Hydrometeorological Processes Research Articles

Mismanaged plastic waste as a predictor for river plastic pollution

Hydrometeorological processes are often assumed to be key drivers of plastic transport. However, the predominant focus on these factors overlooks the impact of anthropogenic factors, such as mismanaged plastic waste (MPW) on plastic transport variability. Here, we investigate the roles of both anthropogenic and hydrometeorological factors on plastic pollution in the Odaw catchment, Ghana. Data on macroplastic transport and density were collected at ten locations between December 2021 and December 2022. We tested for differences between the wet and dry seasons and applied a multiple regression analysis to examine the separate and combined impact of hydrometeorological variables (rainfall, discharge, and windspeed) on macroplastic transport. Additionally, we analyzed the spatial correlation in macroplastic transport/density with MPW and population density. Data collection involved visual counting of floating macroplastics at 10 river locations and counting litter at 9 riverbanks and land locations. Rainfall data was sourced from TAHMO (Trans-African Hydrometeorological Observatory), discharge was measured during field campaigns, and windspeed data sourced from a global climate data provider. We used globally modelled MPW estimates to represent anthropogenic factors. Contrary to previous studies, we found no seasonal differences in macroplastic pollution and only weak correlations were observed between the hydrometeorological variables and macroplastic transport. However, a strong correlation was observed between MPW and macroplastic pollution. We hypothesize that, the influence of hydrometeorological factors on macroplastic transport depend on the relative impact of anthropogenic factors. Our research highlights the limited role of hydrometeorology, showing the significant role of mismanaged plastic waste to field monitored macroplastic pollution variability in the catchment. This insight is essential for future research as it highlights the importance of holistically investigating both anthropogenic and hydrometeorological factors in explaining plastic transport and retention dynamics. This insight is essential for developing interventions that effectively address plastic pollution in catchments.

The influence of hillslope topography on beech water use: a comparative study in two different climates

Abstract. Understanding the interrelation between topography and vegetation across different environments is important to assess how hydrological and climatic conditions affect tree physiological activity. This becomes especially important given the expected reduction in water availability and the increase in water demand driven by climate change. These extremes could enhance the thermal and hydrologic gradients along slopes. Here, we aimed to test if and how different climatic and hydrological conditions affect the physiological response of beech trees (Fagus sylvatica L.) to environmental variables along two different topographic sequences. For this purpose, we set up a comparative study on a gentle hillslope in the Weierbach catchment in Luxembourg (oceanic climate) and on a steep hillslope in the Lecciona catchment in Italy (Mediterranean climate). We combined sap velocity measurements with isotopic measurements of soil, precipitation, stream water, groundwater, and xylem over 2019 and 2020 for the Luxembourgish site and over 2021 for the Italian site. We found that, in the Weierbach catchment, trees' responses to environmental variables (i.e. vapour pressure deficit and relative extractable water in the soil) were similar among hillslope positions and between the two monitored years, resulting from homogeneous growing conditions along the topographic sequence. We also did not find any statistical difference in the isotopic composition of xylem water between positions, suggesting that beech trees relied on similar water sources across the landscape. In the Lecciona catchment, we observed lower sap velocities and shorter growing season in trees growing in the upper portions of the hillslope, likely related to water redistribution and different soil moisture along the hillslope catena. Xylem isotopic composition was significantly lighter at the footslope location throughout the growing season than in the upslope locations, suggesting location-specific water use. These results emphasize how differing hydrometeorological processes occurring at the hillslope scale can lead to contrasting tree responses.

Modeling multi-source plastic pollution yield and transport driven by catchment hydrometeorological processes

