Distinguishing streamflow trends caused by changes in climate,forest cover,and permafrost in a large watershed in northeastern China

Understanding how rivers respond to changes in land cover, climate, and subsurface conditions is critical for sustainably managing water resources and ecosystems. In this study, long‐term hydrologic, climate, and satellite data (1973–2012) from the Upper Tahe River watershed (2359 km²) in the Da Hinggan Mountains of northeast China were analysed to quantify the relative hydrologic effects of climate variability (system input) and the combined influences of forest cover change and permafrost thaw (system characteristics) on average annual streamflow (system response) using 2 methods: the sensitivity‐based method and the Kendall–Theil robust line method. The study period was subdivided into a forest harvesting period (1973–1987), a forest stability period (1988–2001), and a forest recovery period (2002–2012). The results indicated that the combined effects of forest harvesting and permafrost thaw on streamflow (+ 47.0 mm) from the forest harvesting period to the forest stability period was approximately twice as large as the effect associated with climate variability (+20.2 mm). Similarly, from the forest stability period to the forest recovery period, the decrease in average annual streamflow attributed to the combined effects of forest recovery and permafrost thaw (?38.0 mm) was much greater than the decrease due to climate variability (?22.2 mm). A simple method was used to separate the distinct impacts of forest cover change and permafrost thaw, but distinguishing these influences is difficult due to changes in surface and subsurface hydrologic connectivity associated with permafrost thaw. The results highlight the need to consider multiple streamflow drivers in future watershed and aquatic ecosystem management. Due to the ecological and hydrological susceptibility to disturbances in the Da Hinggan Mountains, forest harvesting will likely negatively impact ecohydrological processes in this region, and the effects of forest species transition in the forest recovery process should be further investigated.     

Likely effects of climate change on groundwater availability in a Mediterranean region of Southeastern Spain

Groundwater resources are typically the main fresh water source in arid and semi‐arid regions. Natural recharge of aquifers is mainly based on precipitation; however, only heavy precipitation events (HPEs) are expected to produce appreciable aquifer recharge in these environments. In this work, we used daily precipitation and monthly water level time series from different locations over a Mediterranean region of Southeastern Spain to identify the critical threshold value to define HPEs that lead to appreciable aquifer recharge in this region. Wavelet and trend analyses were used to study the changes in the temporal distribution of the chosen HPEs (≥20 mm day^?1) over the observed period 1953–2012 and its projected evolution by using 18 downscaled climate projections over the projected period 2040–2099. The used precipitation time series were grouped in 10 clusters according to similarities between them assessed by using Pearson correlations. Results showed that the critical HPE threshold for the study area is 20 mm day^?1. Wavelet analysis showed that observed significant seasonal and annual peaks in global wavelet spectrum in the first sub‐period (1953–1982) are no longer significant in the second sub‐period (1983–2012) in the major part of the ten clusters. This change is because of the reduction of the mean HPEs number, which showed a negative trend over the observed period in nine clusters and was significant in five of them. However, the mean size of HPEs showed a positive trend in six clusters. A similar tendency of change is expected over the projected period. The expected reduction of the mean HPEs number is two times higher under the high climate scenario (RCP8.5) than under the moderate scenario (RCP4.5). The mean size of these events is expected to increase under the two scenarios. The groundwater availability will be affected by the reduction of HPE number which will increase the length of no aquifer recharge periods (NARP) accentuating the groundwater drought in the region. Copyright © 2016 John Wiley & Sons, Ltd.     

Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method

Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd.     

