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Global warming has a profound influence on global and regional water cycles, especially in the cold mountainous area. However, detecting and quantifying such changes are still difficult because noise and variability in observed streamflow are relatively larger than the long-term trends. In this study, the impacts of global warming on the catchment water cycles in the Yarlung Zangbo River Basin (YZRB), one of most important catchments in south of the Tibetan Plateau, are quantified using a climatic approach based on the relationship between basin-scale groundwater storage and low flow at the annual time scale. By using a quantile regression method and flow recession analysis, changes in low flow regimes and basin-scale groundwater storage at the Nuxia hydrological station are quantified at the annual time scale during 1961&ndash, 2000. Results show annual low flows (10th and 25th annual flows) of the YZRB have decreased significantly, while long-term annual precipitation, total streamflow, and high flows are statistically unchanged. Annual lowest seven-day flow shows a significantly downward trend (2.2 m3/s/a, p &lt, 0.05) and its timing has advanced about 12 days (2.8 day/10a, p &lt, 0.1) during the study period. Estimated annual basin-scale groundwater storage also shows a significant decreasing trend at a rate of 0.079 mm/a (p &lt, 0.05) over the study period. Further analysis suggests that evaporation increase, decreased snow-fraction, and increased annual precipitation intensity induced by the rising temperature possibly are the drivers causing a significant decline in catchment low flow regimes and groundwater storage in the study area. This highlights that an increase in temperature has likely already caused significant changes in regional flow regimes in the high and cold mountainous regions, which has alarming consequences in regional ecological protection and sustainable water resources management.

Arachis monticola (2n = 4x = 40) is the only allotetraploid wild peanut within the Arachis genus and section, with an AABB-type genome of ∼2.7 Gb in size. The AA-type subgenome is derived from diploid wild peanut Arachis duranensis, and the BB-type subgenome is derived from diploid wild peanut Arachis ipaensis. A. monticola is regarded either as the direct progenitor of the cultivated peanut or as an introgressive derivative between the cultivated peanut and wild species. The large polyploidy genome structure and enormous nearly identical regions of the genome make the assembly of chromosomal pseudomolecules very challenging. Here we report the first reference quality assembly of the A. monticola genome, using a series of advanced technologies. The final whole genome of A. monticola is ∼2.62 Gb and has a contig N50 and scaffold N50 of 106.66 Kb and 124.92 Mb, respectively. The vast majority (91.83%) of the assembled sequence was anchored onto the 20 pseudo-chromosomes, and 96.07% of assemblies were accurately separated into AA- and BB- subgenomes. We demonstrated efficiency of the current state of the strategy for de novo assembly of the highly complex allotetraploid species, wild peanut (A. monticola), based on whole-genome shotgun sequencing, single molecule real-time sequencing, high-throughput chromosome conformation capture technology, and BioNano optical genome maps. These combined technologies produced reference-quality genome of the allotetraploid wild peanut, which is valuable for understanding the peanut domestication and evolution within the Arachis genus and among legume crops.

Climate change has great impacts on hydrological processes worldwide. The Tibetan Plateau (TP), the “Water Tower” of Asia, poses significant influences on Asian climate and is also one of the most sensitive areas to climate change. Therefore, it is of importance to investigate the plausible future hydrological regimes in the TP based on the climate scenarios provided by General Circulation Models (GCMs). In this study, the Variable Infiltration Capacity model was coupled with Shuffled Complex Evolution developed at the University of Arizona to explore the responses of hydrological processes to climate change in the Lhasa River basin, the tributary of the Yarlung Zangbo River in the southern TP. A downscaling framework based on Automatic Statistical Downscaling was used to generate the future climate data from two GCMs (Echam5 and Miroc3.2_Medres) under three scenarios (A1B, A2 and B1) for the period of 2046–2065. Results show increases for both air temperature and annual precipitation in the future climate. Evaporation, runoff and streamflow will experience a rising trend, whereas spring snow cover will reduce dramatically. These changes present significant spatial and temporal variations. The alteration of hydrological processes may challenge the local water resource management. This study is helpful for policy makers to tackle climate change related issues in terms of mitigation and adaptation.

Seed expansion in peanut is a complex biological process involving many gene regulatory pathways. MicroRNAs (miRNAs) play important regulatory roles in plant growth and development, but little is known about their functions during seed expansion, or how they contribute to seed expansion in different peanut lines. We examined seed miRNA expression patterns at 15 and 35 days after flowering (DAF) in two peanut eighth-generation recombinant inbred lines (RIL8); 8106, a medium-pod variety, and 8107, a super-pod variety. Using high-throughput sequencing, we identified 1,082 miRNAs in developing peanut seeds including 434 novel miRNAs. We identified 316 differentially expressed miRNAs by comparing expression levels between the two peanut lines. Interestingly, 24 miRNAs showed contrasting patterns of expression in the two RILs, and 149 miRNAs were expressed predominantly in only one RIL at 35 DAF. Also, potential target genes for some conserved and novel miRNAs were identified by degradome sequencing; target genes were predicted to be involved in auxin mediated signaling pathways and cell division. We validated the expression patterns of some representative miRNAs and 12 target genes by qPCR, and found negative correlations between the expression level of miRNAs and their targets. miR156e, miR159b, miR160a, miR164a, miR166b, miR168a, miR171n, miR172c-5p, and miR319d and their corresponding target genes may play key roles in seed expansion in peanut. The results of our study also provide novel insights into the dynamic changes in miRNAs that occur during peanut seed development, and increase our understanding of miRNA function in seed expansion.