Plastic pollution has emerged as a global environmental concern, impacting both terrestrial and marine ecosystems. However, understanding of plastic sources and transport mechanism at the catchment scale remains limited. This study introduces a multi-source plastic yield and transport model, which integrates catchment economic activities, climate data, and hydrological processes. Model parameters were calibrated using a combination of field observations, existing literature, and statistical random sampling techniques. The model demonstrated robust performance in simulating both plastic yield and transport from 2010 to 2020 in the upper and middle Mulan River Catchment, located in southeast China. The annual average yield coefficients were found to closely align with existing estimations, and the riverine outflow exhibited a high correlation coefficient of 0.97, with biases ranging from -63.0 % to -21.4 % across all monitoring stations. The analysis reveals that, on average, 12.5 ± 2.5 % of the total plastic yield is transported to rivers annually, with solid waste identified as the primary source, accounting for 37.8 ± 20.7 % of the total load to rivers, followed by agricultural film (26.4 ± 9.8 %), impermeable surfaces (21.5 ± 10.3 %), urban and rural sewage (10.4 ± 5.0 % and 3.0 ± 1.5 %, respectively), and industrial wastewater (0.9 ± 0.7 %). The annual average outflow was estimated to between 9.3 and 43.0 ton/year (median: 23.1) at a 95 % confidence level. This study not only provides insights into the primary sources and transport pathways of plastic pollution at the catchment scale, but also offers a valuable tool for informing effective plastic pollution mitigation strategies.

Hydrometeorological response to afforestation in the UK: findings from a kilometer-scale climate model

Afforestation is of international interest for its positive benefits on carbon storage, ecology, and society, but its impacts on terrestrial and atmospheric processes are still poorly understood. This study presents the first use of a coupled land surface and convection permitting atmospheric model (CPM) to quantify hydrometeorological effects of afforestation across the United Kingdom, focusing on atmospheric processes often missing in hydrological models. Generating a scenario of 93 000 km2 (40%) additional woodland across the UK, the periods of 2042–2052 and 2062–2072 are analysed. Simulated afforestation alters seasonal and regional UK hydrometeorology. Countrywide runoff increases in all seasons (between 5.4–11 mm and 4.3–8.6% per season) due to elevated subsurface flows from greater soil moisture. Evaporation decreases in summer (−20.6 mm, −10%) but increases in winter (8.1 mm, 15%) whereas rainfall increases throughout all seasons (between 2.2–6.86 mm and 0.9%–2.2% per season). Greater winter rainfall is detected along Great Britain’s west coastline as increased surface roughness produces prolonged and heavier rainfall. In the summer, lower albedo increases potential evapotranspiration and reduces near surface specific humidity: water is locked in deeper soil layers as transpiration diminishes and the topsoil dries out. However, the magnitude of hydrometeorological change due to altered land cover is smaller than the uncertainty in local climate change projections. This work sets a precedent in illustrating the impacts of afforestation on hydrology using a high-resolution CPM and highlights the importance of coupled hydrometeorological processes when investigating land cover impacts on hydrological processes.

Assessing the relative importance of dry-season incoming solar radiation and water storage dynamics during the 2005, 2010 and 2015 southern Amazon droughts: not all droughts are created equal

Three severe droughts impacted the Amazon in 2005, 2010, and 2015, leading to widespread above-average land surface temperature (LST) (i.e. positive thermal anomalies) over the southern Amazon in the dry season (Aug–Sep) of these years. Below-average dry-season incoming solar radiation (SW↓) and terrestrial water storage anomaly (TWSA) were simultaneously observed in 2005 and 2010, whereas the opposite was observed in 2015. We found that anomalies in precipitation (P), SW↓, and TWSA combined can well explain dry-season thermal anomalies during these droughts (average R2–0.51). We investigated the causes for opposing anomalies in dry-season SW↓ and TWSA, and found different hydro-climatological conditions preceding the drought-year dry seasons. In 2005 and 2010, P was considerably below average during the wet-to-dry transition season (May–July), causing below-average TWSA in the dry season that was favorable for fires. Increased atmospheric aerosols resulting from fires reduced solar radiation reaching the ground. In 2015, although below-average dry-season P was observed, it was above the average during the wet-to-dry transition season, leading to reduced fires and aerosols, and increased dry-season SW↓. To further examine the impact of opposite hydro-climatological processes on the drought severity, we compared dry-season LST during droughts with the maximum LST during non-drought years (i.e. LSTmax) for all grid cells, and a similar analysis was conducted for TWSA with the minimum TWSA (i.e. TWSAmin). Accordingly, the regions that suffered from concurrent thermal and water stress (i.e. LST > LSTmax and TWSA < TWSAmin) were identified. These regions are mainly observed over the southeast in 2005 and southern Amazon in 2010. In 2015, large-scale dry-season thermal stress was found over central and southeast Amazon with little water stress. This study underlines the complex interactions of different hydro-climatological components and the importance of understanding the evolution of droughts to better predict their possible impacts on the Amazon rainforest.

