Much of the world’s water resource infrastructure was designed for specific regional snowmelt regimes under the assumption of a stable climate. However, as climate continues to change, this infrastructure is experiencing rapid regime shifts that test design limits. These changing snowmelt cycles are responsible for extreme hydrologic events occurring across the Contiguous United States (CONUS), such as river flooding from rain-on-snow, which puts infrastructure and communities at risk. Our study uses a new spatial snow regime classification system to track climate driven changes in snow hydrology across CONUS over 40 years (1981 – 2020). Using cloud-based computing and reanalysis data, regime classes are calculated annually, with changes evaluated across decadal and 30-year normal time scales. The snow regime classification designates areas across CONUS as: (1) rain dominated (RD), (2) snow dominated (SD), (3) transitional (R/S), or (4) perennial snow (PS). Classifications are thresholded using a ratio of maximum snow water equivalent (SWE) over accumulated cool-season precipitation, with a comparison of two approaches for selecting maximum SWE. Results indicate that average snow cover duration generally became shorter in each decade over our evaluation period, with rates of decline increasing at higher elevations. Anomalies in SD spatial extents, compared to the 30-year normal, decreased over the first three decades, while anomalies in RD extents increased. Also, previously SD areas have shifted to R/S, with boundary lines moving up in latitude. As water managers adapt to a changing climate, geospatial classification, such as this snow regime approach, may be a critical tool.

The study aims to enhance the accuracy of the European Centre for Medium-Range Weather Forecasts (ECMWF) and Global Ensemble Forecast System (GEFS) reference evapotranspiration forecast at short to medium range (1-7 days) using the post-processing methods: Analog technique (AN) and Simple Linear Regression (LR) over the Indian subcontinent. The FAO, Penman-Monteith (PM) equation, is used for the estimation of reference evapotranspiration (ET0) reforecasts from meteorological reforecasts from ECMWF and GEFS models. The post-processing technique AN and LR was applied to the ET0 reforecasts and compared against the ET0 estimated using observed and reanalysis dataset. The deterministic evaluation metrics, such as  Root Mean Square Error (RMSE) and Correlation Coefficient (R), were used for the performance assessment of raw ET0 forecast and post-processed ET0 forecasts. Results showed that short to medium range ET0 forecasts improved substantially using AN and LR post-processing methods over the Indian region. Assessment across the different climatic zones in India showed that raw and post-processed ET0 forecasts in the Tropical climate zone are more skillful than in the other climatic zones. A comparison of raw and post-processed ET0 forecasts across different seasons in India showed that model forecasts are more skillful during the winter season compared to the rest. Intercomparison of the models also show that overall the raw and post-processed ET0 forecasts from ECMWF are better than GEFS. Results emphasize the use of post-processing methods to enhance the skill of ET0 forecasts over the Indian subcontinent before their application in irrigation scheduling and water demand estimation purposes.

Seasonally warm summers in the Arctic produce supra-permafrost aquifers within the active layer. However, the magnitude of groundwater flow, the amount of dissolved carbon and nutrients, and the solute flow paths are largely unknown, but critical to quantifying downgradient contributions to surface waters (lakes and rivers). To develop approachable methods to quantify groundwater inputs in continuous permafrost watersheds, we selected Imnavait Creek watershed on the North Slope of Alaska as a representative headwater drainage. We conducted 1000 groundwater flow simulations based on topography of the watershed and varying aquifer hydraulic conductivity and saturated thickness values. We fitted a lognormal distribution to the resulting 1000 model outputs, and we derived n=1e6 possible discharge values based on Monte Carlo random sampling on the model outputs. The groundwater discharge values integrated across the watershed generally agree with observed streamflow in Imnavait Creek over 2 months.  When groundwater discharge estimates were combined with in-situ measurements of groundwater-dissolved organic carbon and nitrogen concentrations, we found that Imnavait Creek’s organic matter load is also dominantly sourced from groundwater. Thus, riverine and lacustrine ecological and biogeochemical processes relate strongly to groundwater phenomena in these continuous permafrost settings. As the Arctic warms and the active layer deepens, it will become more important to understand and predict supra-permafrost aquifer dynamics.

