958 resultados para Yangtze River basin
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Experimental and field studies were conducted to evaluate the effects of NH4+ enrichment on growth and distribution of the submersed macrophyte, Vallisneria natans L, in lakes of the Yangtze River in China, based on the balance between free amino acids (FAA) and soluble carbohydrates (SC) in the plant tissue. Increase of NH4+ rather than NO3- concentrations in the water column caused FAA accumulation and SC depletion of the plant. The plant showed a unimodal pattern of biomass distribution along both FAA/SC ratios and external NH4+ concentrations, indicating that a moderate NH4-N concentration (< 0.3 mg L-1) benefited the plant, whereas the high NH4-N concentration (> 0.56 mg L-1) eliminated the plant completely. Therefore, 0.56 mg NH4-N mg L-1 in the water column was taken as the upper limit for V. natans in lakes of the Yangtze River basin. The mesocosm experiment showed that at a high external NH4-N (0.81 mg L-1), V. natans failed to propagate with a loss of half SC content (5 mg g(-1) DW) in the rhizomes, indicating that the consumption of carbohydrates for detoxification of excess NH4+ into non-toxic FAA significantly diminished carbohydrate supply to the rhizomes. This might consequently inhibit the vegetative reproduction of the plant, and also might be an important cause for the decline and disappearance of the plant with eutrophication. The present study for the first time reports substantial ecophysiological evidences for NH4+ stress to submersed macrophytes, and indicates that NH4+ toxicity arising from eutrophication probably plays a key role in the deterioration of submersed macrophytes like V. natans.
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The type species of the cyprinid genus Sinilabeo was misidentified as Varicorhinus tungting, and the species under the generic name belong to Bangana and Linichthys. In order to make Sinilabeo available, its type species is fixed under Article 70.3.2 of the 1999 edition of the International Code of Zoological Nomenclature as S. hummeli, a new species herein described from the upper Yangtze River basin in Chongqing City and Sichuan Province, South China. A re-definition is provided for Sinilabeo. It resembles Qianlabeo in having an upper lip only present in the side of the upper jaw and uncovered by the rostral fold, but missing in the median part of the upper jaw that, instead, bears a thin, flexible, and cornified sheath, covered by the rostral fold, a character that can separate both from all other existing genera of Asian labeonins. However, Sinilabeo is distinguished from Qianlabeo in the presence of a rostral fold disconnected from the lower lip; a broadly interrupted postlabial groove only restricted to the side of the lower jaw; an upper lip, which is only present in the side of the upper separated from it by a groove; 9-10 branched dorsal-fin rays; two pairs of tiny maxillary barbels.
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Sinibrama longianalis, a new cyprinid species from the Wu Jiang (upper Yangtze River basin) in Guizhou, China is distinguished from other congeners in having the following combination of characters: last simple dorsal-fin ray well-ossified; a snout shorter than eye diameter; eye diameter 27.1-31.6% HL; lateral line scales 56-64 (mean 59.5); circumpeduncular scales 18-21; anal fin with 24-28 (mean 25.2) branched rays, originating opposite to or slightly in advance of posterior end of dorsal-fin base, basal length 27.0-31.1% SL; pectoral fin reaching to or slightly beyond pelvic-fin insertion.
Resumo:
Pseudobagrus fui Miao is a valid bagrid species that can be separated from all its congeners in having 27-33 anal-fin rays. Among the species of Pseudobagrus, it forms part of a group of approximately 20 species putatively characterized by having a smooth anterior margin of the pectoral-fin spine and short maxillary barbels not extending to the base of the pectoral-fin spine. Pseudobagrus fui, together with P. nitidus and P. vachelli, can be differentiated from all other fork-tailed species of this group by having no fewer than 20 branched anal-fin rays, the posterior end of the anal-fin base posterior to the vertical through the posterior end of the adipose-fin base, and anal-fin base longer than the adipose-fin base. It further differs from P. nitidus in having a gas bladder without beaded lateral and posterior margins, and from P. vachelli in having short maxillary barbels not extending to the base of the pectoral-fin spine. Pseudobagrus fui is currently known from the main stream of the upper Yangtze River and its tributaries, the Min River, Jialing River, Tuo River, Wu River and Chishui River in Sichuan Province, Guizhou Province, and the Chongqing City. The identity and nomenclature of Pseudobagrus nitidus is also discussed.