A GLUE‐based uncertainty assessment framework for tritium‐inferred transit time estimations under baseflow conditions

The last decade has seen major technical and scientific improvements in the study of water transfer time through catchments. Nevertheless, it has been argued that most of these developments used conservative tracers that may disregard the oldest component of water transfer, which often has transit times greater than 5 years. Indeed, although the analytical reproducibility of tracers limits the detection of the older flow components associated with the most dampened seasonal fluctuations, this is very rarely taken into account in modelling applications. Tritium is the only environmental tracer at hand to investigate transfer times in the 5‐ to 50‐year range in surface waters, as dissolved gases are not suitable due to the degassing process. Water dating with tritium has often been difficult because of the complex history of its atmospheric concentration, but its current stabilization together with recent analytical improvements open promising perspectives. In this context, the innovative contribution of this study lies in the development of a generalized likelihood uncertainty estimation‐based approach for analysing the uncertainties associated with the modelling of transit time due to both parameter identification and tracer analytical precision issues. A coupled resampling procedure allows assessment of the statistical significance of the transfer time differences found in diverse waters. This approach was developed for tritium and the exponential‐piston model but can be implemented for virtually any tracer and model. Stream baseflow, spring and shallow aquifer waters from the Vallcebre research catchments, analysed for tritium in different years with different analytical precisions, were investigated by using this approach and taking into account other sources of uncertainty. The results showed three groups of waters of different mean transit times, with all the stream baseflow and spring waters older than the 5‐year threshold needing tritium. Low sensitivity of the results to the model structure was also demonstrated. Dual solutions were found for the waters sampled in 2013, but these results may be disambiguated when additional analyses will be made in a few years. Copyright © 2016 John Wiley & Sons, Ltd.     

Rain on snow induced urban floods in cold maritime climate: Risk,indicators and trends

Urban floods pose a societal and economical risk. This study evaluated the risk and hydro-meteorological conditions that cause pluvial flooding in coastal cities in a cold climate. Twenty years of insurance claims data and up to 97 years of meteorological data were analysed for Reykjavík, Iceland (64.15°N; <100 m above sea level). One third of the city's wastewater collection system is combined, and pipe grades vary from 0.5% to 10%. Results highlight semi-intensive rain (<7 mm/h; ≤3 year return period) in conjunction with snow and frozen ground as the main cause for urban flood risk in a climate which undergoes frequent snow and frost cycles (avg. 13 and 19 per season, respectively). Floods in winter were more common, more severe and affected a greater number of neighbourhoods than during summer. High runoff volumes together with debris remobilized with high winds challenged the capacity of wastewater systems regardless of their age or type (combined vs. separate). The two key determinants for the number of insurance claims were antecedent frost depth and total precipitation volume per event. Two pluvial regimes were particularly problematic: long duration (13–25 h), late peaking rain on snow (RoS), where snowmelt enhanced the runoff intensity, elongated and connected independent rainfall into a singular, more voluminous (20–76 mm) event; shorter duration (7–9 h), more intensive precipitation that evolved from snow to rain. Closely timed RoS and cooling were believed to trigger frost formation. A positive trend was detected in the average seasonal snow depth and volume of rain and snowmelt during RoS events. More emphasis, therefore, needs to be placed on designing and operating urban drainage infrastructure with regard to RoS co-acting with frozen ground. Furthermore, more detailed, routine monitoring of snow and soil conditions is important to predict RoS flood events.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

A 2000‐year lipid biomarker record preserved in a stalagmite from north‐west Scotland

