@phdthesis{Schmidt2024, author = {Schmidt, Lena Katharina}, title = {Altered hydrological and sediment dynamics in high-alpine areas - Exploring the potential of machine-learning for estimating past and future changes}, doi = {10.25932/publishup-62330}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-623302}, school = {Universit{\"a}t Potsdam}, pages = {xxi, 129}, year = {2024}, abstract = {Climate change fundamentally transforms glaciated high-alpine regions, with well-known cryospheric and hydrological implications, such as accelerating glacier retreat, transiently increased runoff, longer snow-free periods and more frequent and intense summer rainstorms. These changes affect the availability and transport of sediments in high alpine areas by altering the interaction and intensity of different erosion processes and catchment properties. Gaining insight into the future alterations in suspended sediment transport by high alpine streams is crucial, given its wide-ranging implications, e.g. for flood damage potential, flood hazard in downstream river reaches, hydropower production, riverine ecology and water quality. However, the current understanding of how climate change will impact suspended sediment dynamics in these high alpine regions is limited. For one, this is due to the scarcity of measurement time series that are long enough to e.g. infer trends. On the other hand, it is difficult - if not impossible - to develop process-based models, due to the complexity and multitude of processes involved in high alpine sediment dynamics. Therefore, knowledge has so far been confined to conceptual models (which do not facilitate deriving concrete timings or magnitudes for individual catchments) or qualitative estimates ('higher export in warmer years') that may not be able to capture decreases in sediment export. Recently, machine-learning approaches have gained in popularity for modeling sediment dynamics, since their black box nature tailors them to the problem at hand, i.e. relatively well-understood input and output data, linked by very complex processes. Therefore, the overarching aim of this thesis is to estimate sediment export from the high alpine {\"O}tztal valley in Tyrol, Austria, over decadal timescales in the past and future - i.e. timescales relevant to anthropogenic climate change. This is achieved by informing, extending, evaluating and applying a quantile regression forest (QRF) approach, i.e. a nonparametric, multivariate machine-learning technique based on random forest. The first study included in this thesis aimed to understand present sediment dynamics, i.e. in the period with available measurements (up to 15 years). To inform the modeling setup for the two subsequent studies, this study identified the most important predictors, areas within the catchments and time periods. To that end, water and sediment yields from three nested gauges in the upper {\"O}tztal, Vent, S{\"o}lden and Tumpen (98 to almost 800 km² catchment area, 930 to 3772 m a.s.l.) were analyzed for their distribution in space, their seasonality and spatial differences therein, and the relative importance of short-term events. The findings suggest that the areas situated above 2500 m a.s.l., containing glacier tongues and recently deglaciated areas, play a pivotal role in sediment generation across all sub-catchments. In contrast, precipitation events were relatively unimportant (on average, 21 \% of annual sediment yield was associated to precipitation events). Thus, the second and third study focused on the Vent catchment and its sub-catchment above gauge Vernagt (11.4 and 98 km², 1891 to 3772 m a.s.l.), due to their higher share of areas above 2500 m. Additionally, they included discharge, precipitation and air temperature (as well as their antecedent conditions) as predictors. The second study aimed to estimate sediment export since the 1960s/70s at gauges Vent and Vernagt. This was facilitated by the availability of long records of the predictors, discharge, precipitation and air temperature, and shorter records (four and 15 years) of turbidity-derived sediment concentrations at the two gauges. The third study aimed to estimate future sediment export until 2100, by applying the QRF models developed in the second study to pre-existing precipitation and temperature projections (EURO-CORDEX) and discharge projections (physically-based hydroclimatological and snow model AMUNDSEN) for the three representative concentration pathways RCP2.6, RCP4.5 and RCP8.5. The combined results of the second and third study show overall increasing sediment export in the past and decreasing export in the future. This suggests that peak sediment is underway or has already passed - unless precipitation changes unfold differently than represented in the projections or changes in the catchment erodibility prevail and override these trends. Despite the overall future decrease, very high sediment export is possible in response to precipitation events. This two-fold development has important implications for managing sediment, flood hazard and riverine