Rossby waves in May and the Indian summer monsoon rainfall

Large amplitude stationary Rossby wave trains with wavelength in the range 50 degrees to 60 degrees longitude have been identified in the upper troposphere during May, through the analysis of 200 hPa wind anomalies. The spatial phase of these waves has been shown to differ by about 20 degrees of longitude between the dry and wet Indian monsoon years. It has been shown empirically that the Rossby waves are induced by the heat sources in the ITCZ. These heat sources appear in the Bay of Bengal and adjoining regions in May just prior to the onset of the Indian summer monsoon. The inter-annual spatial phase shift of the Rossby waves has been shown to be related to the shift in the deep convection in the zonal direction.

Numerical Modelling of the Observed Diurnal Cycle of Indian Monsoon Rainfall

We have analysed the diurnal cycle of rainfall over the Indian region (10S-35N, 60E-100E) using both satellite and in-situ data, and found many interesting features associated with this fundamental, yet under-explored, mode of variability. Since there is a distinct and strong diurnal mode of variability associated with the Indian summer monsoon rainfall, we evaluate the ability of the Weather Research and Forecasting Model (WRF) to simulate the observed diurnal rainfall characteristics. The model (at 54km grid-spacing) is integrated for the month of July, 2006, since this period was particularly favourable for the study of diurnal cycle. We first evaluate the sensitivity of the model to the prescribed sea surface temperature (SST), by using two different SST datasets, namely, Final Analyses (FNL) and Real-time Global (RTG). It was found that with RTG SST the rainfall simulation over central India (CI) was significantly better than that with FNL. On the other hand, over the Bay of Bengal (BoB), rainfall simulated with FNL was marginally better than with RTG. However, the overall performance of RTG SST was found to be better than FNL, and hence it was used for further model simulations. Next, we investigated the role of the convective parameterization scheme on the simulation of diurnal cycle of rainfall. We found that the Kain-Fritsch (KF) scheme performs significantly better than Betts-Miller-Janjić (BMJ) and Grell-Devenyi schemes. We also studied the impact of other physical parameterizations, namely, microphysics, boundary layer, land surface, and the radiation parameterization, on the simulation of diurnal cycle of rainfall, and identified the “best” model configuration. We used this configuration of the “best” model to perform a sensitivity study on the role of various convective components used in the KF scheme. In particular, we studied the role of convective downdrafts, convective timescale, and feedback fraction, on the simulated diurnal cycle of rainfall. The “best” model simulations, in general, show a good agreement with observations. Specifically, (i) Over CI, the simulated diurnal rainfall peak is at 1430 IST, in comparison to the observed 1430-1730 IST peak; (ii) Over Western Ghats and Burmese mountains, the model simulates a diurnal rainfall peak at 1430 IST, as opposed to the observed peak of 1430-1730 IST; (iii) Over Sumatra, both model and observations show a diurnal peak at 1730 IST; (iv) The observed southward propagating diurnal rainfall bands over BoB are weakly simulated by WRF. Besides the diurnal cycle of rainfall, the mean spatial pattern of total rainfall and its partitioning between the convective and stratiform components, are also well simulated. The “best” model configuration was used to conduct two nested simulations with one-way, three-level nesting (54-18-6km) over CI and BoB. While, the 54km and 18km simulations were conducted for the whole of July, 2006, the 6km simulation was carried out for the period 18 - 24 July, 2006. The results of our coarse- and fine-scale numerical simulations of the diurnal cycle of monsoon rainfall will be discussed.

