Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.     

Soil property variation mapping through data mining of soil category maps

Spatial information on soil properties is an important input to hydrological models. In current hydrological modelling practices, soil property information is often derived from soil category maps by the linking method in which a representative soil property value is linked to each soil polygon. Limited by the area‐class nature of soil category maps, the derived soil property variation is discontinuous and less detailed than high resolution digital terrain or remote sensing data. This research proposed dmSoil, a data‐mining‐based approach to derive continuous and spatially detailed soil property information from soil category maps. First, the soil–environment relationships are extracted through data mining of a soil map. The similarity of the soil at each location to different soil types in the soil map is then estimated using the mined relationships. Prediction of soil property values at each location is made by combining the similarities of the soil at that location to different soil types and the representative soil property values of these soil types. The new approach was applied in the Raffelson Watershed and Pleasant Valley in the Driftless Area of Wisconsin, United States to map soil A horizon texture (in both areas) and depth to soil C horizon (in Pleasant Valley). The property maps from the dmSoil approach capture the spatial gradation and details of soil properties better than those from the linking method. The new approach also shows consistent accuracy improvement at validation points. In addition to the improved performances, the inputs for the dmSoil approach are easy to prepare, and the approach itself is simple to deploy. It provides an effective way to derive better soil property information from soil category maps for hydrological modelling. Copyright © 2014 John Wiley & Sons, Ltd.     

A tree‐ring width based drought reconstruction for southeastern China: links to Pacific Ocean climate variability

We present a drought reconstruction for southeastern China based on a tree‐ring width chronology of Cryptomeria fortunei developed from two sampling sites in central Fujian. A reconstruction of July–February drought variability, spanning AD 1855–2011, was developed by calibrating total tree‐ring width data with the self‐calibrating Palmer drought severity index (scPDSI). The reconstruction was verified against an independent data set, and accounts for 36% of the actual scPDSI variance during the period 1955–2011. Relatively dry intervals were reconstructed between AD 1859–1880, 1899–1911, 1927–1933, 1946–1959, 1964–1970 and 1987–1997. Relatively wet conditions prevailed during 1855–1858, 1881–1898, 1912–1926, 1934–1945, 1960–1963, 1971–1986 and 1998–2011. Comparisons between our scPDSI reconstruction and a moisture‐sensitive tree‐ring width record from Vietnam revealed consistencies between the two data sets, suggesting similar drought regimes. Spectral peaks of 2.2–6.4 years may be indicative of El Niño‐Southern Oscillation (ENSO) activity, as also suggested by the significant correlations with sea surface temperatures (SSTs) in the eastern equatorial and southeastern Pacific Ocean and an extreme event analysis. The analysis of links between our scPDSI reconstruction and the large‐scale regional climatic variation shows that there is a relationship between regional drought variation and East Asian summer monsoon (EASM) intensity.     

The influence of coupled physical swelling and chemical reactions on deformable geomaterials

Coupled thermo‐hydro‐mechanical‐chemical modelling has attracted attention in past decades due to many contemporary geotechnical engineering applications (e.g., waste disposal, carbon capture and storage). However, molecular‐scale interactions within geomaterials (e.g., swelling and dissolution/precipitation) have a significant influence on the mechanical behaviour, yet are rarely incorporated into existing Thermal‐Hydro‐Mechanical‐Chemical (THMC) frameworks. This paper presents a new coupled hydro‐mechanical‐chemical constitutive model to bridge molecular‐scale interactions with macro‐physical deformation by combining the swelling and dissolution/precipitation through an extension of the new mixture‐coupling theory. Entropy analysis of the geomaterial system provides dissipation energy, and Helmholtz free energy gives the relationship between solids and fluids. Numerical simulation is used to compare with the selected recognized models, which demonstrates that the swelling and dissolution/precipitation processes may have a significant influence on the mechanical deformation of the geomaterials.     

Effects of temporal scales and space mismatches on the TRMM 3B42 v7 precipitation product in a remote mountainous area

The accurate measurement of precipitation is essential to understanding regional hydrological processes and hydrological cycling. Quantification of precipitation over remote regions such as the Tibetan Plateau is highly unreliable because of the scarcity of rain gauges. The objective of this study is to evaluate the performance of the satellite precipitation product of tropical rainfall measuring mission (TRMM) 3B42 v7 at daily, weekly, monthly, and seasonal scales. Comparison between TRMM grid precipitation and point‐based rain gauge precipitation was conducted using nearest neighbour and bilinear weighted interpolation methods. The results showed that the TRMM product could not capture daily precipitation well due to some rainfall events being missed at short time scales but provided reasonably good precipitation data at weekly, monthly, and seasonal scales. TRMM tended to underestimate the precipitation of small rainfall events (less than 1 mm/day), while it overestimated the precipitation of large rainfall events (greater than 20 mm/day). Consequently, TRMM showed better performance in the summer monsoon season than in the winter season. Through comparison, it was also found that the bilinear weighted interpolation method performs better than the nearest neighbour method in TRMM precipitation extraction.     

Quantifying the impact of landscape changes on hydrological variables in the alpine and cold region using hydrological model and remote sensing data

Quantifying the impact of landscape on hydrological variables is essential for the sustainable development of water resources. Understanding how landscape changes influence hydrological variables will greatly enhance the understanding of hydrological processes. Important vegetation parameters are considered in this study by using remote sensing data and VIC-CAS model to analyse the impact of landscape changes on hydrology in upper reaches of the Shule River Basin (URSLB). The results show there are differences in the runoff generation of landscape both in space and time. With increasing altitude, the runoff yields increased, with approximately 79.9% of the total runoff generated in the high mountains (4200–5900 m), and mainly consumed in the mid-low mountain region. Glacier landscape produced the largest runoff yields (24.9% of the total runoff), followed by low-coverage grassland (LG; 22.5%), alpine cold desert (AL; 19.6%), mid-coverage grassland (MG; 15.6%), bare land (12.5%), high-coverage grassland (HG; 4.5%) and shrubbery (0.4%). The relative capacity of runoff generation by landscapes, from high to low, was the glaciers, AL, LG, HG, MG, shrubbery and bare land. Furthermore, changes in landscapes cause hydrological variables changes, including evapotranspiration, runoff and baseflow. The study revealed that HG, MG, and bare land have a positive impact on evapotranspiration and a negative impact on runoff and baseflow, whereas AL and LG have a positive impact on runoff and baseflow and a negative impact on evapotranspiration. In contrast, glaciers have a positive impact on runoff. After the simulation in four vegetation scenarios, we concluded that the runoff regulation ability of grassland is greater than that of bare land. The grassland landscape is essential since it reduced the flood peak and conserved the soil and water.     

基于主体功能区约束的大气污染物总量控制目标分配研究

考虑经济发展水平、污染物排放现状、污染物治理水平、空气质量,特别是国家主体功能区环境目标约束等因素,构建大气污染物排放总量分配的指标体系,用改进的等比例分配方法对2015年国家SO2,NOx总量控制目标进行区域分配。分配结果表明:SO2和NOx削减量大的省份主要集中在华北平原及其周围地区,这些地区污染物排放量大、空气质量较差;削减比例较大的地区主要集中在西部地区以及北京、天津2个直辖市,这些地区单位GDP能耗高、工业污染物去除率低、空气质量差;削减量相对较小的地区主要集中在西南和南部一些省份;削减比例较小的地区主要集中在中南部和南部几个省份,这些地区污染物排放量相对较少,空气质量好于其他省份。