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[en] Flash flood disaster is a prominent issue threatening public safety and social development throughout the world, especially in mountainous regions. Rainfall threshold is a widely accepted alternative to hydrological forecasting for flash flood warning due to the short response time and limited observations of flash flood events. However, determination of rainfall threshold is still very complicated due to multiple impact factors, particular for antecedent soil moisture and rainfall patterns. In this study, hydrological simulation approach (i.e., China Flash Flood-Hydrological Modeling System: CNFF-HMS) was adopted to capture the flash flood processes. Multiple scenarios were further designed with consideration of antecedent soil moisture and rainfall temporal patterns to determine the possible assemble of rainfall thresholds by driving the CNFF-HMS. Moreover, their effects on rainfall thresholds were investigated. Three mountainous catchments (Zhong, Balisi and Yu villages) in southern China were selected for case study. Results showed that the model performance of CNFF-HMS was very satisfactory for flash flood simulations in all these catchments, especially for multimodal flood events. Specifically, the relative errors of runoff and peak flow were within ± 20%, the error of time to peak flow was within ± 2 h and the Nash–Sutcliffe efficiency was greater than 0.90 for over 90% of the flash flood events. The rainfall thresholds varied between 93 and 334 mm at Zhong village, between 77 and 246 mm at Balisi village and between 111 and 420 mm at Yu village. Both antecedent soil moistures and rainfall temporal pattern significantly affected the variations of rainfall threshold. Rainfall threshold decreased by 8–38 and 0–42% as soil saturation increased from 0.20 to 0.50 and from 0.20 to 0.80, respectively. The effect of rainfall threshold was the minimum for the decreasing hyetograph (advanced pattern) and the maximum for the increasing hyetograph (delayed pattern), while it was similar for the design hyetograph and triangular hyetograph (intermediate patterns). Moreover, rainfall thresholds with short time spans were more suitable for early flood warning, especially in small rural catchments with humid climatic characteristics. This study was expected to provide insights into flash flood disaster forecasting and early warning in mountainous regions, and scientific references for the implementation of flash flood disaster prevention in China.
[en] The present study indicates the potential projected variation of decadal mean rainfall over Kohistan region of Sindh Province, Pakistan. Precipitation variability is a crucial climatic factor that affects human health and their settlements. In this study, the precipitation variability associated with climate change in Kohistan region, Sindh, Pakistan is simulated using the PRECIS regional climate modeling system. The study analyses the precipitation variability in the future for two spells (2021-2050 and 20712099) with respect to the past (1961-1990) climate under the baseline ECHAM5 dataset for A1B Scenario at a resolution of 25x25 km. Based on this analyses, the precipitation scarcity is projected for 2021-2050 and 2071-2099 decades. The projected results showed a serious precipitation variation and shortfall of 12.60, 53.98, and 48.19% during 2031-2040,2041-2050 and 2081-2090 decades respectively as compared to baseline (1961-1990). The analyzed situation would be harmful to the water resources and agricultural production in the region during the shortfall, which imposes the adverse effect on the recharge of groundwater and quality. That might cause of long drought spell in the region. While during the 20212030 decade shown slight influence on the potential of hill torrents and groundwater recharge. However, the results reveal for the period of 2071-2080 and 2091-2099, the extreme floods with 60.50 and 70.50% are projected as compared to baseline 1961-1990. The increasing trend of precipitation indicates additional recharge of fresh groundwater and quality, with increasing level of aquifers, subsequently more agricultural production would be expected with alternate employment opportunities in the water sector. The projected results, indicating the decadal scenarios of the drought and wet spells in the region by the precipitation variation, which may impact on the hill torrents, groundwater and agricultural production, and employment opportunities. These quantitative projections should enable policymakers and stakeholders to plan for future measures. (author)
[en] The purpose of this work is to analyze the behavior of temperature and rainfall in Ranohira, southern of Madagascar, using climate data from 1961 to 2016 in order to predict future trend. Modeling and Forecasts have been performed using the ARIMA (Auto-Regressive Integrated Moving Average) model. The predictions concern the next 50 years. The data have been subdivided into 13 groups corresponding to each month of the year and the 13th group is the annual average. Before the application of the ARIMA modeling, we performed linear regressions to have a general view of the trends. Results showed that the average annual temperature is strictly increasing with a linear growth rate of about 0.2°C per decade. The mean of the average annual temperatures from 1961 to 2016 is 21.77°C. Total annual rainfall trend shows a decrease of about 47.2 mm per decade. The mean of annual precipitation from 1961 to 2016 is 917.26 mm. Concerning the ARIMA modeling, each of the time series corresponding to the 13 groups of data can be fitted in ARIMA form model(1,1,1)(1,1,1)S. The seasonal part of the model was exploited to have a good description of the fluctuations and the value of S was chosen to obtain the best model for each series. Forecast for the next 50 years permits to predict a significant increase of the average annual temperature which is expected to be equal to 23.94°C in 2066. The mean of the average annual temperatures between 2017 and 2066 is predicted to be equal to 22.8°C. The forecast also shows a decrease of 22.19 % in the mean annual precipitation for the 50 next years compared to the average of the observation years. The dry season, from April to September, will be the most affected, with a deficit of up to 90%. It is predicted that in 2066, the annual rainfall would be 470.18 mm against 605.9 mm in 2016. The economic development of the Ihorombe region eventually requires strategies to deal with these future changes, especially in the water sector, which would be the most affected.