Development of a nonstationary Standardized Precipitation Evapotranspiration Index (NSPEI) and its application across China

Drought is a natural disaster and its occurrence and development can be attributed to a range of driving factors, such as deficient precipitation, wind, temperature, land use/land cover changes, and so on. Therefore, attribution and monitoring of droughts are complicated and challenging. Standardized Precipitation Evapotranspiration Index (SPEI) has been widely used in meteorological drought monitoring. Nonstationarity has not been considered in SPEI-based drought monitoring. Here we propose a modified SPEI considering the nonstationarity of hydrometeorological processes, i.e. NSPEI hereafter. We observe nonstationarity SPEI in the Northeast, the Huang-Huai-Hai Plain, the Yangtze River Delta, and the Qinghai-Tibet Plateau. The goodness-of-fit can be determined for 76% of the total stations. Under the assumption of nonstationarity, the drought monitoring performance of the NSPEI is more robust than the traditional SPEI. Besides, we evaluate drought conditions across China under four shared socioeconomic pathways (SSPs), i.e. SSP126, SSP245, SSP370 and SSP585. We identify intensifying droughts in southwest China but a wetting tendency in northern China in winter under the SSP126 scenario; but a significant wetting tendency in southwest China and a drying tendency in northeast China under SSP245, SSP370 and SSP585 scenarios. This study provides a novel meteorological drought index and highlights a fresh picture of drought conditions across China under different SSPs.

Factors Influencing the Variation in Phytoplankton Functional Groups in Fuchunjiang Reservoir

To explore the distribution characteristics, blooming risk mechanism and driving factors of phytoplankton community structure in Fuchunjiang Reservoir. The variation characteristics of phytoplankton, zooplankton and physicochemical indicators in Fuchunjiang Reservoir and its upper and lower reaches were investigated in 2020 and 2021. Based on the phytoplankton functional groups, non-metric multidimensional scale analysis, redundancy analysis and other statistical methods, the seasonal succession characteristics and driving factors of phytoplankton functional groups were analyzed. A total of 18 phytoplankton functional groups were identified, in of which 10 were predominant. The composition of phytoplankton functional groups in the Fuchunjiang Reservoir was significant different. Spatially, the upstream were dominated by group C and P while the represent species were Cyclotella and Aulacoseira,reflecting the mixed meso-eutrophic environments. However, group P was the main group in Fuchunjiang reservoir, and the dominance decreased gradually along the stream direction. Meanwhile, in the downstream, MP has an absolute advantage at Qiantang River estuary. It reflected the environmental characteristics of frequent disturbance and high turbidity of tide-sensing rivers. In addition, the predominant functional groups demonstrated strong seasonal variations. The dominant functional groups were diverse in summer and consisted of P+L0+J+M+S1+H1+MP. In addition to group P (Aulacoseira), which was dominant throughout the year, it also included several groups represented by cyanobacteria and chlorophyta, reflecting the environmental characteristics of changeable habitats and vigorous productivity. In autumn, the succession was dominated by H1 group represented by Dolichospermum and the representative function groups were P and H1, reflecting the hydrological background of reduced flow and static flow. In winter, the increase of Cyclotella led to the predominance of group C, which was dominated by P+C, reflecting the changing conditions of weakened water exchange and intensified eutrophication problems. In spring, the dominant functional groups were gradually enriched and were composed of C, D, P, and MP, which also reflected the changing environmental habitat characteristics which caused by increasing rainfall and air temperature. According to the results of the C-R-S growth strategy, the Fuchunjiang Reservoir has been in the R strategy for a long time, which was consistent with the habitat characteristics of Fuchunjiang Reservoir and its upper and lower reaches with high disturbance and low stress. In addition, C strategy and S strategy appeared in some reaches, reflecting the variability of water quality and hydrology. RDA analysis showed that water temperature, discharge, zooplankton biomass, permanganate index, total nitrogen and total phosphorus were significantly correlated with the seasonal succession of phytoplankton functional groups (P &lt; 0.05), and temperature and flow pattern were probably the most critical factors for the succession. Studies have shown that the impact of hydrometeorological processes on phytoplankton in the Fuchunjiang Reservoir is crucial:high temperature and changing discharge during the summer may lead to cyanobacterial blooms in the Fuchunjiang reservoir; To reduce the risk of algal blooms, it is still necessary to increase the control of nitrogen and phosphorus load in rivers, and fully consider the coordination of water conservancy dispatch methods.

