Hydrological models differ in the way how hydrological processes are implemented. A rigorous comparison of different hydrological model structures is needed to disentangle the link between similarities and differences in process representations and simulated hydrological processes, states and fluxes. A major challenge in model comparison is to identify effects of individual processes. To move a step in this direction, we developed controlled experiments and compared three hydrological models (HBV, mHM, SWAT+) in nine German catchments (400-3000 km²) along an elevation gradient. We aim at presenting a framework for a consistent comparison of process representations in model structures consisting of three steps: (1) A model comparison protocol was developed for a detailed comparison of process representations in model structures. Consistency was achieved by using the same input data for all models. By grouping the processes in a standardized way, differences and similarities between the models were identified. (2) To investigate the dominant model components, a daily parameter sensitivity analysis was carried out for the three models with different hydrological variables as target variables (e.g. actual evapotranspiration, soil moisture, snow and discharge). The dominant model parameters and associated processes vary more between the models than between the catchments. This also applies to the temporal variability of the parameter sensitivity. (3) The model performance was analysed for a set of different performance criteria. The optimal parameter values differ greatly depending on which performance criteria were selected. This is in particular true for soil and evapotranspiration parameters. Typical patterns can be derived between catchments of different landscapes. The joint analysis of these three methodological steps demonstrates the benefit of a detailed process analysis in model structures for a better understanding of suitable process representations. Therefore, it shows the potentials for improving model structures.

While measured streamflow is commonly used for hydrological model evaluation and calibration, an increasing amount of data on additional hydrological variables is available. These data have the potential to improve process consistency in hydrological modeling and consequently for predictions under change, as well as in data-scarce or ungauged regions. Here, we show how these hydrological data beyond streamflow are currently used for model evaluation and calibration. We consider storage and flux variables, namely snow, soil moisture, groundwater level, terrestrial water storage, evapotranspiration, and altimetric water level. We aim at summarizing the state-of-the-art and providing guidance for the use of additional hydrological variables for model evaluation and calibration. Based on a review of the current literature, we summarize observation methods and uncertainties of currently available data sets, challenges regarding their implementation, and benefits for model consistency. The focus is on catchment modeling studies with study areas ranging from a few km 2 to ~500,000 km 2 . We discuss challenges for implementing alternative variables that are related to differences in the spatio-temporal resolution of observations and models, as well as to variable-specific features, for example, discrepancy between observed and simulated variables. We further discuss advancements required to deal with uncertainties of the hydrological data and to integrate multiple, potentially inconsistent datasets. The increased model consistency and improvement shown by most reviewed studies regarding the additional variables often come at the cost of a slight decrease in streamflow model performance.

Long-term variations in catchment evapotranspiration control water availability for human societies and freshwater ecosystems, with potential negative impacts particularly during low-flow conditions. Previous studies reported increases in water balance-derived evapotranspiration for parts of Central Europe, mostly between 1980s and 2010s. However, knowledge gaps still remain around (i) the extent of these increases in space and time, and (ii) uncertainties from the catchment water balance. Here we analyse trends in water balance-derived evapotranspiration for 461 German near-natural catchments, over multiple time windows in the last six decades. We constrain uncertainties through estimates of storage changes derived from recession analysis and the use of multiple precipitation products. Results show wide-spread, significant increases in catchment evapotranspiration during 1970s–2000s (for example, average regional trends of 3.2 mm year-2 with an uncertainty from precipitation of ±1 mm year-2 for the period 1970–2002). Yet, catchment evapotranspiration shows no significant changes or rather a tendency to the decrease after 2000s (-3.6±1.4 mm year-2 for Pre-Alpine catchments over 2000–2019). The directions of these variations are robust to the considered uncertainties and consistent with sparse in-situ data. We further discuss implications of these variations with respect to low-flow conditions. This study offers a comprehensive synthesis on past variations in catchment evapotranspiration and their uncertainties, which is critical for a proper understanding of recent hydrological changes.

