Mountainous Watershed Research Articles

Relationships between snowpack, low flows and stream temperature in mountain watersheds of the US west coast

AbstractWater temperatures in mountain streams are likely to rise under future climate change, with negative impacts on ecosystems and water quality. However, it is difficult to predict which streams are most vulnerable due to sparse historical records of mountain stream temperatures as well as complex interactions between snowpack, groundwater, streamflow and water temperature. Minimum flow volumes are a potentially useful proxy for stream temperature, since daily streamflow records are much more common. We confirmed that there is a strong inverse relationship between annual low flows and peak water temperature using observed data from unimpaired streams throughout the montane regions of the United States' west coast. We then used linear models to explore the relationships between snowpack, potential evapotranspiration and other climate‐related variables with annual low flow volumes and peak water temperatures. We also incorporated previous years' flow volumes into these models to account for groundwater carryover from year to year. We found that annual peak snowpack water storage is a strong predictor of summer low flows in the more arid watersheds studied. This relationship is mediated by atmospheric water demand and carryover subsurface water storage from previous years, such that multi‐year droughts with high evapotranspiration lead to especially low flow volumes. We conclude that watershed management to help retain snow and increase baseflows may help counteract some of the streamflow temperature rises expected from a warming climate, especially in arid watersheds.

Soil moisture dynamics and associated rainfall-runoff processes under different land uses and land covers in a humid mountainous watershed

Identifying soil moisture dynamics is critical for understanding watershed hydrological processes. Soil moisture responses to rainfall vary with land use and land covers (LULCs) and thus influence rainfall-runoff processes, however, the knowledge about how different LULCs affect such processes was less revealed, especially in humid areas. In this study, we investigated the characteristics of soil moisture content (SMC) and rainfall-related soil moisture responses under four typical LULCs, i.e., waxberry forest, farmland, sloping farmland, and bamboo forest, based on in-situ observations in a humid mountainous watershed. We then used the HYDRUS-1D model to analyze the patterns of the rainfall-infiltration processes and quantified corresponding runoff generation to reveal the partitioning between infiltration and runoff at the event scale. We found significant differences of the average SMC at multiple depths for four LULCs. The deep soil (80 cm) had the largest SMC for the waxberry forest, farmland, and sloping farmland, while the surface soil (10 cm) was the wettest soil layer for the bamboo forest. During the representative rainstorms, although soil moisture responses exhibited depth gradients, the earlier responses at some deep layers indicated the occurrence of preferential flow. Generally, the cumulative infiltration increased while the infiltration rate decreased with the rainfall grade. Farmland and bamboo forest showed the most and the least infiltration, respectively. These results suggested that both the rainfall properties and LULCs had a strong impact on the soil moisture responses. Meanwhile, the estimated average runoff coefficients of four typical LULCs increased with rainfall grade, and the sloping farmland generated the most runoff during the representative rainfall events. Our findings provide new insights into soil moisture dynamics and infiltration regimes under different LULCs in humid areas, which can contribute to hydrological modeling at the field-scale and regional water resource management.

The Role of Parameterizations and Model Coupling on Simulations of Energy and Water Balances – Investigations With the Atmospheric Model WRF and the Hydrologic Model WRF‐Hydro

