Snow Cover Area Research Articles

Estimating Daily Snow Density Through a Spatiotemporal Random Forest Model

AbstractSnow density is of paramount importance in water resource management, snow avalanche warning, and climate change research. However, the lack of competent methods for long‐term and vast‐scale snow density mapping persists due to the intricate spatiotemporal dependencies inherent in snow density, resulting in scarce and inaccurate snow density products. To address this challenge, a spatiotemporal random forest (STRF) model is constructed by leveraging in‐situ measurements, multisource remote sensing, and reanalysis data. It tackles the spatiotemporal dependencies in snow density arising from its inherent heterogeneity and the relations involving snow density and nonlinearly connected meteorological, terrain, vegetation, and snow‐related factors. The effectiveness of the model is substantiated through rigorous validation methods, including random, temporal, and spatial block cross‐validations as well as independent validation, apparently surpassing ERA5‐Land snow density. The estimated snow density is also demonstrated to be able to improve existing snow water equivalent data set using fixed snow density. Utilizing the proposed model, a data set of daily 25‐km snow density from 1980 to 2018 is constructed for stable snow cover areas in China, which holds significant potential for research and applications in the realm of snow hydrology.

Bayesian estimates of snow cover area in Eurasia in the 21st century based on the results of calculations with the CMIP6 ensemble of climate models

Bayesian estimates of snow cover area in Eurasia in the 21st century based on the results of calculations with the CMIP6 ensemble of climate models

Machine learning-based estimation of fractional snow cover in the Hindukush Mountains using MODIS and Landsat data

Accurate estimation of snow-covered area (SCA) is vital for effective water resource management, especially in snowmelt-dependent regions like the Kabul River Basin (KRB). It serves as a reference point for comparing expected variations in water availability driven by climate change, particularly in arid and semi-arid regions like the KRB. In this study, fractional snow cover (FSC) was estimated across the KRB using Landsat and moderate resolution imaging spectroradiometer (MODIS) datasets. For this purpose, 34 Landsat-8 and MODIS image-pairs were acquired covering the snowfall period from 01 October 2018 and 31 March 2021. The training dataset consisted of 31 image-pairs, while the remaining three were used as an independent test dataset. Sample sizes and training strategies (i.e., full, semi, and trimmed-models), three each, were evaluated to understand the relevance of the predictor variables. The full-model incorporated MODIS surface reflectance bands (SRB) 1–7 spectral indices, topography and landcover while the semi-model included MODIS SRB 1–7 and indices. The trimmed-model only utilized SRB 1–7. Random Forests (RF) facilitated FSC mapping with Landsat-8 data as a reference. The findings indicated comparable performance between full and semi models, whereas the trimmed-model exhibited weaker performance. The correlation coefficient (R) of the full, semi and trimmed models ranged from 0.83 to 0.92, 0.83–0.92 and 0.82–0.87 respectively. The models performed strongly in grassland regions (R = 0.89–0.90), but moderately in forested areas (R = 0.43–0.53). This approach results in improved MODIS-based SCA-mapping in the Hindukush Mountains, facilitating better water resource management in the region.

Multivariate analysis of land surface dynamics in Central Asia: patterns of trends and drivers under a changing climate

ABSTRACT With temperatures in Central Asia (CA) increasing more than the global average, this region is one of the global hotspots affected by climate change. CA is mostly characterized by arid climate, which is why available water resources are of paramount importance for the societies, economies, and the environment. In this regard, quantifying changes on the land surface and controlling factors that influence land surface dynamics are of great interest to improve our understanding of climate change impacts in this region. Hence, this study analyzes multivariate time series covering climatic, hydrological and Earth observation (EO)-based land surface variables. The used EO time series characterize the land surface and include data on the normalized difference vegetation index (NDVI), surface water area (SWA), and snow cover area (SCA) between December 2002 to November 2021. To analyze these time series, we employ trend analyses and a causal discovery algorithm. Both analyses were carried out at multiple spatial and temporal scales. The results show that NDVI trends were mostly significantly negative in the Northwest and positive in the Northeast of CA in summer. In summer and autumn, the percentage of significant negative NDVI trends outweighed the positive trends. For SWA, the detected trends were mostly significant negative throughout all scales. Significant negative trends were retrieved for SCA across all seasons, except for autumn regionally. Particularly the Tian Shan and Pamir mountains show significant declines of SCA in winter and spring. The causal analyses revealed that the NDVI is mostly controlled by water availability in summer. In spring and autumn, temperature is the leading driver on the NDVI. Likewise, temperature is found to largely control SWA in spring and autumn. SCA is mostly negatively coupled to temperature during spring and autumn. A positive coupling between SCA and precipitation is identified in winter.