Spatiotemporal scales of precipitation in the Central Tibetan Plateau identified by in-situ soil moisture observations

The spatiotemporal scale of precipitation is crucial for understanding the local hydrometeorological processes. It also plays a significant role in determining the layout of rain gauges. Convectional rainfalls are typical in the Tibetan Plateau (TP), but the spatiotemporal scales of precipitation events in the TP, particularly at scales less than 10 km, remain unclear, which is mainly due to limited in-situ precipitation observations and the relative coarse spatial resolution of satellite precipitation products. Building upon the strong response of soil moisture changes to precipitation, this study attempts to reveal the spatiotemporal scales of precipitation in the central TP during summertime (June–September) by investigating the inter-station correlation of accumulated positive soil moisture increments obtained from in-situ soil moisture measurements in a mesoscale region of the central TP. The soil moisture-implied spatiotemporal scales are further verified against precipitation from the Integrated Multi-satellitE Retrievals for GPM (IMERG) at spatial scales larger than 10 km. Results show that: 1) the variations of surface soil moisture increase and precipitation are fairly consistent, making positive increments in surface soil moisture an effective surrogate for reflecting the spatiotemporal scales of precipitation in central TP; 2) the spatial extents of precipitation as indicated by the precipitation-induced positive soil moisture increments are positively correlated with the temporal scales; 3) precipitation events with relatively short durations (0–6 h) and spatial extents of less than 16 km (and in particular, 0–3 h for scales less than 10 km) dominate the central TP. This study provides a novel approach for characterizing precipitation scales in regions with scarce in-situ precipitation observations, and is expected to inspire new way of assessing large scale soil moisture and precipitation products.

Quantifying the relative contributions of different flood generating mechanisms to floods across CONUS

The occurrence of flooding in a catchment is attributable to various mechanisms. Several researchers have used linear models to quantify the relative contributions of different flood generating mechanisms. However, the use of linear models for this quantification may not be suitable given the complex and nonlinear processes from climate to floods. In this study, we used nonlinear machine learning models with interpretability methods to quantify the contributions of multiple flood generating mechanisms to floods across the continental United States (CONUS). Four flood generating mechanisms were considered comprehensively, which varied in terms of the influence of antecedent soil moisture condition or snowmelt on flood generation. These mechanisms involved three common mechanisms (rainfall, rainfall excess, and snowmelt/rain on snow) and one complex mechanism (effective precipitation) that accounts for snowmelt/rain on snow lost to unsaturated soil zone. Consistent with previous studies, we observed that rainfall alone (i.e., without considering other hydrometeorological processes) is seldom the dominant mechanism for flood generation. Floods in most catchments are largely attributable to rainfall excess and snowmelt. Unlike previous studies, we noted that in the midwest and northeastern CONUS, snowmelt/rain on snow upon saturated soil was the greatest contributor to local flood generation. This finding highlights that both snowmelt and soil moisture condition affect flood generation in the midwest and northeastern CONUS, instead of only snowmelt as has been reported previously. Overall, this study provides new insights into flood generating mechanisms in CONUS and demonstrates the potential of applying interpretable machine learning to examine flood generating mechanisms.

