Assessment of Changes in Annual Maximum Precipitations in the Iberian Peninsula under Climate Change

Drawing Attention to the Measurement Uncertainty Arising from Sampling in Chemical and Physicochemical Analyses: An Overview

Sometimes, analytical chemicals forget that the measurement process begins with the selection of the sample; thus, it must be understood that the measurement uncertainty is constituted by the association of the uncertainty arising from the sampling and the uncertainty arising from the traditional analytical process, that which is carried out in the laboratory. The analytical process is well-controlled, so its uncertainty is well defined; however, the uncertainty arising from sampling, for not having this controlled environment, is often not evident, so that there is still no culture to consider it for the calculation of measurement uncertainty. This study discusses the importance of the sampling uncertainty concerning the analytical uncertainty and details the current approaches available in the literature, such as the classical analysis of variance, the robust analysis of variance, and the range statistics. Moreover, this work highlights the recent manuscripts that are using these mentioned approaches, correlating them to the matrices, chemical and physical-chemical analytes, and analytical techniques. Finally, some case studies using the uncertainty information in compliance assessment show that the measurement uncertainty arising from sampling in chemical and physicochemical analyses cannot always be neglected.

Extreme Floods in Small Mediterranean Catchments: Long-Term Response to Climate Variability and Change

Climate change implies changes in the frequency and magnitude of flood events. The influence of climate variability on flooding was evaluated by an analysis of sedimentary (palaeofloods) and documentary archives. A 500-year palaeoflood record at Montlleó River (657 km2 in catchment area), eastern Spain, revealed up to 31 palaeofloods with a range of discharges of 20–950 m3 s−1, and with at least five floods exceeding 740–950 m3 s−1. This information contrasts with the available gauged flood registers (since year 1971) with an annual maximum daily discharge of 129 m3 s−1. Our palaeoflood dataset indicates flood cluster episodes at (1) 1570–1620, (2) 1775–1795, (3) 1850–1890, and (4) 1920–1969. Flood rich periods 1 and 3 corresponded to cooler than usual (about 0.3 °C and 0.2 °C) climate oscillations, whereas 2 and 4 were characterised by higher inter-annual climatic variability (floods and droughts). This high inter-annual rainfall variability increased over the last 150 years, leading to a reduction of annual maximum flow. Flood quantiles (>50 years) calculated from palaeoflood+gauged data showed 30%–40% higher peak discharges than those using only instrumental records, whereas when increasing the catchment area (1500 km2) the discharge estimation variance decreased to ~15%. The results reflect the higher sensitivity of small catchments to changes on flood magnitude and frequency due to climate variability whereas a larger catchment buffers the response due to the limited extent of convective storms. Our findings show that extended flood records provide robust knowledge about hazardous flooding that can assist in the prioritization of low-regret actions for flood-risk adaptation to climate change.

Effect of Seasonality on the Quantiles Estimation of Maximum Floodwater Levels in a Reservoir and Maximum Outflows

Certain relevant variables for dam safety and downstream safety assessments are analyzed using a stochastic approach. In particular, a method to estimate quantiles of maximum outflow in a dam spillway and maximum water level reached in the reservoir during a flood event is presented. The hydrological system analyzed herein is a small mountain catchment in north Spain, whose main river is a tributary of Ebro river. The ancient Foradada dam is located in this catchment. This dam has no gates, so that flood routing operation results from simple consideration of fixed crest spillway hydraulics. In such case, both mentioned variables (maximum outflow and maximum reservoir water level) are basically derived variables that depend on flood hydrograph characteristics and the reservoir’s initial water level. A Monte Carlo approach is performed to generate very large samples of synthetic hydrographs and previous reservoir levels. The use of extreme value copulas allows the ensembles to preserve statistical properties of historical samples and the observed empirical correlations. Apart from the classical approach based on annual periods, the modelling strategy is also applied differentiating two subperiods or seasons (i.e., summer and winter). This allows to quantify the return period distortion introduced when seasonality is ignored in the statistical analysis of the two relevant variables selected for hydrological risk assessment. Results indicate significant deviations for return periods over 125 years. For the analyzed case study, ignoring seasonal statistics and trends, yields to maximum outflows underestimation of 18% for T = 500 years and 29% for T = 1000 years were obtained.

Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.