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In this paper, we estimate the inputs of phosphorus (P) to the Yangtze River Basin and exports of dissolved inorganic phosphorus (DIP) from the river to the estuary for the period 1970-2003, by using the global NEWS-DIP model. Modeled DIP yields range from 2.5 kg P km(-2) yr(-1) in 1970 to 4.6 kg P km(-2) yr(-1) in 1985, and then dramatically increase to 14.1 kg P km(-2) yr(-1) in 2003. No significant difference between the modeled and measured values at the level of P = 0.05 is observed. The study also demonstrates variable source contributions of P to the modeled DIP during the period 1970-2003. Point sewage P input accounted for approximately 100% in the period 1970-1985 and substantially decreased to 24.8% in 2003. Chemical fertilizer contributed 25.4% of DIP yields in 1986 and increased continuously to 50.3% in 2003, while a stable trend in manure P contribution averaging 22.9% of DIP yields was shown in the same period. The study concludes that P inputs to the Yangtze River Basin and the river DIP export to the estuary have substantially increased during the study period consequence to human pressure.
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Acknowledgements This work was funded by Natural Science Foundation of China under grant numbers of 41071337 and 40830528 and jointly by the Priority Academic Program Development of Jiangsu Higher Education Institutions, China.
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The Yangtze River Basin downstream of China's Three Gorges Dam (TGD) (thereafter referred to as "downstream" basin) hosts the largest cluster of freshwater lakes in East Asia. These lakes are crucial water stocks to local biophysical environments and socioeconomic development. Existing studies document that individual lakes in this region have recently experienced dramatic changes under the context of enduring meteorological drought, continuous population growth, and extensive water regulation since TGD's initial impoundment (i.e., June, 2003). However, spatial and temporal patterns of lake dynamics across the complete downstream Yangtze basin remain poorly characterized. Using daily MODIS imagery and an advanced thematic mapping scheme, this study presents a comprehensive monitoring of area dynamics in the downstream lake system at a 10-day temporal resolution during 2000-2011. The studied lakes constitute ~76% (~11,400 km**2) of the total downstream lake area, including the entire +70 major lakes larger than 20 km**2. The results reveal a decadal net decline in lake inundation area across the downstream Yangtze Basin, with a cumulative decrease of 849 km**2 or 7.4% from 2000 to 2011. Despite an excessive precipitation anomaly in the year 2010, the decreasing trend was tested significant in all seasons. The most substantial decrease in the post-TGD period appears in fall (1.1%/yr), which intriguingly coincides with the TGD water storage season. Regional lake dynamics exhibit contrasting spatial patterns, manifested as evident decrease and increase of aggregated lake areas respectively within and beyond the Yangtze Plain. This contrast suggests a marked vulnerability of lakes in the Yangtze Plain, to not only local meteorological variability but also intensified human water regulations from both the upstream Yangtze main stem (e.g., the TGD) and tributaries (e.g., lakes/reservoirs beyond the Yangtze Plain). The produced lake mapping result and derived lake area dynamics across the downstream Yangtze Basin provides a crucial monitoring basis for continuous investigations of changing mechanisms in the Yangtze lake system.
