ALBERT

Description: The Tibetan Plateau and surroundings contain a large number of debris-covered glaciers, on which debris cover affects glacier response to climate change by altering ice melting rates and spatial patterns of mass loss. Insufficient spatial distribution of debris thickness data makes it difficult to analyze regional debris-cover effects. Mount Gongga glaciers, maritime glaciers in the south-eastern Tibetan Plateau, are characterized by a substantial reduction in glacier length and ice mass in recent decades. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-derived thermal property of the debris layer reveals that 68% of the glaciers have extensive mantles of supraglacial debris in their ablation zones, in which the proportion of debris cover to total glacier area varies from 1.74% to 53.0%. Using a surface energy-mass balance model accounting for the debris-cover effect applied at a regional scale, we find that although the presence of supraglacial debris has a significant insulating effect on heavily debris-covered glaciers, it accelerates ice melting on ~ 10.2% of the total ablation area and produces rapid wastage of ~ 25% of the debris-covered glaciers, resulting in the similar mass losses between debris-covered and debris-free glaciers. Widespread debris cover also facilitates the development of active terminus regions. Regional differences in the debris-cover effect are apparent, highlighting the importance of debris cover for understanding glacier status and hydrology in both the Tibetan Plateau and other mountain ranges around the world.

Description: In this study, the snow physics of a distributed biosphere hydrological model, referred to as the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) is significantly improved by incorporating the three-layer physically based energy balance snowmelt model of Simplified Simple Biosphere 3 (SSiB3) and the Biosphere-Atmosphere Transfer Scheme (BATS) albedo scheme. WEB-DHM with improved snow physics is hereafter termed WEB-DHM-S. Since the in-situ observations of spatially-distributed snow variables with high resolution are currently not available over large regions, the new distributed system (WEB-DHM-S) is at first rigorously tested with comprehensive point measurements. The stations used for evaluation comprise the four open sites of the Snow Model Intercomparison Project (SnowMIP) phase 1 with different climate characteristics (Col de Porte in France, Weissfluhjoch in Switzerland, Goose Bay in Canada and Sleepers River in USA) and one open/forest site of the SnowMIP phase 2 (Hitsujigaoka in Japan). The comparisons of the snow depth, snow water equivalent, surface temperature, snow albedo and snowmelt runoff at the SnowMIP1 sites reveal that WEB-DHM-S, in general, is capable of simulating the internal snow process better than the original WEB-DHM. Sensitivity tests (through incremental addition of model processes) are performed to illustrate the necessity of improvements over WEB-DHM and indicate that both the 3-layer snow module and the new albedo scheme are essential. The canopy effects on snow processes are studied at the Hitsujigaoka site of the SnowMIP2 showing that the snow holding capacity of the canopy plays a vital role in simulating the snow depth on ground. Through these point evaluations and sensitivity studies, WEB-DHM-S has demonstrated the potential to address basin-scale snow processes (e.g., the snowmelt runoff), since it inherits the distributed hydrological framework from the WEB-DHM (e.g., the slope-driven runoff generation with a grid-hillslope scheme, and the flow routing in the river network).

Description: Changes in river discharge due to human activities and climate change would affect the sustainability of freshwater ecosystems. To globally assess how changes in river discharge will affect the future status of freshwater ecosystems, global-scale hydrological simulations need to be connected with a model to estimate the durability of freshwater ecosystems. However, the development of this specific modelling combination for the global scale is still in its infancy. In this study, two statistical methods are introduced to link flow regimes to fish species richness (FSR): one is based on a linear relationship between FSR and mean river discharge (hereafter, FSR-MAD method), and the other is based on a multi-linear relationship between FSR and ecologically relevant flow indices involving several other flow characteristics and mean river discharge (FSR-FLVAR method). The FSR-MAD method has been used previously in global simulation studies. The FSR-FLVAR method is newly introduced here. These statistical methods for estimating FSR were combined with a set of global river discharge simulations to evaluate the potential impact of climate-change-induced flow alterations on FSR changes. Generally, future reductions in FSR with the FSR-FLVAR method are greater and much more scattered than with the FSR-MAD method. In arid regions, both methods indicate reductions in FSR because mean discharge is projected to decrease from past to future, although the magnitude of reductions in FSR is different between the two methods. In contrast, in heavy-snow regions a large reduction in FSR is shown by the FSR-FLVAR method due to increases in the frequency of low and high flows. Although further research is clearly needed to conclude which method is more appropriate, this study demonstrates that the FSR-FLVAR method could produce considerably different results when assessing the global role of flow alterations in changing freshwater ecosystems.

Description: Adequate estimation of the spatial distribution of snowfall is critical in hydrologic modelling. However, this is a well-known problem in estimating basin-scale snowfall, especially in mountainous basins with data scarcity. This study focuses on correction and estimation of this spatial distribution, which considers topographic effects within the basin. A method is proposed that optimises an altitude-based snowfall correction factor (Cfsnow). This is done through multi-objective calibration of a spatially distributed, multilayer energy and water balance-based snowmelt model (WEB-DHM-S) with observed discharge and remotely sensed snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The Shuffled Complex Evolution–University of Arizona (SCE–UA) automatic search algorithm is used to obtain the optimal value of Cfsnow for minimum cumulative error in discharge and snow cover simulations. Discharge error is quantified by Nash–Sutcliffe efficiency and relative volume deviation, and snow cover error was estimated by pixel-by-pixel analysis. The study region is the heavily snow-fed Yagisawa Basin of the Upper Tone River in northeast Japan. First, the system was applied to one snow season (2002–2003), obtaining an optimised Cfsnow of 0.0007 m−1. For validation purposes, the optimised Cfsnow was implemented to correct snowfall in 2004, 2002 and 2001. Overall, the system was effective, implying improvements in correlation of simulated versus observed discharge and snow cover. The 4 yr mean of basin-average snowfall for the corrected spatial snowfall distribution was 1160 mm (780 mm before correction). Execution of sensitivity runs against other model input and parameters indicated that Cfsnow could be affected by uncertainty in shortwave radiation and setting of the threshold air temperature parameter. Our approach is suitable to correct snowfall and estimate its distribution in poorly gauged basins, where elevation dependence of snowfall amount is strong.

Description: The snow physics of a distributed biosphere hydrological model, referred to as the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) is improved by incorporating the three-layer physically based energy balance snowmelt model of Simplified Simple Biosphere 3 (SSiB3) and the Biosphere-Atmosphere Transfer Scheme (BATS) albedo scheme. WEB-DHM with improved snow physics (WEB-DHM-S) can simulate the variability of snow density, snow depth and snow water equivalent, liquid water and ice content in each layer, prognostic snow albedo, diurnal variation in snow surface temperature, thermal heat due to conduction and liquid water retention. The performance of WEB-DHM-S is evaluated at two alpine sites of the Snow Model Intercomparison Project with different climate characteristics: Col de Porte in France and Weissfluhjoch in Switzerland. The simulation results of the snow depth, snow water equivalent, surface temperature, snow albedo and snowmelt runoff reveal that WEB-DHM-S is capable of simulating the internal snow process better than the original WEB-DHM, with the root mean square error and bias error being remarkably reduced. Although WEB-DHM-S is only evaluated at a point scale for the simulation of snow processes, this study provides a benchmark for the application of WEB-DHM-S in cold regions in the assessment of the basin-scale snow water equivalent and seasonal discharge simulation for water resources management.

Description: Changes in river discharge due to human activities and climate change would affect the sustainability of freshwater ecosystem. In order to globally assess the future status of freshwater ecosystem under regime shifts in river discharge, global-scale hydrological simulations need to be connected with a model to estimate the soundness of freshwater ecosystem. However, the explicit combination of those two on a global scale is still in its infancy. A couple of statistical models are introduced here to link flow regimes to fish species richness (FSR): one based on a linear relationship between FSR and mean river discharge, and the other based on a relationship between FSR and ecologically relevant flow indices involving other several flow characteristics as well as mean river discharge. The former one has been sometimes used in global simulation studies, but the latter one is newly introduced here in the context of global simulation. These statistical models for estimating FSR were combined with a set of global river discharge simulations to evaluate the potential impact of flow alterations due to climate change on FSR changes. Generally, future reductions in FSR by the latter method are larger and much more scattered rather than by the former method. In arid regions, both models provide reductions in FSR because mean discharge is projected to decrease from past to future, although the magnitude of reduction in FSR is different. On the other hand, large reductions in FSR only by the latter model are detected in heavy-snow regions due to the increases of mean discharge and frequency of low and high flows. Although we need further research to conclude which is more relevant, this study demonstrates that the new model could show a considerably different behavior in assessing the global impact of flow alteration on freshwater ecosystem change.

Description: Adequate estimation of the spatial distribution of snowfall is critical in hydrologic modeling. However, this is a well-known problem in estimating basin-scale snowfall, especially in mountainous basins with data scarcity. This study focuses on correction and estimation of this spatial distribution, which considers topographic effects within the basin. A method is proposed that optimizes an altitude-based snowfall correction factor (Cfsnow). This is done through multi-objective calibration of a spatially distributed, multilayer energy and water balance-based snowmelt model (WEB-DHM-S) with observed discharge and remotely sensed snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The Shuffled Complex Evolution – University of Arizona automatic search algorithm is used to obtain the optimal value of Cfsnow for minimum cumulative error in discharge and snow cover simulations. Discharge error is quantified by Nash–Sutcliffe efficiency and relative volume deviation, and snow cover error was estimated by pixel-by-pixel analysis. The study region is the heavily snow-fed Yagisawa Basin of the Upper Tone River in northeast Japan. First, the system was applied to one snow season (2002–2003), obtaining an optimized Cfsnow of 0.0007 m−1. For validation purposes, the optimized Cfsnow was implemented to correct snowfall in 2004, 2002 and 2001. Overall, the system was effective, implying improvements in correlation of simulated vs. observed discharge and snow cover. The 4 yr mean of basin-average snowfall for the corrected spatial snowfall distribution was 1160 mm (780 mm before correction). Execution of sensitivity runs against other model input and parameters indicated that Cfsnow could be affected by uncertainty in shortwave radiation and setting of the threshold air temperature parameter. Our approach is suitable to correct snowfall and estimate its distribution in poorly-gauged basins, where elevation dependence of snowfall amount is strong.

Description: The Working Group II contribution to the IPCC Sixth Assessment Report assesses the impacts of climate change, looking at ecosystems, biodiversity, and human communities at global and regional levels. It also reviews vulnerabilities and the capacities and limits of the natural world and human societies to adapt to climate change.