Two decades of high-resolution aerosol product over the Sierra Nevada Mountain region (SE Spain): Spatio-temporal distribution and impact on ecosystems

Atmospheric aerosols play a pivotal role in shaping our environment, impacting climate, human health, and ecosystems. Characterizing the influence of aerosols on ecosystems, especially in mountain environments, is a challenging task due to their complex-orography and scarcity of aerosol ground stations. Satellite-based aerosol data can improve our knowledge over such complex-orography areas. Thus, we have analyzed the Aerosol Optical Depth (AOD) product from the MODerate resolution Imaging Spectrometer (MODIS) sensor produced by the inversion algorithm MultiAngle Implementation of Atmospheric Correction (MAIAC) over the last two decades for the period 2001–2022 with a spatial resolution of 1 × 1 km. Our study focuses on the Sierra Nevada Mountain region and National Park in Southeastern Spain. As a first step, we have validated the AOD from MODIS+MAIAC against three AERONET stations at different altitudes (680 m, 1800 m, and 2500 m above sea level (a.s.l.)). MODIS+MAIAC AOD showed good agreement with the ground-based AOD observations, with R values ranging from 0.75 to 0.82, RMSE values ranging from 0.047 to 0.066 and having 80% of the samples within the expected error (EE) of the product. The MODIS+MAIAC AOD product is able to characterize the fine-scale features of such complex-orography area and hence evaluate the spatio-temporal distribution of the AOD over the mountainous region. We have generated the most extended AOD dataset for a mountainous region, spanning the past two decades. We have deepened into the spatial and seasonal AOD patterns from 2001 to 2022, unveiling elevated AOD values near valleys and urban areas. In general, the AOD values decrease with increasing altitude with the exception of snow-covered areas at high altitudes (>2800 m a.s.l.), which might affect aerosol retrieval and provide bias due to higher-reflecting surfaces and pixel removal. For the first time, the relationship of aerosol loading with ecosystem type has been assessed in the protected environment of Sierra Nevada Natural Park. Monthly AOD trends across different ecosystem types and altitudinal ranges are analyzed in detail over the last two decades. In addition, Generalized Linear Models (GLM) are applied to reveal significant correlations between ecosystems and AOD, irrespective of altitude, latitude or longitude. Based on the interannual variation of AOD over the last two decades, we have analyzed the relationship of AOD with the different ecosystems of Sierra Nevada at 500 m elevation ranges. The patterns of the ecosystem's types are maintained over the elevation ranges 1200–1700 m and 1700–2200 m a.s.l., which demonstrates that land-type has an impact on the AOD product. Furthermore, it is observed that forest-like ecosystems tend to present lower AOD compared with bare-soil or low-growth vegetation ecosystems. In addition, the areas of the mountain closer to Granada city present generalized higher AOD values on the western part of the mountain, regardless of the ecosystem, showing the significant influence of the proximity of urban sites over the ecosystems and the potential impact on the environment.

