Abstract. To date, knowledge on the effects of decadal-scale changes in climatic forcing on sediment export from glaciated high alpine areas is still limited. This is primarily due to the extreme scarcity of sufficiently long records of suspended sediment concentrations (SSC), which precludes robust explorations of longer-term developments. Aggravatingly, insights are not necessarily transferable from one catchment to another, as sediment export can heavily depend on local preconditions (such as geology or connectivity). However, gaining a better understanding of past sediment export is an essential step towards estimating future changes, which will affect downstream hydropower production, flood hazard, water quality and aquatic habitats. Here we test the feasibility of reconstructing decadal-scale sediment export from short-term records of SSC and long time series of the most important hydro-climatic predictors, discharge, precipitation and air temperature (QPT). Specifically, we test Quantile Regression Forest (QRF), a non-parametric, multivariate approach based on Random Forests. We train independent models for the two nested and partially glaciated catchments Vent (98 km2) and Vernagt (11.4 km2) in the Upper Ötztal in Tyrol, Austria (1891 to 3772 m a.s.l.), to gain a comprehensive overview of sediment dynamics. In Vent, daily QPT records are available since 1967, alongside 15 years of SSC measurements. At gauge Vernagt, QPT records started in 1975 in hourly resolution, which allows comparing model performances in hourly and daily resolution (Validation A). Challengingly, only four years of SSC measurements exits at gauge Vernagt, yet consisting of two 2-year datasets, that are almost 20 years apart, which provides an excellent opportunity for validating the model’s ability to reconstruct past sediment dynamics (Validation B). As a second objective, we aim to assess changes in sediment export by analyzing the reconstructed time series for trends (using Mann-Kendall test and Sen’s slope estimator) and step-like changes (using two complementary change point detection methods, the widely used Pettitt’s test and a Bayesian approach implemented in the R package ‘mcp’). Our findings demonstrate that QRF performs well in reconstructing past daily sediment export (Nash-Sutcliffe efficiency of 0.73) as well as the derived annual sediment yields (Validation B), despite the small training dataset. Further, our analyses indicate that the loss of model skill in daily as compared to hourly resolution is small (Validation A). We find significant positive trends in the reconstructed annual suspended sediment yields at both gauges, with distinct step-like increases around 1981. This coincides with a crucial point in glacier melt dynamics: we find co-occurring change points in annual and summer mass balances of the two largest glaciers in the Vent catchment. This is also reflected in a coinciding step-like increase in discharge at both gauges as well as a considerable increase in the accumulation area ratio of the Vernagtferner glacier. We identify exceptionally high July temperatures in 1982 and 1983 as a likely cause, as July is the most crucial month with respect to firn and ice melt. In contrast, we did not find coinciding change points in precipitation. This study demonstrates that the presented QRF approach is a promising tool with the ability to deepen our understanding of the response of high-alpine areas to decadal climate change. This in turn will aid estimating future changes and preparing management or adaptation strategies.
