Temporal Characteristics Of Rainfall Research Articles

Future Change in Urban Flooding Using New Convection‐Permitting Climate Projections

AbstractRainfall intensity in the United Kingdom is projected to increase under climate change with significant implications for rainfall‐driven (combined pluvial and fluvial) flooding. In the UK, the current recommended best practice for estimating changes in pluvial flood hazard under climate change involves applying a simple percentage uplift to spatially uniform catchment rainfall, despite the known importance of the spatial and temporal characteristics of rainfall in the generation of pluvial floods. The UKCP Local Convective Permitting Model (CPM) has for the first time provided the capacity to assess changes in flood hazard using hourly, 2.2 km CPM precipitation data that varies in space and time. Here, we use an event set of ∼13,500 precipitation events across the three UKCP Local epochs (1981–2000, 2021–2040, and 2061–2080) to simulate rainfall‐driven flooding using the LISFLOOD‐FP hydrodynamic model at 20 m resolution over a 750 km2 area of Bristol and Bath, UK. We find that both the event set and uplift approaches indicate an increase in flood hazard under near‐term (2021–2040) and future (2061–2080) climate change. However, the event set produces markedly higher estimates of flood hazard when compared to the uplift approach, ranging from 19% to 49% higher depending on the return period. This suggests including the full spatiotemporal rainfall variability and its future change in rainfall‐driven flood modeling is critical for future flood risk assessment.

The Combined Power of Double Mass Curves and Bias Correction for the Maximisation of the Accuracy of an Ensemble Satellite-Based Precipitation Estimate Product

Precise estimation of the spatial and temporal characteristics of rainfall is essential for producing the reliable catchment response needed for proper management of water resources. However, in most parts of the world, gauged rainfall stations are sparsely distributed and fail to properly capture the spatial variability of rainfall. Furthermore, the gauged rainfall data can sometimes be of short length or require validation. Following this, we present a procedure that enhances the trustworthiness of gauged rainfall data and the accuracy of the rainfall estimations of five satellite-based precipitation estimate (SPE) products by validating them using the 1779 gauged rainfall stations across Thailand. The five SPE products considered include CMORPH-BLD; TRMM-3B42; CHIRPS; CHIRPS-PL; and TRMM-3B42RT. Prior to validation, the gauged rainfall dataset was verified using double mass curve (DMC) analysis to eliminate questionable and inconsistent readings. This led to the improvement of the Nash–Sutcliffe Efficiency (NSE) between the station of interest and its surroundings by 13.9% (0.758–0.863), together with an average 11.8% increase with SPE products, whilst dropping only 7% of questionable dataset. Three different bias correction (BC) procedures were applied to correct SPE products using gauge-based gridded rainfall (GGR). Once DMC and BC procedures were implemented together, the performance of the SPE products was found to increase significantly. Finally, the application of the ensemble weighted average of the three best-performing bias-corrected SPE products (Bias-CMORPH-BLD, Bias-TRMM-3B42, and Bias-CHIRPS) further enhanced the NSE to 0.907 and 0.880 in calibration and validation time periods, respectively. The proposed DMC-based correction SPE and the weighting procedure of multiple SPE products allows for an easy means of obtaining daily rainfall in remote locations with sufficient accuracy.

An Event-Based Stochastic Parametric Rainfall Simulator (ESPRS) for Urban Stormwater Simulation and Performance in a Sponge City

The temporal heterogeneity of rainfall is substantial in urban catchments, and it often has huge impacts on stormwater simulation and management. Using a design storm with a fixed pattern may cause uncertainties in hydrological modeling. Here, we propose an event-based stochastic parametric rainfall simulator (ESPRS) for stormwater simulation in a sponge city with green roofs, permeable pavements, and bioretention cells. In the ESPRS, we used five distributions to fit the measured rainfall events and evaluated their performance using Akaike’s Information Criterion, Anderson—Darling goodness-of-fit test, and p-values. The vast rainfall time series data generated using the ESPRS were used to run the storm water management model for outflow simulations in the catchment, thus revealing the influence of temporal rainfall characteristics on the hydrological responses. The results showed the following: (1) The ESPRS outperforms the Chicago method in predicting extreme precipitation events, and its control factors are the rainfall peak period, rainfall peak fraction, and cumulative rainfall fraction at the peak period. (2) The best-fit functions for the rainfall depth in each period have different distributions, mostly being in lognormal, gamma, and generalized extreme value distributions. (3) Rear-type precipitation events with high peak fractions are the most negative pattern for outflow control. The developed ESPRS can suitably reproduce rainfall time series for urban stormwater management.