Measured black carbon deposition over the central Himalayan glaciers: Concentrations in surface snow and impact on snow albedo reduction

Deposition of ambient black carbon (BC) aerosols over snow-covered areas reduces surface albedo and accelerates snowmelt. Based on in-situ atmospheric BC data and the WRF-Chem model, we estimated the dry and wet deposition of BC over the Yala glacier of the central Himalayan region in Nepal during 2016–2018. The maximum and minimum BC dry deposition was reported in pre- and post-monsoon respectively. Approximately 50% of annual dry deposition occurred in the pre-monsoon season (March to May) and 27% of the annual dry deposition occurred in April. The total dry BC deposition rate was estimated as ∼4.6 μg m−2 day−1 providing a total deposition of 531 μg m−2 during the pre-monsoon season. The contribution of biomass burning and fossil fuel sources to BC deposition on an annual basis was 28% and 72% respectively. The annual accumulated wet deposition of BC was 196 times higher than the annual dry deposition. The ten months of observed dry deposition of BC (October 1, 2016 to August 31, 2017 – except December 2016) was ∼39% lower than that of WRF-Chem's estimated annual dry deposition from September 1, 2016 to August 31, 2017 partially due to model bias. The deposited content of BC over the snow surface has an important role in albedo reduction, therefore snow samples were collected from the surface of the Yala Glacier and the surrounding region in April 2016, 2017, and 2018. Samples were analyzed for BC mass concentration through the thermal optical analysis and single particle soot photometer method. The BC calculated via the thermal optical method was in the range of 352–854 ng g−1, higher than the BC calculated through the particle soot photometer method and estimated BC in 2 cm surface snow (imperial equation). The maximum surface snow albedo reduction due to BC was 8.8%, estimated by a widely used snow radiative transfer model and a linear regression equation.

A Study of the Relationship between Human Behavior and Urban Design during the Winter in a High-Snowfall Urban Area

This study focuses on the relationship between the outdoor environment and usage behavior of open spaces in cities with snowy and cold climates (winter cities), using an outdoor survey conducted at AKAPURA Plaza in Sapporo, Japan. This study seeks to understand walking and staying behaviors and analyzes their relationships to snowy outdoor environments. An analysis of the survey data shows that while the number of pedestrians using AKAPLA Plaza decreased as temperatures dropped, the ratio of staying behavior to the number of pedestrians did not decrease despite dropping temperatures. This study identified the following three design principles that can be applied to encourage the usage of open urban spaces during the winter. In winter, the number of pedestrians decreases in correlation with decreases in temperature; however, during the snowy season, walking can be encouraged by providing areas with less snow (PATH). Partially snow-covered areas can encourage photography and snow play behavior (STORAGE). Finally, providing walking routes to snow-covered areas can encourage staying behavior (APPROACH). These design principles were established based on the study in Sapporo and have the potential to be widely applied in other winter cities across the world through future research and analysis.

Remote sensing of mountain snow from space: status and recommendations

The spatial and temporal variation of the seasonal snowpack in mountain regions is recognized as a clear knowledge gap for climate, ecology and water resources applications. Here, we identify three salient topics where recent developments in snow remote sensing and data assimilation can lead to significant progress: snow water equivalent, high resolution snow-covered area and long term snow cover observations including snow albedo. These topics can be addressed in the near future with institutional support.

Elevation dependent climate assessment and its influence on snow cover variability in Hindu Kush Himalayan region using Google Earth Engine for the period of 2003–2021

The Hindu Kush Himalayan (HKH) regions are taken for this study, which is predominantly characterized by amplified climatic uncertainty and rapid snow melting. Because at the present there is a lack of studies estimating the elevation dependent and climate influenced snow cover variability in the HKH region. This present study examines elevation-dependent (2003–2021) distribution and trends in precipitation, temperature, and snow cover area (SCA) by employing numerous non-parametric statistical applications using the Google Earth Engine (GEE). The Modified Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover data is employed to estimate SCA along with the Climate Hazards Group Infrared Precipitation with Station Data version 2.0 (CHIRPS-V2.0) for precipitation and MODIS land surface temperature (LST). The study reveals statistically significant decreasing annual, seasonal, and monthly trends of SCA in most of the elevation zones typically during winter season (−0.256%/year), whereas temperature and precipitation exhibit strong spatial variability. Inclusive investigation portrays maximum mean annual and seasonal increased precipitation at the low-elevated regions, whereas seasonal disparities in temperature ostensible marginal decreased at high-elevation, albeit increased at lowland. Moreover, topographic importance on SCA distribution showcases maximum snow (43.4%) at north-facing less surface inclination with flat topography (14.6%). The comprehensive elevation wise analysis improves our understanding of the overall hydrological challenge and assist in the development of more reliable flood forecasting and water resource management.