The use of accurate streamflow estimates is widely recognized in the hydrological field. However, due to the model’s structural error, they often yield suboptimal streamflow estimates. Past studies have shown that soil moisture assimilation improves the performance of the hydrological model which often results in enhanced model estimates. Due to this reason, it is widely studied in the hydrological field.  However, the efficiency of the assimilation largely relies on the correct placement of the observation into the model. Ingesting futile observations often results in the degradation of model performance. On the contrary, performing assimilation only at those time steps when the assimilating variable is sensitive to the model output may yield desirable output. Further, it will avoid the assimilation of spurious observations. In this view, this study proposes a new approach where sensitivity-based sequential assimilation is performed on a conceptual Two Parameter Model (TPM). To demonstrate this approach, ASCAT soil moisture observations are assimilated into TPM using Ensemble Kalman Filter (EnKF) sequential approach. At first, the temporal evolution of the soil moisture sensitivity with respect to streamflow is established. Later, at those time steps when the soil moisture is sensitive, EnKF assimilation is performed. For this purpose, a moderately sized catchment in the Krishna basin, India is selected as the study area. Model calibration and validation are performed between 2000 to 2006 and 2007 to 2011 respectively. Model run without assimilation is considered as open-loop simulation. Streamflow simulation after assimilation showed a significant improvement when compared against the open-loop simulation. KGE value increased from 0.70 to 0.79 and PBIAS value reduced from 18.31 to 1.80. The highlighting factor is that only 39% of the total observations were used during the assimilation process. The initial results are encouraging and looks that the proposed approach shall be highly useful at those locations where data availability for assimilation purpose is a serious concern.  

Cosmic-ray neutron sensors (CRNS) have been used in many studies for measuring soil moisture and snow pack over intermediate scales. Corrections for geomagnetic latitude, barometric pressure and atmospheric humidity are well established, however, corrections for the effect of solar activity on neutron count rates have been overly simplistic, typically relying on one neutron monitor station and accounting for latitude and elevation crudely or not at all. Recognizing the lack of a generalised and scientifically robust approach to neutron intensity correction, we developed a new approach for correcting CRNS count rates based on analysis of data from 110 quality-controlled neutron monitor stations from around the world spanning more than seven decades. Count rates from each monitor were plotted against the count rates from Climax, CO, USA or Jungfraujoch, Switzerland depending on the time period covered. Relationships between relative counting rates at the site of interest versus the reference neutron monitors were found to be strongly linear. The dimensionless slope of this linear relation, referred to as τ, was shown to increase with increasing geomagnetic latitude and elevation. This dependence of τ on geomagnetic latitude and elevation was represented using an empirical relationship based on a single reference neutron monitor. This generalised approach enables τ to be derived for any location on Earth and also lends itself to roving CRNS studies. The correction procedure also includes a location-dependent normalisation factor which enables easy substitution of an alternative reference neutron monitor into the correction procedure.

A document by Fernando Aristizabal. Click on the document to view its contents.

The Gravity Recovery and Climate Experiment (GRACE) data help to determine the total water storage anomalies (TWS) across the global scale. The various other important components such as Groundwater storage (GWS) and evapotranspiration for the region of South –East Asia have been determined. With the study of the gravity variation across the globe the long-term changes in the hydrological cycle can be determined which can be related to climate science or the influence of anthropogenic activities. The variation between the Groundwater storage (GWS) and the Total water storage (TWS) of the study area has been calculated for the pre and post-monsoon season of the study area. The variation between groundwater storage and total water storage can be visualized through geospatial analysis. Therefore, the regions with a substantial decrease in water storage can be related to various climate and anthropogenic factors hence implying a sustainable use of groundwater as a resource. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

Machine Learning and Remote sensing method to determine the relationship between Climate and Groundwater Recharge. Adya Aiswarya Dash1, Abhijit Mukherjee1,2,3. 1Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, West Bengal 721302, India 2School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India 3Applied Policy Advisory for Hydrogeoscience (APAH) Group, Indian Institute of Technology Kharagpur, West Bengal 721302, India Abstract Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