AbstractThe distributed hydrologic model WRF‐Hydro can operate in a fully‐coupled mode with the atmospheric Weather, Research and Forecasting (WRF) model. WRF‐Hydro enhances the modeling of terrestrial hydrologic processes in coupled WRF/WRF‐Hydro by simulating lateral surface and subsurface water flows. The objectives of this study are (a) to examine the effect of WRF‐Hydro on the surface energy and water balance in fully‐coupled WRF/WRF‐Hydro simulations and (b) to examine the impact of five WRF physics parameterizations on WRF‐Hydro streamflow. The study area is the Mediterranean island of Cyprus and 31 mountainous watersheds. The domain‐average soil moisture was 20% higher in the coupled WRF/WRF‐Hydro, relative to the standalone WRF model, during a 1‐year simulation. The higher soil moisture could explain the increase in latent heat (36%) and evapotranspiration (33%). The increase in these fluxes was less with a modification in the model transpiration parameterization to represent nocturnal transpiration and the use of remote sensing leaf area index data. The simulated precipitation of the coupled model increased up to 3%, relative to WRF. Two‐year long WRF‐Hydro simulations gave a median Nash‐Sutcliffe Efficiency for daily streamflow of the 31 watersheds of 0.5 for observed precipitation forcing and between −1.9 and 0.2 for the forcing of the five WRF parameterizations. This study showed that the enhancement of the standalone WRF model with lateral water flow processes in the coupled mode with WRF‐Hydro modifies the terrestrial energy and water balance. The improved terrestrial process representation should be considered for future hydrological cycle studies with WRF.

Climate‐Driven Increases in Stream Metal Concentrations in Mineralized Watersheds Throughout the Colorado Rocky Mountains, USA

AbstractIncreasing stream metal concentrations apparently caused by climate warming have been reported for a small number of mountain watersheds containing hydrothermally altered bedrock with abundant sulfide minerals (mineralized watersheds). Such increases are concerning and could negatively impact downstream ecosystem health, water resources, and mine‐site remediation efforts. However, the pervasiveness and typical magnitude of these trends remain uncertain. We aggregated available streamwater chemistry data collected from late summer and fall over the past 40 years for 22 mineralized watersheds throughout the Colorado Rocky Mountains. Temporal trend analysis performed using the Regional Kendall Test indicates significant regional upward trends of ∼2% of the site median per year for sulfate, zinc, and copper concentrations in the 17 streams affected by acid rock drainage (ARD; median pH ≤ 5.5), equivalent to concentrations roughly doubling over the past 30 years. An examination of potential load trends utilizing streamflow data from eight “index gages” located near the sample sites provides strong support for regionally increasing sulfate and metal loads in ARD‐affected streams, particularly at higher elevations. Declining streamflows are likely contributing to regionally increasing concentrations, but increasing loads appear to be on average an equal or greater contributor. Comparison of selected site characteristics with site concentration trend magnitudes shows the highest correlation for mean annual air temperature and mean elevation (R2 of 0.42 and 0.35, respectively, with all others being ≤0.14). Future research on climate‐driven controlling mechanisms should therefore focus on processes such as melting of frozen ground directly linked to site mean temperature and elevation.

Interpreting and modelling the daily extreme sediment events in karst mountain watersheds

Increasingly frequent extreme rainfall as a result of climate change is strongly damaging the global soil and water environment. However, few studies have focused on daily extreme sediment events (DESE) in heterogeneous karst watersheds based on long-term in-situ observations. This study quantitatively assessed the time effect of DESE on rainfall response, decoupled the impact of environmental factors on DESE by using structural equation modelling, and finally explored the modelling scheme of DESE based on the hybrid model. The results showed that DESE had the highest frequency of occurrence in May–July, with dispersed distribution in the value domain. Rainfall with a time lag of 1 day and a time accumulation of 2 or 3 days was an important contribution to DESE (P < 0.01, R = 0.47–0.68). Combined effects of environmental factors explained 53.6 %–64.1 % of the variation in DESE. Runoff and vegetation exerted the strongest direct and indirect effects on DESE, respectively (β = 0.66/−0.727). Vegetation was the dominant driver of DESE in Dabanghe and Yejihe (β = −0.725/−0.758), while the dominant driver in Tongzhihe was climate (β = 0.743). In the future, the risk of extreme sediments should be prevented and resolved through the comprehensive regulation of multiple paths, such as runoff and vegetation. Hybrid models significantly improved the modelling performance of machine learning models. Generalized additive model-Extreme gradient boost had the best performance, while Partial least squares regression-Extreme gradient boost was the most valuable when considering performance and input data cost. Two methods can be used as recommended solutions for DESE modelling. This study provides new and in-depth insights into DESE in karst watersheds and helps the region develop forward-looking soil and water management models to cope with future extreme erosion hazards.