ecology. This thesis shows that QRF can be a very useful tool to model sediment export in high-alpine areas. Several validations in the second study showed good performance of QRF and its superiority to traditional sediment rating curves - especially in periods that contained high sediment export events, which points to its ability to deal with threshold effects. A technical limitation of QRF is the inability to extrapolate beyond the range of values represented in the training data. We assessed the number and severity of such out-of-observation-range (OOOR) days in both studies, which showed that there were few OOOR days in the second study and that uncertainties associated with OOOR days were small before 2070 in the third study. As the pre-processed data and model code have been made publically available, future studies can easily test further approaches or apply QRF to further catchments.}, language = {en} } @phdthesis{Reich2023, author = {Reich, Marvin}, title = {Advances in hydrogravimetry}, doi = {10.25932/publishup-60479}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604794}, school = {Universit{\"a}t Potsdam}, pages = {ix, 142}, year = {2023}, abstract = {The interest of the hydrological community in the gravimetric method has steadily increased within the last decade. This is reflected by numerous studies from many different groups with a broad range of approaches and foci. Many of those are traditionally rather hydrology-oriented groups who recognized gravimetry as a potential added value for their hydrological investigations. While this resulted in a variety of interesting and useful findings, contributing to extend the respective knowledge and confirming the methodological potential, on the other hand, many interesting and unresolved questions emerged. This thesis manifests efforts, analyses and solutions carried out in this regard. Addressing and evaluating many of those unresolved questions, the research contributes to advancing hydrogravimetry, the combination of gravimetric and hydrological methods, in showing how gravimeters are a highly useful tool for applied hydrological field research. In the first part of the thesis, traditional setups of stationary terrestrial superconducting gravimeters are addressed. They are commonly installed within a dedicated building, the impermeable structure of which shields the underlying soil from natural exchange of water masses (infiltration, evapotranspiration, groundwater recharge). As gravimeters are most sensitive to mass changes directly beneath the meter, this could impede their suitability for local hydrological process investigations, especially for near-surface water storage changes (WSC). By studying temporal local hydrological dynamics at a dedicated site equipped with traditional hydrological measurement devices, both below and next to the building, the impact of these absent natural dynamics on the gravity observations were quantified. A comprehensive analysis with both a data-based and model-based approach led to the development of an alternative method for dealing with this limitation. Based on determinable parameters, this approach can be transferred to a broad range of measurement sites where gravimeters are deployed in similar structures. Furthermore, the extensive considerations on this topic enabled a more profound understanding of this so called umbrella effect. The second part of the thesis is a pilot study about the field deployment of a superconducting gravimeter. A newly developed field enclosure for this gravimeter was tested in an outdoor installation adjacent to the building used to investigate the umbrella effect. Analyzing and comparing the gravity observations from both indoor and outdoor gravimeters showed performance with respect to noise and stable environmental conditions was equivalent while the sensitivity to near-surface WSC was highly increased for the field deployed instrument. Furthermore it was demonstrated that the latter setup showed gravity changes independent of the depth where mass changes occurred, given their sufficiently wide horizontal extent. As a consequence, the field setup suits monitoring of WSC for both short and longer time periods much better. Based on a coupled data-modeling approach, its gravity time series was successfully used to infer and quantify local water budget components (evapotranspiration, lateral subsurface discharge) on the daily to annual time scale. The third part of the thesis applies data from a gravimeter field deployment for applied hydrological process investigations. To this end, again at the same site, a sprinkling experiment was conducted in a 15 x 15 m area around the gravimeter. A simple hydro-gravimetric model was developed for calculating the gravity response resulting from water redistribution in the subsurface. It was found that, from a theoretical point of view, different subsurface water distribution processes (macro pore flow, preferential flow, wetting front advancement, bypass flow and perched water table rise) lead to a characteristic shape of their resulting