Sustainably managing resources in a transboundary freshwater basin is a complex problem, particularly when considering the compounding impacts of climate change, hydropower development, and evolving water governance paradigms. In this study, we used a mixed methods approach to analyze potential impacts of climate change on regional hydrology, the ability of dam operation rules to keep downstream flow within acceptable limits, and the present state of water governance in Laos, Vietnam, and Cambodia. Our results suggest that future river flows in the 3S river system could move closer to natural (i.e., pre-development) conditions during the dry season and experience increased floods during the wet season. This anticipated new flow regime in the 3S region would require a shift in the current dam operations, from maintaining minimum flows to reducing flood hazards. Moreover, our Governance and Stakeholders survey assessment results revealed that existing water governance systems in Laos, Vietnam, and Cambodia are ill-prepared to address such anticipated future water resource management problems. Our results indicate that the solution space for addressing these complex issues in the 3S river basins will be highly constrained unless major deficiencies in transboundary water governance, strategic planning, financial capacity, information sharing, and law enforcement are remedied in the next decade. This work is part of an ongoing research partnership between the National Aeronautical and Space Agency (NASA) and the Conservation International (CI) dedicated to improving natural resources assessment for conservation and sustainable management.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

The combined utilization of spatiotemporal clustering and deep learning neural network models were designed to evaluate the applicability of the multi-year and multi-sites precipitation δ18O forecasting method based on the precipitation isotope data of 10 stations in Germany from 1988 to 2012. In the overall forecasting, the performance of single-site multi-year forecasting is in the order of the Bi-directional Long Short-Term Memory (Bi-LSTM), CNN-Bi-LSTM, and the Convolutional Neural Network (CNN), with CNN-Bi-LSTM being the optimal model for multi-site multi-year forecasts. The seasonal forecasting does not demonstrate a significant improvement compared to the overall forecasting. For forecasting based on spatiotemporal clustering, cluster 1 improved accuracy, and cluster 2 improved error reduction and variance consistency. Nevertheless, the accuracy of forecasts depends solely on the amount of input data when the proportion of forecasting increases to a certain level. Overall, the seasonal forecasting is more appropriate for improving forecasting within a specific season, while spatiotemporal clustering can improve forecasting accuracy to some degree. In addition, optimal solutions exist for the type and number of model clusters. In terms of model types, CNN-Bi-LSTM generally has better forecasting performance than CNN and Bi-LSTM.

Stream confluences are ubiquitous interfaces in freshwater networks and serve as junctions of previously independent landscapes. However, few studies have investigated how confluences influence transport, mixing, and fate of organic matter and inorganic nutrients at the scale of river networks. To understand how network biogeochemical fluxes may be altered by confluences, we conducted two sampling campaigns at five confluences in summer and fall 2021 spanning the extent of a mixed land use stream network. We sampled the confluence mainstem and tributary reaches as well as throughout the mixing zone downstream. We predicted that biologically reactive solutes would mix non-conservatively downstream of confluences and that alterations to downstream biogeochemistry would be driven by differences in chemistry and size of the tributary and upstream reaches. In our study, confluences were geomorphically distinct downstream compared to reaches upstream of the confluence. Dissolved organic matter and nutrients mixed non-conservatively downstream of the five confluences. Biogeochemical patterns downstream of confluences were only partially explained by contributing reach chemistry and drainage area. We found that the relationship between geomorphic variability, water residence time, and microbial respiration differed between reaches upstream and downstream of confluences. The lack of explanatory power from network-scale drivers suggests that non-conservative mixing downstream of confluences may be driven by biogeochemical processes within the confluence mixing zone. The unique geomorphology, non-conservative biogeochemistry, and ubiquity of confluences highlights a need to account for the distinct functional role of confluences in water resource management in freshwater networks.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