Monitoring Snow Cover in Typical Forested Areas Using a Multi-Spectral Feature Fusion Approach

Accurate snow cover monitoring is greatly significant for research on the hydrology model and regional climate variation, especially in Northeast China where forests cover almost forty percent of the total area. However, effectively monitoring snow cover under the forest canopy is still challenging with either in situ or remote sensing observations. The global SNOWMAP algorithm pertinent to the fixed normalized difference snow index (NDSI) threshold is, therefore, no longer applicable in a typical forested region of Northeast China. In order to achieve the goal of improving the accuracy of monitoring snow cover in areas with forest, utilizing MOD09GA and MOD13A1 products, a new approach of snow mapping was developed in this study, and it exploits the fusion and coupling of spectral features by integrating and analyzing the normalized difference forest snow index (NDFSI), the normalized difference vegetation index (NDVI), and the NDSI index. Then, Landsat 8 OLI images of high resolution were used to evaluate snow cover mapping precision. The experimental results indicated that the NDFSI index combined with the NDVI index showed great potential for extracting the snow cover distribution in forested regions. Compared with the snow distribution obtained from the Landsat 8 images, the average bias and FAR (false alarm ratio) values of snow cover mapping obtained by this algorithm were 1.23 and 13.54%, which were reduced by 1.98 and 29.36%, respectively. The overall accuracy of 81.31% was reached, which is improved by 20.19%. Thus, the snow classification scheme combining multiple spectral features from MODIS data works effectively in improving the precision of automatic snow cover mapping in typical forested areas of Northeast China, which provides essential support and significant perspectives for the next step of establishing a runoff model and rationally regulating forest water resources.

Streamflow prediction using support vector regression machine learning model for Tehri Dam

Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time series Models, Data-driven models, Artificial intelligence, etc. Still, there needs to be generalised method practise in the real world. The resolution of this issue lies in selecting different methods for a particular study area. This paper uses the Support vector regression machine learning model to predict the streamflow for the Tehri Dam, Uttarakhand, India, at the Daily and Ten Daily time steps. Two cases are considered in predicting daily and ten daily time steps. The first case includes four input variables: Discharge, Rainfall, Temperature, and Snow cover area. The second case comprises only three input variables: Rainfall, Temperature, and Snow cover area. Radial Kernel is used to overcome the space complexity in the datasets. The K-fold cross-validation is suitable for prediction as it averages the prediction error rate after evaluating the SVR model’s performance on various subsets of the training data. The streamflow data for daily and ten daily time steps have been collected from 2006 to 2020. The calibration period is from 2006 to 2016, and the validation period is from 2017 to 2020. Nash Sutcliffe Efficiency (NSE) and Coefficient of determination (R2) are used as the accuracy indicator in this manuscript. The lag has been observed in the daily prediction time series when three input variables are considered. For other scenarios, the respective model shows excellent results at both the temporal scale and the parametres, which play a vital role in prediction. The study also enhances the effect on the potential use of input parametres in the machine learning model.

Soil quality assessment in low human activity disturbance zones: a study on the Qinghai-Tibet Plateau.