The Tibetan Plateau (TP) plays a key role on both hydrology and climate for southern and eastern Asia. Improving runoff predictions in ungauged catchments in the TP is critical for surface water hydrology and water resources management in this region. However, a detailed runoff prediction study in this region has not been reported yet. To fill the gap, this study evaluates two regionalization approaches, spatial proximity and physical similarity, for predicting runoff using two rainfall-runoff models (SIMHYD and GR4J). These models are driven by meteorological inputs from eight large non-nested catchments (4000-50,000 km(2)) in the Yarlung Tsangpo River basin located in southeast TP. For each catchment, the two models are calibrated using data from the first two-thirds of the observation period and validated over the remaining period. The calibrated and validated Nash-Sutcliffe Efficiency of monthly runoff (NSE) varies from 0.73 to 0.93 for the SIMHYD model, and are similar to or slightly better than those obtained for the GR4J model. The incorporation of snowfall-snowmelt processes into the rainfall-runoff models does not noticeably improve the runoff predictions in the study area. The main reason is that monthly runoff is dominated by summer precipitation and snowfall in winter accounts for a small percentage (less than 14%). The results from both models show that the spatial proximity approach marginally outperforms the physical similarity approach and both approaches are better than random selection of a donor catchment. This is consistent with recent regionalization studies carried out in Europe and Australia. The study suggests that conceptual rainfall-runoff models are powerful and simple tools for monthly runoff predictions in large catchments in southeast TP, and incorporation of more catchments into regionalization can further improve prediction skills. Crown Copyright (c) 2014 Published by Elsevier B.V. All rights reserved.

Summary The Tibetan Plateau (TP) is the “water tower of Asia” and strongly influences both the hydrology and climate of southern and eastern Asia. Exploring the impact of climate change on the runoff of TP rivers is critical to improve water resources management. However, thorough studies on the runoff response to climate change are seldom conducted on large TP river systems. To complement the current body of work, this study uses two rainfall–runoff models (SIMHYD and GR4J) to simulate the monthly and annual runoff across the Yarlung Tsangpo River (YTR) basin in the southeastern TP (i.e., upstream of the Brahmaputra) under historical (1962–2002) and future (up to approximately 2030) climate conditions. The future climate series are obtained by using 20 Global Climate Models (GCMs) outputs from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to reflect a 1 °C increase in the global average surface air temperature. The two rainfall–runoff models successfully simulate the historical runoff for the eight catchments in the YTR basins with median monthly runoff Nash–Sutcliffe Efficiencies (NSE) of 0.86 for SIMHYD and 0.83 for GR4J. The mean annual future precipitation and runoff across the region are projected to increase by most of the modeling results. The mean annual precipitation changes obtained from the 20 GCMs are −15%, 7% and 16% for the 10th percentile, median and 90th percentile of GCM outputs, respectively, and the corresponding changes in the simulated mean annual runoffs are −24%, 13% and 29% for the SIMHYD model outputs and −22%, 11% and 26% for the GR4J model outputs. The projected increase in the runoff at the median percentile mainly occurs in the middle reaches of the YTR and its two tributaries, the Lhasa River and the Nyangqu River, with a 12% increase in annual runoff that mainly occurs in the wet season from May to September. The present work is the first comprehensive study on the hydrological response to climate change covering the entire upstream area of the Brahmaputra, and the results found in this study are not only helpful for local water resource management but also for the lower reaches of the Brahmaputra.

The Tibetan Plateau (TP) is the “water tower of Asia” and it plays a key role on both hydrology and climate for southern and eastern Asia. It is critical to explore the impact of climate change on runoff for better water resources management in the TP. However, few studies pay attention to the runoff response to climate change in large river systems on the TP, especially in data-sparse upstream area. To complement the current body of work, this study uses two rainfall-runoff models (SIMHYD and GR4J) to simulate the monthly and annual runoff in the upstream catchments of the Yarlung Tsangpo River basin (YTR) under historical (1962–2002) and future (2046–2065 A1B scenario) climate conditions. The future climate series are downscaled from a global climate model (MIROC3.2_hires) by a high resolution regional climate model (RegCM3). The two rainfall-runoff models successfully simulate the historical runoff for the eight catchments in the YTR basin, with median monthly runoff Nash–Sutcliffe Efficiency of 0.86 for SIMHYD and 0.83 for GR4J. The mean annual future temperature in eight catchments show significant increase with the median of +3.8 °C. However, the mean annual future precipitation shows decrease with the median of −5.8 % except in Lhatse (+2.0 %). The two models show similar modeling results that the mean annual future runoff in most of catchments (seven in eight) shows decrease with the median of −13.9 % from SIMHYD and −15.2 % from GR4J. The results achieved in this study are not only helpful for local water resources management, but also for future water utilization planning in the lower reaches region of the Brahmaputra.

Vault sink of tunnel contains a lot of random error.In order to eliminate or weaken interference of random error,the measured data was processed by wavelet de-noising that made the data more authenticity in the paper.Aiming at problems such as poor precision and slow convergence about BP neural network prediction,de-noising data was predicted by the improved BP neural network,which compared with traditional BP neural network.Experimental results showed the improved neural network of wavelet de-noising made convergence rate accelerate,accuracy improve,prediction result significantly enhance,it was true to prediction research of vault sink.

Based on the observed data in 1961–2009 and future projected data in 2010–2100, past and future climate change over the Yarlung Tsangpo River (YTR) basin has been investigated integrally. To promote modeling accuracy, spatial interpolation method considering altitude has been used to observed data, and regional average mean value was used to check the climate change state in the YTR basin. Results show that annual and four seasonal mean air temperature show statistically significant increasing trend in the past and future. Winter warming tendency is more obvious. Annual precipitation shows significant increasing trend of 15.38 mm/decade in the past and 18.57 (A1B), 23.89 (A2), 9.16 (B1) mm/decade in the future. Observed data show a warming and wetting tendency in the YTR basin, while future projected data shows a warmer and wetter future in the 21st century.