საქართველოს ცალკეულ რეგიონებისათვის, ლოკალური ოროგრაფიის როლის შეფასება, ატმოსფეროს მიწისპირა ფენის ჰაერის ტურბულენტური ნაკადის დინამიკაში

There are many micro-regions on Earth in which the study of evolution hydrometeorological processes is relevant and has great practical value. Such regions include various caverns, highways, open mining areas, Hydropower plant construction areas. To study local events in such areas in order to set the assignment special theoretical and model approach is given in the paper. The mathematical justification of this approach is presented also case studies and calculation procedures are given. The materials presented in the article will explain, substantiate and give practical value to the physical and climatic features that characterize the local area and are useful for ​​interest persons involved

Flood Simulations Using a Sensor Network and Support Vector Machine Model

This study aims to couple the support vector machine (SVM) model with a hydrometeorological wireless sensor network to simulate different types of flood events in a montane basin. The model was tested in the mid-latitude montane basin of Vydra in the Šumava Mountains, Central Europe, featuring complex physiography, high dynamics of hydrometeorological processes, and the occurrence of different types of floods. The basin is equipped with a sensor network operating in headwaters along with the conventional long-term monitoring in the outlet. The model was trained and validated using hydrological observations from 2011 to 2021, and performance was assessed using metrics such as R2, NSE, KGE, and RMSE. The model was run using both hourly and daily timesteps to evaluate the effect of timestep aggregation. Model setup and deployment utilized the KNIME software platform, LibSVM library, and Python packages. Sensitivity analysis was performed to determine the optimal configuration of the SVR model parameters (C, N, and E). Among 125 simulation variants, an optimal parameter configuration was identified that resulted in improved model performance and better fit for peak flows. The sensitivity analysis demonstrated the robustness of the SVR model, as different parameter variations yielded reasonable performances, with NSE values ranging from 0.791 to 0.873 for a complex hydrological year. Simulation results for different flood scenarios showed the reliability of the model in reconstructing different types of floods. The model accurately captured trend fitting, event timing, peaks, and flood volumes without significant errors. Performance was generally higher using a daily timestep, with mean metric values R2 = 0.963 and NSE = 0.880, compared to mean R2 = 0.913 and NSE = 0.820 using an hourly timestep, for all 12 flood scenarios. The very good performance even for complex flood events such as rain-on-snow floods combined with the fast computation makes this a promising approach for applications.

Distribution, driving forces, and risk assessment of 2-MIB and its producer in a drinking water source-oriented shallow lake.

Freshwater blooms of harmful cyanobacteria in drinking water source-oriented shallow lakes affect public health and ecosystem services worldwide. Therefore, identifying 2-methylisoborneol (2-MIB)-producing cyanobacteria and predicting the risks of 2-MIB are critical for managing 2-MIB-infected water sources. Previous studies on the potential producers and risks of 2-MIB have focused on reservoirs or have been limited by the ecosystems of phytoplankton-dominated areas. We investigated the producers, distribution, and occurrence of 2-MIB in East Taihu Lake-a drinking water source-oriented shallow lake with macrophyte- and phytoplankton-dominated areas-from August 2020 to November 2021. We observed that Pseudanabaena sp. produces 2-MIB in this lake, as determined by the maximum correlation coefficient (R = 0.71, p < 0.001), maximum detection rate, and minimum false positive/negative ratio exhibited by this genus. Extreme odor events occurred in this lake during late summer and early autumn in 2021, with the mean 2-MIB concentration increasing to 727 ± 426ng/L and 369 ± 176ng/L in August and September, respectively. Moreover, the macrophyte-dominated area, particularly the wetland area, exhibited a significant decrease (p < 0.01) in bloom intensity and 2-MIB production during these extreme odor events. Pseudanabaena sp. outbreak was likely owing to eutrophication, seasonal gradients, and macrophyte reduction, considering that temporal trends were consistent with high water temperature, high total phosphorus levels, and low-light conditions. Moreover, 2-MIB production was sensitive to short-term hydrometeorological processes, with high water levels and radiant intensity enhancing 2-MIB production. The risk assessment results showed that the probability of 2-MIB concentration exceeding the odor threshold (10ng/L) is up to 90% when the cell density of Pseudanabaena sp. reaches 1.8 × 107 cell/L; this risk is reduced to 50 and 25% at densities of < 3.8 × 105 cell/L and 5.6 × 104 cell/L, respectively. Our findings support calls for shallow lake management efforts to maintain a macrophyte-dominated state and control odorous cyanobacteria growth.