Watershed rehabilitation with forest gardens in Moneragala District, Sri Lanka

Forests in mountainous watersheds sustain water flows, providing a valuable lifeline in times of no rain. The clearance and destruction of forests, for plantations and modern chena cultivation, degrades watersheds, resulting in the loss of these ecosystem services, impoverishing people and diminishing watershed sustainability. The Neo Synthesis Research Centre (NSRC) has been involved in rehabilitating Sri Lankan watersheds for over 30 years, which has included implementing forest gardens (FGs) in Maragalakanda. The article describes the rehabilitation of one farmer's land (referred to as Rani), which resulted in increased household livelihood security, reversed forest loss, and sustained watershed health. The rehabilitation efforts followed a successional process, using regenerative agriculture, analog forestry, and conservation forestry. The landscape design divided Rani’s landholding into several land uses. In addition to the forest gardens which were modelled on the adjacent forest, there were paddy fields, chena and cash crop areas, and a buffer zone between the disturbed natural forest and the landholding. Comprised of 175 different species, the forest gardens provide various regulatory ecosystem services, and over half provide food, medicine, timber, firewood, ornamentals and biopesticides, which are either used for household consumption or sold to generate income. Monitoring and evaluation revealed positive impacts on biodiversity, soil fertility, and food and income security. By 2013, Rani's landholding had become a biodiversity corridor, attracting more birds and increasing her total income to USD 32 241, mainly thanks to her forest gardens. As a result, Rani achieved financial stability, enabling her to invest in and educate all five of her children, eliminating any need to clear forests for livelihood. Despite an average FG profit of USD 24 413, challenges of increased rainfall variability, animal pests, and rising costs of purchases stress her newfound livelihood. Nonetheless, watershed rehabilitation with forest gardens succeeded in reversing forest loss, restoring ecosystem services, and increasing livelihood security. To ensure more households benefit as Rani’s did, policymakers and planners must consider population growth and allocate new lands for expanding watershed populations while implementing laws to prevent forest destruction.

Comparison of Google Earth Engine Machine Learning Algorithms for Mapping Smallholder Irrigated Areas in a Mountainous Watershed, Upper Blue Nile Basin, Ethiopia

Comparison of Google Earth Engine Machine Learning Algorithms for Mapping Smallholder Irrigated Areas in a Mountainous Watershed, Upper Blue Nile Basin, Ethiopia

Sensitivity of GNSS‐Derived Estimates of Terrestrial Water Storage to Assumed Earth Structure

AbstractGeodetic methods can monitor changes in terrestrial water storage (TWS) across large regions in near real‐time. Here, we investigate the effect of assumed Earth structure on TWS estimates derived from Global Navigation Satellite System (GNSS) displacement time series. Through a series of synthetic tests, we systematically explore how the spatial wavelength of water load affects the error of TWS estimates. Large loads (e.g., &gt;1,000 km) are well recovered regardless of the assumed Earth model. For small loads (e.g., &lt;10 km), however, errors can exceed 75% when an incorrect model for the Earth is chosen. As a case study, we consider the sensitivity of seasonal TWS estimates within mountainous watersheds of the western U.S., finding estimates that differ by over 13% for a collection of common global and regional structural models. Errors in the recovered water load generally scale with the total weight of the load; thus, long‐term changes in storage can produce significant uplift (subsidence), enhancing errors. We demonstrate that regions experiencing systematic and large‐scale variations in water storage, such as the Greenland ice sheet, exhibit significant differences in predicted displacement (over 20 mm) depending on the choice of Earth model. Since the discrepancies exceed GNSS observational precision, an appropriate Earth model must be adopted when inverting GNSS observations for mass changes in these regions. Furthermore, regions with large‐scale mass changes that can be quantified using independent data (e.g., altimetry, gravity) present opportunities to use geodetic observations to refine structural properties of seismologically derived models for the Earth's interior structure.