The Qinghai-Tibet Plateau, located at the Third Pole and known as the "Asian water tower," serves as a crucial ecological barrier for China. Grasping the soil quality on the Qinghai-Tibet Plateau holds paramount importance for the rational and scientific exploitation of soil resources within the region and is essential for vegetation restoration and ecological reconstruction. This study, conducted in Maqin County, Qinghai Province, collected 1647 soil samples (0-20cm) within a study area of 6300km2. Sixteen soil indicators were selected that were split into beneficial (N, P, S, and B), harmful (Cr, Hg, As, Pb, Ni, and Cd), and essential (Cu, Zn, Se, Ga, K, and Ca) elements. The Soil Quality Index (SQI) was computed to assess soil quality across diverse geological contexts, land cover classifications, and soil profiles. The results indicate that the overall SQI in the study area was comparatively high, with most regions having an SQI between 0.4 and 0.6, categorized as moderately to highly satisfactory. Among the different geological backgrounds, the highest SQI was found in the Quaternary alluvium (0.555) and the lowest in the Precambrian Jinshuikou Formation (0.481). Regarding different land-use types, the highest SQI was observed in glacier- and snow-covered areas (0.582) and the lowest in other types of grassland (0.461). The highest SQI was recorded in typical alpine meadow soil (0.521) and the lowest in leached brown soil (0.460). The evaluation results have significant reference value for the sustainable utilization and management of soil in Maqin County, Qinghai Province, China.

An intercomparison of empirical schemes for partitioning precipitation phase

Study regionPrecipitation phase in China has changed with global warming, and accurate partitioning of the precipitation phase is crucial for understanding the hydrological processes and energy balance. Study focusBased on a 36-year daily meteorological dataset from the China Meteorological Administration, this study conducted a comprehensive evaluation of six empirical methods in estimating the precipitation phase nationwide and explored the applicability and distinction across different meteorological, topographic and geographic categories. These methods utilized inputs of air temperature, with two variants incorporating relative humidity, and encompassed a range of combined non-linear air temperature and hydrometeor temperature methods to more simple air temperature threshold methods. New hydrological insights for the regionMethods implementing non-linear functions without air temperature thresholds performed better than those relying solely on air temperature thresholds. The exponential functions with air temperature and relative humidity exhibited the best performance, while those relying only on air temperature thresholds exhibited the worst overall performance. Optimal methods were identified and recommended under different scenarios (i.e., multivariate meteorological and geographic parameters, different geographic regions). The rain–precipitation ratio exhibited a significant increasing trend, and the meteorological stations showing an increase were primarily concentrated in the three major snow-covered areas, the west and vicinity of the “Hu Huanyong Line”. This study can be applied to other regions and offers valuable insights for analyzing precipitation data and developing hydrological models.

Modification and Validation of the Soil–Snow Module in the INM RAS Climate Model

This paper describes the modification of a simple land snow cover module of the INM RAS climate model. The possible liquid water and refreezing of meltwater in the snow layer are taken into account by the proposed parameterization. This is particularly important for modelling the transition season, as this phenomenon is mainly observed during the formation and melting of the snow cover when the surface temperature fluctuates around 0 °C. The snow density evolution simulation is also added. This parameterization is implemented in the INM-CM snow module and verified on observation data using the ESM-SnowMIP-like protocol. As a result, the INM-CM mean climate snow melt periods are refined, particularly in middle and high latitudes. The snow-covered area according to the model is also improved. In the future, a modified version of the land snow module can be used, coupled with a snow albedo model that takes into account snow metamorphism. This module can also be applied to sea ice snow.

Integrated modeling of snow-covered areas and hydrological processes in the Larji Sub-Basin, Himachal Pradesh, India, using SRM and SWAT models

Abstract Alpine snow is crucial for the water cycle, as the runoff and environment of arid and semi-arid regions rely entirely on these glaciers. Mountain-fed rivers provides the water for domestic, agricultural irrigation, hydroelectric power, and other uses. Due to climate variability, river catchment flows may shift, causing floods and droughts that will aggravate the economy. This study estimated the SCA using Google Earth Engine (GEE) for the Larji River basin, situated in Himachal Pradesh, from the year 2001 to 2021. The snow cover area (SCA) varies from 0.7–22% in the basin. The present study highlights the effectiveness of cloud computing for assessing trends in snow-cover regions in the basin. Moderate Resolution Imaging Spectroradiometer (MODIS) snow product (MOD10A1 V6 Snow Cover Daily Global 500m product) is utilized for SCA calculation. This study simulates runoff from the Larji river, using Snow-melt Runoff Model (SRM) and Soil Water Assessment Tool (SWAT) for years (2019-2021). Furthermore, the Coefficient of determination (R2) Coefficient of corelation (r), Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) were used to evaluate the efficacy of models. This study will help the policymakers and stakeholders to carry out the water resource management practices in the study area.