gravity response curve. Although by using this approach it was possible to identify a dominating subsurface water distribution process for this site, some clear limitations stood out. Despite the advantage for field installations that gravimetry is a non-invasive and integral method, the problem of non-uniqueness could only be overcome by additional measurements (soil moisture, electric resistivity tomography) within a joint evaluation. Furthermore, the simple hydrological model was efficient for theoretical considerations but lacked the capability to resolve some heterogeneous spatial structures of water distribution up to a needed scale. Nevertheless, this unique setup for plot to small scale hydrological process research underlines the high potential of gravimetery and the benefit of a field deployment. The fourth and last part is dedicated to the evaluation of potential uncertainties arising from the processing of gravity observations. The gravimeter senses all mass variations in an integral way, with the gravitational attraction being directly proportional to the magnitude of the change and inversely proportional to the square of the distance of the change. Consequently, all gravity effects (for example, tides, atmosphere, non-tidal ocean loading, polar motion, global hydrology and local hydrology) are included in an aggregated manner. To isolate the signal components of interest for a particular investigation, all non-desired effects have to be removed from the observations. This process is called reduction. The large-scale effects (tides, atmosphere, non-tidal ocean loading and global hydrology) cannot be measured directly and global model data is used to describe and quantify each effect. Within the reduction process, model errors and uncertainties propagate into the residual, the result of the reduction. The focus of this part of the thesis is quantifying the resulting, propagated uncertainty for each individual correction. Different superconducting gravimeter installations were evaluated with respect to their topography, distance to the ocean and the climate regime. Furthermore, different time periods of aggregated gravity observation data were assessed, ranging from 1 hour up to 12 months. It was found that uncertainties were highest for a frequency of 6 months and smallest for hourly frequencies. Distance to the ocean influences the uncertainty of the non-tidal ocean loading component, while geographical latitude affects uncertainties of the global hydrological component. It is important to highlight that the resulting correction-induced uncertainties in the residual have the potential to mask the signal of interest, depending on the signal magnitude and its frequency. These findings can be used to assess the value of gravity data across a range of applications and geographic settings. In an overarching synthesis all results and findings are discussed with a general focus on their added value for bringing hydrogravimetric field research to a new level. The conceptual and applied methodological benefits for hydrological studies are highlighted. Within an outlook for future setups and study designs, it was once again shown what enormous potential is offered by gravimeters as hydrological field tools.}, language = {en} } @phdthesis{Kemter2022, author = {Kemter, Matthias}, title = {River floods in a changing world}, doi = {10.25932/publishup-55856}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-558564}, school = {Universit{\"a}t Potsdam}, pages = {xvii, 120}, year = {2022}, abstract = {River floods are among the most devastating natural hazards worldwide. As their generation is highly dependent on climatic conditions, their magnitude and frequency are projected to be affected by future climate change. Therefore, it is crucial to study the ways in which a changing climate will, and already has, influenced flood generation, and thereby flood hazard. Additionally, it is important to understand how other human influences - specifically altered land cover - affect flood hazard at the catchment scale. The ways in which flood generation is influenced by climatic and land cover conditions differ substantially in different regions. The spatial variability of these effects needs to be taken into account by using consistent datasets across large scales as well as applying methods that can reflect this heterogeneity. Therefore, in the first study of this cumulative thesis a complex network approach is used to find 10 clusters of similar flood behavior among 4390 catchments in the conterminous United States. By using a consistent set of 31 hydro-climatological and land cover variables, and training a separate Random Forest model for each of the clusters, the regional controls on flood magnitude trends between 1960-2010 are detected. It is shown that changes in rainfall are the most important drivers of these trends, while they are regionally controlled by land cover conditions. While climate change is most commonly associated with flood magnitude trends, it has been shown to also influence flood timing. This can lead