs well capture the rainfall characteristics of different rainfall event classes. The multi‐model ensembles suggest that compared to the historical period, the frequency of rainfall events with an extreme depth, short duration and long inter‐event time will increase in the two future periods and the change in 2071–2100 is generally larger than that in 2041–2070, indicating that more extreme climate conditions may occur in Qu River basin in the future. Moreover, the temporal patterns of heavy rainfall events will become more non‐uniform with more concentrated peak rainfall. The frequency of the delayed rainfall type (i.e., peaks occurring at the end of the rainfall event) will increase in the future, which can probably cause more severe floods and is very detrimental to flood defence in this study area.
With global warming, the frequency of rainfall events with an extreme depth, short duration and long inter‐event time will probably increase in the future. The rainfall patterns of the delayed rainfall type (i.e., peaks occurring at the end of a rainfall event) are becoming more non‐uniform with more rainfall concentrating in the peak parts, particularly for heavy rainfall events. The future changes of these rainfall event characteristics pose a great challenge to flood defence in the study area.
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.
The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide quasi-global (60° N–60° S) precipitation estimates, beginning March 2014, from the ...combined use of passive microwave (PMW) and infrared (IR) satellites comprising the GPM constellation. The IMERG products are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account. In this study, IMERG version 3 Early, Late, and Final (IMERG-E,IMERG-L, and IMERG-F) half-hourly rainfall estimates are compared with gauge-based gridded rainfall data from the WegenerNet Feldbach region (WEGN) high-density climate station network in southeastern Austria. The comparison is conducted over two IMERG 0.1° × 0.1° grid cells, entirely covered by 40 and 39 WEGN stations each, using data from the extended summer season (April–October) for the first two years of the GPM mission. The entire data are divided into two rainfall intensity ranges (low and high) and two seasons (warm and hot), and we evaluate the performance of IMERG, using both statistical and graphical methods. Results show that IMERG-F rainfall estimates are in the best overall agreement with the WEGN data, followed by IMERG-L and IMERG-E estimates, particularly for the hot season. We also illustrate, through rainfall event cases, how insufficient PMW sources and errors in motion vectors can lead to wide discrepancies in the IMERG estimates. Finally, by applying the method of Villarini and Krajewski (2007), we find that IMERG-F half-hourly rainfall estimates can be regarded as a 25 min gauge accumulation, with an offset of +40 min relative to its nominal time.
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 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.
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