Decreases in streamflow (Q) during dry periods can negatively affect river ecosystems and human societies, and understanding their causes is crucial to anticipate them. The contribution of increases in catchment actual evapotranspiration (E) to decreases in Q during dry periods remains poorly quantified. To address this gap, we performed a data-based analysis for 363 small (< 1000 km(2)) catchments without substantial water management influences in Germany over 1970-2019. We quantified trends in the magnitude of summer low flows, i.e. the minimum 7 d Q during summer months (7 dQ(min, JJA)). We attributed these trends to their main potential predictors, namely, long-term variations in E; summer precipitation, P; and spring and winter P as proxies for storage. Furthermore, we assessed potential changes in the annual P-Q relationship of the catchments during a multi-year drought in the early 1990s and investigated whether these changes were related with trends and anomalies in E and P. Summer low flows generally showed a decreasing tendency (median trend of -3.7 % decade(-1) and interquartile range of -7.5/-0.6 % decade(-1) across all catchments), significant negative trends in 31 % of the catchments, and significant positive trends in 2 % of them only. Increases in E were a relevant driver of these decreases, particularly in relatively more arid eastern catchments (contribution to long-term dynamics of 7 dQ(min, JJA) of 35 % based on multiple linear regression and correlation coefficient between trends in 7 dQ(min, JJA) and in E of -0.74). Changes in the P-Q relationship occurred in 26 % of the catchments that experienced a multi-year drought between 1989 and 1993, with lower Q than expected from the relationship before the drought. These changes occurred in catchments with concurrent strong increases in E (median trend of 6.1 % decade(-1)). Our findings point to the importance of increases in E, especially in more arid catchments, when assessing potential future decreases in Q during dry periods for water management and climate adaptation strategies.

Increasing climate variability, especially prolonged droughts, shapes forest water consumption and constrains vegetation growth. However, the effects of drought-induced changes on evapotranspiration (ET) fluxes vary due to species-specific differences and drought characteristics. Here, we analyzed the seasonal variations in ET in pine, beech, and mixed forest stands in northeast Germany (2012-2021) and explored the ability of a process-based ecohydrological model (EcH2O) in reproducing the water balance components observed at three forested lysimeters. To better understand how individual climate variables control ET fluxes, we performed simulation experiments with detrended climate inputs. Multi-variable calibration showed that the model reproduced well in-situ soil moisture, seepage, and interception (EI) in the three stands. Precipitation (P) was the main driver of ET anomalies, with above-average ET in wet years and below-average ET in dry years. However, only small reductions in ET were observed during the dry year 2018. This could be attributed to high P in the previous year, i.e., P legacy effects, which led to only small reductions or even positive anomalies in ET. The beech stand, with a seasonal leaf cycle, had lower ET and interception losses compared to the pine and mixed stands, which maintain year-round foliage. This resulted in greater percolation to deeper soil layers in beech forests. These findings suggest that broadleaf species such as beech by allowing greater water transfer to groundwater, offer a distinct hydrological advantage in terms of promoting deep percolation. Our results therefore provide a process-based rationale for the strategic selection of broadleaf species in forest management to enhance groundwater recharge and promote sustainable water management. Additionally, model testing at such data-rich sites will be valuable for improving the process-consistency and reliability of other hydrological models, particularly in studies aimed at investigating the effects of different vegetation cover.

Understanding long-term changes in evapotranspiration and their drivers is crucial due to direct impacts on water availability. Increasing evapotranspiration rates can exacerbate droughts and jeopardise water availability, especially in the summer months with higher water demands. Uncertainties of multi-decadal variations in evapotranspiration at local to regional scale and their drivers are, however, still large. In this data-based study, we derive changes in evapotranspiration from the catchment water balance for a large number of catchments in Central Europe over 1982–2016. We further analyse changes in potential drivers including vegetation and land cover based on a remote-sensing derived vegetation index and a land cover product, water availability based on changes in seasonal precipitation and available energy and atmospheric demand based on changes in reference evapotranspiration. We find wide-spread increases in catchment evapotranspiration until about the year 2000 and only small changes with a decreasing tendency after 2000. The observed variations in regional evapotranspiration are significantly correlated with variations in precipitation, reference evapotranspiration and vegetation activity. High evapotranspiration around 2000 can be related to high values of reference evapotranspiration, precipitation and vegetation activity. Lower evapotranspiration in the early 1980s despite relatively high precipitation is linked to lower values of reference evapotranspiration and vegetation activity, while the halt of further evapotranspiration increases after 2000 despite high values of reference evapotranspiration may be explained by low precipitation. The study contributes to expand our knowledge on the drivers of changes in the water balance in Central Europe over recent decades, which is of great importance for water management in a changing climate.