Previous studies on lipid biomarkers preserved in Chinese stalagmites have indicated that ratios of low‐molecular‐weight (LMW) to high‐molecular‐weight (HMW) n‐alkanes, n‐alkan‐2‐ones, n‐alkanols and n‐alkanoic acids can be used as an index of vegetation versus microbial organic matter input to the system and, by extension, a marker of climatic changes, with increases in the proportion of LMW compounds coinciding with colder periods. Here we test whether this hypothesis is equally applicable to a different geographical region (north‐west Scotland), by examining a stalagmite record of the past 200 years, and a wider range of lipid markers. We also test the applicability of other lipid proxies in this context, including the use of n‐alkane ratios, to interpret vegetation changes, and unsaturated alkanoic acid ratios as climatic indicators. The results show that lipid proxies preserved in stalagmites, and especially those related to vegetation, are potentially extremely useful in palaeoenvironmental research. Of particular value is the use of C₂₇/C₃₁ n‐alkane ratios as a proxy for vegetation change, clearly indicating variations between herbaceous and arboreal cover. This proxy has now been successfully applied to samples from diverse environments, and can be considered sufficiently robust to be of use in analysing future stalagmite records. It will be of particular value in areas where reliable pollen records are not available, as is often the case with deeper cave deposits. However, the division between LMW and HMW aliphatic compounds is not a clear‐cut case of microbial versus plant activity, with the changes in LMW compounds relating more closely to those in their HMW analogues than in specific bacterial biomarkers. The use of unsaturated alkanoic acid ratios here gives conflicting results, with the observed variation through time depending on the isomer measured. The discrepancies between the findings of this study and previous work are likely to be due to the varying controls on the lipids (original organic matter input, and compound degradation), which in turn will be affected by whether the main climatic limiting factor on the soil is temperature or precipitation. This suggests that lipid proxies preserved in stalagmites must be interpreted with care, particularly in the case of bacterial compounds which may be derived from within the cave or from the soil. However, many of these issues can be resolved by the use of multi‐proxy studies. Copyright © 2011 John Wiley & Sons, Ltd.     

Dynamic response of a pile considering the interaction of pile variable cross section with the surrounding layered soil

Assuming that the pile variable cross section interacts with the surrounding soil in the same way as the pile toe does with the bearing stratus, the interaction of pile variable cross section with the surrounding soil is represented by a Voigt model, which consists of a spring and a damper connected in parallel, and the spring constant and damper coefficient are derived. Thus, a more rigid pile–soil interaction model is proposed. The surrounding soil layers are modeled as axisymmetric continuum in which its vertical displacements are taken into account and the pile is assumed to be a Rayleigh–Love rod with material damping. Allowing for soil properties and pile defects, the pile–soil system is divided into several layers. By means of Laplace transform, the governing equations of soil layers are solved in frequency domain, and a new relationship linking the impedance functions at the variable‐section interface between the adjacent pile segments is derived using a Heaviside step function, which is called amended impedance function transfer method. On this basis, the impedance function at pile top is derived by amended impedance function transfer method proposed in this paper. Then, the velocity response at pile top can be obtained by means of inverse Fourier transform and convolution theorem. The effects of pile–soil system parameters are studied, and some conclusions are proposed. Then, an engineering example is given to confirm the rationality of the solution proposed in this paper. Copyright © 2017 John Wiley & Sons, Ltd.     

Trend and extreme occurrence of precipitation in a mid‐latitude Eurasian steppe watershed at various time scales

The confounding effects of step change invalidate the stationarity assumption of commonly used trend analysis methods such as the Mann–Kendall test technique, so previous studies have failed to explain inconsistencies between detected trends and observed large precipitation anomalies. The objectives of this study were to (1) formulate a trend analysis approach that considers nonstationarity due to step changes, (2) use this approach to detect trends and extreme occurrences of precipitation in a mid‐latitude Eurasian steppe watershed in North China, and (3) examine how runoff responds to precipitation trends in the study watershed. Our results indicate that annual precipitation underwent a marginal step jump around 1995. The significant annual downward trend after 1994 was primarily due to a decrease in summer rainfall; other seasons exhibited no significant precipitation trends. At a monthly scale, July rainfall after 1994 exhibited a significant downward trend, whereas precipitation in other months had no trend. The percentage of wet days also underwent a step jump around 1994 following a significant decreasing trend, although the precipitation intensity exhibited neither a step change nor any significant trend. However, both low‐frequency and high‐frequency precipitation events in the study watershed occurred more often after than before 1994; probably as either a result or an indicator of climate change. In response to these precipitation changes, the study watershed had distinctly different precipitation‐runoff relationships for observed annual precipitations of less than 300 mm, between 300 and 400 mm, and greater than 400 mm. Copyright © 2013 John Wiley & Sons, Ltd.