An analysis of change in alpine annual maximum discharges: implications for the selection of design discharges

The paper presents an analysis of 17 long annual maximum series (AMS) of flood flows for Swiss Alpine basins, aimed at checking the presence of changes in the frequency regime of annual maxima. We apply Pettitt's change point test, the nonparametric sign test and Sen's test on trends. We also apply a parametric goodness‐of‐fit test for assessing the suitability of distributions estimated on the basis of annual maxima collected up to a certain year for describing the frequency regime of later observations. For a number of series the tests yield consistent indications for significant changes in the frequency regime of annual maxima and increasing trends in the intensity of annual maximum discharges. In most cases, these changes cannot be explained by anthropogenic causes only (e.g. streamflow regulation, construction of dams). Instead, we observe a statistically significant relationship between the year of change and the elevation of the catchment outlet. This evidence is consistent with the findings of recent studies that explain increasing discharges in alpine catchments with an increase in the temperature controlling the portion of mountain catchments above the freezing point. Finally, we analyse the differences in return periods (RPs) estimated for a given flood flow on the basis of recent and past observations. For a large number of the study AMS, we observe that, on average, the 100‐year flood for past observations corresponds to a RP of approximately 10 to 30 years on the basis of more recent observation. From a complementary perspective, we also notice that estimated RP‐year flood (i.e. flood quantile (FQ) associated with RP) increases on average by approximately 20% for the study area, irrespectively of the RP. Practical implications of the observed changes are illustrated and discussed in the paper. Copyright © 2011 John Wiley & Sons, Ltd.     

CHOSEN: A synthesis of hydrometeorological data from intensively monitored catchments and comparative analysis of hydrologic extremes

Comparative hydrology has been hampered by limited availability of geographically extensive, intercompatible monitoring data on comprehensive water balance stores and fluxes. These limitations have, for example, restricted comprehensive assessment of multiple dimensions of wetting and drying related to climate change and hampered understanding of why widespread changes in precipitation extremes are uncorrelated with changes in streamflow extremes. Here, we address this knowledge gap and underlying data gap by developing a new data synthesis product and using that product to detect trends in the frequencies and magnitudes of a comprehensive set of hydroclimatic and hydrologic extremes. CHOSEN (Comprehensive Hydrologic Observatory Sensor Network) is a database of streamflow, soil moisture, and other hydroclimatic and hydrologic variables from 30 study areas across the United States. An accompanying data pipeline provides a reproducible, semi-automated approach for assimilating data from multiple sources, performing quality assurance and control, gap-filling and writing to a standard format. Based on the analysis of extreme events in the CHOSEN dataset, we detected hotspots, characterized by unusually large proportions of monitored variables exhibiting trends, in the Pacific Northwest, New England, Florida and Alaska. Extreme streamflow wetting and drying trends exhibited regional coherence. Drying trends in the Pacific Northwest and Southeast were often associated with trends in soil moisture and precipitation (Pacific Northwest) and evapotranspiration-related variables (Southeast). In contrast, wetting trends in the upper Midwest and the Rocky Mountains showed few univariate associations with other hydroclimatic extremes, but their latitudes and elevations suggested the importance of changing snowmelt characteristics. On the whole, observed trends are incompatible with a ‘drying-in-dry, wetting-in-wet’ paradigm for climate-induced hydrologic changes over land. Our analysis underscores the need for more extensive, longer-term observational data for soil moisture, snow and evapotranspiration.     