Hydrological models often do not properly simulate streamflow (Q) during extreme events, including droughts. Limited abilities in simulating Q during droughts may arise from a misrepresentation of Q generating processes during these periods, but little research has focused on distributed, process-based models over human-affected areas and extreme events. To shed more light into model consistency during these periods, we evaluated the ability of the hydrological model Continuum in simulating Q over the human-affected Po river basin in Italy during droughts of different severity over the last 13 years, including the severe 2022 event. To investigate the causes for potential model deterioration during severe droughts, we assessed the simulation of evapotranspiration (ET) and Terrestrial Water Storage (TWS) against independent remote sensing-based benchmarks, and possible inconsistencies in forcing and benchmark data. Finally, we included a moderate drought in the calibration period, as potential strategy to improve model performances during severe droughts. The model represented well Q (KGE = 0.81 for the outlet of the basin), ET (r = 0.94) and TWS (r = 0.76) over the whole study period. Focusing on Q and specific sub-periods, model performances were comparable during wet years (2014 and 2020) and moderate droughts (2012 and 2017), with KGE across the 38 study sub-catchments of 0.59±0.32 (mean ± standard deviation) during wet years and 0.55±0.25 during moderate droughts. The model simulated Q well for the outlet section of the basin also during the severe 2022 drought (KGE = 0.82). However, performances across the subcatchments declined in 2022 (KGE = 0.18±0.69). For the severe drought, we detected a decrease in model performances for ET, in particular over human-affected croplands (mean decrease in r by 105% and mean increase in nRMSE by 86%). Furthermore, calibrating during a moderate drought did not improve model performances in 2022 (KGE = 0.18±0.63), pointing to the fairly unique conditions of this period in terms of hydrological processes and human interference on them. Our study highlighted decreased model skills specifically during a severe drought and identified the neglection of irrigation as the most plausible cause for this. Given projected increases in severe droughts and the frequent modelling simplification of human activities, despite their heavy interference in many regions, our findings are highly relevant to move towards more robust hydrological modelling in a changing climate and the Anthropogenic era, to support management and adaptation strategies.

Distributed hydrological models can correctly simulate spatial patterns if the model parameters adequately represent the spatial heterogeneity of the basin. However, determining realistic values of these parameters is often difficult. Considering remote sensing-derived land-surface states and fluxes in combination with streamflow observations is a suitable strategy to better constrain parameters and improve process-consistency. For ecohydrological modelling, an accurate representation of vegetation characteristics is important to capture the vegetation response to varying moisture availability. Land surface temperature (Ts) may serve as a valuable diagnostic because it is pivotal to the surface energy and water balance, and conveys information about ecosystem stress and water use. This study aims at assessing the benefits of integrating spatial patterns of Landsat-derived Ts into calibration of a process-based ecohydrological model to improve process representation of catchment-scale energy fluxes and vegetation response to moisture deficits. We explicitly analyze the trade-off between streamflow and Ts performance, and explore the value of adding an increasing number of Ts images in the calibration process. The study is performed in a mixed land cover catchment in Germany using the ecohydrological model EcH2O. Our results demonstrate the value of satellite-derived Ts data for reducing uncertainties of energy-balance related vegetation pa-rameters, which are hardly constrained in streamflow-only calibration. Including satellite-derived Ts for model calibration reduced the mean absolute error of spatial anomalies in simulated Ts patterns by 15 % with negligible deterioration of streamflow performance. Inclusion of spatial Ts patterns improved capturing the dif-ferences in Ts between vegetation types, and affected simulated evapotranspiration fluxes. Improvements in simulated Ts could already be achieved by including only few (4-5) images of satellite-derived Ts in calibration. Based on our results, we advocate a wider use of satellite-based Ts data for multivariate calibration to improve model parameter identifiability and process consistency of highly parameterised, process-based distributed ecohydrological models, especially when capturing hydrology-vegetation interactions is crucial.