<p>Under the influence of climate change, high mountain areas like the European Alps are in a transient state where catchment conditions and processes that determine sediment dynamics are changing. Hydro-sedimentary events can account for a substantial proportion the annual sediment yield in alpine catchments, and are often associated with heavy rainfall and rainfall-triggered mass-movements. It is therefore of interest to study the driving conditions and processes of these events, especially due to the potential downstream impacts they can have to eco- and human systems.</p> <p>The dynamics, characteristics and, in particular, (suspended) sediment-discharge hysteresis are often used in conjunction with hydro-meteorological and catchment state variables to identify driving processes and conditions of events. However, is it possible to elucidate the determining conditions and processes or determine meaningful event classes based solely on metrics derived from the suspended sediment and discharge data of the event?&#160;</p> <p>Using two catalogs of manually and automatically detected hydro-sedimentary events from Rofental, Austria, we attempt to answer this question. We perform a cluster analysis with various approaches on event metrics (e.g. hysteresis class, suspended sediment yield, peak discharge, time since last event). To avoid biasing the results towards a specific number of event types, we explicitly use clustering algorithms which do not require the number of clusters (i.e. event types) to be specified. We then look for commonalities within the identified event clusters in terms of catchment conditions and processes during the event (e.g. high temperatures, snowmelt, intense rainfall, wet antecedent conditions, mass movement occurrences). Finally, we discuss the advantages and disadvantages of grouping events on their characteristics alone.</p>
<p>The genesis of riverine floods in large river basins often is complex. Streamflow originating from precipitation and snowmelt and different tributaries can superimpose and cause high water levels threatening cities and communities along the river banks. In this study, we develop an analytical framework that captures and shares the story behind major historic and projected streamflow peaks in the large and complex basin of the Rhine River. Our analysis is based on hydrological simulations with the mesoscale Hydrolgical Model (mHM) forced with an ensemble of climate projections. The spatio-temporal analysis of the flood formation includes the assessment and mapping of antecedent liquid precipitation, snow cover changes, generated and routed runoff, flood extent and the excess runoff from major sub-basins up to ten days before a streamflow peak. An interactive web-based viewer provides easy access to result figures of major historic and projected streamflow peaks at four locations along the Rhine River. Our results indicate that each streamflow peak is driven by a specific sequence of precipitation and snowmelt from different areas in the Rhine River basin. Furthermore, we map how rising temperatures increase liquid precipitation in the Alps, in turn, increasing streamflow peaks along the Rhine River. The highest streamflow peak simulated at Cologne using climate projections exceeds the historic record by almost 50 % and was driven by excessive rainfall over several days over large parts of the Rhine River basin. Such an event taking place today would have catastrophic consequences. Further research is required to assess the impacts of changes in the persistence of circulation patterns on flood extent and hazard.</p>
<p>The impact of a warming climate on snow- and rain-dominated river basins such as the Garhwal Himalayas basin constitutes both a major research challenge and the potential of a severe socio-economic risk. The particular combination at the Garhwal, of hydrometeorological and hydrographic conditions entails merging and superposing two presently distinct seasonal phenomena: snowmelt induced spring floods and rainfall generated summer floods. This study focuses on the projection of seasonality changes in floods in a Garhwal Himalayas basin under global warming. The research in this context is rather uncertain in the proposed study area of the Himalayas, mainly due to the scarcity and unavailability of&#160;long-term and high-resolution meteorological data in that region. But after setting up Automatic Weather Stations and Gauge and Discharge sites in the Garhwal region in 2016, the observed data of the past five years lay the basis for understanding the different flood generating regimes. We have analysed the IMD historical maximum monthly rainfall (1901-2020) and maximum temperature (1951-2020) over the study region and found evidence of shifting of maximum rainfall peak backward up to the month of June and maximum temperature peak shifting forward to June (earlier triggering snowmelt induced peak then); if warmer climate scenarios are experienced in future. We also compared the different precipitation datasets available with respect to the observed data at daily, monthly, quarterly and yearly time scales. Those data are crucial for any analysis of possible changes in seasonal hydro-meteorological conditions. We found that the IMD precipitation dataset matches best the observations and the projected climate ensemble of chosen dataset (NEX-GDDP) required significant correction with respect to observed data to counter underestimation. Therefore, we have used quantile-based mapping to adjust the biased projected climate dataset of NEX-GDDP. Also, the corrected projected precipitation of time window 2071-2099 of RCP 4.5 and 8.5 scenarios is found to be magnitude wise higher than that of the corrected historical time window 1971-2000. This clearly indicates the possible occurrences of changes in floods, though we are well aware about the high uncertainties of projected future precipitation conditions. Thus, our analysis poses the potential of bridging the gaps of understanding different flood generating regimes and their future possibilities for better preparedness against natural hazards in the Himalayan region.</p>
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