Mountain System Recharge (MRS) processes are the natural recharge pathways in arid and semi-arid mountainous regions. However, MSR processes are often poorly understood and characterized in hydrologic models. Mountains are the primary source of water supply to valley aquifers via multiple pathways including lateral groundwater flow from the mountain block (Mountain-block Recharge, MBR) and focused recharge from mountain streams contributing to mountain front recharge (MFR) at the piedmont zone. Here, we present a multi-tool isogeochemical approach to characterize mountain flow paths and MSR processes in the northern Tulare basin, California. We used groundwater chemistry data to delineate hydrochemical facies and explain the chemical evolution of groundwater from the Sierra Nevada to the Central Valley aquifer. Isotope tracers helped to validate MSR processes. Novel application of End-Member Mixing Analysis (EMMA) using conservative chemical components revealed three MSR end-members: (1) evaporated Ca-HCO3 water type associated with MFR, (2) non-evaporated Ca-HCO3 and Na-HCO3 water types with short residence times associated with shallow MBR, and (3) Na-HCO3 groundwater type with long residence time associated with deep MBR. We quantified the contribution of each MSR process to the valley aquifer using mixing ratio calculation (MIX). Our results show that deep MBR is a significant component of recharge representing more than 50% of the valley groundwater. Greater hydraulic connectivity between the Sierra Nevada and Central Valley has significant implications for parameterizing Central Valley groundwater flow models and improving groundwater management. Our framework is useful for understanding MSR processes in other snow-dominated mountain watersheds.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

As all kinds of physics-based and data-driven models are emerging in the fields of hydrologic and hydraulic engineering, Bayesian model averaging (BMA) is one of the popular multi-model methods used to account for the various uncertainty sources in the flood modeling process and generate robust ensemble predictions based on multiple competitive candidate models. The reliability of BMA parameters (weights and variances) determines the accuracy of BMA predictions. However, the uncertainty in the BMA parameters with fixed values, which are usually obtained from the Expectation-Maximization (EM) algorithm, has not been adequately investigated in BMA-related applications over the past few decades. Given the limitations of the commonly used EM algorithm, the Metropolis-Hastings (M-H) algorithm, which is one of the most widely used algorithms in the Markov Chain Monte Carlo (MCMC) method, is proposed to estimate the BMA parameters and quantify their associated uncertainty. Both numerical experiments and the one-dimensional HEC-RAS models are employed to examine the applicability of the M-H algorithm with multiple independent Markov chains. The performances of the EM and M-H algorithms in the BMA analysis are compared based on the daily water stage predictions from 10 model configurations. The results show that the BMA weights estimated from both algorithms are comparable, while the BMA variances obtained from the M-H MCMC algorithm are closer to the given variances in the numerical experiment. Moreover, the normal proposal distribution used in the M-H algorithm can yield narrower distributions for the BMA weights than those from the uniform prior. Overall, the MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its prediction performance is better than the default EM algorithm in terms of multiple evaluation metrics as well as algorithm flexibility.

Evaluation of the performance of hydrologic and hydraulic models is a crucial step in the modeling process. Considering the limitations of single statistical metrics, such as the Nash Sutcliffe efficiency (NSE), the Kling Gupta efficiency (KGE), and the coefficient of determination (R2), which are widely used in the evaluation of model performance, an evaluation framework that incorporates multiple criteria and based on the generalized likelihood uncertainty estimation (GLUE) is proposed to demonstrate the uncertainty in the evaluation criteria and hence to quantify the overall uncertainty of flood models in a comprehensive way. This framework is applied to the one-dimensional HEC-RAS models of six reaches located in States of Indiana and Texas of the United States to quantify the uncertainty associated with the channel roughness and upstream flow input. Specifically, the effects of different prior distributions of the uncertainty sources, multiple high-flow scenarios, and various types of measurement errors (white noise, positive bias, and negative bias) in observations on the evaluation metrics are investigated by using the bootstrapping method and Monte Carlo simulations. The results show that the model performances based on the uniform and normal priors are comparable. The distributions of all the evaluation metrics in the framework are significantly different for the flood model under different high-flow scenarios, and it further indicates that the metrics are essentially random statistical variables. Additionally, the white-noise error in observations has the least impact on the metrics, while the positive and the negative biases would have opposite impacts, which depends on whether the model overestimated or underestimated the hydrologic variable.