AbstractUnderstanding variations in catchment evapotranspiration (EC) is critical as it directly affects water availability for humans and ecosystems. Previous studies found increases in EC in Central Europe over recent decades, but fixed study periods may not fully reveal inter‐decadal hydroclimatological variability. We performed a multi‐temporal trend analysis of water balance‐derived EC for 461 German catchments and the period 1964–2019. We accounted for previously often neglected changes in storage and uncertainties in precipitation. EC generally increased throughout Germany during 1970s–2000s (>2 mm year−2), while it showed milder changes and decreases afterward. These variations were robust to uncertainties in precipitation (median relative uncertainty of 26%) and broadly coherent with sparse plot‐scale data. Variations in EC were related with variations in precipitation and radiation, with a potentially increasing influence of precipitation after 2000s. These findings provide a reference for synthesizing current knowledge on variations in EC and their uncertainties.

Temporal sensitivity analyses can be used to detect dominant model parameters at different time steps (e.g. daily or monthly) providing insights on their temporal patterns and reflecting the temporal variability in dominant hydrological processes. However, hydrological processes do not only vary in time under different hydrometeorological conditions, but also the time scales of implemented processes are different. Here, the impact of different time scales (e.g. daily vs. monthly) on sensitivity patterns is investigated.A temporal parameter sensitivity analysis is applied to three hydrological models (HBV, mHM and SWAT) for nine catchments in Germany. These catchments represent the variability of landscapes in Germany and are dominated by different runoff generation processes. In addition to discharge, further model fluxes and states such as evapotranspiration or soil moisture are used as target variables for the sensitivity analysis.To analyse the impact of different time scales, two approaches are compared. In a first approach, daily simulated time series are used for the sensitivity analysis and aggregated then to monthly averaged sensitivities (Post-Agg). In a second approach, the simulated time series is first aggregated to a monthly time series and than used as input for the sensitivity analysis (Pre-Agg).Our analysis shows that monthly averaged sensitivity patterns of different model outputs vary between Post- and Pre-Aggregation approach. Model parameters that are related to fast-reacting runoff processes, e.g. surface runoff or fast subsurface flow, are more sensitive when using daily time series for the sensitivity analysis (Post-Agg). In contrast, model parameters related processes with longer time scales such as snowmelt or evapotranspiration are more emphasized in monthly time series (Pre-Agg). These differences in the sensitivity results between Post-Agg and Pre-Agg are in particularly pronounced when using the integrated value of discharge as the target variable. Instead, the differences are smaller when applying the sensitivity analysis directly to represent model fluxes.Moreover, our analysis shows changes in dominant parameters along a north-south gradient which can be explained by the physiographic characteristics of the catchments. The differences in the sensitivity results between the models can be related to the different model structures.Based on our analysis, we recommend to either using model outputs of the major hydrological variables or different time scales for the sensitivity analysis to derive the maximum information from the diagnostic model analysis and to understand how model parameters describe hydrological systems.

