TY  - JOUR
A1  - Ayzel, Georgy
A1  - Izhitskiy, Alexander
T1  - Climate Change Impact Assessment on Freshwater Inflow into the Small Aral Sea
JF  - Water
N2  - During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007-2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash-Sutcliffe efficiency of 0.72 and a Kling-Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century.
KW  - Small Aral Sea
KW  - hydrology
KW  - climate change
KW  - modeling
KW  - machine learning
Y1  - 2019
U6  - https://doi.org/10.3390/w11112377
SN  - 2073-4441
VL  - 11
IS  - 11
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - Muster, Sina
A1  - Riley, William J.
A1  - Roth, Kurt
A1  - Langer, Moritz
A1  - Aleina, Fabio Cresto
A1  - Koven, Charles D.
A1  - Lange, Stephan
A1  - Bartsch, Annett
A1  - Grosse, Guido
A1  - Wilson, Cathy J.
A1  - Jones, Benjamin M.
A1  - Boike, Julia
T1  - Size distributions of arctic waterbodies reveal consistent relations in their statistical moments in space and time
JF  - Frontiers in Earth Science
N2  - Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (<5 m) circum-Arctic Permafrost Region Pond and Lake (PeRL) database recently becoming available. We have used this database to make the first consistent, high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0.0001 km(2) (100 m(2)) to 1 km(2). We found that the size distributions varied greatly across the thirty study regions investigated and that there was no single universal size distribution function (including power-law distribution functions) appropriate across all of the study regions. We did, however, find close relationships between the statistical moments (mean, variance, and skewness) of the waterbody size distributions from different study regions. Specifically, we found that the spatial variance increased linearly with mean waterbody size (R-2 = 0.97, p < 2.2e-16) and that the skewness decreased approximately hyperbolically. We have demonstrated that these relationships (1) hold across the 30 Arctic study regions covering a variety of (bio)climatic and permafrost zones, (2) hold over time in two of these study regions for which multi-decadal satellite imagery is available, and (3) can be reproduced by simulating rising water levels in a high-resolution digital elevation model. The consistent spatial and temporal relationships between the statistical moments of the waterbody size distributions underscore the dominance of topographic controls in lowland permafrost areas. These results provide motivation for further analyses of the factors involved in waterbody development and spatial distribution and for investigations into the possibility of using statistical moments to predict future hydrologic dynamics in the Arctic.
KW  - permafrost
KW  - hydrology
KW  - waterbodies
KW  - size distribution
KW  - thermokarst
KW  - statistical moments
KW  - ponds
KW  - lakes
Y1  - 2019
U6  - https://doi.org/10.3389/feart.2019.00005
SN  - 2296-6463
VL  - 7
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  -