Surface and subsurface flow of a glacierised catchment in the cold-arid region of Ladakh, Trans-Himalaya

Hydrological assessments of high-altitude catchments in Trans-Himalayan Ladakh are necessary for a better understanding of water availability in the context of irrigated cultivation under conditions of insufficient quantitative information on cryospheric meltwater discharge. In this study, an integrated spatially distributed temperature index model and a coupled surface/subsurface flow model were used to simulate daily, seasonal, and annual surface and subsurface flows to assess the proportion of corresponding source contributors from the Stok catchment. Snow and glacier meltwater discharge secures irrigated agriculture of more than 300 households in this catchment. The models were forced by temperature, precipitation, ice- and snow-covered areas at daily time steps with calibration (2019; 108 days) and validation (2018; 93 days) against the observed discharge. The simulated discharge shows a good agreement with the observed discharge with R2 and RMSE of 0.8 (p < 0.01) and 0.6 m3/s, respectively. The results between 2003 and 2019 show that the snowmelt contribution to the total annual discharge is largest with 65 %, followed by glacier melt and rainfall contributions of approximately 19 % and 16 %, respectively. A reduction in glacierised areas by 4.2 % was observed while snow-covered areas showed high inter-annual variation. Simulated subsurface flow makes up 62 % (mean = 37.2 × 106 m3) of the total discharge with less inter-annual variation. The simulation suggests that while surface flow ceases during the winter period and peaks in August, the annualized mean flow amounts to ∼23.7 × 106 m3. More than 50 % of the melt occurs in the summer months of June, July and August, when the intensity of snowmelt, ice melt, and rainfall reach its maximum. The findings of this study on meltwater availability and surface/subsurface flow is important for irrigated agriculture of Stok village on a local scale, and it might also help to better understand socio-hydrological dynamics and situations of water scarcity in the wider cold-arid region of Ladakh.

Isotope data-constrained hydrological model improves soil moisture simulation and runoff source apportionment

Multiple-objective calibration helps constrain the parameter uncertainties and improve the performances of hydrological models. Previous studies have indicated that calibration toward soil moisture data could improve the streamflow simulation, but its influence on the runoff source apportionment quantification still needs to be analyzed. Meanwhile, although isotope calibration has proved to improve the representation of internal hydrological processes, the value of isotope on the simulation of internal state variables such as soil moisture has yet to be examined. This study utilized the tracer-aided hydrological model THREW-T (Tsinghua Representative Elementary Watershed – Tracer-aided version) in two mountainous basins on the Tibetan Plateau (The Upper Brahmaputra and Upper Yangtze basins) to evaluate the value of soil moisture and isotope data on model calibration. The result shows that: (1) The THREW-T model produced good simulation on streamflow, snow cover area, soil moisture, and stream water isotope simultaneously in the two study areas. Calibration toward soil moisture and isotope caused slight (∼0.03) but statistically significant (p < 0.01) decrease on the Nash-Sutcliffe coefficient of streamflow simulation compared to the baseline calibration variant only toward streamflow. (2) Calibration toward soil moisture brought no improvement to streamflow simulation for the validation period and stations in both basins, only improving soil moisture simulation. However, calibration toward the isotope improved the simulations of internal streamflow and soil moisture's spatiotemporal variation. (3) Different calibration variants resulted in different estimations of the runoff source apportionment, and independent evidence indicated that the results obtained by isotope calibration were most reasonable. Calibrations toward streamflow and soil moisture underestimated and overestimated the contributions from subsurface runoff, respectively. Isotope was the most sensitive objective to the runoff source apportionment and significantly reduced the uncertainty. Our study found a lower value of soil moisture data than the isotope on model calibration. However, we believe that the full potential of soil moisture data was not utilized due to the current limitations in soil moisture simulation and measurement methods, and the development of relevant technologies will make the soil moisture data more valuable for model calibration.

Identifying components and controls of streamflow in a cold and arid headwater catchment

Streamflow is increasingly vulnerable to climate change over cold-arid regions. To understand the underlying mechanisms of streamflow changes, it is essential to distinguish streamflow components and identify their drivers due to various meltwater and rainfall runoff processes. This study combines the conceptual temperature-index Snowmelt Runoff Model (SRM) and wavelet coherence analysis to unravel the impacts of environmental factors on streamflow and its components over the semiarid-and-cold headwater catchment of the Manas River in Northwest China during 2001–2015. Our results show that SRM reproduces monthly observed streamflow with determination coefficients of > 0.91, Nash-Sutcliffe Efficiency of > 0.82, and absolute relative error of < 5 % during SRM calibration (2001–2007) and validation (2008–2015) periods. The meltwater runoff and rainfall runoff contribute to 42 % and 31 % of the total streamflow, respectively. The wavelet-based spectral analysis, partial wavelet coherency analysis, and multiple-wavelet coherence together demonstrate that total streamflow (rainfall runoff) is predominantly influenced by snow cover area (precipitation) at a scale of 32 (4–16) months. But the meltwater runoff is simultaneously controlled by precipitation, potential evapotranspiration, and normalized difference vegetation index on scales of 4–16 and > 32 months. Our study provides technical support for streamflow partitioning and controlling factors identification, and has implication to sustainable water resources management in cold and arid regions.

Improving glacio-hydrological model calibration and model performance in cold regions using satellite snow cover data

Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables.

Glaciers determine the sensitivity of hydrological processes to perturbed climate in a large mountainous basin on the Tibetan Plateau

Abstract. The major rivers on the Tibetan Plateau supply important freshwater resources to riparian regions but have been undergoing significant climate change in recent decades. Understanding the sensitivities of hydrological processes to climate change is important for water resource management, but large divergences exist in previous studies because of the uncertainties of hydrological models and climate projection data. Meanwhile, the spatial pattern of local hydrological sensitivities was poorly explored despite the strong heterogeneity on the Tibetan Plateau. This study adopted the climate perturbation method to analyze the hydrological sensitivities of a typical large mountainous basin (Yarlung Tsangpo River, YTR) to climate change. We utilized the tracer-aided hydrological model Tsinghua Representative Elementary Watershed-Tracer-aided version (THREW-T) to simulate the hydrological and cryospheric processes in the YTR basin. Multiple datasets and internal stations were used to validate the model to provide confidence in the baseline simulation and the sensitivity analysis. Results indicated that (1) the THREW-T model performed well in simulating the streamflow, snow cover area (SCA), glacier mass balance (GMB) and stream water isotope, ensuring good representation of the key cryospheric processes and a reasonable estimation of the runoff components. The model performed acceptably in simulating the streamflow at eight internal stations located in the mainstream and two major tributaries, indicating that the spatial pattern of hydrological processes was reflected by the model. (2) Increasing temperature led to decreasing annual runoff, smaller inter-annual variation, more even intra-annual distribution and an earlier maximum runoff. It also influenced the runoff regime by increasing the contributions of rainfall and glacier melt overland runoff but decreasing the subsurface runoff and snowmelt overland runoff. Increasing precipitation had the opposite effect to increasing temperature. (3) The local runoff change in response to increasing temperature varied significantly, with a changing rate of −18.6 % to 54.3 % for 5∘ of warming. The glacier area ratio (GAR) was the dominant factor in the spatial pattern of hydrological sensitivities to both perturbed temperature and precipitation. Some regions had a non-monotonic runoff change rate in response to climate perturbation, which represented the most dynamic regions within the basin, as they kept shifting between energy- and water-limited stages. The GAR and mean annual precipitation (MAP) of the non-monotonic regions had a linear relation and formed the boundary of regions with different runoff trends in the GAR–MAP plot.