to trends in the size of the area across which floods occur simultaneously, the flood synchrony scale. The second study is an analysis of data from 3872 European streamflow gauges and shows that flood synchrony scales have increased in Western Europe and decreased in Eastern Europe. These changes are attributed to changes in flood generation, especially a decreasing relevance of snowmelt. Additionally, the analysis shows that both the absolute values and the trends of flood magnitudes and flood synchrony scales are positively correlated. If these trends persist in the future and are not accounted for, the combined increases of flood magnitudes and flood synchrony scales can exceed the capacities of disaster relief organizations and insurers. Hazard cascades are an additional way through which climate change can influence different aspects of flood hazard. The 2019/2020 wildfires in Australia, which were preceded by an unprecedented drought and extinguished by extreme rainfall that led to local flooding, present an opportunity to study the effects of multiple preceding hazards on flood hazard. All these hazards are individually affected by climate change, additionally complicating the interactions within the cascade. By estimating and analyzing the burn severity, rainfall magnitude, soil erosion and stream turbidity in differently affected tributaries of the Manning River catchment, the third study shows that even low magnitude floods can pose a substantial hazard within a cascade. This thesis shows that humanity is affecting flood hazard in multiple ways with spatially and temporarily varying consequences, many of which were previously neglected (e.g. flood synchrony scale, hazard cascades). To allow for informed decision making in risk management and climate change adaptation, it will be crucial to study these aspects across the globe and to project their trajectories into the future. The presented methods can depict the complex interactions of different flood drivers and their spatial variability, providing a basis for the assessment of future flood hazard changes. The role of land cover should be considered more in future flood risk modelling and management studies, while holistic, transferable frameworks for hazard cascade assessment will need to be designed.}, language = {en} } @article{Ayzel2021, author = {Ayzel, Georgy}, title = {Deep neural networks in hydrology}, series = {Vestnik of Saint Petersburg University. Earth Sciences}, volume = {66}, journal = {Vestnik of Saint Petersburg University. Earth Sciences}, number = {1}, publisher = {Univ. Press}, address = {St. Petersburg}, issn = {2541-9668}, doi = {10.21638/spbu07.2021.101}, pages = {5 -- 18}, year = {2021}, abstract = {For around a decade, deep learning - the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers - modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources. identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of "Gartner Hype Curve", which in the general details describes a life cycle of modern technologies.}, language = {en} } @techreport{HuđekŽganecPusch2020, author = {Huđek, Helena and Žganec, Krešimir and Pusch, Martin T.}, title = {A review of hydropower dams in Southeast Europe}, series = {Renewable \& sustainable energy reviews}, volume = {117}, journal = {Renewable \& sustainable energy reviews}, publisher = {Elsevier Science}, address = {Amsterdam [u.a.]}, issn = {1364-0321}, doi = {10.1016/j.rser.2019.109434}, pages = {11}, year = {2020}, abstract = {Currently, Southeast Europe (SEE) is witnessing a boom in hydropower plant (HPP) construction, which has not even spared protected areas. As SEE includes global hotspots of aquatic biodiversity, it is expected that this boom will result in a more severe impact on biodiversity than that of other regions. A more detailed assessment of the environmental risks resulting from HPP construction would have to rely on the existence of nearby hydrological and biological monitoring stations. For this reason, we review the distribution and trends of HPPs in the area, as well as the availability of hydrological and biological monitoring data from national institutions useable for environmental impact assessment. Our analysis samples tributary rivers of the Danube in Slovenia, Croatia, Bosnia and Herzegovina, Serbia, and Montenegro, referred to hereafter as TRD rivers. Currently, 636 HPPs are operating along the course of TRD rivers, most of which are small (<1 MW). An additional 1315 HPPs are currently planned to be built, mostly in Serbia and in Bosnia and Herzegovina. As official monitoring stations near HPPs are rare, the impact of those HPPs on river flow, fish and macro-invertebrates is difficult to assess. This manuscript represents the first regional review of hydropower use and of available data sources on its environmental impact for an area outside of the Alps. We conclude that current hydrological and biological monitoring in TRD rivers is insufficient for an assessment of the ecological impacts of HPPs. This data gap also prevents an adequate assessment of the ecological impacts of planned HP projects, as well as the identification of appropriate measures to mitigate the environmental effects of existing HPPs.