An assessment of terrestrial water storage, rainfall and river discharge over Northern India from satellite data

Terrestrial water storage (TWS) plays a key role in the global water cycle and is highly influenced by climate variability and human activities. In this study, monthly TWS, rainfall and Ganga-Brahmaputra river discharge (GBRD) are analysed over India for the period of 2003-12 using remote sensing satellite data. The spatial pattern of mean TWS shows a decrease over a large and populous region of Northern India comprising the foothills of the Himalayas, the Indo-Gangetic Plains and North East India. Over this region, the mean monthly TWS exhibits a pronounced seasonal cycle and a large interannual variability, highly correlated with rainfall and GBRD variations (r > 0.8) with a lag time of 2 months and 1 month respectively. The time series of monthly TWS shows a consistent and statistically significant decrease of about 1 cm year(-1) over Northern India, which is not associated with changes in rainfall and GBRD. This recent change in TWS suggests a possible impact of rapid industrialization, urbanization and increase in population on land water resources. Our analysis highlights the potential of the Earth-observation satellite data for hydrological applications.

Modeling non-stationarity in intensity, duration and frequency of extreme rainfall over India

Significant changes are reported in extreme rainfall characteristics over India in recent studies though there are disagreements on the spatial uniformity and causes of trends. Based on recent theoretical advancements in the Extreme Value Theory (EVT), we analyze changes in extreme rainfall characteristics over India using a high-resolution daily gridded (1 degrees latitude x 1 degrees longitude) dataset. Intensity, duration and frequency of excess rain over a high threshold in the summer monsoon season are modeled by non-stationary distributions whose parameters vary with physical covariates like the El-Nino Southern Oscillation index (ENSO-index) which is an indicator of large-scale natural variability, global average temperature which is an indicator of human-induced global warming and local mean temperatures which possibly indicate more localized changes. Each non-stationary model considers one physical covariate and the best chosen statistical model at each rainfall grid gives the most significant physical driver for each extreme rainfall characteristic at that grid. Intensity, duration and frequency of extreme rainfall exhibit non-stationarity due to different drivers and no spatially uniform pattern is observed in the changes in them across the country. At most of the locations, duration of extreme rainfall spells is found to be stationary, while non-stationary associations between intensity and frequency and local changes in temperature are detected at a large number of locations. This study presents the first application of nonstationary statistical modeling of intensity, duration and frequency of extreme rainfall over India. The developed models are further used for rainfall frequency analysis to show changes in the 100-year extreme rainfall event. Our findings indicate the varying nature of each extreme rainfall characteristic and their drivers and emphasize the necessity of a comprehensive framework to assess resulting risks of precipitation induced flooding. (C) 2014 Elsevier B.V. All rights reserved.

Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data

This study presents a comprehensive evaluation of five widely used multisatellite precipitation estimates (MPEs) against 1 degrees x 1 degrees gridded rain gauge data set as ground truth over India. One decade observations are used to assess the performance of various MPEs (Climate Prediction Center (CPC)-South Asia data set, CPC Morphing Technique (CMORPH), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks, Tropical Rainfall Measuring Mission's Multisatellite Precipitation Analysis (TMPA-3B42), and Global Precipitation Climatology Project). All MPEs have high detection skills of rain with larger probability of detection (POD) and smaller ``missing'' values. However, the detection sensitivity differs from one product (and also one region) to the other. While the CMORPH has the lowest sensitivity of detecting rain, CPC shows highest sensitivity and often overdetects rain, as evidenced by large POD and false alarm ratio and small missing values. All MPEs show higher rain sensitivity over eastern India than western India. These differential sensitivities are found to alter the biases in rain amount differently. All MPEs show similar spatial patterns of seasonal rain bias and root-mean-square error, but their spatial variability across India is complex and pronounced. The MPEs overestimate the rainfall over the dry regions (northwest and southeast India) and severely underestimate over mountainous regions (west coast and northeast India), whereas the bias is relatively small over the core monsoon zone. Higher occurrence of virga rain due to subcloud evaporation and possible missing of small-scale convective events by gauges over the dry regions are the main reasons for the observed overestimation of rain by MPEs. The decomposed components of total bias show that the major part of overestimation is due to false precipitation. The severe underestimation of rain along the west coast is attributed to the predominant occurrence of shallow rain and underestimation of moderate to heavy rain by MPEs. The decomposed components suggest that the missed precipitation and hit bias are the leading error sources for the total bias along the west coast. All evaluation metrics are found to be nearly equal in two contrasting monsoon seasons (southwest and northeast), indicating that the performance of MPEs does not change with the season, at least over southeast India. Among various MPEs, the performance of TMPA is found to be better than others, as it reproduced most of the spatial variability exhibited by the reference.

