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Glacier evolution across Tien Shan and Pamir is heterogeneous in space and time. This heterogeneity is believed to be mainly driven by contrasting climatic settings and changing atmospheric conditions. However, a systematic and consistent region-wide analysis of the climatic and static morphological drivers remains limited to date. Meteorological reanalysis and remote sensing products, and novel approaches to derive region-wide annual mass balance time series, all provide the basis to investigate the drivers behind the observed heterogeneous glacier response. Here, we investigate the consistency of interpretations derived from available datasets through correlation analyses between climatic and static drivers with mass balance estimates in Tien Shan and Pamir. Our results show that even supposedly similar datasets lead to different and partly contradicting assumptions on dominant drivers of mass balance variability. Only when considering all glaciers in the Pamir and Tien Shan together, we find a similar picture of dominant meteorological drivers over space. Using either existing mountain subdivisions or glacier subdivisions based on mass balance variability, no consistencies can be found. Within different mass balance and meteorological datasets the results suggest very different drivers. This conclusion is even more prominent in the temporal correlation analysis where contradicting patterns of dominant drivers result from presumably similar meteorological datasets. Clear non-climatic drivers could not be identified. Even with newly available mass balance and meteorological data, a knowledge gap about the main mechanisms behind the heterogeneous glacier response in Central Asia remains. The results highlight that apparent but false consistencies across studies using a single dataset might largely relate to the chosen dataset rather than to the processes or involved environmental variables. As long as no glaciological, meteorological, or hydrological in situ observation network provides data for direct calibration and validation of extensive datasets, we cannot predict a realistic improvement in our understanding of the changing cryosphere at regional scale for Tien Shan and Pamir.

Central Asia is a climate change hot spot, facing an unprecedented juxtaposition of regional climate- and water-related issues. Meltwater from the Pamir Mountains plays a crucial role in Central Asia's hydrological cycle, yet there is a little detailed investigation on their response to climate change. Increasing water demand and the assumed ongoing transition from a snow-glacier runoff regime to a volatile rainfall regime in a changing climate demands for a better understanding of the processes governing surface flow in this region. Glacial-hydrological models are key tools for understanding the hydrological response to climate change, informing the planning of possible responses and adaptation. However, modeling is challenging here due to the lack of proper calibration and validation data. Most of the modeling studies in Pamir regions calibrated the model with runoff data only, which is fairly simple but potentially hides internal inconsistencies due to the complexity of feedback mechanisms that govern melt and runoff generation. Further, factors such as model forcing and/or data errors also contribute to simulation uncertainties. There is a clear need for a more detailed investigation of the regional hydrological processes, with systematic calibration and validation methods that allow more robust parameter constraints. This work employed a conceptual distributed hydrological model (SPHY) and tended to unravel the hydrological cycle in a representative catchment in the central Pamir, the Gunt River basin. We tested the usefulness of several state-of-the-art remote sensing, regional climate model, and reanalysis precipitation and temperature data sets in a hydrological application. We found none of them can be equally good in reproducing all targeted hydrological and cryospheric variables (runoff, snow cover fraction, and glacier mass balance). The aims of this study are hence (1) to develop a set of precipitation and temperature correction factors that could improve the model performance, (2) to develop a systematic calibration strategy that could guarantee internal consistency in the simulation of the single hydrological components, and (3) to test whether the corrected forcing data and model calibration strategy can be upscaled to other basins in the Parmir Mountains.

The Tien Shan and Pamir mountains host over 28,000 glaciers providing essential water resources for increasing water demand in Central Asia. A disequilibrium between glaciers and climate affects meltwater release to Central Asian rivers, challenging the region's water availability. Previous research has neglected temporal variability. We present glacier mass balance estimates based on transient snowline and geodetic surveys with unprecedented spatiotemporal resolution from 1999/00 to 2017/18. Our results reveal spatiotemporal heterogeneity characterized by two mass balance clusters: (a) positive, low variability, and (b) negative, high variability. This translates into variable glacial meltwater release (≈1–16%) of annual river runoff for two watersheds. Our study reveals more complex climate forcing‐runoff responses and importance of glacial meltwater variability for the region than suggested previously., Key Points Annual glacier mass balance for Central Asia (1999/00–2017/18) is derived by combining transient snowlines, geodetic surveys, and modelingStrong spatiotemporal heterogeneity with contrasting patterns of mass gain and loss are foundHot spots of heterogeneous mass balance variability are associated with highly variable glacier melt water runoff

