The Ganga plains represent the abode of more than 400 million people and a region of severe anthropogenic disturbance to natural processes. Changing agricultural practices, inefficient use of water, contamination of groundwater systems, and decrease in soil fertility are some of the issues that have affected the long-term resilience of hydrological processes. The quantification of these processes demands a network of hydro-meteorological instrumentation, low-cost sensors, continuous engagement of stakeholders and real time data transmission at a fine interval. We have therefore set up a Critical Zone Observatory (CZO) in a small watershed (21 square Km ) that forms an intensively managed rural landscape consisting of 92% of agricultural land in the Pandu River Basin (a small tributary of the Ganga River). Apart from setting up a hydrometeorological observatory, the major science questions we want to address relate to development of water balance model, understanding the soil-water interaction and estimation of nutrient fluxes in the watershed. This observatory currently has various types of sensors that are divided into three categories: (a) spatially not dense but temporally fine data, (b) spatially dense but temporally not fine data and(c) spatially dense and temporally fine data. The first category represent high cost sensors namely automatic weather stations that are deployed at two locations and provide data at 15 minute interval. The second category includes portable soil moisture, discharge and groundwater level at weekly/ biweekly interval. The third category comprises low-cost sensors including automatic surface and groundwater level sensors installed on open wells to monitor the continuous fluctuation of water level at every 15 minutes. In addition to involving the local communities in data collection (e.g. manual rainfall measurement, water and soil sampling), this CZO also aims to provide relevant information to them for improving their sustainability. The preliminary results show significant heterogeneity in soil type, cropping system, fertilizer application, water quality, irrigation source etc. within a small catchment.

Smart stormwater systems equipped with real-time controls are transforming urban drainage management by enhancing the flood control and water treatment potential of previously static infrastructure. Real-time control of detention basins, for instance, has been shown to improve contaminant removal by increasing hydraulic retention times while also reducing downstream flood risk. However, to date, few studies have explored optimal real-time control strategies for achieving both water quality and flood control targets. This study advances a new model-predictive control (MPC) algorithm for stormwater detention ponds that determines the outlet valve control schedule needed to maximize pollutant removal and minimize flooding using forecasts of the incoming pollutograph and hydrograph. We illustrate that, compared to rule-based controls, MPC more effectively prevents overflows, reduces peak discharges, improves water quality, and adapts to changing hydrologic inputs. Moreover, when paired with an online data assimilation scheme based on Extended Kalman Filtering (EKF), we find that MPC is robust to uncertainty in both pollutograph forecasts and water quality measurements. By providing an integrated control strategy that optimizes both water quality and quantity goals while remaining robust to uncertainty in hydrologic and pollutant dynamics, our study paves the way for real-world smart stormwater systems that will achieve improved flood and nonpoint source pollution management.

Access to groundwater leaves riparian plants in drylands resistant to atmospheric drought but vulnerable to changes in climate or water use that reduce streamflow and groundwater tables. Despite the vulnerability of riparian vegetation to water balance changes few extensible methods have been developed to automatically map riparian plants at the scale of individual stands or stream reaches, to assess their response to changes in moisture due to drought and climate change, and to contrast those responses across plant functional types. We used LiDAR and a sub-annual timeseries of NDVI to map vegetation and then assessed drought response by comparing a drought index to variation in a remotely sensed metric of plant health. First, a random forest model was built to classify vegetation communities based on phenological changes in Sentinel-2 NDVI. This model produced community classes with an overall accuracy of 97.9%; accuracy for the riparian vegetation class was 98.9%. Following this initial classification, LiDAR measurements of vegetation height were used to split the riparian class into structural subclasses. Multiple Endmember Spectral Mixture Analysis was applied to a timeseries of Landsat imagery from 1984 to 2018, producing annual sub-pixel fractions of green vegetation, non-photosynthetic vegetation, and soil. Relationships were assessed within structural subclasses between mid-summer green vegetation fraction (GV) and the Standardized Precipitation-Evapotranspiration Index (SPEI), a measure of soil moisture drought. Among riparian vegetation subclasses, all groups showed significant positive correlations between SPEI and GV, indicating an increase in healthy plant material during wetter years. However, the relationship was strongest for herbaceous plants (R^2=0.509, m=0.0278), intermediate for shrubs (R^2=0.339, m=0.0262), and weakest for the largest trees (R^2=0.1373, m=0.0145). This implies decoupling of larger riparian plants from the impacts of atmospheric drought due to subsidies provided by groundwater resources. Our method was extended successfully to multiple climatically-dissimilar dryland systems in the American Southwest, and the results provide a basis for ongoing studies on the fine-scale drought response and climatic vulnerability of riparian woodlands.

Whole-stream metabolism models are generally implemented with a steady flow assumption that does not hold true for many systems with sub-daily flow variation, such as river sections downstream of dams. The steady flow assumption has confined metabolism estimation to a limited range of river environments, thus limiting our understanding about the influence of hydrology on biological production in rivers. Therefore, we couple a flow routing model with the two-station stream metabolism model to estimate metabolism under unsteady flow conditions in rivers. The model’s applicability is further extended by including advection-dispersion processes to facilitate metabolism estimation in transient storage zones. Metabolism is estimated using two approaches: (1) an accounting approach similar to the conventional two-station method and (2) an inverse approach that estimates metabolism parameters using least-squares minimisation method. Both approaches are complementary since we use outputs of the accounting approach to constrain the inverse model parameters. The model application is demonstrated using a case study of an 11 km long stretch downstream of a hydropower plant in the River Otra in southern Norway. We present and test different formulations of the model to show that users can make an appropriate selection that best represents hydrology and solute transport mechanism in the river system of interest. The inclusion of unsteady flows and transient storage zones in the model unlocks new possibilities for studying metabolism controls in altered river ecosystems.

The local temperarture cannot explain the inter-annual variation in δ18Oprecip in the coastal Antarctic in past few decades. To understand this enigmatic variation, we have used long-term modern δ18Oprecip value of three coastal Antarctic sites. Using the δ18O-d-excess relationship and modelled δ18O value of vapor at source, we have shown that δ18Oprecip inherits the signature of moisture source parameters (MSPs). Furthermore, the wavelet analysis suggests that the variation in the MSPs impacts the seasonal cycle of δ18Oprecip which lead to disparity in the seasonal isotope-temperature relationship. The Southern Ocean surface stratification, due to increase in the freshwater flux by glacier melting, led to alignment of MSPs in such a manner that altogether significantly lowered the isotopic composition of initially formed vapor, which is reflected in δ18Oprecip at inter-annual scale. Our observations suggest that the palaeothermometry will underestimate the Antarctic temperature change for the periods characterized by warming and high glacier-melt.

The dissolution and mobilization of non-aqueous phase liquids (NAPL) blobs in Surfactant-Enhanced Aquifer Remediation (SEAR) processes are upscaled using dynamic pore network modelling of three-dimensional and unstructured networks. We considered corner flow and micro-flow mechanisms including snap-off and piston-like movement for two-phase flow. