Climate change impacts water supply dynamics in the Upper Rio Grande (URG) watersheds of the US Southwest, where declining snowpack and altered snowmelt patterns have been observed. While temperature and precipitation effects on streamflow often receive the primary focus, other hydroclimate variables may provide more specific insight into runoff processes, especially at regional scales and in mountainous terrain where snowpack is a dominant water storage. The study addresses the gap by examining the mechanisms of generating streamflow through multi-modal inferences, coupling the Bayesian Information Criterion (BIC) and Bayesian Model Averaging (BMA) techniques. We identified significant streamflow predictors, exploring their relative influences over time and space across the URG watersheds. Additionally, the study compared the BIC-BMA-based regression model with Random Forest Regression (RFR), an ensemble Machine Learning (RFML) model, and validated them against unseen data. The study analyzed seasonal and long-term changes in streamflow generation mechanisms and identified emergent variables that influence streamflow. Moreover, monthly time series simulations assessed the overall prediction accuracy of the models. We evaluated the significance of the predictor variables in the proposed model and used the Gini feature importance within RFML to understand better the factors driving the influences. Results revealed that the hydroclimate drivers of streamflow exhibited temporal and spatial variability with significant lag effects. The findings also highlighted the diminishing influence of snow parameters (i. e., snow cover, snow depth, snow albedo) on streamflow while increasing soil moisture influence, particularly in downstream areas moving towards upstream or elevated watersheds. The evolving dynamics of snowmelt-runoff hydrology in this mountainous environment suggest a potential shift in streamflow generation pathways. The study contributes to the broader effort to elucidate the complex interplay between hydroclimate variables and streamflow dynamics, aiding in informed water resource management decisions.

Multi-source precipitation products (MSPs) are critical for hydrologic modeling, but their spatial and temporal heterogeneity and uncertainty present challenges to simulation accuracy that need to be addressed urgently. This study assessed the impact of different precipitation data sources on hydrologic modeling in an arid basin. There were seven precipitation products and meteorological station interpolated data that were used to drive the hydrological model, and we evaluated their performance by fusing the six precipitation products through the dynamic bayesian averaging algorithm. Ultimately, the runoff simulation uncertainty was quantified based on the DREAM algorithm, and the information transfer entropy was used to quantify the differences in hydrologic simulation processes driven by different precipitation data. The results showed that CMFD and ERA5 weights were higher, and the DBMA fused precipitation annual mean value was about 309.83 mm with good simulation accuracy (RMSE of 1.46 and R-2 of 0.75). The simulation was satisfactory (NSE >0.80) after parameter calibration and data assimilation for all driving data, with CHIRPS and TRMM performed better in the common mode, and HRLT and CMFD performed excellently in the glacier mode. The DREAM algorithm indicated less uncertainty for DBMA, CHIRPS and HRLT data. The entropy of information transfer revealed that precipitation occupied a significant position in information transfer, especially affecting evapotranspiration and surface soil moisture. CMFD and TPS CMADS were highest in snow water equivalent information entropy, and CHIRPS and TPS CMADS were highest in evapotranspiration information entropy. CDR, CHIRPS, ERA5-Land and IDW STATION had the highest snow water equivalent information entropy, DBMA and CMORPH had the highest runoff information entropy, CHIRPS and TRMM had the highest soil moisture information entropy, whereas ERA5, HRLT, and TPS CMADS had the highest evapotranspiration information entropy in glacial mode. This study reveals significant differences between different precipitation data sources in hydrological modeling of arid basin, which is an important reference for future water resources management and climate change adaptation strategies.

Reconstructing historical climate change from deep ground temperature measurements in cold regions is often complicated by the presence of permafrost. Existing methods are typically unable to account for latent heat effects due to the freezing and thawing of the active layer. In this work, we propose a novel method for reconstructing historical ground surface temperature (GST) from borehole temperature measurements that accounts for seasonal thawing and refreezing of the active layer. Our method couples a recently developed fast numerical modeling scheme for two-phase heat transport in permafrost soils with an ensemble-based method for approximate Bayesian inference. We evaluate our method on two synthetic test cases covering both cold and warm permafrost conditions as well as using real data from a 100 m deep borehole on Sardakh Island in northeastern Siberia. Our analysis of the Sardakh Island borehole data confirms previous findings that GST in the region have likely risen by 5-9 degrees C between the pre-industrial period of 1750-1855 and 2012. We also show that latent heat effects due to seasonal freeze-thaw have a substantial impact on the resulting reconstructed surface temperatures. We find that neglecting the thermal dynamics of the active layer can result in biases of roughly -1 degrees C in cold conditions (i.e., mean annual ground temperature below -5 degrees C) and as much as -2.6 degrees C in warmer conditions where substantial active layer thickening (>200 cm) has occurred. Our results highlight the importance of considering seasonal freeze-thaw in GST reconstructions from permafrost boreholes. Plain Language Summary Long-term changes in the temperature of the atmosphere are recorded in the solid Earth due to the insulating properties of soil and rock. As a result, it is possible to estimate past changes in temperature at the interface between the ground and the atmosphere by measuring ground temperatures deep below Earth's surface. In cold regions, the presence of permafrost, that is, ground that remains frozen throughout the year, complicates such analyses due to the effects of water freezing and thawing in the soil. In this work, we present a new method for reconstructing past changes in ground surface temperature from boreholes situated in permafrost using a computational model of heat flow that accounts for these effects. We evaluate our method on both synthetic test cases as well as real data from a 100 m deep borehole in northeastern Siberia. Our results demonstrate that annual freezing and thawing of water near the surface has a substantial impact on the reconstructed ground surface temperature (GST), especially in regions where permafrost is thawing. The proposed method is the first to be widely applicable to ground temperatures measured in permafrost and thus constitutes a valuable new tool for understanding past and present climate change in cold regions.

It is proposed to build a high-speed railway through the China -Mongolia -Russia economic corridor (CMREC) which runs from Beijing to Moscow via Mongolia. However, the frozen ground in this corridor has great impacts on the infrastructure stability, especially under the background of climate warming and permafrost degradation. Based on the Bayesian Network Model (BNM), this study evaluates the suitability for engineering construction in the CMREC, by using 21 factors in five aspects of terrain, climate, ecology, soil, and frozen-ground thermal stability. The results showed that the corridor of Mongolia's Gobi and Inner Mongolia in China is suitable for engineering construction, and the corridor in Amur, Russia near the northern part of Northeast China is also suitable due to cold and stable permafrost overlaying by a thin active layer. However, the corridor near Petropavlovsk in Kazakhstan and Omsk in Russia is not suitable for engineering construction because of low freezing index and ecological vulnerability. Furthermore, the sensitivity analysis of influence factors indicates that the thermal stability of frozen ground has the greatest impact on the suitability of engineering construction. These conclusions can provide a reference basis for the future engineering planning, construction and risk assessment.

The Tarim River, the largest inland river in China, sits in the Tarim River Basin (TRB), which is an arid area with the ecosystem primarily sustained by water from melting snow and glaciers in the headstream area. To evaluate the pressures of natural disasters in this climate-change-sensitive basin, this study projected flash droughts in the headstream area of the TRB. We used the variable infiltration capacity (VIC) model to describe the hydrological processes of the study area, Markov chain Monte Carlo to quantify the parameter uncertainty of the VIC model. Ten downscaled general circulation models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to drive the VIC model, and the standardized evaporative stress ratio was applied to identify flash droughts. The results demonstrated that the VIC model after Bayesian parameters uncertainty analysis can efficiently describe the hydrological processes of the study area. In the future (2021-2100), compared with the plain region, the alpine region has higher flash drought frequency and intensity. Compared with the historical period (1961-2014), the frequency, duration, and intensity of flash droughts tend to increase throughout the study area, especially for the alpine area. Moreover, based on variance decomposition, CMIP6 model is the most important uncertainty source for flash drought projection, followed by the shared socioeconomic pathway of climate change scenario and VIC model parameters.

Rising global temperatures are a threat to the current state of the Arctic. In particular, permafrost degradation has been impacting the terrestrial cryosphere in many ways, including effects on carbon cycling and the global climate, regional hydrological connectivity and ecosystem dynamics, as well as human health and infrastructure. However, the ability to simulate permafrost dynamics under future climate projections is limited, and model outputs are often associated with large uncertainties. A model structured on a Bayesian Network is presented to address existing limitations in the representation of physically complex processes and the limited availability of observational data. A strength of Bayesian methods over more traditional modeling methods is the ability to integrate various types of evidence (i.e., observations, model outputs, expert assessments) into a single model by mapping the evidence into probability distributions. Here, we outline PermaBN, a new modeling framework, to simulate permafrost thaw in the continuous permafrost region of the Arctic. Pre-validation and expert assessment validation results show that the model produces estimations of permafrost thaw depth that are consistent with current research, i.e., thaw depth increases during the snow-free season under initial conditions favoring warming temperatures, lowered soil moisture conditions, and low active layer ice content. Using a case study from northwestern Canada to evaluate PermaBN, we show that model performance is enhanced when certainty about the system components increases for known scenarios described by observations directly integrated into the model; in this case, insulation properties from vegetation were integrated to the model. Overall, PermaBN could provide informative predictions about permafrost dynamics without high computational cost and with the ability to integrate multiple types of evidence that traditional physics-based models sometimes do not account for, allowing PermaBN to be applied to carbon modeling studies, infrastructure hazard assessments, and policy decisions aimed at mitigation of, and adaptation to, permafrost degradation.

Northern circumpolar permafrost thaw affects global carbon cycling, as large amounts of stored soil carbon becomes accessible to microbial breakdown under a warming climate. The magnitude of carbon release is linked to the extent of permafrost thaw, which is locally variable and controlled by soil thermodynamics. Soil thermodynamic properties, such as thermal diffusivity, govern the reactivity of the soil-atmosphere thermal gradient, and are controlled by soil composition and drainage. In order to project permafrost thaw for an Alaskan tundra experimental site, we used seven years of site data to calibrate a soil thermodynamic model using a data assimilation technique. The model reproduced seasonal and interannual temperature dynamics for shallow (5-40 cm) and deep soil layers (2-4 m), and simulations of seasonal thaw depth closely matched observed data. The model was then used to project permafrost thaw at the site to the year 2100 using climate forcing data for three future climate scenarios (RCP 4.5, 6.0, and 8.5). Minimal permafrost thawing occurred until mean annual air temperatures rose above the freezing point, after which we measured over a 1 m increase in thaw depth for every 1 degrees C rise in mean annual air temperature. Under no projected warming scenario was permafrost remaining in the upper 3 m of soil by 2100. We demonstrated an effective data assimilation method that optimizes parameterization of a soil thermodynamic model. The sensitivity of local permafrost to climate warming illustrates the vulnerability of sub-Arctic tundra ecosystems to significant and rapid soil thawing.

We develop a Bayesian model to predict the maximum thickness of seasonally frozen ground (MTSFG) using historical air temperature and precipitation observations. We use the Stefan solution and meteorological data from 11 stations to estimate the MTSFG changes from 1961 to 2016 in the Yellow River source region of northwestern China. We employ an antecedent precipitation index model to estimate changes in the liquid soil water content. The marginal posterior probability distributions of the antecedent precipitation index parameters are estimated using Markov chain Monte Carlo sampling methods. We compare the results of our stochastic method with those obtained from the traditional deterministic method and find that they are consistent in general. The stochastic approach is effective for estimating the historical changes in the frozen ground depth (root-mean-square errors = 0.13-0.35 m), and it provides more information on model uncertainty regarding soil moisture variations. Additionally, simulation shows that the MTSFG has decreased by 0.31 cm per year over the last 56 years on the northeastern Qinghai-Tibet Plateau. This decrease in frost depth accelerated in the 1990s and 2000s. Considering the lack of data on seasonally frozen soil monitoring, the Bayesian method provides a pragmatic approach to statistically model frozen ground changes using available meteorological data.

Little is known about the ecological impacts of permafrost degradation on water fluxes in boreal ecosystems, such as those in Interior Alaska. Low plant water stress suggests a reliance on a diversity of water sources. In addition to rainfall, we hypothesize that deep soil water derived from thawing seasonal ground ice (TSGI) supports plants during dry periods. We analyzed water stable isotopes from soils, plants, ice, and rain collected from stable and unstable permafrost sites. We found that TSGI provides a background water source for plants during wet years (at least 10-20%) and a stable source during dry years (at least 30-50%) and early in the growing season (60-80% in wet and dry years). Plant water uptake patterns track the soil thawing front, using deep and shallow layers in wet years and deep layers during dry years. This plasticity allows boreal plants to cope with seasonal drought and exploit available water sources. The availability of TGSI depends on the amount of rainfall the prior year and on permafrost stability. Thawing permafrost may reduce the buffering capacity of TGSI due to less seasonal ice from greater drainage and/or a deeper active layer. This study demonstrates the importance of two buffering mechanisms for plants to cope with rainfall variability within boreal forest underlain by permafrost-availability of TSGI and plasticity in water uptake patterns. We suggest that plant utilization of stored water may be why evapotranspiration in northern latitudes can exceed growing season precipitation.

The effect of thawing permafrost on boreal ecosystem water cycling represents a significant knowledge gap of how climate change will affect northern landscapes. Evapotranspiration, particularly transpiration, may be changing in response to changes in permafrost conditions, vegetation, and climate. This study focuses on the effect of permafrost thaw on boreal plant transpiration over two summers with contrasting weather conditions. We quantified the response of stomatal conductance (gs), from which transpiration was calculated, of deciduous and evergreen plants to soil environmental factors that permafrost thaw affects: soil water content (S), depth of seasonal thaw (D), and soil temperature (T). We found that gs was least sensitive to T compared with S and D at both sites and across both years. At the thawing site, gs was more sensitive to S in a dry year (2009) and to D in a wet year (2010). In the wet year, S of similar to 50cm represented a threshold wherein the sensitivity of gs to T and D switched between positive (S50cm). However, the sensitivities to T and D were negative when S was consistently less than 50cm in the dry year. This is one of the first studies to explore the effect of permafrost thaw on boreal plant gs and transpiration, and our model predicted higher transpiration rates from deciduous plants located on thawing permafrost. Copyright (c) 2013 John Wiley & Sons, Ltd.