The time when a climate signal permanently exceeds its natural variability is called time of emergence (ToE). ToE shall serve policy makers as an indication of when to expect the climate and the environment to undergo significant changes. Identifying ToE, however, is challenging, primarily because of the lack of a standard to quantify exceedance, which, in turn, requires a definition of a natural background variability. Existing approaches often rely on a high level of arbitrary parameter values, e.g. selecting a specific number of times the standard deviation of a reference period as natural variability, selecting specific moving window widths to smooth a signal, or the arbitrary choice of a significance level for a statistical test. Such choices of course have a large influence on the final results and would in theory require exhaustive sensitivity analyses and discussion.In order to minimize the level of parameterization for ToE estimates, we have developed a novel approach. It assesses exceedance of a climate signal by measuring distances between probability density functions (PDF) of the signal at different times (reference vs. target periods), using the Hellinger distance (HD) metric. The HD metric can be understood as the geometrical overlap of the respective PDFs and we adjusted it to describe the emergence as dissimilarity (0%-100%). In order to derive the PDFs, we use a kernel density estimator (KDE). This, however, introduces the KDE-bandwidth hyperparameter, which determines how smoothly the PDF is generated. Together with the choices for the length of the target and reference periods, and the end of the reference period, a set of less numerous but unavoidable hyperparameters are present that affect the outcome of ToE estimates. We present an extensive sensitivity analysis and highlight strengths and shortcomings of our approach with respect to the frequently used Kolmogorov–Smirnov (KS) test, and the used distance metric within it. We consider a set of synthetic datasets that show similar features as climate model temperature time series. In these datasets, we control the onset of change, variability levels, or trends in the data. Results show that our approach can more precisely identify the changes as compared to the KS-based approach. In particular when the changes in the signal are of low amplitude and sample sizes are small, our approach performs superior. The sensitivity of our approach in the considered tests to varying KDE-bandwidths is less than 5%. The approach has so far been applied on time-series of annual temperature and precipitation. Changes in the distribution of various other climate variables are potential fields of application. Associated challenges with non normally-distributed data, for example high temporal resolution precipitation data, are discussed.

Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems, and the population's traditional livelihoods. In the Lena River catchment, eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as a PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scales. The method yields dissimilarities or emergence levels ranging from 0 % to 100 % and the direction of change as a continuous time series itself. First, we showcase the method's advantage over the Kolmogorov–Smirnov metric using a synthetic dataset that resembles signals observed in the utilized climate models. Then, we focus on the Lena River catchment, where significant environmental changes are already apparent. On average, the emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50 %–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by 2040. For precipitation the analysis is less conclusive because of high uncertainties in existing reanalysis datasets that also impede an evaluation of the climate models. Model projections suggest hardly any emergence by 2000 but a strong emergence thereafter, reaching 60 % by the end of the investigated period (2089). The presented ToE method provides more versatility than traditional parametric approaches and allows for a detailed temporal analysis of climate signal evolutions. An original strategy to select the most realistic model simulations based on the available observational data significantly reduces the uncertainties resulting from the spread in the 65 climate models used. The method comes as a toolbox available at https://github.com/pohleric/toe_tools (last access: 19 May 2020).

A clear understanding of erosion processes is fundamental in order to comprehend the evolution of actively deforming mountain ranges. However, the relative contributions of tectonic and climatic factors and their feedbacks remain highly debated. In order to contribute to the debate, we quantify basin-wide denudation rates from cosmogenic 10Be concentrations in modern river sediments in the Pamir. This mountain range is a unique natural laboratory because the ongoing India–Eurasia collision sustains high deformation rates and, on account of its position at the transition between Westerlies and monsoon, a strong regional climatic variability arises. Sample acquisition and preparation for accelerator mass spectrometry measurements were challenging due to difficult field accessibility, low quartz and high feldspar concentrations and crystal coating. Six samples along the main draining river, the Panj, and five samples within the major, east–west elongated tributary basins allow us to quantify basin-wide denudation rates for the first time in this orogen. An average denudation rate of ~ 0.64 mm yr−1 reveals a rapid evolution of the entire Pamir. Denudation rates of tributary sub-basins highlight the strong contrast between the Pamir Plateau (0.05 to 0.16 mm yr−1) and its margins (0.54 to 1.45 mm yr−1). The intensity of denudation is primarily correlated with geometric properties of the surface, such as slope steepness (0.75 quartiles; R2 of 0.81), and to a lesser extent to climatic factors such as precipitation. We thus argue that either tectonic uplift or base-level lowering are the main contributors to denudation processes. Multiple linear regression analysis (best R2 of 0.93) suggests that precipitation may act as a limiting factor to denudation. The highest denudation rates coincide with areas of the northwestern Pamir margin that receive precipitation predominantly from the Westerlies during winter. There, the concentrated discharge during spring and early summer may sustain the pronounced denudation and allow the rapid sediment transport out of the basins. Low slope angles and dry conditions hamper the sediment flux on the plateau and, consequently, denudation. The magnitude of denudation in the Pamir is similar to rates determined in the southern Himalaya despite very different climatic and tectonic conditions. The discrepancy between rates of basin-wide denudation and the fluvial incision that is up to 10 times higher evidences a transient landscape in the Pamir. This underpins the hypothesis that river captures may have caused the strong base-level lowering that drives the enhanced incision of the Panj and its main tributaries.