Fuzzy State Real-Time Reservoir Operation Model for Irrigation with Gridded Rainfall Forecasts

A short-term real-time operation model with fuzzy state variables is developed for irrigation of multiple crops based on earlier work on long-term steady-state policy. The features of the model that distinguish it from the earlier work are (1) apart from inclusion of fuzziness in reservoir storage and in soil moisture of crops, spatial variations in rainfall and soil moisture of crops are included in the real-time operation model by considering gridded command area with a grid size of 0.5 degrees latitude by 0.5 degrees longitude; (2) the water allocation model and soil moisture balance equations are integrated with the real-time operation model with consideration of ponding water depth for Paddy crop; the model solution specifies reservoir releases for irrigation in a 10-day time period and allocations among the crops on a daily basis at each grid by maintaining soil moisture balance at the end of the day; and (3) the release policy is developed using forecasted daily rainfall data of each grid and is implemented for the current time period using actual 10-day inflow and actual daily rainfall of each grid. The real-time operation model is applied to Bhadra Reservoir in Karnataka, India. The results obtained using the real-time operation model are compared with those of the standard operating policy model. Inclusion of fuzziness in reservoir storage and soil moisture of crops captures hydrologic uncertainties in real time. Considerations of irrigation decisions on a daily basis and the gridded command area result in variations in allocating water to the crops, variations in actual crop evapotranspiration, and variations in soil moisture of the crops on a daily basis for each grid of the command area. (C) 2015 American Society of Civil Engineers.

Temporal and spatial variations in the quality of water in El Gergal Reservior, Seville, Spain

It is often difficult to define ‘water quality’ with any degree of precision. One approach is that suggested by Battarbee (1997) and is based on the extent to which individual lakes have changed compared with their natural ‘baseline’ status. Defining the base-line status of artificial lakes and reservoirs however, is, very difficult. In ecological terms, the definition of quality must include some consideration of their functional characteristics and the extent to which these characteristics are self-sustaining. The challenge of managing lakes in a sustainable way is particularly acute in semi-arid, Mediterranean countries. Here the quality of the water is strongly influenced by the unpredictability of the rainfall as well as year-to-year variations in the seasonal averages. Wise management requires profound knowledge of how these systems function. Thus a holistic approach must be adopted and the factors influencing the seasonal dynamics of the lakes quantified over a range of spatial and temporal scales. In this article, the authors describe some of the ways in which both long-term and short-term changes in the weather have influenced the seasonal and spatial dynamics of phytoplankton in El Gergal, a water supply reservoir situated in the south of Spain. The quality of the water stored in this reservoir is typically very good but surface blooms of algae commonly appear during warm, calm periods when the water level is low. El Gergal reservoir is managed by the Empresa Municipal de Abastecimiento y Saneamiento (EMASESA) and supplies water for domestic, commercial and industrial use to an area which includes the city of Seville and twelve of its surrounding towns (ca. 1.3 million inhabitants). El Gergal is the last of two reservoirs in a chain of four situated in the Rivera de Huelva basin, a tributary of the Guadalquivir river. It was commissioned by EMASESA in 1979 and since then the company has monitored its main limnological parameters on, at least, a monthly basis and used this information to improve the management of the reservoir. As a consequence of these intensive studies the physical, chemical and biological information acquired during this period makes the El Gergal database one of the most complete in Spain. In this article the authors focus on three ‘weather-related’ effects that have had a significant impact on the composition and distribution of phytoplankton in El Gergal: (i) the changes associated with severe droughts; (ii) the spatial variations produced by short-term changes in the weather; (iii) the impact of water transfers on the seasonal dynamics of the dinoflagellate Ceratium.

