Temporal Scaling of Streamflow Elasticity to Precipitation: A Global Analysis

Streamflow elasticity to precipitation, defined as the percent change of streamflow resulting from a 1% change in precipitation, is sometimes used as an alternative to rainfall-runoff models in climate impact analyses. Elasticity is usually estimated from long streamflow and precipitation series aggregated at annual time steps while the climate impact analyses are usually geared toward changes at decadal scales. The purpose of this paper is therefore to understand how the elasticity depends on the aggregation time scale and the process controls of such a dependence. We analyze streamflow records of 7,053 catchments around the world over the period 1950–2016, and select 5,327 with reliable elasticity estimates for aggregation time ranging from 13 to 60 months. We find a significant scaling of streamflow elasticity to precipitation with aggregation time in 66% of the catchments which is much larger than expected by chance. Positive scaling occurs much more frequently than negative scaling. More arid/less rainy catchments, less forested catchments and catchments with a large base flow contribution to streamflow are more frequently characterized by a positive scaling. A random forest classification model identifies aridity index, latitude, mean annual precipitation, the potential evapotranspiration seasonality, the base flow index and the precipitation seasonality as relevant explanatory variables of the scaling. We interpret the sign of the scaling by non-linear runoff generation in arid regions, by the effect of climate modes and snow processes, and by the regulation capacity of vegetation to transpire more water if the past years were wet. It is suggested to use decadal elasticities instead of annual elasticities in climate impact analyses in order to account for their scaling behavior

Performance Characteristics of qPCR Assays Targeting Human- and Ruminant-Associated Bacteroidetes for Microbial Source Tracking across Sixteen Countries on Six Continents

Numerous quantitative PCR assays for microbial fecal source tracking (MST) have been developed and evaluated in recent years. Widespread application has been hindered by a lack of knowledge regarding the geographical stability and hence applicability of such methods beyond the regional level. This study assessed the performance of five previously reported quantitative PCR assays targeting human-, cattle-, or ruminant-associated Bacteroidetes populations on 280 human and animal fecal samples from 16 countries across six continents. The tested cattle-associated markers were shown to be ruminant-associated. The quantitative distributions of marker concentrations in target and nontarget samples proved to be essential for the assessment of assay performance and were used to establish a new metric for quantitative source-specificity. In general, this study demonstrates that stable target populations required for marker-based MST occur around the globe. Ruminant-associated marker concentrations were strongly correlated with total intestinal Bacteroidetes populations and with each other, indicating that the detected ruminant-associated populations seem to be part of the intestinal core microbiome of ruminants worldwide. Consequently tested ruminant-targeted assays appear to be suitable quantitative MST tools beyond the regional level while the targeted human-associated populations seem to be less prevalent and stable, suggesting potential for improvements in human-targeted methods

Significance testing of rank cross-correlations between autocorrelated time series with short-range dependence

Statistical dependency measures such as Kendall’s Tau or Spearman’s Rho are frequently used to analyse the coherence between time series in environmental data analyses. Autocorrelation of the data can, however, result in spurious cross correlations if not accounted for. Here, we present the asymptotic distribution of the estimators of Spearman’s Rho and Kendall’s Tau, which can be used for statistical hypothesis testing of cross-correlations between autocorrelated observations. The results are derived using U-statistics under the assumption of absolutely regular (or β-mixing) processes. These comprise many short-range dependent processes, such as ARMA-, GARCH- and some copula-based models relevant in the environmental sciences. We show that while the assumption of absolute regularity is required, the specific type of model does not have to be specified for the hypothesis test. Simulations show the improved performance of the modified hypothesis test for some common stochastic models and small to moderate sample sizes under autocorrelation. The methodology is applied to observed climatological time series of flood discharges and temperatures in Europe. While the standard test results in spurious correlations between floods and temperatures, this is not the case for the proposed test, which is more consistent with the literature on flood regime changes in Europe

Flood trends in Europe: Are changes in small and big floods different?

Recent studies have revealed evidence of trends in the median or mean flood discharge in Europe over the last 5 decades, with clear and coherent regional patterns. The aim of this study is to assess whether trends in flood discharges also occurred for larger return periods, accounting for the effect of catchment scale. We analyse 2370 flood discharge records, selected from a newly available pan-European flood database, with record length of at least 40 years over the period 1960-2010 and with contributing catchment area ranging from 5 to 100 000 km2. To estimate regional flood trends, we use a non-stationary regional flood frequency approach consisting of a regional Gumbel distribution, whose median and growth factor can vary in time with different strengths for different catchment sizes. A Bayesian Markov chain Monte Carlo (MCMC) approach is used for parameter estimation. We quantify regional trends (and the related sample uncertainties), for floods of selected return periods and for selected catchment areas, across Europe and for three regions where coherent flood trends have been identified in previous studies. Results show that in northwestern Europe the trends in flood magnitude are generally positive. In small catchments (up to 100 km2), the 100-year flood increases more than the median flood, while the opposite is observed in medium and large catchments, where even some negative trends appear, especially in northwestern France. In southern Europe flood trends are generally negative. The 100-year flood decreases less than the median flood, and, in the small catchments, the median flood decreases less compared to the large catchments. In eastern Europe the regional trends are negative and do not depend on the return period, but catchment area plays a substantial role: the larger the catchment, the more negative the trend