Natural hazards such as floods, hurricanes, heatwaves, and wildfires cause significant economic losses (e.g., agricultural and property damage) as well as a high number of fatalities. Natural hazards are often driven by univariate or multivariate hydrometeorological drivers. Therefore, it is crucial to understand how and which hydrometeorological variables (i.e., drivers) combine to contribute to the impacts of these hazards. Additionally, when multiple drivers are associated with a hazard, traditional univariate risk assessment approaches are insufficient to cover the full spectrum of impact-relevant conditions originating from different combinations of multiple drivers. Based on historical socioeconomic loss data, we develop an impact-based approach to assess the influence of different hydrometeorological drivers on the impacts caused by different hazard event types. We use the Spatial Hazard Events and Losses Database for the United States (SHELDUS™) to identify the historical hazard events that caused socioeconomic impacts (property and crop damage, injuries, and fatalities) in our case study area, Miami-Dade County, in south Florida. For 9 different hazard types, we obtained data for 13 hydrometeorological drivers from historical in-situ observations and reanalysis products corresponding to the timing and locations of the hazard events found in the SHELDUS database. The relative importance of each hazard driver in generating impacts and the frequency of multiple drivers was then assessed. We found that many high-impact events were caused by multiple hydrometeorological drivers (i.e., compound events). For example, 61% of the recorded flooding events were compound events rather than univariate hazards and these contributed 99% of total property damage and 98.2% of total crop damage in Miami-Dade County. For several hazards, such as hurricanes/tropical storms and wildfires, all the events that caused damage are classified as compound events in our framework. Our findings emphasize the benefit of including socioeconomic impact information when analyzing hazard events, as well as the importance of analyzing all relevant hydrometeorological drivers to identify compound events.

Weather generators based on resampling simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are coherent with the observed ones. These weather generators are fully data-driven and simple to implement, do not rely on parametric distributions, and can reproduce the dynamics among the weather variables under analysis. However, although the simulated time series is new, the produced weather fields at arbitrary timesteps are copies of the weather fields found in the training dataset. Consequently, the spatial variability of simulations is restricted. Furthermore, these weather generators cannot create weather fields with out-of-sample extreme values because the scope of the resampling method is constrained to the observed values. In this work, we embedd the Direct Sampling algorithm — a data-driven method for producing simulations — into resampling-based weather generators to improve the spatial variability of the produced weather fields, and for generating extreme weather fields. We increase the spatial variability by applying Direct Sampling as a post-processing step on the weather generator outputs. Furthermore, we produce out-of-sample extreme weather fields using Direct Sampling in two ways: 1) applying quantile mappings on the Direct Sampling simulations for a given return period, and 2) using a set of control points jointly with Direct Sampling with values informed by return period analysis. We validate our approach using precipitation, temperature, and cloud cover weather-fields time series datasets, for a region in northwest India. The results are analyzed using a set of statistical and connectivity metrics.

Sustainable river management can be supported by models predicting long-term morphological developments. Even for one-dimensional morphological models, run times can be up to several days for simulations over multiple decades. Alternatively, analytical tools yield metrics that allow estimation of migration celerity and damping of bed waves, which have potential for being used as rapid assessment tools to explore future morphological developments. We evaluate the use of analytical relations based on linear stability analyses of the St. Venant-Exner equations, which apply to bed waves with spatial scales much larger than the water depth. With a one-dimensional numerical morphological model, we assess the validity range of the analytical approach. The comparison shows that the propagation of small bed perturbations is well-described by the analytical approach. For Froude numbers over 0.3, diffusion becomes important and bed perturbation celerities reduce in time. A spatial-mode linear stability analysis predicts an upper limit for the bed perturbation celerity. For longer and higher bed perturbations, the dimensions relative to the water depth and the backwater curve length determine whether the analytical approach yields realistic results. For higher bed wave amplitudes, non-linearity becomes important. For Froude numbers ≤0.3, the celerity of bed waves is increasingly underestimated by the analytical approach. The degree of underestimation is proportional to the ratio of bed wave amplitude to water depth and the Froude number. For Froude numbers exceeding 0.3, the net impact on the celerity depends on the balance between the decrease due to damping and the increase due to non-linear interaction.