Statistical characterization of rainfall fields based upon a 12-year high-resolution radar archive of Belgium

Spatial and temporal variability of rainfall input plays a critical role on the performance of the urban runoff simulations. For this reason, urban hydrological application demands accurate products of high-resolution and high-quality rainfall estimates, which use and understanding become critical for more robust models.In this study, the spatial and temporal characteristics of rainfall in Belgium are analyzed at fine scales. This is done by constructing a conceptual rain storm model using historical long-term radar data archive. This application involved the use of a state-of-the-art tracking algorithm, developed by Muñoz et al. (2018), to associate the spatial and temporal variations in rain storms and cells This algorithm enables better handling of high-resolution details and is capable of extracting rainfall objects/entities across various scales. Through tracking and conceptualizing these storm entities, spatial and temporal rainfall properties, such as geometric features, intensities, velocity, rainfall types or spatial patterns, can be extracted and analyzed. To accomplish this, twelve years of continuous high-resolution radar data are utilized in this work. The resulting analysis of the spatial and temporal characteristics of rainfall radar data is intended to set the ground for the future applications. More specifically, it can be used to refine and improve the existing long-term spatial rainfall generators, such as that the one implemented by Willems' in 2001.

The effect of spatial–temporal characteristics of rainfall on urban inundation processes

AbstractThe spatial–temporal characteristics of rainfall is one of the important elements affecting urban inundation. In this work, the effect of spatial–temporal characteristics of rainfall on urban inundation processes was investigated. To solve the problem that it is difficult to acquire the spatial–temporal characteristics of rainfall, the weather generator model was used to generate rainfall data in accordance with the climatic conditions of the study area. Then, precise rainfall data with 1 km2 spatial resolution and 5 min temporal resolution were obtained through the downscaling method. Finally, the GAST hydrodynamic model was used to simulate the urban inundation processes under different rainfall scenarios. The simulated results show that the inundation area and the inundation water volume increase with the rainfall spatial–temporal heterogeneity, and the inundation peak time lags behind. The impact of rainfall spatial–temporal heterogeneity on inundation weakens as the rainfall return period prolongs. When the rainfall spatial heterogeneity increases, the high‐risk inundation area enlarges, while the low‐risk inundation area shrinks. The temporal distribution of rainfall exerts the greatest impact on the inundation peak time. The research results can help researchers understand the key effects of the spatial–temporal characteristics of rainfall on urban inundation, and obtain relatively reliable simulated inundation results even without detailed information of the spatial–temporal characteristics of rainfall.

An adaptive approach to quantify weather-within-climate rainfall characteristics

Knowledge of subseasonal-to-seasonal (S2S) rainfall characteristics such as onset, duration, and demise of the rainfall cycle, and characteristics of dry periods that are between rainfall seasons, can provide important insight for the agriculture, health, disaster, and energy industries. Several methods have attempted to calculate temporal characteristics of rainfall and few have worked with intermittent dry periods and calculate intermittent dry period characteristics. Issues arise when applying these methods for complex rainfall regimes, or regions that have one or more of the following: (1) multiple rainfall seasons in a given year, (2) a relatively wet dry season, (3) unclear transitions between wet and dry seasons. This study develops a new and comprehensive method for calculating S2S rainfall characteristics, especially for regions that have complex annual rainfall cycles. The method consists of three steps: (1) create the annual cycle climatology of rainfall and classify its modality by the presence or absence of intermittent dry period(s), (2) identify seasonal windows of the rainfall cycle based on its climatological modality, (3) calculate yearly intermittent dry period characteristics and calculate temporal rainfall characteristics for each year using information on daily rainfall amount and the concurrent rate of change. Application and results of the method are given for the Caribbean, which exhibits the properties of a region with complex rainfall regimes. The method can be utilized under a meteorological or agronomical lens and is able to delineate false meteorological and agronomical onsets and demises. Meteorological onsets and demises that are calculated via each year’s Early-Rainy Season (ERS) and Late-Rainy Season (LRS) mean thresholds effectively characterize the seasonal evolution of mean onsets and demises in the Caribbean. The year-to-year variability of Mid-Summer Drought (MSD) characteristics, and onsets and demises that are calculated by climatological ERS and LRS mean thresholds resemble the variability of seasonal rainfall totals in the Caribbean and are statistically significantly correlated with the identified dynamical processes that impact each seasonal component of the rainfall cycle. The method has important implications for prediction, modeling capabilities, and understanding the variability of rainfall across timescales.