}, language = {en} } @phdthesis{Pilz2020, author = {Pilz, Tobias}, title = {Pursuing the understanding of uncertainties in hydrological modelling}, doi = {10.25932/publishup-47664}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-476643}, school = {Universit{\"a}t Potsdam}, pages = {136}, year = {2020}, abstract = {Hydrological models are important tools for the simulation and quantification of the water cycle. They therefore aid in the understanding of hydrological processes, prediction of river discharge, assessment of the impacts of land use and climate changes, or the management of water resources. However, uncertainties associated with hydrological modelling are still large. While significant research has been done on the quantification and reduction of uncertainties, there are still fields which have gained little attention so far, such as model structural uncertainties that are related to the process implementations in the models. This holds especially true for complex process-based models in contrast to simpler conceptual models. Consequently, the aim of this thesis is to improve the understanding of structural uncertainties with focus on process-based hydrological modelling, including methods for their quantification. To identify common deficits of frequently used hydrological models and develop further strategies on how to reduce them, a survey among modellers was conducted. It was found that there is a certain degree of subjectivity in the perception of modellers, for instance with respect to the distinction of hydrological models into conceptual groups. It was further found that there are ambiguities on how to apply a certain hydrological model, for instance how many parameters should be calibrated, together with a large diversity of opinion regarding the deficits of models. Nevertheless, evapotranspiration processes are often represented in a more physically based manner, while processes of groundwater and soil water movement are often simplified, which many survey participants saw as a drawback. A large flexibility, for instance with respect to different alternative process implementations or a small number of parameters that needs to be calibrated, was generally seen as strength of a model. Flexible and efficient software, which is straightforward to apply, has been increasingly acknowledged by the hydrological community. This work further elaborated on this topic in a twofold way. First, a software package for semi-automated landscape discretisation has been developed, which serves as a tool for model initialisation. This was complemented by a sensitivity analysis of important and commonly used discretisation parameters, of which the size of hydrological sub-catchments as well as the size and number of hydrologically uniform computational units appeared to be more influential than information considered for the characterisation of hillslope profiles. Second, a process-based hydrological model has been implemented into a flexible simulation environment with several alternative process representations and a number of numerical solvers. It turned out that, even though computation times were still long, enhanced computational capabilities nowadays in combination with innovative methods for statistical analysis allow for the exploration of structural uncertainties of even complex process-based models, which up to now was often neglected by the modelling community. In a further study it could be shown that process-based models may even be employed as tools for seasonal operational forecasting. In contrast to statistical models, which are faster to initialise and to apply, process-based models produce more information in addition to the target variable, even at finer spatial and temporal scales, and provide more insights into process behaviour and catchment functioning. However, the process-based model was much more dependent on reliable rainfall forecasts. It seems unlikely that there exists a single best formulation for hydrological processes, even for a specific catchment. This supports the use of flexible model environments with alternative process representations instead of a single model structure. However, correlation and compensation effects between process formulations, their parametrisation, and other aspects such as numerical solver and model resolution, may lead to surprising results and potentially misleading conclusions. In future studies, such effects should be more explicitly addressed and quantified. Moreover, model functioning appeared to be highly dependent on the meteorological conditions and rainfall input generally was the most important source of uncertainty. It is still unclear, how this could be addressed, especially in the light of the aforementioned correlations. The use of innovative data products, e.g.