A Tale of Two Catchments: Causality Analysis and Isotope Systematics Reveal Mountainous Watershed Traits That Regulate the Retention and Release of Nitrogen

AbstractMountainous watersheds are characterized by variability in functional traits, including vegetation, topography, geology, and geomorphology, which determine nitrogen (N) retention, and release. Coal Creek and East River are two contrasting catchments within the Upper Colorado River Basin that differ markedly in total nitrate (NO3−) export. The East River has a diverse vegetation cover, and sinuous floodplains, and is underlain by N‐rich marine shale. At 0.21 ± 0.14 kg ha−1 yr−1, the East River exports ∼3.5 times more NO3− relative to the conifer‐dominated Coal Creek (0.06 ± 0.02 kg ha−1 yr−1). While this can partly be explained by the larger size of the East River, the distinct watershed traits of these two catchments imply different mechanisms controlling the aggregate N‐export signal. A causality analysis shows physical and biogenic processes were critical in determining NO3− export from the East River catchment. Stable isotope ratios of NO3− (δ15NNO3 and δ18ONO3) show the East River catchment is a strong hotspot for biogeochemical processing of NO3− at the hillslope soil‐saprolite. By contrast, the conifer‐dominated Coal Creek retained nearly all atmospherically deposited NO3−, and its export was controlled by catchment hydrological traits (i.e., snowmelt periods and water table depth). The conservative N‐cycle within Coal Creek is likely due to the abundance of conifer trees, and smaller riparian regions, retaining more NO3− overall and reduced processing prior to export. This study highlights the value of integrating isotope systematics to link watershed functional traits to mechanisms of watershed element retention and release.

Elastic deformation as a tool to investigate watershed storage connectivity

Storage-discharge relationships and dynamic changes in storage connectivity remain key unknowns in understanding and predicting watershed behavior. In this study, we use Global Positioning System measurements of load-induced Earth surface displacement as a proxy for total water storage change in four climatologically diverse mountain watersheds in the western United States. Comparing total water storage estimates with stream-connected storage derived from hydrograph analysis, we find that each of the investigated watersheds exhibits a characteristic seasonal pattern of connection and disconnection between total and stream-connected storage. We investigate how the degree and timing of watershed-scale connectivity is related to the timing of precipitation and seasonal changes in dominant hydrologic processes. Our results show that elastic deformation of the Earth due to water loading is a powerful new tool for elucidating dynamic storage connectivity and watershed discharge response across scales in space and time.

Determining sediment deposition dynamics influenced by check dams in a semi‐arid mountainous watershed

AbstractSemi‐arid environments are characterized by infrequent large magnitude rainfalls that produce flash flood events with high sediment concentration. Control structures such as check dams are widely used in this environment for mitigation. However, their impact on the overall sediment balance of watersheds, particularly those severely affected by anthropogenic activity, is sparsely documented. This study used topographic measurements, sediment analysis, and fallout isotope techniques to assess the effectiveness and service life of 18 rock and masonry check dams that were constructed in the 1930s for controlling sediment fluxes in an 11 ha mountainous watershed in southern Arizona. All of the dams are currently filled with sediment resulting in reduction of local channel gradients by 35 to 71% to between 0.04 and 0.28 depending on location within the reach. Sedimentation occurred over multiple decades at a relatively slow average rate of 0.59 t ha−1 y−1 indicating low instantaneous retention efficiency. The smaller headwater dams were filled soon after construction; however, their share of the overall storage capacity was minor. Although evaluation of 137Cs was an effective method for dating sediment, the 210Pb dating method was not satisfactory because of sediment sorting effects and complex deposition patterns. The abundance of similar control structures in the region points to the opportunity to better understand the process impacts of check dams over multiple decades to inform planning and design of their use in future mitigation projects.