Temporal and spatial distribution of phytoplankton with emphasis on Skeletonema costatum in the Mathamuhuri river – estuary (Chakaria mangrove ecosystem), Bangladesh

A total of 91 species under 44 genera were identified among the phytoplankton community during the course of one year's investigation between May 1982 and April 1983. Bacillariophyta was the most dominant group with 72 specie, Chlorophyta 11 spp, Cyanophyta 6 spp and Pyrrophyta was represented by 2 species. The yearly percentage composition of 4 groups of phytoplankton in order of abundance were Bacillariophyta 50.77%, Cyanophyta 47.70%, Chlorophyta 1.5% and Pyrrophyta 0.02%. The highest densities of phytoplankton were recorded in monsoon months (June-July) with a peak in July (31550 cells/l) and the minimum in February (770 cells/1). Higher concentration of phytoplankton was recorded at station 2, nearer to the Chakaria Sundarbans (mangroves), but abundance of phytoplankton showed no significant difference in the two stations (Mann Whitney U test, P=0.64, Z=-0.642, U=64). Phytoplankton population in this area were positively correlated with rainfall (r=0.655, P=<0.5, df.22) and water temperature (r=0.523, P=<0.05). Skeletonema costatum was the dominant member of phytoplankton and occupied 35.23% of the annual population and occurred throughout the period of study except in September and January. Its abundance was recorded during the monsoon months (April- July) with a maximum density (24185 cells/l) in July. No significant correlation was found between abundance of S. costatum and the hydro-meteorological parameters recorded in the Chakaria mangrove area.

Modelling raindrop impact and splash erosion processes within a spatial cell: a stochastic approach

A new approach is proposed to simulate splash erosion on local soil surfaces. Without the effect of wind and other raindrops, the impact of free-falling raindrops was considered as an independent event from the stochastic viewpoint. The erosivity of a single raindrop depending on its kinetic energy was computed by an empirical relationship in which the kinetic energy was expressed as a power function of the equivalent diameter of the raindrop. An empirical linear function combining the kinetic energy and soil shear strength was used to estimate the impacted amount of soil particles by a single raindrop. Considering an ideal local soil surface with size of I m x I m, the expected number of received free-failing raindrops with different diameters per unit time was described by the combination of the raindrop size distribution function and the terminal velocity of raindrops. The total splash amount was seen as the sum of the impact amount by all raindrops in the rainfall event. The total splash amount per unit time was subdivided into three different components, including net splash amount, single impact amount and re-detachment amount. The re-detachment amount was obtained by a spatial geometric probability derived using the Poisson function in which overlapped impacted areas were considered. The net splash amount was defined as the mass of soil particles collected outside the splash dish. It was estimated by another spatial geometric probability in which the average splashed distance related to the median grain size of soil and effects of other impacted soil particles and other free-falling raindrops were considered. Splash experiments in artificial rainfall were carried out to validate the availability and accuracy of the model. Our simulated results suggested that the net splash amount and re-detachment amount were small parts of the total splash amount. Their proportions were 0.15% and 2.6%, respectively. The comparison of simulated data with measured data showed that this model could be applied to simulate the soil-splash process successfully and needed information of the rainfall intensity and original soil properties including initial bulk intensity, water content, median grain size and some empirical constants related to the soil surface shear strength, the raindrop size distribution function and the average splashed distance. Copyright (c) 2007 John Wiley & Sons, Ltd.

A spatial sampling optimization package using MSN theory

The density and distribution of spatial samples heavily affect the precision and reliability of estimated population attributes. An optimization method based on Mean of Surface with Nonhomogeneity (MSN) theory has been developed into a computer package with the purpose of improving accuracy in the global estimation of some spatial properties, given a spatial sample distributed over a heterogeneous surface; and in return, for a given variance of estimation, the program can export both the optimal number of sample units needed and their appropriate distribution within a specified research area. (C) 2010 Elsevier Ltd. All rights reserved.