\ remote sensing data in combination with station measurements, and efficient processing methods for the improvement of rainfall input and explicit consideration of associated uncertainties is advisable to bring more insights and make hydrological simulations and predictions more reliable.}, language = {en} } @article{MarquartEldridgeGeissleretal.2020, author = {Marquart, Arnim and Eldridge, David J. and Geissler, Katja and Lobas, Christoph and Blaum, Niels}, title = {Interconnected effects of shrubs, invertebrate-derived macropores and soil texture on water infiltration in a semi-arid savanna rangeland}, series = {Land degradation \& development}, volume = {31}, journal = {Land degradation \& development}, number = {16}, publisher = {Wiley}, address = {Chichester, Sussex}, issn = {1085-3278}, doi = {10.1002/ldr.3598}, pages = {2307 -- 2318}, year = {2020}, abstract = {Many semi arid savannas are prone to degradation, caused for example, by overgrazing or extreme climatic events, which often lead to shrub encroachment. Overgrazing by livestock affects vegetation and infiltration processes by directly altering plant composition (selective grazing) or by impacting soil physical properties (trampling). Water infiltration is controlled by several parameters, such as macropores (created by soil-burrowing animals or plant roots) and soil texture, but their effects have mostly been studied in isolation. Here we report on a study, in which we conducted infiltration experiments to analyze the interconnected effects of invertebrate-created macropores, shrubs and soil texture (sandy soil and loamy sand) on infiltration in two Namibian rangelands. Using structural equation modeling, we found a direct positive effect of shrub size on infiltration and indirectly via invertebrate macropores on both soil types. On loamy sands this effect was even stronger, but additionally, invertebrate-created macropores became relevant as a direct driver of infiltration. Our results provide new insights into the effects of vegetation and invertebrates on infiltration under different soil textures. Pastoralists should use management strategies that maintain a heterogeneous plant community that supports soil fauna to sustain healthy soil water dynamics, particularly on soils with higher loam content. Understanding the fundamental functioning of soil water dynamics in drylands is critical because these ecosystems are water-limited and support the livelihoods of many cultures worldwide.}, language = {en} } @article{AichnerMakhmudovRajabovetal.2019, author = {Aichner, Bernhard and Makhmudov, Zafar and Rajabov, Iljomjon and Zhang, Qiong and Pausata, Francesco Salvatore R. and Werner, Martin and Heinecke, Liv and Kuessner, Marie L. and Feakins, Sarah J. and Sachse, Dirk and Mischke, Steffen}, title = {Hydroclimate in the Pamirs Was Driven by Changes in Precipitation-Evaporation Seasonality Since theLast Glacial Period}, series = {Geophysical research letters}, volume = {46}, journal = {Geophysical research letters}, number = {23}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0094-8276}, doi = {10.1029/2019GL085202}, pages = {13972 -- 13983}, year = {2019}, abstract = {The Central Asian Pamir Mountains (Pamirs) are a high-altitude region sensitive to climatic change, with only few paleoclimatic records available. To examine the glacial-interglacial hydrological changes in the region, we analyzed the geochemical parameters of a 31-kyr record from Lake Karakul and performed a set of experiments with climate models to interpret the results. delta D values of terrestrial biomarkers showed insolation-driven trends reflecting major shifts of water vapor sources. For aquatic biomarkers, positive delta D shifts driven by changes in precipitation seasonality were observed at ca. 31-30, 28-26, and 17-14 kyr BP. Multiproxy paleoecological data and modelling results suggest that increased water availability, induced by decreased summer evaporation, triggered higher lake levels during those episodes, possibly synchronous to northern hemispheric rapid climate events. We conclude that seasonal changes in precipitation-evaporation balance significantly influenced the hydrological state of a large waterbody such as Lake Karakul, while annual precipitation amount and inflows remained fairly constant.}, language = {en} } @misc{AyzelIzhitskiy2019, author = {Ayzel, Georgy and Izhitskiy, Alexander}, title = {Climate change impact assessment on freshwater inflow into the Small Aral Sea}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1071}, issn = {1866-8372}, doi = {10.25932/publishup-47279}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-472794}, pages = {21}, year = {2019}, abstract = {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.}, language = {en} } @article{AyzelIzhitskiy2019, author = {Ayzel, Georgy and Izhitskiy, Alexander}, title = {Climate Change Impact Assessment on Freshwater Inflow into the Small Aral Sea}, series = {Water}, volume = {11}, journal = {Water}, number = {11}, publisher = {MDPI}, address = {Basel}, issn = {2073-4441}, doi = {10.3390/w11112377}, pages = {19}, year = {2019}, abstract = {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.}, language = {en} }