X-ray chemical imaging for assessing redox microsites within soils and sediments

Redox reactions underlie several biogeochemical processes and are typically spatiotemporally heterogeneous in soils and sediments. However, redox heterogeneity has yet to be incorporated into mainstream conceptualizations and modeling of soil biogeochemistry. Anoxic microsites, a defining feature of soil redox heterogeneity, are non-majority oxygen depleted zones in otherwise oxic environments. Neglecting to account for anoxic microsites can generate major uncertainties in quantitative assessments of greenhouse gas emissions, C sequestration, as well as nutrient and contaminant cycling at the ecosystem to global scales. However, only a few studies have observed/characterized anoxic microsites in undisturbed soils, primarily, because soil is opaque and microsites require µm-cm scale resolution over cm-m scales. Consequently, our current understanding of microsite characteristics does not support model parameterization. To resolve this knowledge gap, we demonstrate through this proof-of-concept study that X-ray fluorescence (XRF) 2D mapping can reliably detect, quantify, and provide basic redox characterization of anoxic microsites using solid phase “forensic” evidence. First, we tested and developed a systematic data processing approach to eliminate false positive redox microsites, i.e., artefacts, detected from synchrotron-based multiple-energy XRF 2D mapping of Fe (as a proxy of redox-sensitive elements) in Fe-“rich” sediment cores with artificially injected microsites. Then, spatial distribution of FeII and FeIII species from full, natural soil core slices (over cm-m lengths/widths) were mapped at 1–100 µm resolution. These investigations revealed direct evidence of anoxic microsites in predominantly oxic soils such as from an oak savanna and toeslope soil of a mountainous watershed, where anaerobicity would typically not be expected. We also revealed preferential spatial distribution of redox microsites inside aggregates from oak savanna soils. We anticipate that this approach will advance our understanding of soil biogeochemistry and help resolve “anomalous” occurrences of reduced products in nominally oxic soils.

Soil temperature and moisture as key controls of phosphorus export in mountain watersheds

Oligotrophic mountain lakes act as sensitive indicators of landscape-scale changes in mountain regions due to their low nutrient concentration and remote, relatively undisturbed watersheds. Recent research shows that phosphorus (P) concentrations are increasing in mountain lakes around the world, creating more mesotrophic states and altering lake ecosystem structure and function. The relative importance of atmospheric deposition and climate-driven changes to local biogeochemistry in driving these shifts is not well established. In this study, we test whether increasing temperatures in watershed soils may be contributing to the observed increases in mountain lake P loading. Specifically, we test whether higher soil temperatures increase P mobilization from mountain soils by accelerating the rate of geochemical weathering and soil organic matter decomposition. We used paired soil incubation (lab) and soil transplant (field) experiments with mountain soils from around the western United States to test the effects of warming on rain-leachable P concentration, soil P mobilization, and soil respiration. Our results show that while higher temperature can increase soil P mobilization, low soil moisture can limit the effects of warming in some situations. Soils with lower bulk densities, higher pH, lower aluminum oxide contents, and lower ratios of carbon to nitrogen had much higher rain-leachable P concentration across all sites and experimental treatments. Together, these results suggest that mountain watersheds with high-P soils and relatively high soil moisture could have the largest increases in P mobilization with warming. Consequently, lakes and streams in such watersheds could become especially susceptible to soil-driven eutrophication as temperatures rise.

Linking soil erosion and sediment yield with landscape heterogeneity: Exploration using the lower Jinsha River Basin, China