From source to sea: Floating macroplastic transport along the Rhine river

Rivers are pathways and storage zones for plastic pollution. Land-based plastic waste enters river systems through anthropogenic and hydrometeorological processes, after which they are transported and retained. Only a small fraction (&lt;2%) is assumed to make it into the ocean. Understanding and quantifying river plastic transport are important to optimize prevention and reduction strategies and to evaluate the efficacy of new regulations and interventions. To achieve this, consistent and reliable data are crucial. River plastic pollution monitoring is still an emerging field, especially since river-scale plastic pollution assessments are limited to date. Here, we present an estimate of floating plastic transport and polymer characterization along the Rhine, from Switzerland to the river mouth in Netherlands. We show plastic transport is highly variable along the river, but with a significant increase towards the river mouth. High plastic transport was observed close to urban areas, and confluences with tributaries, suggesting both are likely entry points of plastic pollution. The largest plastic transport was measured in the estuary, which is explained by the tidal dynamics, limiting the transport of plastic into the sea. Our results can be used as a baseline to compare with future assessments. Furthermore, the plastic transport and composition estimates can be directly compared to other rivers that applied the same approach, which may reduce the uncertainty in global river plastic emission simulations. With our study, we aim to contribute to the development of a simple harmonized plastic monitoring approach to quantify plastic pollution at the river basin scale.

Direct and Indirect Drivers of Energy and Nutrient Availability in Freshwater Ecosystems Across Spatial Scales

AbstractFreshwater ecosystems reflect the landscapes in which they are embedded. The biogeochemistry of these systems is fundamentally linked to climate and watershed processes that control fluxes of water and the mobilization of energy and nutrients imprinting as variation in stream water chemistry. Disentangling these processes is difficult as they operate at multiple scales varying across space. We examined the relative importance of climate, soil, and watershed characteristics in mediating direct and indirect pathways that influence carbon and nitrogen availability in streams and rivers across spatial scales. Our data set comprised landscape and climatic variables and 37,995 chemistry measurements of carbon and nitrogen across 459 streams and rivers spanning the continental United States. Models explained a small fraction of carbon and nitrogen concentrations at the continental scale (25% and 6%, respectively) but 61% and 40%, respectively, at smaller spatial scales. Hydrometeorological processes were always important in mediating the availability of solutes but the mechanistic implications were variable across spatial scales. The influence of hydrometeorology on concentrations was often not direct, rather it was mediated by soil characteristics for carbon and watershed characteristics for nitrogen. For example, the seasonality of precipitation was often important in determining carbon concentrations through its influence on soil moisture at biogeoclimatic spatial scales, whereas it had a direct influence on concentrations at the continental scale. Our results suggest that hydrometeorological forcing remains the consistent driver of energy and nutrient concentrations but the mechanism influencing patterns varies across broad spatial scales.

Dynamic evolution of recent droughts in Central Asia based on microwave remote sensing satellite products

Under warm climates, the more complex and malleable relationship between water and heat and the changes in the water components at the land–atmosphere interface pose greater challenges to understanding hydro-meteorological processes. However, the emergence of the advanced microwave scanning radiometer (AMSR) has facilitated the precise monitoring of these changes. Therefore, this study investigated and analyzed the spatiotemporal transformation characteristics of drought–wetness and its driving mechanisms in Central Asia from July 2002 to December 2018 based on the AMSR surface wetness index (ASWI) coupled with multiple water components. Three major conclusions were drawn from this investigation. First, the spatiotemporal differences in multiple water components in Central Asia caused substantial spatiotemporal differences in the extent of changes in drought–wetness. For example, arid areas were mainly concentrated in the lower reaches of the Amu Darya and Syr Darya river basins, where the dominant drought periods were 2007 and 2018. Second, the centroids of the largest drought patches were mainly located in the northeast, northwest, and southwest regions of Central Asia. Central Asia experienced a continuous drought from 2002 to 2005, and then a gradual wetting from 2008 to 2017. Third, the drought–wetness variation can be attributed to the variation of water vapor flux in the typical barometric layer as well as the variation of precipitation and temperature. The ASWI may play an essential role in drought monitoring and tracking prediction, particularly for drought risk mitigation and aversion.