A Comparative Study on the Skill of CMIP6 Models to Preserve Daily Spatial Patterns of Monsoon Rainfall Over India

South Asian monsoon is a phenomena that plays out during June-September every year, due to the northward shift of the ITCZ which causes heavy rainfall over many countries of South Asia, including India. These rains are directly related to the lives and economic well-being of over a billion people. Indian monsoon is highly heterogeneous, due to the vast physiographic variations across the country. There is considerable interest among scientists and other stake-holders about possible future changes to Indian monsoon due to worldwide climate change. Simulations of future climate by global climate models under various scenarios can provide important clues for this. However, simulations of Indian monsoon in the historical period by global climate models under the CMIP5 family were found to be inaccurate in several aspects. Simulations by the new global climate models from the CMIP6 family are now available, and scientists are evaluating their ability to simulate Indian monsoon. In this work, we focus on one particular aspect of simulations by these models: the spatial distribution over daily rainfall over the Indian landmass during monsoon. We use a Machine Learning based probabilistic graphical model that can identify frequent spatial patterns of rainfall after creating a binary representation of rainfall. This model also helps us to identify spatial clusters, i.e., homogeneous regions within the Indian landmass with similar temporal characteristics of rainfall. We identify such frequent spatial patterns and spatial clusters from observed monsoon rainfall data, and also from simulations of monsoon rainfall by different CMIP6 models during the period 2000–2014. We evaluate the models by comparing the patterns and clusters identified from their simulations with those identified from observed data. We find that some of the CMIP6 models can simulate the spatial distribution of monsoon rainfall to a reasonable degree, but there are various limitations—most models underestimate extreme rainfall events and are unable to reproduce the regions of the landmass that are homogeneous with respect to rainfall.

Analysis of spatial and temporal rainfall characteristics of the North East region of India

Analysis of spatial and temporal rainfall characteristics of the North East region of India

Ability of an Australian reanalysis dataset to characterise sub-daily precipitation

Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the fractions skill score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location, with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA is found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75th percentile (90th percentile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.

The Spatial and Temporal Characteristics of Rainfall over the Lake Victoria Basin of Kenya in 1987-2016

Climate extremes have increased in the recent past and they are further being exacerbated by climate change and variability. In this paper, we sought to determine rainfall characteristics over the Lake Victoria Basin of Kenya in 1987-2016, as a basis of understanding climate variability. The methodology used included; Standardized Precipitation Index to depict variability, coefficient of variation for spatial analysis and the Mann-Kendall test to test the presence of trends in data. We established that Lake Victoria basin is relatively wet through-out the year, with two distinct rainfall seasons March-April-May (MAM) and October-November-December (OND) that support human livelihood and ecology. The normal wetness conditions have declined over time, paving way for both dry and wet extremes conditions between 1997-1998 and 2002-2006, respectively. The rainfall extremes have become frequent in the last decade in 2007-2016. We also established a decline in the MAM rainfall seasons, and an increase during the October-December rainfall seasons in 1987-2016. Furthermore, the number of rainy days has declined with the onset and cessations of both long rains and short rains having shown a variability of at least 50% and 30% respectively, in a range of about 100 to 200 Julian days. The decline in wet condition is likely to affect economic activities especially the rainfed agriculture. The changing rainfall trends over the basin therefore, call for proper human livelihood planning and ecological monitoring in order to achieve ecological sustainability.

Spatial and temporal characteristics of rainfall over a forested river basin in NW Borneo