&lt;p&gt;Considering different types of hydrologic observations for model calibration in addition to streamflow is a suitable strategy to better constrain model parameters and improve process-consistency of hydrologic models. In this regard, land surface temperature (&lt;em&gt;T&lt;/em&gt;&lt;sub&gt;s&lt;/sub&gt;) is an interesting variable as it is at the core of the surface energy and water balance. This study aims at evaluating the benefits of integrating spatial patterns of satellite-derived &lt;em&gt;T&lt;/em&gt;&lt;sub&gt;s&lt;/sub&gt; into calibration of the process-based ecohydrologic model EcH&lt;sub&gt;2&lt;/sub&gt;O. We furthermore explore the value of an increasing number of &lt;em&gt;T&lt;/em&gt;&lt;sub&gt;s&lt;/sub&gt; images in the calibration period. The study is performed in a mixed land cover catchment in NE Germany and makes use of Landsat-derived &lt;em&gt;T&lt;/em&gt;&lt;sub&gt;s&lt;/sub&gt; data. Our results show that satellite-derived &lt;em&gt;T&lt;/em&gt;&lt;sub&gt;s&lt;/sub&gt; is useful for reducing uncertainties of energy-balance related vegetation parameters, which are hardly constrained when the model is calibrated to streamflow only. Good model performance with respect to streamflow does not preclude low performance in terms of &lt;em&gt;T&lt;/em&gt;&lt;sub&gt;s&lt;/sub&gt; and including satellite-derived &lt;em&gt;T&lt;/em&gt;&lt;sub&gt;s&lt;/sub&gt; for model calibration clearly improves simulated spatial patterns of &lt;em&gt;T&lt;/em&gt;&lt;sub&gt;s&lt;/sub&gt;. Spatial patterns in observed &lt;em&gt;T&lt;/em&gt;&lt;sub&gt;s&lt;/sub&gt; are shown to be strongly related to land cover class and a vegetation index, and our results indicate that further model improvements may be possible by better representing observed variations of leaf area index within the ecohydrologic model.&lt;/p&gt;

Hydrological models often do not simulate properly streamflow (Q) during droughts, because of a poor representation of the interactions among precipitation deficits, actual evapotranspiration (ET), and terrestrial water storage anomalies (TWSA) during these periods. However, there is little research comprehensively evaluating model skills during droughts of varying intensity in a spatially distributed way. To shed further light into these drops in model skills and step toward more robust models in an anthropogenic era and a changing climate, we evaluated Q, ET, and TWSA simulations during moderate and severe droughts, and we tested if calibrating during a moderate drought could enhance model performances during a severe one. We applied the distributed hydrological model Continuum over the heavily human-affected Po river basin in northern Italy and the period 2010 – 2022. Moreover, we exploited independent ground- and remote sensing-based datasets to evaluate the temporal and spatial variability of Q, ET, and TWSA monthly simulations across the whole basin and 38 sub-catchments. Model performances for Q across the study sub-catchments were comparable during both wet years (2014 and 2020, mean KGE = 0.59±0.32) and moderate droughts (2012 and 2017, mean KGE = 0.55±0.25). Further, Continuum simulated well Q for the basin outlet even during a severe drought (KGE = 0.82 in 2022), while its performances generally decreased among the sub-catchments (mean KGE = 0.18±0.69 in 2022). In general, the model well represented ET and TWSA seasonality over the study area, and a decline in TWSA over the more recent years. Yet, during the severe 2022 drought we detected an increased uncertainty in ET anomalies, especially in human-affected croplands, that could explain the Q performance drop along with an increased anthropogenic disturbance. Including a moderate drought (2017) in the calibration period did not lead to a significant improvement in model skills during the severe event (mean KGE = 0.18±0.63 for Q during 2022), meaning that the severe 2022 drought was fairly unique for the study area both in terms of hydrological processes and human disturbance on them. By unveiling an increase in model uncertainty during a severe drought and possible causes for it, our findings are relevant to assess and possibly enhance model robustness in a changing climate and the anthropogenic era for adequate water management, disaster risk reduction, and climate change adaptation.

ABSTRACT Tarim River basin is the largest endorheic river basin in China. Due to the extremely arid climate the water supply solely depends on water originating from the glacierised mountains with about 75% stemming from the transboundary Aksu River. The water demand is linked to anthropogenic (specifically agriculture) and natural ecosystems, both competing for water. Ongoing climate change significantly impacts the cryosphere. The mass balance of the glaciers in Aksu River basin was clearly negative since 1975. The discharge of the Aksu headwaters has been increasing over the last decades mainly due to the glacier contribution. The average glacier melt contribution to total runoff is 30–37% with an estimated glacier imbalance contribution of 8–16%. Modelling using future climate scenarios indicate a glacier area loss of at least 50% until 2100. River discharge will first increase concomitant with glacier shrinkage until about 2050, but likely decline thereafter. The irrigated area doubled in the Aksu region between the early 1990s and 2020, causing at least a doubling of water demand. The current water surplus is comparable to the glacial runoff. Hence, even if the water demand will not grow further in the future a significant water shortage can be expected with declining glacial runoff. However, with the further expansion of irrigated agriculture and related industries, the water demand is expected to even further increase. Both improved discharge projections and planning of efficient and sustainable water use are necessary for further socioeconomic development in the region along with the preservation of natural ecosystems.

Glacierised river catchments are highly sensitive to climate change, while large populations may depend on their water resources. The irrigation agriculture and the communities along the Tarim River, NW China, strongly depend on the discharge from the glacierised catchments surrounding the Taklamakan Desert. While recent increasing discharge has been beneficial for the agricultural sector, future runoff under climate change is uncertain. We assess three climate change scenarios by forcing two glacio-hydrological models with output of eight general circulation models. The models have different glaciological modelling approaches but were both calibrated to discharge and glacier mass balance observations. Projected changes in climate, glacier cover and river discharge are examined over the twenty-first century and generally point to warmer and wetter conditions. The model ensemble projects median temperature and precipitation increases of + 1.9–5.3 °C and + 9–24%, respectively, until the end of the century compared to the 1971–2000 reference period. Glacier area is projected to shrink by 15–73% (model medians, range over scenarios), depending on the catchment. River discharge is projected to first increase by about 20% in the Aksu River catchments with subsequent decreases of up to 20%. In contrast, discharge in the drier Hotan and Yarkant catchments is projected to increase by 15–60% towards the end of the century. The large uncertainties mainly relate to the climate model ensemble and the limited observations to constrain the glacio-hydrological models. Sustainable water resource management will be key to avert the risks associated with the projected changes and their uncertainties.

Drought risk will increase in the next decades due to anthropogenic warming, especially in the Mediterranean region. Therefore, robust hydrological models during droughts are essential tools for disaster risk reduction and climate change adaptation strategies. Yet, many studies showed drops in model performance when simulating periods with different climatic conditions from those of the calibration period, which poses challenges in properly simulating discharge (Q) during droughts. Some works also revealed that these issues may be related to the simulation of evapotranspiration (ET) and changes in terrestrial water storage (TWS) in the catchment, which has been shown to be highly sensitive to the calibration period too. Here, we analyzed how the simulation of Q, ET, and TWS differs according to the selected calibration period and during droughts, thus expanding on previous work on this matter that has mostly focused on Q. We compared two parameterizations of the distributed hydrological model Continuum for the Po river basin over 2009 – 2019. The northern Italian study area is characterized by a transition from continental to Mediterranean climates and experienced two major drought events during the study period (2012 and 2017). The two model parameterizations result from an iterative semi-automated calibration against Q data during a wet period for the first model variant (2018-2019), and during a dry period for the second model variant (2016-2017). We then evaluated the modelling skills in simulating Q, ET, and TWS for the whole river basin and 43 subcatchments in terms of both temporal and spatial variability, using ground-based and satellite-derived data as benchmark. Calibrating during a dry period improved the simulation of Q during low-flow conditions, as expected, though at the expense of model internal consistency, ET, and TWS representation. We also detected a general deterioration of modelling skills in reproducing Q and ET temporal dynamics, as well as ET and TWS spatial patterns, during droughts for both the model variants. Results call for (i) comprehensive evaluation of the output and states of hydrological models across the whole water balance, rather than only Q, to verify their internal consistency and (ii) the development of alternative calibration procedures to improve the distributed modelling of Q, ET, and TWS during dry periods. This is highly needed to properly predict water availability in the different compartments of the hydrological cycle in a changing climate.

It is widely acknowledged that calibrating and evaluating hydrological models only against streamflow may lead to inconsistencies of internal model states and large parameter uncertainties. Soil moisture is a key variable for the energy and water balance, which affects the partitioning of solar radiation into latent and sensible heat as well as the partitioning of precipitation into direct runoff and catchment storage. In contrast to ground-based measurements, satellite-derived soil moisture (SDSM) data are widely available and new data products benefit from improved spatio-temporal resolutions. Here we use a soil water index product based on data fusion of microwave data from METOP ASCAT and Sentinel 1 CSAR for calibrating the process-based ecohydrological model EcH2O-iso in the 66 km² Demnitzer Millcreek catchment in NE Germany. Available field measurements in and close to this intensively monitored catchment include soil moisture data from 74 sensors and water stable isotopes in precipitation, stream and soil water. Water stable isotopes provide information on flow pathways, storage dynamics, and the partitioning of evapotranspiration into evaporation and transpiration. Accounting for water stable isotopes in the ecohydrologic model therefore provides further insights regarding the consistency of internal processes. We first compare the SDSM data to the ground-based measurements. Based on a Monte Carlo approach, we then investigate the trade-off between model performance in terms of soil moisture and streamflow. In situ soil moisture and water stable isotopes are further consulted to evaluate the internal consistency of the model. Overall, we find relatively good agreements between satellite-derived and ground based soil moisture dynamics. Preliminary results suggest that including SDSM in the model calibration can improve the simulation of internal processes, but uncertainties of the SDSM data should be accounted for. The findings of this study are relevant for reliable ecohydrological modelling in catchments that lack detailed field measurements for model evaluation.

Quantifying the contributions of runoff components (CRCs) to streamflow is of significant importance for understanding the dynamics of water resources under changing climate in glacierized basins. This article presents a meta-analysis on different approaches for quantifying runoff components in glacierized basins, including the tracer-based end-member mixing method and the hydrological modeling approach. We collected estimated CRCs from 312 glacierized basin cases, as well as values of five characteristics in these basins including mean basin elevation (MBE), mean annual air temperature (MAT), mean annual precipitation (MAP), winter precipitation fraction (WPF) and glacierized area ratio (GAR). Relations between CRCs and the basin characteristics were assessed using a random forest (RF) algorithm. The review showed that CRCs were most often quantified by the hydrological modeling approach (73% of the basin cases). Compared to hydrological modeling, the tracer-based approach (applied in 19% of the basin cases) was more likely to be used in smaller basins < 50 km(2), rather at the seasonal than annual time scale and within shorter study periods of <=5 years. Meta-analysis results indicate that: (1) At the annual time scale, the most important influencing basin characteristics were GAR and MBE for the ice melt contribution, WPF and MAP for the snow melt contribution, and GAR and WPF for the rainfall contribution. RF algorithm based on the five basin characteristics was able to explain 56%, 40%, and 40% of the variability of the reported annual contributions of ice melt, snowmelt and rainfall, respectively; the variability of seasonal CRCs and annual contribution of groundwater could be less well explained by the five basin characteristics. (2) Comparing different definitions of runoff components based on water-input or flow-pathway indicated that the ice melt contribution to total water input (sum of rainfall and melt water) based on the water-input definition was close to the contribution of ice melt-induced surface flow to total runoff based on the flow-pathway definition. In contrast, based on the reviewed studies, rainfall and snowmelt contributions based on the water-input definition were around 9%-14% higher than the contributions of rainfall and snowmelt induced surface flow to total runoff. (3) The tracer-based end-member mixing method tended to estimate larger uncertainties of CRCs than hydrological modeling, but uncertainties of modeled CRCs were likely underestimated as often only one or two of the three uncertainty sources of model parameter, model input and model structure were considered in the modeling studies. We propose that more efforts are required to cross validate CRCs estimated by the tracer-based and hydrological modeling methods, and to reduce uncertainties of CRCs by integrations of hydro-meteorological data and water tracer data.