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Floods are among the most devastating events that affect primarily tropical, archipelagic countries such as the Philippines. With the current predictions of climate change set to include rising sea levels, intensification of typhoon strength and a general increase in the mean annual precipitation throughout the Philippines, it has become paramount to prepare for the future so that the increased risk of floods on the country does not translate into more economic and human loss. Field work and data gathering was done within the framework of an internship at the former German Technical Cooperation (GTZ) in cooperation with the Local Government Unit of Ormoc City, Leyte, The Philippines, in order to develop a dynamic computer based flood model for the basin of the Pagsangaan River. To this end, different geo-spatial analysis tools such as PCRaster and ArcGIS, hydrological analysis packages and basic engineering techniques were assessed and implemented. The aim was to develop a dynamic flood model and use the development process to determine the required data, availability and impact on the results as case study for flood early warning systems in the Philippines. The hope is that such projects can help to reduce flood risk by including the results of worst case scenario analyses and current climate change predictions into city planning for municipal development, monitoring strategies and early warning systems. The project was developed using a 1D-2D coupled model in SOBEK (Deltares Hydrological modelling software package) and was also used as a case study to analyze and understand the influence of different factors such as land use, schematization, time step size and tidal variation on the flood characteristics. Several sources of relevant satellite data were compared, such as Digital Elevation Models (DEMs) from ASTER and SRTM data, as well as satellite rainfall data from the GIOVANNI server (NASA) and field gauge data. Different methods were used in the attempt to partially calibrate and validate the model to finally simulate and study two Climate Change scenarios based on scenario A1B predictions. It was observed that large areas currently considered not prone to floods will become low flood risk (0.1-1 m water depth). Furthermore, larger sections of the floodplains upstream of the Lilo- an’s Bridge will become moderate flood risk areas (1 - 2 m water depth). The flood hazard maps created for the development of the present project will be presented to the LGU and the model will be used to create a larger set of possible flood prone areas related to rainfall intensity by GTZ’s Local Disaster Risk Management Department and to study possible improvements to the current early warning system and monitoring of the basin section belonging to Ormoc City; recommendations about further enhancement of the geo-hydro-meteorological data to improve the model’s accuracy mainly on areas of interest will also be presented at the LGU.
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The multifractal properties of daily rainfall time series at the stations in Pearl River basin of China over periods of up to 45 years are examined using the universal multifractal approach based on the multiplicative cascade model and the multifractal detrended fluctuation analysis (MF-DFA). The results from these two kinds of multifractal analyses show that the daily rainfall time series in this basin have multifractal behavior in two different time scale ranges. It is found that the empirical multifractal moment function K(q)K(q) of the daily rainfall time series can be fitted very well by the universal multifractal model (UMM). The estimated values of the conservation parameter HH from UMM for these daily rainfall data are close to zero indicating that they correspond to conserved fields. After removing the seasonal trend in the rainfall data, the estimated values of the exponent h(2)h(2) from MF-DFA indicate that the daily rainfall time series in Pearl River basin exhibit no long-term correlations. It is also found that K(2)K(2) and elevation series are negatively correlated. It shows a relationship between topography and rainfall variability.
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In this paper, downscaling models are developed using a support vector machine (SVM) for obtaining projections of monthly mean maximum and minimum temperatures (T-max and T-min) to river-basin scale. The effectiveness of the model is demonstrated through application to downscale the predictands for the catchment of the Malaprabha reservoir in India, which is considered to be a climatically sensitive region. The probable predictor variables are extracted from (1) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1978-2000, and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 1978-2100. The predictor variables are classified into three groups, namely A, B and C. Large-scale atmospheric variables Such as air temperature, zonal and meridional wind velocities at 925 nib which are often used for downscaling temperature are considered as predictors in Group A. Surface flux variables such as latent heat (LH), sensible heat, shortwave radiation and longwave radiation fluxes, which control temperature of the Earth's surface are tried as plausible predictors in Group B. Group C comprises of all the predictor variables in both the Groups A and B. The scatter plots and cross-correlations are used for verifying the reliability of the simulation of the predictor variables by the CGCM3 and to Study the predictor-predictand relationships. The impact of trend in predictor variables on downscaled temperature was studied. The predictor, air temperature at 925 mb showed an increasing trend, while the rest of the predictors showed no trend. The performance of the SVM models that are developed, one for each combination of predictor group, predictand, calibration period and location-based stratification (land, land and ocean) of climate variables, was evaluated. In general, the models which use predictor variables pertaining to land surface improved the performance of SVM models for downscaling T-max and T-min