A clear understanding of the hydrology is required to capture surface processes and potential inherent hazards in orogens. Complex climatic interactions control hydrological processes in high mountains that in their turn regulate the erosive forces shaping the relief. To unravel the hydrological cycle of a glaciated watershed (Gunt River) considered representative of the Pamir Mountains' hydrologic regime, we developed a remote-sensing-based approach. At the boundary between two distinct climatic zones dominated by the Westerlies and Indian summer monsoon, the Pamir Mountains are poorly instrumented and only a few in situ meteorological and hydrological data are available. We adapted a suitable conceptual distributed hydrological model (J2000g). Interpolations of the few available in situ data are inadequate due to strong, relief-induced, spatial heterogeneities. Instead of these we use raster data, preferably from remote sensing sources depending on availability and validation. We evaluate remote-sensing-based precipitation and temperature products. MODIS MOD11 surface temperatures show good agreement with in situ data, perform better than other products, and represent a good proxy for air temperatures. For precipitation we tested remote sensing products as well as the HAR10 climate model data and the interpolation-based APHRODITE data set. All products show substantial differences both in intensity and seasonal distribution with in situ data. Despite low resolutions, the data sets are able to sustain high model efficiencies (NSE ≥ 0.85). In contrast to neighbouring regions in the Himalayas or the Hindu Kush, discharge is dominantly the product of snow and glacier melt, and thus temperature is the essential controlling factor. Eighty percent of annual precipitation is provided as snow in winter and spring contrasting peak discharges during summer. Hence, precipitation and discharge are negatively correlated and display complex hysteresis effects that allow for the effect of interannual climatic variability on river flow to be inferred. We infer the existence of two subsurface reservoirs. The groundwater reservoir (providing 40 % of annual discharge) recharges in spring and summer and releases slowly during autumn and winter, when it provides the only source for river discharge. A not fully constrained shallow reservoir with very rapid retention times buffers meltwaters during spring and summer. The negative glacier mass balance (-0.6 m w.e. yr-1) indicates glacier retreat, which will ultimately affect the currently 30 % contribution of glacier melt to annual stream flow. The spatiotemporal dependence of water release from snow and ice during the annual cycle likewise implies spatiotemporally restricted surface processes, which are essentially confined to glaciated catchments in late summer, when glacier runoff is the only source of surface runoff. Only this precise constraint of the hydrologic cycle in this complex region allows for unravelling of the surface processes and natural hazards such as floods and landslides as well as water availability in the downstream areas. The proposed conceptual model has a tremendous importance for the understanding of the denudation processes in the region. In the Pamirs, large releases of running water that control erosion intensity are primarily controlled by temperature and the availability of snow and glaciers, thus making the region particularly sensitive to climatic variations. ________________________________________