The spatial and temporal patterns of rainfall over the Baram River Basin (BR) in Sarawak (Malaysian Borneo) were characterised through cluster analysis and multivariate statistics for the 25 year period from 1990 to 2014. Baram River Basin recorded an average annual rainfall of 3654 mm with high spatial and temporal variations. Ward’s method based analytical hieratical clustering (AHC) identified three homogeneous clusters (HC’s) of rain gauging stations (HC-I, HC-II, and HC-III). Rain gauges in HC-I are located in high rainfall domain, HC-II gauges are in moderate rainfall domain and HC-III gauges are in low rainfall domains. The moderate elevation regions in the Baram basin recorded the highest amounts of rainfall compared to lower and upper elevated regions. Multivariate statistics show variation in the distribution of monthly and annual rainfall within and between individual HC’s. Mean annual rainfall recorded at individual HC’s varies by around 1000 mm. Though the mean monthly rainfall distribution in HC’s varies by more than 70 mm, all clusters show a common pattern of high and low rainfall seasons related to monsoon characteristics of the region. The northeast monsoon (NEM) is the wettest period with highest mean rainfall falling in the months of November and December at all stations. In comparison, the southwest monsoon (SWM) is the driest season with the lowest mean rainfall falling in July. It was also noted that the inter-monsoon periods are actually wetter than the SWM season. Overall, rainfall in the Baram shows high inter-annual variability with dominant low rainfall domain in the lower (coastal) and upper (plateau) reach with highly localised high rainfall domain surrounded by fluctuating moderate rainfall areas in the middle reaches of the river basin. The localised nature of high rainfall domain in the central (south and southwest) part of the basin suggests that the surrounding elevated mountains and hills in that region influence the high rainfall recorded. The findings of the present research will aid in formulating water resource management plans, large scale plantation and agricultural practices and any other hydrological development projects in the region.

Downscaling Rainfall in Benin: A Deltaic City of the Niger Delta Region, under two Emission Scenarios

This study endeavored to downscale rainfall in Benin City so as to reveal what pathway would be more tolerable in the metropolis both now and in the future, putting the current effect of rainfall in the area in mind. Quasi-experimental research design was adopted and data for rainfall (1985-2015) was collected from the archive of the Nigerian meteorological agency (NIMET). While data of large scale predictors were collected from the HadCM3 data achieve. Calibration period was from 1985 to 2001, while validation was 2001 to 2015. Analysis of variance was used to trace if there was a significant difference in the temporal rainfall characteristics for A2 and B2 emission scenarios. However the study found that the predictors that explain rainfall patterns in the area were Shum, Rhum, R850 and R500; and during calibration it was found that rainfall significantly depended on these predictors at P<0.05. However, during validation, statistics show that the models performed considerably well in which case the modeled data related well with the observed data for all seasons of the year i.e. DJF season r-0.92 (r2-0.85; RMSE 0.31; RSD, 0.89) at P<0.05; MAM r-0.81 (r2-0.66; RMSE 0.41; RSD, 0.91) at P<0.05; JJA r-0.76 (r2-0.58; RMSE 0.31; RSD, 0.97) at P<0.05; SON r-0.82 (r2-0.67; RMSE 0.26; RSD, 0.82) at P<0.05. However there are some uncertainties in the data set for which caution must be taken while relying on the out puts of the model of the current study. As a result, the study recommends the use of clean technology, and a development of flood risk preparedness to deal with flood effects as a result increasing amounts of rainfall in the metropolis amongst others.

Effect of rainfall spatial distribution and duration on minimum spatial resolution of rainfall data for accurate surface runoff prediction

The effect of rainfall spatial distribution and duration on determining rainfall spatial resolution for runoff discharge prediction and equilibrium time estimation was studied. We simulated hydrographs and equilibrium times of storms which have the same spatial averaged rainfall intensity with wide range of spatial distributions and durations under stationary storm conditions on an idealized plane. The equilibrium times under various rainfall conditions were estimated with an approximate solution based on the kinematic wave assumption. The runoff hydrographs were simulated using a kinematic wave runoff model. The approximate and computed results were used to investigate the relationship between the spatial–temporal rainfall characteristics and rainfall spatial resolution. From the simulated results, it was found that (i) A required rainfall spatial resolution to achieve threshold accuracy depended on rainfall spatial distribution. For the simulated cases, the critical rainfall spatial resolution was higher as the slope of rainfall spatial distribution was steeper. (ii) The rainfall duration, as well as rainfall spatial distribution, was an important factor for determining a required rainfall spatial resolution for accurate flood prediction. The required rainfall spatial resolution to achieve threshold prediction accuracy was higher as the rainfall duration was shorter. (iii) The rainfall spatial distribution and duration which were traditionally neglected need to be considered in lumped runoff modelling or measurement as an important factor. Although limited to the ideal test cases, the maximum errors due to the low rainfall spatial resolution for the equilibrium time and peak discharge were about 25% and 35%, respectively.

