The influence of climate,hydrology and permafrost on Holocene peat accumulation at 3500 m on the eastern Qinghai–Tibetan Plateau

Peatland of the eastern Qinghai–Tibetan Plateau lies at the convergence of the East Asian and Indian monsoon systems in eastern Asia. To understand the evolution of this peatland and its potential to provide new insights into the Holocene evolution of the East Asian monsoon a 6 m peat core was collected from the undisturbed central part of a peat deposit near Hongyuan. The age-depth profile was determined using 16 ¹⁴C-AMS age dates, the peat analysed for a range of environmental variables including carbon, nitrogen and hydrogen concentration, bulk density, δ¹³C and the associated spring water analysed for hydrogen and oxygen isotopes. The age-depth profile of the recovered peat sequence covers the period from 9.6 to 0.3 kyr BP and is linear indicating that the conditions governing productivity and decay varied little over the Holocene. Using changes in carbon density, organic carbon content and its δ¹³C, cold dry periods of permafrost characterised by low density and impeded surface drainage were identified. The low δ¹⁸O and δD values of the spring water emanating around the peat deposit, down to ?13.8 and ?102‰ (VSMOW), respectively, with an inverse relationship between electrical conductivity and isotopic composition indicate precipitation under colder and drier conditions relative to the present day. In view of the current annual mean air temperature of 1 °C this suggests conditions in the past have been conducive to permafrost. Inferred periods of permafrost correspond to independently recognised cold periods in other Holocene records from across China at 8.6, 8.2–7.8, 5.6–4.2, 3.1 and 1.8–1.5 kyr BP. The transition to a cold dry climate appears to be more rapid than the subsequent recovery and cold dry periods at Hongyuan are of longer duration than equivalent cold dry periods over central and eastern China. Light–dark banding peat on a scale of 15–30 years from 9.6 to 5.5 kyr BP may indicate a strong influence of decadal oscillations possibly the Pacific Decadal Oscillation and a potential link between near simultaneous climatic changes in the northwest Pacific, ENSO, movement of the Intertropical Convergence Zone and the East Asian Monsoon.     

Performance assessment of evapotranspiration estimated from different data sources over agricultural landscape in Northern India

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydrometeorological datasets. This study addresses the performance analysis of reference evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state-of-the-art Hamon’s and Penman-Monteith’s methods were utilized for the ETo estimation in the Northern India. The performance indices such as bias, root mean square error (RMSE), and correlation (r) were calculated, which showed the values 0.242, 0.422, and 0.959 for NCEP data (without downscaling) and 0.230, 0.402, and 0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with bias, RMSE, and correlation values of 0.154, 0.348, and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less computationally expensive and easily available than performing WRF simulations.     

Compositional evolution of grossular garnet from leucotonalitic pegmatite at Ruda nad Moravou, Czech Republic; a complex EMPA, LA-ICP-MS, IR and CL study

Five distinct paragenetic, morphological and compositional types of grossular garnet (G1, G2, G3, G4, G5) were distinguished within the individual (sub)units of the zoned leucotonalitic pegmatite cutting serpentinized lherzolite with rodingite dikes at ??ár near Ruda nad Moravou, Staré Město Unit, Northern Moravia. Detailed study using Electron Microprobe Analysis, Laser Ablation Inductively Coupled Plasma Mass Spectrometry, Cathodoluminiscence and Infrared Spectroscopy revealed distinct compositional trends in major, minor and trace elements. The contents of Fe³⁺, Mn, Mg and Ti increase from early garnet (G1) in the outermost grossular subunit through the interstitial garnet (G2) in the leucocratic subunit to graphic intergrowths of quartz+garnet (G3) in the coarse-grained unit. Then these constituents decrease in inclusions of garnet (G4) from the blocky unit and large crystals of garnet (G5) from the quartz core. Some trace elements (V, Ni, Y) exhibit the same trends, only Be evidently increases in garnet from border zone to the centre. Fluorine has negative correlation with Fe³⁺ as well as some trace elements (Ta, Pb). Concentrations of H₂O in garnets, up to 0.22 wt.% H₂O, are comparable with spessartine-almandine garnets from the Rutherford No. 2 pegmatite, Virginia, and grossular garnets from high-temperature calc-silicate rocks (skarns). Water contents correlate positively with Fe³⁺, but inversely with F. The use of water contents in garnet to elucidate the fluctuations of activity of H₂O during the pegmatite formation is only limited; the incorporation of hydrous defects seems to be controlled instead by crystal-structural constraints. However, the sum of all volatile components (H₂O + F) increases about twice from the outermost subunit to the centre of the pegmatite body.     