Runoff evolution in the Swiss Alps: projections for selected high‐alpine catchments based on ENSEMBLES scenarios

The Alps are often referred to as the ‘water tower of Europe’. In Switzerland, many branches of the economy, especially the hydropower industry, are closely linked to and dependent on the availability of water. Assessing the impact of climate change on streamflow runoff is, thus, of great interest. Major efforts have already been made in this respect, but the analyses often focus on individual catchments and are difficult to intercompare. In this article, we analysed nine high‐alpine catchments spread over the Swiss Alps, selected for their relevance to a wide range of morphological characteristics. Runoff projections were carried out until the end of the current century by applying the Glacier Evolution Runoff Model (GERM) and climate scenarios generated in the framework of the ENSEMBLES project. We focused on assessing the uncertainty induced by the unknown climate evolution and provided general, statistically based statements, which should be useful as a ‘rule of thumb’ for analyses addressing questions related to water management. Catchments with a high degree of glacierization will undergo the largest changes. General statements about absolute variations in discharge are unreliable, but an overall pattern, with an initial phase of increased annual discharge, followed by a phase with decreasing discharge, is recognizable for all catchments with a significant degree of glacierization. In these catchments, a transition from glacial and glacio‐nival regime types to nival will occur. The timing of maximal annual runoff is projected to occur before 2050 in all basins. The time of year with maximal daily discharges is expected to occur earlier at a rate of 4·4 ± 1·7 days per decade. Compared to its present level, the contribution of snow‐ and icemelt to annual discharge is projected to drop by 15 to 25% until the year 2100. Copyright © 2011 John Wiley & Sons, Ltd.     

Long-term variations in orographic rainfall: analysis and implications for upland catchments

Abstract

Climatic changes could alter the frequency and magnitude of rainfall events and the distribution of rainfall with altitude, with important consequences for management of aquatic ecosystems, water resources and flood risk. This study investigates changes in observed rainfall amounts across a range of altitudes in the Lake District region, northwest England, and spatial and temporal changes to the orographic “rainshadow” effect. Between the 1970s and 1990s there have been marked changes to the seasonality of precipitation, such that winters have become wetter, and increasingly dominated by heavy precipitation events. The intensity of these events has increased most markedly at higherelevation sites. Such changes could hinder recovery of sensitive upland sites from acidification and increase the risk of downstream flooding. An inter-decadal weakening of the region's rainshadow suggests a greater proportion of winter precipitation crosses the high-elevation Lake District dome. This is linked to changes in the frequency and character of wet weather patterns.     

Alteration of hydrologic indicators for Korean catchments under CMIP5 climate projections

The change of hydrological regimes may cause impacts on human and natural system. Therefore, investigation of hydrologic alteration induced by climate change is essential for preparing timely proper adaptation to the changes. This study employed 24 climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Pathway (RCP) 4.5 scenario. The climate projections were downscaled at a station‐spacing for seven Korean catchments by a statistical downscaling method that preserves a long‐term trend in climate projections. Using an ensemble of future hydrologic projections simulated by three conceptual rainfall‐runoff models (GR4J, IHACRES, and Sacramento models), we calculated Hydrologic Alteration Factors (HAFs) to investigate degrees of variations in Indicators of Hydrologic Alteration (IHAs) derived from the hydrologic projections. The results showed that the seven catchments had similar trend in terms of the HAFs for the 24 IHAs. Given that more frequent severe floods and droughts were projected over Korean catchments, sound water supply strategies are definitely required to adapt to the alteration of streamflow. A wide range of HAFs between rainfall‐runoff models for each catchment was detected by large variations in the magnitude of HAFs with the hydrologic models and the difference could be the hydrologic prediction uncertainty. There were no‐consistent tendency in the order of HAFs between the hydrologic models. In addition, we found that the alterations of hydrologic regimes by climate change are smaller as the size of catchment is larger. Copyright © 2016 John Wiley & Sons, Ltd.     

Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis

Understanding and modelling pluvial flood patterns is pivotal for the estimation of flood impacts in urban areas, especially in a climate change perspective. However, urban flood modelling under climate change conditions poses several challenges. On one hand, the identification and collection of climate change data suitable for flood-related evaluations requires consistent computational and scientific effort. On the other hand, large difficulties can arise in the reproduction of the rainfall-runoff transformation process in cases when only little information about the subsurface processes is known. In this perspective, a simplified approach is proposed to address the challenges regarding the quantitative estimation of climate change effects on urban flooding for real case applications. The approach is defined as “bottom-up” because climate change information is not included in flood modelling, but it is only invoked for the interpretation of results. In other words, the challenge faced in this work is the development of a modelling strategy that is expeditious, because it does not require flood simulations for future rainfall scenarios, but only under current climate conditions, thus reducing the overall computational effort; and it is flexible, because results can be easily updated once new climate change data, scenarios or methods become available, without the need of additional flood simulations. To simulate real case applications, the approach is tested for a scenario analysis, where different return periods and hyetograph shapes are used as input for urban inundation modelling in Naples, Italy. The approach can support public and private stakeholders, such as land administrators and water systems managers; moreover, it represents a valuable and effective basis for climate change risk communication strategies.     

Long‐term trend analysis for major climate variables in the Yellow River basin*

Some previous studies have shown that drying‐up of the lower Yellow River resulted from decreasing precipitation and excessive industrial and agricultural consumption of water from the middle and downstream regions of the Yellow River. On the basis of average air temperature, precipitation, and pan evaporation data from nearly 80 gauging stations in the Yellow River basin, the monotonic trends of major climate variables over the past several decades are analysed. The analysis was mainly made for 12 months and the annual means. The isograms for annual and typical months are given in the paper. The result shows that the average temperature in the study area exhibits an increasing trend, mainly because of the increase of temperature in December, January and February. The largest trend is shown in December and the smallest is in August. There are 65 of 77 stations exhibiting a downward trend for annual precipitation. In all seasons except summer, there is a similar trend in the upstream region of the Yellow River, south of latitude 35°N. It is interesting to note that the pan evaporation has decreased in most areas of the Yellow River basin during the past several decades. April and July showed the greatest magnitude of slope, and the area from Sanmenxia to Huayuankou as well as the Yiluo River basin exhibited the strongest declining trend. The conclusion is that the decreasing pan evaporation results from complex changes of air temperature, relative humidity, solar radiation, and wind speed, and both climate change and human activities have affected the flow regime of the Yellow River during the past several decades. Copyright © 2007 John Wiley & Sons, Ltd.     

Artificial intelligence for identifying hydrologically homogeneous regions: A state‐of‐the‐art regional flood frequency analysis

Due to the severity related to extreme flood events, recent efforts have focused on the development of reliable methods for design flood estimation. Historical streamflow series correspond to the most reliable information source for such estimation; however, they have temporal and spatial limitations that may be minimized by means of regional flood frequency analysis (RFFA). Several studies have emphasized that the identification of hydrologically homogeneous regions is the most important and challenging step in an RFFA. This study aims to identify state‐of‐the‐art clustering techniques (e.g., K ‐means, partition around medoids, fuzzy C‐means, K ‐harmonic means, and genetic K ‐means) with potential to form hydrologically homogeneous regions for flood regionalization in Southern Brazil. The applicability of some probability density function, such as generalized extreme value, generalized logistic, generalized normal, and Pearson type 3, was evaluated based on the regions formed. Among all the 15 possible combinations of the aforementioned clustering techniques and the Euclidian, Mahalanobis, and Manhattan distance measures, the five best were selected. Several watersheds' physiographic and climatological attributes were chosen to derive multiple regression equations for all the combinations. The accuracy of the equations was quantified with respect to adjusted coefficient of determination, root mean square error, and Nash–Sutcliffe coefficient, whereas, a cross‐validation procedure was applied to check their reliability. It was concluded that reliable results were obtained when using robust clustering techniques based on fuzzy logic (e.g., K ‐harmonic means), which have not been commonly used in RFFA. Furthermore, the probability density functions were capable of representing the regional annual maximum streamflows. Drainage area, main river length, and mean altitude of the watershed were the most recurrent attributes for modelling of mean annual maximum streamflow. Finally, an integration of all the five best combinations stands out as a robust, reliable, and simple tool for estimation of design floods.