Soil Moisture Memory of Land Surface Models Utilized in Major Reanalyses Differ Significantly From SMAP Observation

AbstractWeather and climate forecast predictability relies on hydrometeorological processes occurring at different time scales. However, evaluation of parameterizations of such processes in current land surface models (LSMs) is challenging since they are complex, and large‐scale observations are scarce and uncommon. Recent advancements in satellite observations, in this light, provide a unique opportunity to evaluate the models' performances at large spatial scales. Using 5‐year soil moisture memory (SMM) from Soil Moisture Active and Passive observations, we evaluate hydrometeorological behaviors of four LSMs in major reanalyses. Multi‐model mean comparison at the global scale shows that current LSMs tend to overestimate SMM that is controlled by water‐limited processes whereas underestimate SMM controlled by energy‐limitations. Large model spreads in SMM are also observed between individual models. The SMM biases are highly dependent on models' parameterizations, while showing minor relevance to the models' online/offline simulating schemes. Further analyses of two important terrestrial water cycle‐related variables indicate current LSMs may underestimate soil moisture that is directly available for water cycle rate and water‐limited evapotranspiration. Finally, the comparison of two soil moisture thresholds indicates the soil parameters utilized in LSMs play an essential role in producing the model's biases. This study provides an observation‐based reference for hydrometeorological evaluations in multiple LSMs, shedding lights on improving land surface simulations in future.

Moisture sources and isotopic composition of a record-breaking heavy Meiyu-Baiu rainfall in southwestern Japan in early July 2020

Torrential Meiyu-Baiu rainfall events have become more frequent in East Asia this decade. The moisture source and isotopic composition of precipitation are crucial for understanding the underlying dynamical and thermodynamic processes of extreme heavy Meiyu-Baiu rainfall. To clarify the moisture source and isotopic composition, we simulated a record-breaking heavy Meiyu-Baiu rainfall over Kyushu in southwestern Japan in early July 2020 using an isotopic regional spectral model. In addition, we observed the isotopic composition of the rainfall in Kyushu to validate the simulations. Moisture sources were classified into three groups: Asian monsoon (AM) moisture from the Indian Ocean, the South China Sea, and the East Asian Continent; North Pacific subtropical high (NPSH) moisture from the Pacific Ocean and the Philippine Sea; and local moisture from the East China Sea, the Kuroshio region, and the Sea of Japan. The remote AM and NPSH moisture was dominant. The NPSH moisture, transported by the NPSH at low levels, gradually increased in the early period. While AM moisture, transported by the monsoonal southwesterly flow at the mid-level, rapidly increased in association with a Baiu frontal depression and merged with the NPSH moisture in the middle period. Meanwhile, a quasi-stationary convective band (QSCB) formed and then triggered heavy rainfall. As for the isotopic composition, condensation exhibited lower δ2H and higher d-excess with more AM moisture in the beginning and on the north side of the QSCB. Regarding the effects of hydrometeorological processes on the isotopic composition, the lower δ2H and higher d-excess of AM moisture were principally attributable to more rainout and below-cloud evaporation during the transport process, rather than surface evaporation in moisture source regions. This study systematically clarified the major moisture sources and their corresponding isotopic dynamics with respect to the hydrometeorological processes of surface evaporation, moisture transport, rainout, and below-cloud evaporation for torrential Meiyu-Baiu rainfall. The findings may be useful in weather forecasting and disaster prevention for heavy Meiyu-Baiu rainfall in East Asia.