Exploration of optimal time steps for daily precipitation bias correction: a case study using a single grid of RCM on the River Exe in southwest England

ABSTRACT

Bias correction is a necessary post-processing procedure in order to use regional climate model (RCM)-simulated local climate variables as the input data for hydrological models due to systematic errors of RCMs. Most of the present bias-correction methods adjust statistical properties between observed and simulated data based on a predefined duration (e.g. a month or a season). However, there is a lack of analysis of the optimal period for bias correction. This study attempted to address the question whether there is an optimal number for bias-correction groups (i.e. optimal bias-correction period). To explore this we used a catchment in southwest England with the regional climate model HadRM3 precipitation data. The proposed methodology used only one grid of RCM in the Exe catchment, one emissions scenario (A1B) and one member (Q0) among 11 members of HadRM3. We tried 13 different bias-correction periods from 3-day to 360-day (i.e. the whole of one year) correction using the quantile mapping method. After the bias correction a low pass filter was used to remove the high frequencies (i.e. noise) followed by estimating Akaike’s information criterion. For the case study catchment with the regional climate model HadRM3 precipitation, the results showed that a bias-correction period of about 8 days is the best. We hope this preliminary study on the optimum number bias-correction period for daily RCM precipitation will stimulate more research to improve the methodology with different climatic conditions. Future efforts on several unsolved problems have been suggested, such as how strong the filter should be and the impact of the number of bias correction groups on river flow simulations.

Editor M.C. Acreman Associate editor S. Kanae     

Indications for pedogenic formation of perylene in a terrestrial soil profile: Depth distribution and first results from stable carbon isotope ratios

Concentrations and isotope compositions of polycyclic aromatic hydrocarbons (PAHs) were determined in natural soils of Southern Germany. In selected profiles perylene concentrations increased with soil depth when compared to the other PAH compounds present. However, its low solubility made vertical transport by seepage water unlikely. Therefore two mechanisms are discussed that could have caused the unusual distribution of perylene in these soils:

(a) Atmospheric deposition of combustion-derived (i.e. pyrogenic) perylene in the top-soil and

(b) in situ generation in the sub-soil of these specific terrestrial environments. This could have been caused by microbial activities or other catalytic processes yet unknown.

In order to distinguish between pyrogenic and natural generation compound-specific ¹³C/¹²C ratios (δ¹³C) were compared between perylene and other PAHs in samples from the top-soil and sub-soil. Despite successful clean-up of the extracts, low perylene concentrations and peak overlaps with benzo(e)pyrene and benzo(a)pyrene prevented determination of a unique δ¹³C value for perylene in the upper horizon. However, the δ¹³C value of perylene in the sub-soil was 5.7 permille more negative than other equal-mass PAHs (with m/z of 252) in the top-soil, which rather supports in situ generation of perylene in the sub-soil.     

Long-term perspective on the relationship between phytoplankton and nutrient concentrations in a southeastern Australian estuary

Sixteen years (1997–2013) of physicochemical, nutrient and phytoplankton biomass (Chlorophyll-a (Chl-a)) data and a decade (2003-2013) of phytoplankton composition and abundance data were analyzed to assess how the algal community in a temperate southeastern Australian estuary has responded to decreased chronic point source nitrogen loading following effluent treatment upgrade works in 2003. Nitrogen concentrations were significantly lower (P < 0.05) following enhanced effluent treatment and Chl-a levels decreased (P < 0.05) during the warmer months. Temperature and nutrient concentrations significantly influenced temporal changes of Chl-a (explaining 55% of variability), while salinity, temperature, pH and nutrient concentrations influenced phytoplankton abundance and composition (25% explained). Harmful Algal Bloom (HAB) dynamics differed between sites likely influenced by physical attributes of the estuary. This study demonstrates that enhanced effluent treatment can significantly decrease chronic point source nitrogen loading and that Chl-a concentrations can be lowered during the warmer months when the risk of blooms and HABs is greatest.     

Estimating reference evapotranspiration using numerical weather modelling

Evapotranspiration is an important hydrological process and its estimation usually needs measurements of many weather variables such as atmospheric pressure, wind speed, air temperature, net radiation and relative humidity. Those weather variables are not easily obtainable from in situ measurements in practical water resources projects. This study explored a potential application of downscaled global reanalysis weather data using mesoscale modelling system 5 (MM5). The MM5 is able to downscale the global data down to much finer resolutions in space and time for use in hydrological investigations. In this study, the ERA‐40 reanalysis data are downscaled to the Brue catchment in southwest England. The results are compared with the observation data. Among the studied weather variables, atmospheric pressure could be derived very accurately with less than 0·2% error. On the other hand, the error in wind speed is about 200–400%. The errors in other weather variables are air temperature (<10%), relative humidity (5–21%) and net radiation (4–23%). The downscaling process generally improves the data quality (except wind speed) and provides higher data resolution in comparison with the original reanalysis data. The evapotranspiration values estimated from the downscaled data are significantly overestimated across all the seasons (27–46%) based on the FAO Penman–Monteith equation. The dominant weather variables are net radiation (during the warm period) and relative humidity (during the cold period). There are clear patterns among some weather variables and they could be used to correct the biases in the downscaled data from either short‐term in situ measurements or through regionalization from surrounding weather stations. Artificial intelligence tools could be used to map the downscaled data directly into evapotranspiration or even river runoff if rainfall data are available. This study provides hydrologists with valuable information on downscaled weather variables and further exploration of this potentially valuable data source by the hydrological community should be encouraged. Copyright © 2010 John Wiley & Sons, Ltd.