Spatial and temporal distribution of rainfall and drought characteristics across the Pearl River basin

Understanding rainfall trends as well as drought characteristics plays a key role in watershed development and management. In this study, the spatial and temporal characteristics of rainfall and drought based on temperature and precipitation data observed in 48 meteorological stations from 1959 to 2012 across the Pearl River Basin in China were analyzed. The possible influence of El Nino-Southern Oscillation (ENSO) and El Nino Modoki (ENSO_M) events on seasonal drought based on the Standardized Precipitation-evapotranspiration Index (SPEI) were also investigated. The results show that annual and seasonal rainfall decreased slightly in most areas, annual and seasonal daily precipitation concentration decreased in a few areas, monthly rainfall had an irregular distribution but with no significant trend detected, and rainfall seasonality increased in most areas. Drought tended to worsen during recent years, especially in the upper reaches, and seasonal drought also tended to become serious or occurred frequently across the Pearl River Basin. The drought patterns are not only related to the decreasing trends in rainfall but also to changes in the daily rainfall concentration, monthly rainfall heterogeneity, and rainfall seasonality. Both ENSO and ENSO_M events had an influence on summer drought in the middle-upper reaches. The ENSO events dominated the patterns of autumn drought in the Pearl River Delta, and the ENSO_M events strongly affected the changing patterns of autumn drought in the middle-upper reaches and northern parts of the middle-lower reaches.

Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China

In this study, efforts are focused on the comparison and validation of standard Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products—Version-7 3B42RT estimates before and after assimilation by using a Kalman filter with independent rain gauge networks located within the Jinghe basin of China. Generally, the direct comparison of TMPA precipitation estimates to 200 collocated rain gauges from 2006 to 2008 demonstrate that the spatial and temporal rainfall characteristics over the region are well captured by the assimilation estimates. Especially, results also show that using Kalman filter to assimilate TRMM-based multi-satellite real-time precipitation estimates tends to perform well over regions, where gauge network is rather sparse. Last, this study highlights that accurate detection and estimation of precipitation in the summer season by Kalman filter, particularly for nonlinear convective precipitation events, is still a challenging task for the future development of assimilation technique for improving the satellite-based precipitation accuracy.

Spatial and temporal characteristics of rainfall in a data-scarce region: case of Kinshasa and Bas-Congo in Democratic Republic of Congo

ABSTRACTThe aim of the present paper was to improve understanding of the rainfall dynamics in Bas-Congo and Kinshasa provinces, in Democratic Republic of Congo (DRC). The first objective of the study was achieved by analysing the spatial correlations of monthly, seasonal, annual and individual monthly rainfall amounts of Kinshasa and Bas-Congo. The second objective was achieved through investigating and quantifying the temporal trends and their spatial variations. The results demonstrated notably high average inter-station correlation of +0.63 for dry season series, followed by monthly rainfall series with an average inter-station correlation of +0.58. However, there was no station with a stable monthly rainfall regime, i.e. with mean precipitation concentration index lower than 10% (it varies between 14.2 and 21.9%). Moreover, Kinshasa experienced an increase of rainfall with an average annual rate of change of +4.59 mm/year for the period 1961–2006. The results will be helpful for efficient water resources management and for mitigating the adverse impacts of future extreme drought or flood occurrences.Editor M.C. Acreman Associate editor N. Verhoest

Spatial and temporal characteristics of rainfall across Ganjiang River Basin in China

The hydrological variations of Ganjiang River Basin have significant influence on the ecological environment of Poyang Lake, which is one of the largest freshwater lake and wetland in the world. This study analyzed the spatial and temporal characteristics of rainfall across Ganjiang River Basin. The analyses include annual total rainfall amount (ATRA), annual total rainy day (ATRD) and annual mean daily rainfall intensity (AMDRI). To detect changes in the hydrological trends, data from 19 rainfall stations from 1953 to 2013 were used in the analyses. First, quality control and homogeneity detection was carried out to examine the annual rainfall series. Second, the spatial correlation analysis was used to identify the spatial relationship among the measurements from different stations. Finally, the statistics of coefficient of variation (CV) and average were used to analyze the interannual variation trend. The modified Mann–Kendall (MMK) trend test method was used to detect the temporal characteristics of rainfall. The results include: (1) Some outliers were detected and corrected. (2) The correlation of ATRA and ATRD series of all the stations had lower spatial variability while the AMDRI series of all the stations had higher spatial variability. On the other hand, the ATRA, ATRD and AMDRI series of single stations show similar spatial variability and the influencing radius of the rainfall events was less than 50 km. (3) the rainfall at the northern and southern parts of the basin had smaller CV. Further, by applying the MMK test method to the ATRA, ATRD, and AMDRI series, the ATRA series has an increasing trend which is opposite to that of the ATRD series. The increasing trend in the ATRA series also led to the increasing trend in the AMDRI series. These stations are mainly in central and northern parts of the basin which are more likely to experience droughts and floods. Thus, for the central and northern parts of the basin, they should receive particular attention on the prevention and treatment of droughts and floods.