Windmapper: An Efficient Wind Downscaling Method for Hydrological Models

AbstractEstimates of near‐surface wind speed and direction are key meteorological components for predicting many surface hydrometeorological processes that influence critical aspects of hydrological and biological systems. However, observations of near‐surface wind are typically spatially sparse. The use of these sparse wind fields to force distributed models, such as hydrological models, is greatly complicated in complex terrain, such as mountain headwaters basins. In these regions, wind flows are heavily impacted by overlapping influences of terrain at different scales. This can have a great impact on calculations of evapotranspiration, snowmelt, and blowing snow transport and sublimation. The use of high‐resolution atmospheric models allows for numerical weather prediction (NWP) model outputs to be dynamically downscaled. However, the computation burden for large spatial extents and long periods of time often precludes their use. Here, a wind‐library approach is presented to aid in downscaling NWP outputs and terrain‐correcting spatially interpolated observations. This approach preserves important spatial characteristics of the flow field at a fraction of the computational costs of even the simplest high‐resolution atmospheric models. This approach improves on previous implementations by: scaling to large spatial extents O(1M km2); approximating lee‐side effects; and fully automating the creation of the wind library. Overall, this approach was shown to have a third quartile RMSE of 1.8 and a third quartile RMSE of 58.2° versus a standalone diagnostic windflow model. The wind velocity estimates versus observations were better than existing empirical terrain‐based estimates and computational savings were approximately 100‐fold versus the diagnostic model.

Crowdsourced Data Highlight Precipitation Phase Partitioning Variability in Rain‐Snow Transition Zone

AbstractTo increase the number of direct observations of rain and snow, we started a citizen science project that crowdsources precipitation phase reports from volunteers using a smartphone app. We focused on the Lake Tahoe region of California and Nevada, USA which forms part of the rain‐snow transition zone, an area where both solid and liquid precipitation occur in winter months. In two study years, we received 2,495 reports, of which 2,248 (90.1%) passed our quality control checks. Snow was the most frequent phase (64.0%), followed by rain (21.0%) and mixed precipitation (15.0%). We compared these values to estimates from 14 common precipitation phase partitioning methods that use near‐surface meteorology as well as to two remote sensing products from the Global Precipitation Measurement mission (GPM). We found the meteorology‐based methods tended to underestimate snowfall on average (60.9%) with a sizable standard deviation of 18%. The Integrated Multi‐satellitE Retrievals for GPM level 3 probabilityLiquidPrecipitation product also underestimated snowfall (57.5%) relative to the crowdsourced data, while the Dual‐frequency Precipitation Radar level 2A phaseNearSurface product had little spatiotemporal overlap with the observations. We also found slight differences in the rain‐snow line elevations measured by a freezing‐level radar versus those estimated from the crowdsourced data, with the former being 165 m lower than the latter on average. These findings underscore the importance of collecting ground‐truth observations of precipitation phase in the rain‐snow transition zone. We hope future studies will consider the use of crowdsourced data for improved insights into and better representation of hydrometeorological processes.

Assessing the hydrological controls on spatio-temporal patterns of streamwater in glacierized mountainous Upper Indus River Basin (UIRB), western Himalayas

The meltwater generated from the Himalayan mountain system is an important source of freshwater sustaining hydrological, ecological and biological activities in upstream and downstream regions. Under a warming climate, the cryospheric cover is shrinking, altering the availability of freshwater to ∼53 million people living in the upper and lower parts of the Himalayas. Therefore, it becomes essential to understand the hydrological processes and regimes influencing the regional hydrological cycle for proper water resource management and ecosystem restoration policies. In this study, we measured the patterns of isotopic values (δ18O and δ2H) of river/streamwater to assess the factors controlling its spatio-temporal variability and mean residence time in glacierized mountainous Upper Indus River Basin, India to better understand the significant hydrological processes and controls. The isotopic values of stream/river water exhibited significant spatio-temporal variance ascribed to changing physiographic, hydrometeorological, and climatic factors persisting at the local and regional scales. The slope and intercept of the River Water Line [δ2H = (8.3 ± 0.2) × δ18O + (15 ± 3.9, p > 0.001)] is higher than global meteoric water line and local meteoric water line of the UIRB, India. The seasonality in source water ratios suggested that stream water is more biased to winter precipitation. The estimated mean residence time in six sub-basins ranged from 188 ± 17 to 387 ± 38 days with the longest and shortest mean residence time observed in Indus (387 ± 38 days) and Tangtse (188 ± 17 days) sub-basins. The polynomial (second‐order) relationship suggests that the variability in the paleo-precipitation δ18O values reflect a change in the moisture source and/or upliftment along airmass transport pathways. Our study highlighted the significance of physiographical and hydrometeorological processes in controlling the spatio-temporal variance of stable isotopic composition of river/stream water bodies. The current research work also improved the knowledge and understanding of the local and regional hydrological processes.