Summary In water resources modeling, meteorological data scarcity can be compensated by various global data sets, but those data sets can differ tremendously. In the literature, hydrological models of differing complexity are proposed for estimating the water resources of semi-arid catchments, and also to evaluate rainfall data sets. The goal of this paper is to provide a joint analysis of modeling uncertainty due to different input data and increasing model complexity. Impacts of mutually concealed uncertainties on model performance and model outputs are exemplified in two data sparse semi-arid catchments in Ethiopia. We applied a semi-distributed and a fully distributed hydrological model, having different levels of complexity. Three different satellite-based rainfall data sets and two temperature products were used as model inputs. The semi-distributed model demonstrated good validation performances, while the fully distributed model was more sensitive to data uncertainties. The application of TRMM version 6 completely failed and the high-resolution CMORPH precipitation estimate outperformed TRMM version 7. In contrast, the use of high-resolution temperature data did not improve the model results. The large differences in remotely sensed input data were buffered inside the hydrological models. Therefore, data set evaluations regarding only the simulated hydrographs were less meaningful. In contrast, the investigation of parameter evolution and distributed outputs’ variability appeared to be a valuable tool to uncover the interdependencies of data and model uncertainties. We suggest this procedure to be applied by default in water resources estimations that are affected by data scarcity, but especially when data sets are evaluated using hydrological models. Our case study demonstrates that estimations of groundwater recharge and actual evapotranspiration vary largely, depending on the applied data sets and models. The joint analysis reveals large interdependencies between data and model evaluations. It shows that traditional studies focusing only on one part of uncertainty, either the input uncertainty or the uncertainty arising from the choice of model structure are limited in their explanatory power of the modeling performance, particularly in poorly gauged regions.

Surface processes involve complex feedback effects between tectonic and climatic influences in the high mountains of Pamir. The ongoing India–Asia collision provokes the development of east–west-trending mountain ranges that impose structural control on flow directions of the Pamir rivers. The evolving relief is further controlled by strong moisture gradients. The decreasing precipitations from the southern and western margins of the Pamir Plateau to its center, in their turn, control the emplacement of glaciers. Chronologies of glacial records from the Pamir Plateau attest for strong climatic variability during the Quaternary. Corresponding remnants of glacial advances suggest glacial morphodynamic restricted to >4,000 m a.s.l. since marine isotope stage 4. The Panj, the trunk river of Pamir, deflects from the predominant westward drainage, connecting its main tributaries at the western margin of the drainage basin. The geometry of the river network and the pattern of incision characterize the Panj as a composite river. River reaches of indicated low incision coincide with west-trending valleys, parallel to domes and their bounding faults. Valley shape ratios reflect increased incision in north-trending sections, but do not match with changes in the catchment geometry or erodibility of rock types. Modelled riverbed profiles distinguish three Panj reaches. The upstream increase in convexity suggests successive river captures in response to local base-level changes. The northward-deflected river reaches link the local base levels, which coincide with the southern boundaries of the Shakhdara and Yazgulom Dome and Darvaz Range. We argue that tectonics plays a large role controlling the drainage system of the Panj and hence surface processes in the Pamir mountains.

Complex climatic interactions control hydrological processes in high mountains that in their turn regulate the erosive forces shaping the relief. To unravel the hydrological cycle of a glaciated watershed (Gunt River) considered representative of the Pamirs' hydrologic regime we developed a remote sensing-based approach. At the boundary between two distinct climatic zones dominated by Westerlies and Indian summer monsoon, the Pamir is poorly instrumented and only a few in situ meteorological and hydrological data are available. We adapted a suitable conceptual distributed hydrological model (J2000g). Interpolations of the few available in situ data are inadequate due to strong, relief induced, spatial heterogeneities. Instead we use raster data, preferably from remote sensing sources depending on availability and validation. We evaluate remote sensing-based precipitation and temperature products. MODIS MOD11 surface temperatures show good agreement with in situ data, perform better than other products and represent a good proxy for air temperatures. For precipitation we tested remote sensing products as well as the HAR10 climate model data and the interpolation-based APHRODITE dataset. All products show substantial differences both in intensity and seasonal distribution with in-situ data. Despite low resolutions, the datasets are able to sustain high model efficiencies (NSE ≥0.85). In contrast to neighbouring regions in the Himalayas or the Hindukush, discharge is dominantly the product of snow and glacier melt and thus temperature is the essential controlling factor. 80% of annual precipitation is provided as snow in winter and spring contrasting peak discharges during summer. Hence, precipitation and discharge are negatively correlated and display complex hysteresis effects that allow to infer the effect of inter-annual climatic variability on river flow. We infer the existence of two subsurface reservoirs. The groundwater reservoir (providing 40% of annual discharge) recharges in spring and summer and releases slowly during fall and winter. A not fully constrained shallow reservoir with very rapid retention times buffers melt waters during spring and summer. This study highlights the importance of a better understanding of the hydrologic cycle to constrain natural hazards such as floods and landslides as well as water availability in the downstream areas. The negative glacier mass balance (−0.6 m w.e. yr−1) indicates glacier retreat, that will effect the currently 30% contribution of glacier melt to stream flow.