Landscape heterogeneity, including compositional and spatial configuration, changes soil erosion and sediment yield (SESY) in watersheds by regulating hydrological processes. However, existing research has paid little attention to landscape composition. The Lower Jinsha River Basin (LJRB) has significant landscape heterogeneity and the highest sediment yields in the Yangtze River Basin, China. The spatially distributed soil erosion model WaTEM/SEDEM was used to evaluate the spatio-temporal changes in SESY in the LJRB from 1990 to 2020. Area information conservation method and semi-variogram were utilized to determine the optimal grain and extent size of landscape analysis. The landscape heterogeneity characteristics of the LJRB were analyzed for landscape composition and spatial configuration. Finally, statistical analysis was performed to determine the differences in SESY among different landscape composition types (LCTs) and to explore the relationship between landscape configuration and SESY. The optimal grain and extent size for analysis of the landscape pattern in the LJRB were 150 and 2100 m, respectively. Based on this, three types and ten subtypes of landscape composition were identified. Landscapes composed of forest, shrubs, grassland, and farmland were predominant in the study area, with a relatively even distribution of patches and minimum dominance of specific patch types. Additionally, the landscapes composed of forest, shrubs, grassland, farmland, and impervious surfaces were diverse, with the highest fragmentation. Landscapes comprising farmland and impervious surfaces occupied a minor proportion of the study area, but specific patches exhibited higher landscape connectivity. The magnitude of SESY in the LJRB gradually decreased during 1990–2020. Farmland and grassland were the land types with the highest magntidue of SESY in this region. Substantial sediment has been deposited in Xiangjiaba and Xiluodu reservoirs since they commenced operations. Increased proportions of farmland patches in some LCTs have significantly enhanced SESY. For LCTs dominated by farmland and impervious surfaces, reducing the dominance of the largest patch, decreasing patch aggregation, and increasing landscape-type diversity and evenness have helped mitigate SESY. The opposite is true for LCTs dominated by forests, shrubs, and grasslands. Collectively, the results of this study provide valuable progress in the capacity to support landscape restoration and comprehensive management of soil and water degradation in mountainous watersheds with similar characteristics to the LJRB.

Effects of forest damage on rainfall thresholds to initiate bedload transport in mountain watersheds, Republic of Korea

Effects of forest damage on rainfall thresholds to initiate bedload transport in mountain watersheds, Republic of Korea

Simulation and Evaluation of Runoff in Tributary of Weihe River Basin in Western China

Model simulation plays a significant role in the water resources cycle, and the simulation accuracy of models is the key to predicting regional water resources. In this research, the Qianhe tributary at the Weihe River basin in Western China was selected as the study area. The tributary was divided into 29 sub-basins and 308 hydrological response units according to the spatial raster data and attribute data of the hydrology, meteorology, topography, land use, and soil types. On this basis, a soil and water assessment tool (SWAT) model for runoff simulation and evaluation of this region was established. A sensitivity test and parameter calibration were then executed on 15 parameters involved with surface runoff, soil flow, and shallow underground runoff. The simulation results demonstrate a calibration and verification error of 3.06–10.08%, with very small uncertainties throughout the simulation, whereas they exhibit relatively large errors in the simulation of the dry period (winter) but, in contrast, quite small errors in the rainy period (summer). In addition, the simulated runoff with a low value is overestimated. When the annual, monthly, and daily runoff are 4–13.5 m3/s, 4–69.8 m3/s, and 40–189.3 m3/s, respectively, the relative error is smaller, and the simulation results are more accurate. The sensitive parameters predominantly affecting the runoff simulation of the basin include soil evaporation compensation, runoff curve coefficient, vegetation transpiration compensation, and saturated hydraulic conductivity in this region. In the case of hypothetical land use change scenarios, we observe a great reduction in simulated runoff in arable land, woodland, and grassland, while we observe an increment in construction and residential land and wasteland. The annual and monthly runoff are increased by above 54.5%. With the increase in cultivated land and forestland, the annual and monthly runoff decrease by 24.6% and 6.8%, respectively. In the case of hypothetical scenarios under 24 climate combinations, if the precipitation remains unchanged, the increase and decrease in temperature by 1 °C leads to a decline and increment of runoff by −0.72% and 5.91%, respectively. With regard to the simulation for the future under the RCP2.6 and RCP8.5 climate scenarios, downscaling was employed to predict the runoff trend of the future. In short, this study provides a method for runoff inversion and water resources prediction in small mountainous watersheds lacking hydrological and meteorological observation stations.