Total evaporation from the vast terrain of the Tibetan Plateau (TP) may strongly influence downwind regions. However, the ultimate fate of this moisture remains unclear. This study tracked and quantified TP-originating moisture. The results show that the TP moisture participation in downwind regions’ precipitation is the strongest around the eastern edge of the TP and then weakens gradually toward the east. Consequently, TP moisture in the composition of precipitation over the central-eastern TP is the largest of over 30%. 44.9-46.7% of TP annual evaporation is recycled over the TP, and about 2/3 of the TP evaporation is reprecipitated over terrestrial China. Moisture cycling of TP origin shows strong seasonal variation, with seasonal patterns largely determined by precipitation, evaporation and wind fields. High levels of evaporation and precipitation over the TP in summer maximize local recycling intensity and recycling ratios. Annual precipitation of TP origin increased mainly around the northeastern TP during 2000-2020. This region consumed more than half of the increased TP evaporation. Further analyses showed that changes in reprecipitation of TP origin were consistent with precipitation trends in nearby downwind areas: when intensified TP evaporation meets intensified precipitation, more TP moisture is precipitated out. The model estimated an annual precipitation recycling ratio (PRR) of 26.9-30.8% in forward moisture tracking. However, due to the non-closure issue of the atmospheric moisture balance equation, the annual PRR in backward tracking can be ~6% lower.

Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Hence, understanding and continuously monitoring mass movements is crucial to mitigate their risk. We propose to use Distributed Acoustic Sensing (DAS) to measure strain rate along a fiber-optic cable to characterize ground deformation induced by avalanches. We recorded 12 snow avalanches of various dimensions at the Vallée de la Sionne test site in Switzerland, utilizing existing fiber-optic infrastructure and a DAS interrogation unit during the winter 2020/2021. By training a Bayesian Gaussian Mixture Model, we automatically characterize and classify avalanche-induced ground deformations using physical properties extracted from the frequency-wavenumber and frequency-velocity domain of the DAS recordings. The resulting model can estimate the probability of avalanches in the DAS data and is able to differentiate between the avalanche-generated seismic near-field, the seismo-acoustic far-field and the mass movement propagating on top of the fiber. By analyzing the mass-movement propagation signals, we are able to identify group velocity packages within an avalanche that propagate faster than the phase velocity of the avalanche front, indicating complex internal structures. Importantly, we show that the seismo-acoustic far-field can be detected before the avalanche reaches the fiber-optic array, highlighting DAS as a potential research and early warning tool for hazardous mass movements.

Extraction of critical hydrologic features from high-resolution topographic data is challenging using existing grid coarsening approaches, such as surface flow path, river network, and slope, which limits the application of hydrological models. In this research, the influence of various grid coarsening techniques on the prediction outcomes was measured by a numerical experiment based on the integrated hydrological model ParFlow-Common Land Model (ParFlow.CLM). Three grid coarsening methods (Nearest Neighbor Coarsening, Majority Coarsening, Hydrography-Driven Coarsening) were applied to simulate evapotranspiration(E), soil temperature(ST), streamflow, soil moisture(SM) and latent(LE) heat fluxes in central China’s Sixi Valley, a classic example of a karstic basin. As a result, the three grid coarsening methods perform uniform in simulating latent heat fluxes and soil temperature. However, their ability to predict soil moisture surface flow and evapotranspiration are more diverging. The hydrography-driven coarsening extracts significantly more accurate valleys, rivers network, and slopes closer to the actual terrain than existing coarsening strategies. Slopes derived from hydrography-driven coarsening methods can be used to predict more accurately the top soil moisture, evapotranspiration, and streamflow dynamics processes. This study stresses that a hydrography-driven coarsening strategy is advocated for all those cases in which topographic slope extracted using a coarse-grid digital elevation model is an important influence on the ParFlow.CLM simulation of essential hydrographic features.