Hydrologic Evaluation of the TRMM Multisatellite Precipitation Analysis Over Ganjiang Basin in Humid Southeastern China

This study assesses the successive Version-6 and Version-7 TRMM Multisatellite Precipitation Analysis (TMPA) products including near-real-time products (3B42RTV6 and 3B42RTV7) and post-real-time research products (3B42V6 and 3B42V7) from March 2002 to February 2008 over Ganjiang Basin in the humid southeastern China, located in the lower reach of Yangtze River. Direct comparison of TMPA rainfall estimates with ground observation shows that the spatial and temporal rainfall characteristics over this region are well captured by 3B42V6, 3B42RTV7, and 3B42V7. In terms of daily grid-based comparison, 3B42RTV7 has been improved over 3B42RTV6 by reducing relative bias (RB) from -30.25% to 4.93%; 3B42V6, 3B42RTV7, and 3B42V7 show close performance with each other with RB less than 5% and moderate correlative coefficient (CC, 0.59). Daily hydrologic simulation with Xin'anjiang hydrologic model using these TMPA products as input shows that: 1) 3B42V6 and 3B42V7 demonstrate very comparable hydrologic skills, which are close to those of the reference rainfall with high Nash-Sutcliffe index (NSCE, 0.71 and 0.72, respectively) and strong correlation (CC = 0.88); and 2) 3B42RTV7 displays a better hydrologic performance than 3B42RTV6 by increasing the NSCE from 0.56 to 0.59, improving CC from 0.81 to 0.87, and reducing RB from -33.15% to 20.93%. This improvement of real-time TMPA product shows its potential hydrologic utility in water resource management and flood forecast. Finally, this study provides useful reference for TMPA developers and insights for the end users in their applications.

Spatial and temporal characteristics of rainfall in Africa: Summary statistics for temporal downscaling

AbstractAn understanding of rainfall characteristics at multiple spatiotemporal scales is of great importance for hydrological, biogeochemical, and land surface modeling studies. In the present study, patterns of rainfall are analyzed over the African continent based on 3 hourly 0.25° Tropical Rainfall Measuring Mission (TRMM) estimates between 1998 and 2012 to produce monthly statistical summaries. The selected rain event properties are multiyear means of precipitation total amount (mm), event frequency (number), rate (mm/h), and duration (h) calculated independently for each calendar month. Analysis of 3 hourly and daily events in the 1998–2012 period suggests that rainfall amount can be summarized using gamma probability density functions. Assuming stationarity, gamma probability density functions of the total depth of 3 hourly and daily events are estimated and then used for temporal downscaling of monthly rainfall estimates (past or future). As a result, we generate 3 hourly and daily rainfall estimates that are pixelwise statistically indistinguishable from the observations while preserving monthly totals. Example scripts are provided that can be used to access monthly statistics and implement downscaling using archival (or projected) monthly rainfall estimates. These statistics could also be utilized for the assessment of rainfall from atmospheric models.

Spatial and Temporal Variability of Rainfall in Anand District of Gujarat State

Rainfall Variability is one of the key factors that affect agricultural production in any region. Hence the proper understanding of rainfall pattern and its trends may help water resources development and to take decisions for the developmental activities of that place. The present study is an attempt to evaluate the spatial and temporal rainfall variability of Anand district of Gujarat State in Western India. Anand in Gujarat State popularly known as the milk capital of India is located in the Southern part of Gujarat near Gulf of Cambay. The district has eight Talukas with district headquarter at Anand. Khambhat Taluka of this district has natural border with Gulf of Cambay. The normal rainfall of this district is 687mm from South-West Monsoon with an average of 40 rainy days. The normal onset of monsoon is from 3rd week of June and the normal cessation is in the 4th week of September. In the present study the spatial and temporal rainfall characteristics of this district are studied. Long-term annual and monthly rainfall data (Monsoon months) are considered from 1901 to 2014. The daily rainfalls of monsoon months of all the eight Talukas are analysed for the last five years from 2010 to 2014. It was found out that spatial and temporal variability is high in the District.