Rating curve development and uncertainty analysis in mountainous watersheds for informed hydrology and resource management

Accurate measurement of continuous stream discharge poses both excitement and challenges for hydrologists and water resource planners, particularly in mountainous watersheds. This study centers on the development of rating curves utilizing the power law at three headwaters of the lesser Himalayas—Aglar, Paligaad, and Balganga—through the installation of water level recorders for stage measurement and salt dilution for discharge measurement from 2014 to 2016. The stream stage–discharge relationship, crucially known as the rating curve, is susceptible to numerous factors in mountainous watersheds that are often challenging to comprehend or quantify. Despite significant errors introduced during the rating curve development, such as stemming from observations, modeling, and parameterization, they are frequently overlooked. In this study, acknowledging the inherent uncertainty, we employ the maximum-likelihood method to assess uncertainty in the developed rating curve. Our findings reveal substantial inconsistency in the stage–discharge relationship, particularly during high flows. A novel contribution of this study is introducing a weighing factor concept that correlates uncertainty with the morphological parameters of the watershed. The higher value of the weighting factor in Paligaad (0.37) as compared to Balganga (0.35) and less in the case of Aglar (0.27) will have more uncertainty. The authors contend that precise rating curves and comprehensive uncertainty analyses can mitigate construction costs, foster robust decision-making, and enhance the perceived credibility of decisions in hydrology and water resource management.

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

Assessment of sediment transport in Luxiapuqu watershed using RUSLE-TLSD and InSAR techniques: Yarlung Tsangpo River, China.

The Yarlung Tsangpo River Basin is characterized by its intricate topography and a significant presence of erosive materials. These often coincide with heavy localized precipitation, resulting in pronounced hydraulic erosion and geological hazards in mountainous regions. To tackle this challenge, we integrated the RUSLE-TLSD (Revised Universal Soil Loss Equation-Transportation-limited sediment delivery) model with InSAR (Interferometric Synthetic Aperture Radar) data, aiming to explore the sediment transport process and pinpoint hazard-prone sites within mountainous small watershed. The RUSLE-TLSD model aids in evaluating multi-year sediment transport dynamics in mountainous zones. And, the InSAR data precisely delineates changes in sediment scouring and siltation at sites vulnerable to hazards. Our research estimates that the potential average soil erosion within the watershed stands at 52.33 t/(hm2 a), with a net soil erosion of 0.69 t/(hm2 a), the sediment transport pathways manifest within the watershed's gullies and channels. Around 4.32% of the watershed area undergoes sedimentation, predominantly at the base of slopes and within channels. Notably, areas (d) and (e) emerge as the most susceptible to disasters within the watershed. Further analysis of the InSAR data highlighted four regions in the typical area (e) from 2017 that are either sedimentation- or erosion-prone, referred to as "hotspots." Among them, R1 exhibits a strong interplay between water and sediment, rendering it highly sensitive to environmental factors. In contrast, R4, characterized by a sharp bend in siltation, remains relatively impervious to external elements. The NDVI (normalized difference vegetation index) stands out as the pivotal determinant influencing sediment transport within the watershed, exerting a pronounced impact on the outlet section, especially in spring. By employing this approach, we gained a deeper understanding of sediment transport mechanisms and potential hazards in small watershed in uninformative mountainous areas. This study furnishes a robust scientific framework beneficial for erosion mitigation and disaster surveillance in mountainous watersheds.

Performance evaluation of various techniques in estimating precipitation record of a sparsely gauged mountainous watershed.

Comprehensive precipitation data is essential for hydrological, agricultural, and climatological studies. Yet, gaps and sparse rain gauge distribution pose challenges, requiring imputation algorithms to fill data gaps. The aim of this research is to evaluate the performance of several approaches for estimating incomplete precipitation data in the Upper Indus Basin (UIB). Eight various imputation approaches were used on sparsely gauged mountainous UIB on a monthly time series of twenty-four meteorological observatories. Following that, the estimation approaches were evaluated using a rank-based approach comprising four different statistical indicators. The results indicate that multiple linear regression is the best-performing strategy for most of the stations regardless of season or orography, followed by the arithmetic average method and inverse distance weighing method.