Half-precessional dynamics of monsoon rainfall near the East African Equator

External climate forcings-such as long-term changes in solar insolation-generate different climate responses in tropical and high latitude regions(1). Documenting the spatial and temporal variability of past climates is therefore critical for understanding how such forcings are translated into regional climate variability. In contrast to the data-richmiddle and high latitudes, high-quality climate-proxy records from equatorial regions are relatively few(2-4), especially from regions experiencing the bimodal seasonal rainfall distribution associated with twice-annual passage of the Intertropical Convergence Zone. Here we present a continuous and well-resolved climate-proxy record of hydrological variability during the past 25,000 years from equatorial East Africa. Our results, based on complementary evidence from seismic-reflection stratigraphy and organic biomarker molecules in the sediment record of Lake Challa near Mount Kilimanjaro, reveal that monsoon rainfall in this region varied at half-precessional (similar to 11,500-year) intervals in phase with orbitally controlled insolation forcing. The southeasterly and northeasterly monsoons that advect moisture from the western Indian Ocean were strengthened in alternation when the inter-hemispheric insolation gradient was at a maximum; dry conditions prevailed when neither monsoon was intensified and modest local March or September insolation weakened the rain season that followed. On sub-millennial timescales, the temporal pattern of hydrological change on the East African Equator bears clear high-northern-latitude signatures, but on the orbital timescale it mainly responded to low-latitude insolation forcing. Predominance of low-latitude climate processes in this monsoon region can be attributed to the low-latitude position of its continental regions of surface air flow convergence, and its relative isolation from the Atlantic Ocean, where prominent meridional overturning circulation more tightly couples low-latitude climate regimes to high-latitude boundary conditions.

Deriving meteorological variables across Africa for the study and control of vector-borne disease:a comparison of remote sensing and spatial interpolation of climate

This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) onboard the European Meteorological Satellite programme's (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study anti control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy.

Spatial and temporal influences of in-stream factors on the chemistry and epilithic biomasses of upland stream metal deposits

The density and composition of stream bed metal deposits are affected by physical, chemical and biological processes. In this paper we investigate the importance of these processes and their relation to algal and non-photosynthetic detrital (NPD) biomass in a set of upland streams in Northern Ireland. Deposit density and Fe, Mn, Al and P concentrations varied with stream pH across sites but not seasonally. No effects of stream bed erosion or photoreduction were detected on deposit densities. Seasonal variation in stream water metal concentrations was correlated with rainfall. NPD biomass was a significant predictor of both spatial and seasonal variation in deposit concentrations. There were strong, non-linear, relations between NPD biomass and deposit metal concentrations, with Fe and Mn becoming relatively more important and algal biomass declining above threshold deposit/NPD densities. The results suggest that NPD biomass influences deposit density and reduces the biomass of photosynthetic autotrophs above a threshold deposit density.

Spatial and temporal variation of commercially important bivalve species in the Algarve coast, Portugal

Dissertação de mestrado, Biologia Marinha, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

South-West Monsoon Rainfall of Kerala and Its Variability

This study focuses on the south –west monsoon rainfall over Kerala and its variability both on the spatial and temporal scales. The main objectives of the study are, interanual, long-term and decadal variabilities in MRF(monsoon rain fall),relationship between antecedent global circulation parameters, diurnal variability using data of a large number of stations in Kerala and the spatial distribution of rainfall under two large scale synoptic. Kerala gets nearly 190cm of rainfall during the south-west monsoon season 1st June to 30th September. This is more than twice the monsoon rainfall of India. A good part of kerala’s rainfall is caused by the orography of the Western Ghats Mountain ranges. The state receives 286cm of annual rainfall of which 68%is during the south-west monsoon season. The summer monsoon rainfall of Kerala shows a decreasing trend of 12.0%in 96 years. The study shows that the Intra Seasonal Oscillations(ISO) of the monsoon season has large interanual variability,some years having long period and other years having short period ISO. It is seen that Western Ghats has a strong control on the east west profile on the monsoon rainfall.