[en] The Biosphere Reserve of Sian Ka'an located in the Mexican Caribbean, where greatest diversity of seaweeds has been recorded. Despite of diversity and species richness there are limited phycological studies in the study area, suggesting that species diversity is not completely known. We reported species that complement previous reports of brown algae collected from the study area. In addition, brown algae recorded in the literature and the species present in the herbaria for the Biosphere Reserve of Sian Ka'an were also included, and data obtained from sampling at eleven localities during 2009 to 2012 were added. In all 50 infrageneric taxa are reported. Families Dictyotaceae and Sargassaceae comprised the most species richness. The presence of 11 species of Phaeophyceae is reported for the first time for the study area. Of them, Symphyocarpus strangulans is a new record for the Mexican Atlantic; eight species are new records for study area and two for Quintana Roo. The highest number of species was recorded for Punta Xoquem and Pulticub, while the lowest number of taxa was in Cayo Valencia. The highest number of species was found in summer rains, the lowest in winter rains. The obtained data highlights the high specific richness of Phaeophyceae indicating that the Sian Ka'an Reserve should be considered a priority area due to its great biodiversity. Moreover, these results will be a basis for future ecological, utilization and conservation studies. (author)
[en] The primary goal of present study is to investigate the impact of assimilation of conventional and satellite radiance observations in simulating the mesoscale convective system (MCS) formed over south east India. An assimilation methodology based on Weather Research and Forecasting model three dimensional variational data assimilation is considered. Few numerical experiments are carried out to examine the individual and combined impact of conventional and non-conventional (satellite radiance) observations. After the successful inclusion of additional observations, strong analysis increments of temperature and moisture fields are noticed and contributed to significant improvement in model’s initial fields. The resulting model simulations are able to successfully reproduce the prominent synoptic features responsible for the initiation of MCS. Among all the experiments, the final experiment in which both conventional and satellite radiance observations assimilated has showed considerable impact on the prediction of MCS. The location, genesis, intensity, propagation and development of rain bands associated with the MCS are simulated reasonably well. The biases of simulated temperature, moisture and wind fields at surface and different pressure levels are reduced. Thermodynamic, dynamic and vertical structure of convective cells associated with the passage of MCS are well captured. Spatial distribution of rainfall is fairly reproduced and comparable to TRMM observations. It is demonstrated that incorporation of conventional and satellite radiance observations improved the local and synoptic representation of temperature, moisture fields from surface to different levels of atmosphere. This study highlights the importance of assimilation of conventional and satellite radiances in improving the models initial conditions and simulation of MCS.
[en] Full text: For sustainable cotton production against climate change in Myanmar, well-developed, fresh and uniform cotton seeds were irradiated with 50 to 500Gy of gamma ray. According to the germination test and field condition, we selected the doses from 200 to 350Gy for further generation studies. The desirable mutants having higher yield, early maturity, resistant to CLCuV disease etc. were selected and advanced to M3 generation. Ten mutant plants of each treatment were evaluated for agromorphological characters compared with the control plants. The mean value of plant height (141.2cm), no. of square/plant (48.7), no. of flower/ plant, (2.5) and total boll number/plant (48.8) of 300Gy were higher than other treatments and control. Even the shortest plant height was observed in 200Gy, early maturity and larger boll size was also found in 200 and 250Gy. The highest no. of square/plant was observed in 350Gy after 300Gy, however, after heavy rain, defoliation rate is higher than other treatments and control. Yield and fibre quality test will be studied for specific line selection of desirable trait and screening to heat tolerant. (author)
[en] Full text: TILLING (Targeting Induced Local Lesions IN Genomes) is a mutagen-based, non-transgenic, effective, reverse genetic technology, which is utilized for functional genomics studies. Gossypium herbaceum (2n = 2x = 26) withstand drought, thus it maximum cultivation occurs in the rain-fed regions of Asia. The cotton genome(s) and transcriptome(s) sequencing studies of Gossypium spp. provide information for candidate genes that determine different traits. Mutagenesis generated several new alleles of the interesting gene(s). They need functional validation before being used in breeding. In current EMS mutagenized TILLING population of G. herbaceum (cv. VAGAD), 70 mM EMS was used and we developed 5473 M3 plants. The morphological data for 11 agronomical traits were recorded for 4453 matured M3 plants. The range of dispersion, mean performance and coefficient of variation (CV), was more variable in the mutants as compared to control plants. The phenotyping of M3 population showed 31.63% plants having some variation as compared to the control untreated plants. The visual inspection of plant morphology showed that 2.29% of plants have visible changes in leaf morphology, leaf colour, sterility, and plant habits. this mutant population provides the opportunity for functional genomics studies of cotton that might potentially be useful in breeding. (author)
[en] In the present study, an attempt is made to understand the influence of land surface parameters (such as soil moisture conditions, soil type and vegetation type) and forcing parameters on the model spin-up behaviour of a land surface model (LSM), namely Noah LSM, over the Indian sub-continent. The work presented here primarily aims to understand the optimum initial conditions to achieve the least spin-up time over the subtropical conditions that exist over the region of interest. The study is presented in three major parts. In the first part, a multivariate statistical analysis, namely principle component analysis is employed to investigate how parameters such as precipitation, air temperature, soil moisture, radiation components as well as various parameters that characterize soil and vegetation types influence the model spin-up. The second part deals with the study of the impact of soil and vegetation parameters in different seasons on the model spin-up behaviour. Finally, the third part looks into the influence of initial soil moisture condition and precipitation forcing on the spin-up behaviour of the model in different seasons to obtain the optimum initial conditions for the minimum spin-up time of the model. From the study, it is seen that the soil and vegetation type, as well as the soil moisture content influence the model spin-up significantly. The present study reports that the experiments initialized just before a continuous rainfall event has the least spin-up unless the initial soil is saturated.
[en] The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain .