Chapter 8 The Status and Role of the alpine Cryosphere in Central Asia

The alpine cryosphere including snow, glaciers and permafrost are critical to water management in the Aral Sea Basin (ASB) and larger Central Asia (CA) under changing climate: as they store large amounts of water in its solid forms. Most cryospheric components in the Aral Sea Basin are close to melting point, and hence very vulnerable to a slight increase in air temperature with significant consequences to long-term water availability and to water resources variability and extremes. Current knowledge about different components of cryosphere and their connection to climate in the Basin and in the entire Central Asia, varies. While it is advanced in the topics of snow and glaciers, knowledge on permafrost it rather limited. Observed trends in runoff point in the direction of increasing water availability in July and August at least until mid-century and increasing possibility for water storage in reservoirs and aquifers. However, eventually this will change as glaciers waste away. Future runoff may change considerably after mid-century and start to decline if not compensated by increasing precipitation. Cryosphere monitoring systems are the basis for sound estimates of water availability and water-related hazards associated with snow, glaciers and permafrost. They require a well-distributed observational network for all cryospheric variables. Such systems need to be re-established in the Basin after the breakup of the Soviet Union in the early 1990s. This process is slowly emerging in the region. Collaboration between local operational hydro-meteorological services and academic sector, and with international research networks may improving the observing capabilities in high mountain regions of CA Asia in general and the ASB specifically

Mass-balance reconstruction for Glacier No. 354, Tien Shan, from 2003 to 2014

This study presents a reconstruction of the seasonal mass balance of Glacier No. 354, located in the Akshiirak range, Kyrgyzstan, from 2003 to 2014. We use a distributed accumulation and temperature-index melt model driven by daily air temperature and precipitation from a nearby meteorological station. The model is calibrated with in situ measurements of the annual mass balance collected from 2011 to 2014. The snow-cover depletion pattern observed using satellite imagery provides additional information on the dynamics of mass change throughout the melting season. Two digital elevation models derived from high-resolution satellite stereo images acquired in 2003 and 2012 are used to calculate glacier volume change for the corresponding period. The geodetic mass change thus derived is used to validate the modelled cumulative glacier-wide balance. For the period 2003–12 we find a cumulative mass balance of –0.40±10mw.e.a-1. This result agrees well with the geodetic balance of –0.48±0.07mw.e.a-1over the same period

Constraining hydrological model parameters using water isotopic compositions in a glacierized basin, Central Asia

Water stable isotope signatures can provide valuable insights into the catchment internal runoff processes. However, the ability of the water isotope data to constrain the internal apportionments of runoff components in hydrological models for glacierized basins is not well understood. This study developed an approach to simultaneously model the water stable isotopic compositions and runoff processes in a glacierized basin in Central Asia. The fractionation and mixing processes of water stable isotopes in and from the various water sources were integrated into a glacio- hydrological model. The model parameters were calibrated on discharge, snow cover and glacier mass balance data, and additionally isotopic composition of streamflow. We investigated the value of water isotopic compositions for the calibration of model parameters, in comparison to calibration methods without using such measurements. Results indicate that: (1) The proposed isotope-hydrological integrated modeling approach was able to reproduce the isotopic composition of streamflow, and improved the model performance in the evaluation period; (2) Involving water isotopic composition for model calibration reduced the model parameter uncertainty, and helped to reduce the uncertainty in the quantification of runoff components; (3) The isotope-hydrological integrated modeling approach quantified the contributions of runoff components comparably to a three-component tracer-based end-member mixing analysis method for summer peak flows, and required less water tracer data. Our findings demonstrate the value of water isotopic compositions to improve the quantification of runoff components using hydrological models in glacierized basins

Fernerkundungsbasierte Wasserbilanzmodellierung : Schwerpunkt Zentralasien

The whole Central Asian population is dependent on water resources stored as solid in high mountains and feed the population in summer. The agriculture in Central Asia is possible only by irrigating the land in regular basis. The water for it comes from snow and glacier melt in high mountains. Thus, it is important to estimate possible water stored in mountains during planting season to better plan agricultural activities in summer. As an example, the year of 2002 or 2007 were drought years with extreme shortages of water for iriigation purposes, but the agricultural activities were planned as usual. As a consequence, the agricultural yeild has reduced very much so that it had an e ffect on the economy of countries. Another essential aspect for the importance of water balance modeling in this region is ongoing large reservoir construction plans that should mainly serve as energy generator for Central Asian population. In order to carry out such large projects, detailed hydrological studies are very important that should help to define the location or the size of the reservoir to be constructed. Such activities will continue in the future as population is increasing and the demand for energy as well as for water will be increasing. For the reasons mentioned above, water balance modeling studies are very essential for Central Asia. These studies require data availability to be representative in quality and quantity. Data availability for such studies in this region is limited. It is limited also due to very heterogeneous topography of the region where environmental parameters have their spatial heterogeneity as well. Nowadays, remote sensing has become one of the very promising alternative data sources for such regions with limited data availbility. The advantages of remote sensing information is that this data is spatially distributed and it is based on true reflection of surface properties of each spatial scale which can be better than inter-or extrapolated data for modeling purposes. For these reasons, testing the possibility of applying remote sensing information for water balance studies in data limited regions such as Central Asia was a challenge to this study. The main objective of this research is to carry out water balance study for mountainous regions with data limited conditions using remote sensing information. This study will be focused on the use of mainly snow cover information from remote sensing that can be integrated for hydrological modeling studies. Additionally, possibility of air temperature estimation using remote sensing Land Surface Temperature (LST) will also be investigated. Available, but few station data will be used for the validation of remote sensing information. These two parameters are most important environmental parameters in Central Asia where snow and glacier melt according to available energy (temperature) dominates summer discharge in rivers. The main open question arising from the motivation for this study can be devided into two parts: - how can we compensate data gaps in data scarce regions in mountainous areas? - can remote sensing information be used in water balance modeling purposes in Central Asia?In Zentralasien ist Wasser eine limitierende Ressource, deren Knappheit sich durch einem künftig zu erwartenden Klimawandel und dem daraus resultierenden Anstieg der Lufttemperatur noch verstärken kann. Die Wasserknappheit ergibt sich vor allem dadurch, dass Zentralasien eine aride Zone ist, die fast nur durch Schnee und Gletscherschmelze bewässert wird. Bei einer Reduktion der Gletschermassen würde sich die Wasserkanappheit noch weiter vergrößern. Das ist insbesondere deshalb relevant, da ökonomisch gesehen einige der Zentralasiatischen Staaten stark an die Landwirtschaft gebunden sind, die im Sommer eine Bewässerung benötigt. Der Sommerabfluss ist von der Wasserspeicherung in den Gebirgen als Schnee oder Gletscher abhängig. Um die verfügbaren Wasserresourcen für die Sommermonate vorhersagen zu können, werden daher Informationen über Schnee und Gletscher benötigt. Diese sind in der Regel über meteorologische Stationen messbar, deren Zahl jedoch innerhalb der Region begrenzt ist. Außerdem ist das der Abflussbildung topographisch gesehen sehr heterogen, so dass die Stationswerte nur lokal repräsentativ werden sein können. Durch die Entwicklung der Fernerkundung in den letzten Dekaden sind die räumlich verteilten Fernerkundungsdaten eine aussichtsreiche Option für Datenlücken, insbesondere in Hochgebirgszonen geworden, da diese Gebiete sonst schwer oder gar nicht erreichbar sind. Bislang wurde in Zentralasien wenig mit Fernerkundungsdaten gearbeitet. Die zwei zu beantwortenden Hauptfragestellungen dieser Arbeit sind: - Wie können Datenlücken in Gebirgsregionen mit limitierende Datenverfügbarkeit kompensiert werden? - Können Fernerkundungsdaten zurWasserbilanzmodelierung in Zentralasien angewendet werden? Um diese Fragen beantworten zu können wird sich diese Arbeit mit der Validierung und Anwendung räumlich verteilter Fernerkundungsdaten in Zentralasien beschäftigen. Hierzu werden hauptsächlich Schnee und Bodentemperaturdaten aus der Fernerkundung verwendet

Snow cover distribution in the Aksu catchment (Central Tien Shan) 1986-2013 Based on AVHRR and MODIS data

Variability in snow cover strongly influences mass budgets of glaciers, permafrost distribution, and seasonal discharge of rivers. In times of a changing climate, the spatiotemporal patterns of snow cover are of high interest. In this study, snow cover time series for the Aksu catchment in Central Tien Shan have been generated from optical remote sensing imagery. The analyses span a period between 1986 and 2013 and imbed Advanced Very High Resolution Radiometer (AVHRR) level 1b scenes, which were classified using a dichotomous decision scheme, as well as the preprocessed Moderate Resolution Imaging Spectrometer (MODIS) snow cover product. High congruence of the results could be achieved in spite of different sensors involved. However, a small bias appears especially at high elevations. The results from 2000 to 2013 reveal that snow accumulation begins in October and melting starts in March. Above an elevation of around 5200 m a.s.l., permanent snow cover can be expected, which is mirrored by a zonal mean of more than 85% of snow for the whole period 1986–2013. Anomalies are very indicative and reveal a high interannual variability of snow cover in terms of quantity and spatial distribution. Change detection of snow cover probability (SCP) shows a slight decrease in lower altitudes up to 4000 m a.s.l. and an opposite trend above. However, the negative trends are not significant. Significant gradients have been found only at high elevations where the two data sources could not perfectly be harmonized. Comparisons with climatic variables show a similar temporal behavior of SCP and temperatures

Snow Cover Distribution in the Aksu Catchment (Central Tien Shan) 1986-2013 Based on AVHRR and MODIS Data

Variability in snow cover strongly influences mass budgets of glaciers, permafrost distribution, and seasonal discharge of rivers. In times of a changing climate, the spatio-Temporal patterns of snow cover are of high interest. In this study, snow cover time series for the Aksu catchment in Central Tien Shan have been generated from optical remote sensing imagery. The analyses span a period between 1986 and 2013 and imbed Advanced Very High Resolution Radiometer (AVHRR) level 1b scenes, which were classified using a dichotomous decision scheme, as well as the preprocessed Moderate Resolution Imaging Spectrometer (MODIS) snow cover product. High congruence of the results could be achieved in spite of different sensors involved. However, a small bias appears especially at high elevations. The results from 2000 to 2013 reveal that snow accumulation begins in October and melting starts in March. Above an elevation of around 5200 m a.s.l., permanent snow cover can be expected, which is mirrored by a zonal mean of more than 85% of snow for the whole period 1986-2013. Anomalies are very indicative and reveal a high interannual variability of snow cover in terms of quantity and spatial distribution. Change detection of snow cover probability (SCP) shows a slight decrease in lower altitudes up to 4000 m a.s.l. and an opposite trend above. However, the negative trends are not significant. Significant gradients have been found only at high elevations where the two data sources could not perfectly be harmonized. Comparisons with climatic variables show a similar temporal behavior of SCP and temperatures.</p

Hydrological Forecasting under Climate Variability Using Modeling and Earth Observations in the Naryn River Basin, Kyrgyzstan

The availability of water resources in Central Asia depends greatly on snow accumulation in the mountains of Tien-Shan and Pamir. It is important to precisely forecast water availability as it is shared by several countries and has a transboundary context. The impact of climate change in this region requires improving the quality of hydrological forecasts in the Naryn river basin. This is especially true for the growing season due to the unpredictable climate behavior. A real-time monitoring and forecasting system based on hydrological watershed models is widely used for forecast monitoring. The study’s main objective is to simulate hydrological forecasts for three different hydrological stations (Uch-Terek, Naryn, and Big-Naryn) located in the Naryn river basin, the main water formation area of the Syrdarya River. We used the MODSNOW model to generate statistical forecast models. The model simulates the hydrological cycle using standard meteorological data, discharge data, and remote sensing data based on the MODIS snow cover area. As for the forecast at the monthly scale, the model considers the snow cover conditions at separate elevation zones. The operation of a watershed model includes the effects of climate change on river dynamics, especially snowfall and its melting processes in different altitude zones of the Naryn river basin. The linear regression models were produced for monthly and yearly hydrological forecasts. The linear regression shows R2 values of 0.81, 0.75, and 0.77 (Uch-Terek, Naryn, and Big-Naryn, respectively). The correlation between discharge and snow cover at various elevation zones was used to examine the relationship between snow cover and the elevation of the study. The best correlation was in May, June, and July for the elevation ranging from 1000–1500 m in station Uch-Terek, and 1500–3500 m in stations Naryn and Big-Naryn. The best correlation was in June: 0.87; 0.76; 0.84, and May for the elevation ranging from 1000–3500 m in station Uch-Terek, and 2000–3000 m in stations Naryn and Big-Naryn. Hydrological forecast modeling in this study aims to provide helpful information to improve our under-standing that the snow cover is the central aspect of water accumulation