Estimating parameter values of a socio-hydrological flood model

Socio-hydrological modelling studies that have been published so far show that dynamic coupled human-flood models are a promising tool to represent the phenomena and the feedbacks in human-flood systems. So far these models are mostly generic and have not been developed and calibrated to represent specific case studies. We believe that applying and calibrating these type of models to real world case studies can help us to further develop our understanding about the phenomena that occur in these systems. In this paper we propose a method to estimate the parameter values of a socio-hydrological model and we test it by applying it to an artificial case study. We postulate a model that describes the feedbacks between floods, awareness and preparedness. After simulating hypothetical time series with a given combination of parameters, we sample few data points for our variables and try to estimate the parameters given these data points using Bayesian Inference. The results show that, if we are able to collect data for our case study, we would, in theory, be able to estimate the parameter values for our socio-hydrological flood model

Impacts of land use and land cover change on surface runoff, discharge and low flows: evidence from East Africa

Region: East Africa. Focus: A review of catchment studies (n=37) conducted in East Africa evaluating the impacts of Land Use and Land Cover Changes (LULCC) on discharge, surface runoff, and low flows. New hydrological insights: Forest cover loss is accompanied by increased stream discharges and surface runoff. No significant difference in stream discharge is observed between bamboo and pine plantation catchments, and between cultivated and tea plantation catchments. Trend analyses show that despite forest cover loss, 63% of the watersheds show non-significant changes in annual discharges while 31% show increasing trends. Half of the watersheds show non-significant trends in wet season flows and low flows while 35% reveal decreasing trends in low flows. Modeling studies estimate that forest cover loss increases annual discharges and surface runoff by 16 ± 5.5% and 45 ± 14%, respectively. Peak flows increased by a mean of 10 ± 2.8% while low flows decreased by a mean of 7 ± 5.3%. Increased forest cover decreases annual discharges and surface runoff by 13 ± 1.9% and 25 ± 5%, respectively. Weak correlations between forest cover and runoff (r=0.42, p < 0.05), mean discharge (r=0.63, p < 0.05) and peak discharge (r=0.67, p < 0.05) indicate that forest cover alone is not an accurate predictor of hydrological fluxes in East African catchments. The variability in these results supports the need for long-term field monitoring to better understand catchment responses and to improve the calibration of currently used simulation models

Understanding Heavy Tails of Flood Peak Distributions

Statistical distributions of flood peak discharge often show heavy tail behavior, that is, extreme floods are more likely to occur than would be predicted by commonly used distributions that have exponential asymptotic behavior. This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior, and the heaviness of the tails is very difficult to predict, which may lead to unnecessarily high flood damage. Despite its high importance, the literature on the heavy tail behavior of flood distributions is rather fragmented. In this review, we provide a coherent overview of the processes causing heavy flood tails and the implications for science and practice. Specifically, we propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions related to processes in the atmosphere, the catchment, and the river system. We then discuss to which extent the current knowledge supports or contradicts these hypotheses. We also discuss the statistical conditions for the emergence of heavy tail behavior based on derived distribution theory and relate them to the hypotheses and flood generation mechanisms. We review the degree to which the heaviness of the tails can be predicted from process knowledge and data. Finally, we recommend further research toward testing the hypotheses and improving the prediction of heavy tails

Editorial: toward 50 years of 'Water Resources Research'

The first issue of 'Water Resources Research' (WRR) was published in March 1965 and, therefore, the year 2015 will present the exciting opportunity to celebrate the 50th anniversary of the journal. Naturally, this milestone will be seen as an occasion to look back on 50 years of research activity. The history of WRR provides a very interesting perspective on the development of hydrology and the legacy of the worldwide water resources community

Changing climate shifts timing of European floods

A warming climate is expected to have an impact on the magnitude and timing of river floods; however, no consistent large-scale climate change signal in observed flood magnitudes has been identified so far. We analyzed the timing of river floods in Europe over the past five decades, using a pan-European database from 4262 observational hydrometric stations, and found clear patterns of change in flood timing. Warmer temperatures have led to earlier spring snowmelt floods throughout northeastern Europe; delayed winter storms associated with polar warming have led to later winter floods around the North Sea and some sectors of the Mediterranean coast; and earlier soil moisture maxima have led to earlier winter floods in western Europe. Our results highlight the existence of a clear climate signal in flood observations at the continental scale