Review of submitted manuscript: The importance of spatio-temporal snowmelt variability for isotopic hydrograph separation in a high-elevation catchment by Schmieder et al.

2016 ◽  
Author(s):  
Stefan Pohl
2016 ◽  
Author(s):  
Jan Schmieder ◽  
Florian Hanzer ◽  
Thomas Marke ◽  
Jakob Garvelmann ◽  
Michael Warscher ◽  
...  

Abstract. Seasonal snow cover is an important temporary water storage in high-elevation regions. Especially in remote areas, the available data is often insufficient to explicitly quantify snowmelt contributions to streamflow. The unknown spatio-temporal variability of the snowmelt isotopic content, as well as pronounced spatial variations of snowmelt rates lead to high uncertainties in applying the isotopic hydrograph separation method. This study presents an approach that uses a distributed snowmelt model to support the traditional isotopic hydrograph separation technique. The stable isotopic signatures of snowmelt water samples collected during two spring 2014 snowmelt events at a north- and a south-facing slope were volume-weighted with snowmelt rates derived from a distributed physics-based snow model in order to transfer the measured plot-scale isotopic content of snowmelt water to the catchment scale. The observed δ18O values and modelled snowmelt rates showed distinct inter- and intra-event variations, as well as marked differences between north- and south-facing slopes. Accounting for those differences, two-component isotopic hydrograph separation revealed snowmelt contributions of 35 ± 3 % and 75 ± 14 % for the early and peak melt season, respectively. Differences to formerly used weighting methods (e.g. using observed plot-scale melt rates) or considering either the north- or south-facing slope were up to 5 and 15 %, respectively.


2020 ◽  
Author(s):  
Michael McCarthy ◽  
Flavia Burger ◽  
Alvaro Ayala ◽  
Stefan Fugger ◽  
Thomas E Shaw ◽  
...  

<p>The Andean cryosphere is a vital water resource for downstream populations. In recent years, it has been in steep decline as a whole, but shown strong spatio-temporal variability due to climatic events such as the current mega drought in central Chile. Glacio-hydrological models are necessary to understand and predict changes in water availability as a result of changes to the cryosphere. However, due to a lack of data for initialisation, forcing, calibration and validation, they are rarely used, especially in the Andes, for periods longer than a few years or decades. While useful insights can be gained from short-term modelling, there is a gap in our understanding of how glaciers impact hydrology on longer timescales, which may prevent local communities and governments from achieving effective planning and mitigation. Here we use the glacio-hydrological model TOPKAPI-ETH – initialised, forced, calibrated and validated using unique and extensive field and remote sensing datasets – to investigate glacier contributions to the streamflow of the high-elevation Rio Yeso catchment, Chile, over the past 50 years. We focus in particular on: 1) fluctuations in glacier surface mass balance and runoff and associated climatic variability; 2) if peak water has already occurred and when; 3) the effect of supraglacial debris cover on seasonal and long-term hydrographs. We offer insights into some of the challenges of running glacio-hydrological models on longer timescales and discuss the implications of our findings in the context of a shrinking Andean cryosphere.</p>


2021 ◽  
Vol 6 (1) ◽  
pp. 64
Author(s):  
Paul David Carchipulla-Morales ◽  
Xavier Zapata-Ríos

This study presents the spatio–temporal assessment of the Pugllohuma peatland’s soil saturation and water level variability. The Pugllohuma is a high elevation wetland located within the Sustainable Water Conservation Area Antisana in the northern Andes of Ecuador above 4100 m.a.s.l. This assessment provides information of the dry and wet seasons in the Pugllohuma peatland. The temporal variability was investigated considering variables such as: atmospheric pressure, rainfall, relative humidity, air temperature, wind speed and direction records of two near meteorological stations, while the spatial variability was investigated through images of the Sentinel-1 mission from 2017 to 2019, and terrain characteristics such as: elevation and slope. Image analysis and degree of soil saturation classification were carried out using the R programming language and Google Earth Engine, and the results were published in the UI service in Google Apps Script.


2014 ◽  
Vol 8 (6) ◽  
pp. 2293-2312 ◽  
Author(s):  
P. M. Alexander ◽  
M. Tedesco ◽  
X. Fettweis ◽  
R. S. W. van de Wal ◽  
C. J. P. P. Smeets ◽  
...  

Abstract. Accurate measurements and simulations of Greenland Ice Sheet (GrIS) surface albedo are essential, given the role of surface albedo in modulating the amount of absorbed solar radiation and meltwater production. In this study, we assess the spatio-temporal variability of GrIS albedo during June, July, and August (JJA) for the period 2000–2013. We use two remote sensing products derived from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as outputs from the Modèle Atmosphérique Régionale (MAR) regional climate model (RCM) and data from in situ automatic weather stations. Our results point to an overall consistency in spatio-temporal variability between remote sensing and RCM albedo, but reveal a difference in mean albedo of up to ~0.08 between the two remote sensing products north of 70° N. At low elevations, albedo values simulated by the RCM are positively biased with respect to remote sensing products by up to ~0.1 and exhibit low variability compared with observations. We infer that these differences are the result of a positive bias in simulated bare ice albedo. MODIS albedo, RCM outputs, and in situ observations consistently indicate a decrease in albedo of −0.03 to −0.06 per decade over the period 2003–2013 for the GrIS ablation area. Nevertheless, satellite products show a decline in JJA albedo of −0.03 to −0.04 per decade for regions within the accumulation area that is not confirmed by either the model or in situ observations. These findings appear to contradict a previous study that found an agreement between in situ and MODIS trends for individual months. The results indicate a need for further evaluation of high elevation albedo trends, a reconciliation of MODIS mean albedo at high latitudes, and the importance of accurately simulating bare ice albedo in RCMs.


2018 ◽  
Vol 32 (12) ◽  
pp. 1828-1843 ◽  
Author(s):  
Jan Schmieder ◽  
Jakob Garvelmann ◽  
Thomas Marke ◽  
Ulrich Strasser

2019 ◽  
Vol 11 (16) ◽  
pp. 4334 ◽  
Author(s):  
Xiaowan Liu ◽  
Zongxue Xu ◽  
Dingzhi Peng

Spatiotemporal vegetation patterns are of great importance for regional development. As one of the largest transnational rivers in China, the Yarlung Zangbo River in the Qinghai-Tibetan Plateau was selected as the study site, and the spatiotemporal patterns of vegetation during 1998–2014 were analyzed using the normalized difference vegetation index (NDVI). The results show that the NDVI increased with decreasing elevation, and the largest value was observed for the broadleaf forest. The lag time of NDVI to precipitation for most of the vegetation units was distinguished as approximately one month. In the region with an elevation of over 5000 m, the NDVI for the alpine vegetation was negatively correlated with the precipitation. Most NDVI variations were due to precipitation and temperature (approximately 75%). These results could provide a reference for ecological protection at a similar high elevation in the future.


2020 ◽  
Vol 25 (2) ◽  
pp. 39-48
Author(s):  
Kalpana Hamal ◽  
Nitesh Khadka ◽  
Samresh Rai ◽  
Bharat Badayar Joshi ◽  
Jagdish Dotel ◽  
...  

Precipitation is a fundamental component of the water cycle and integral to the society and the ecosystem. Further, continuous monitoring of precipitation is essential for predicting severe weather, monitoring droughts, and high-intensity related extremes. The present study evaluated the spatio-temporal distribution of precipitation and trends between 1998– 2018 using Tropical Rainfall Measuring Mission (TRMM) (3B43-V7) with reference to 142-gauge observations over Nepal. TRMM moderately captured precipitation patterns' overall characteristics, although underestimated the mean annual precipitation during the study period. TRMM precipitation product well captured the seasonal variation of the observed precipitation with the highest correlation in the winter season. The decreasing seasonal and annual trend was found in both observed and TRMM products, with the highest (lowest) decreasing trend observed during the monsoon (winter) season. It was also noted that the TRMM product showed a smaller bias before 2007, while a large error was found after 2007, especially in the monsoon months. In general, the TRMM product is a good alternative to observe rain gauge measurement in Nepal. However, there is still space for further improvement in rainfall retrieval algorithms, especially in high-elevation areas during the winter season.


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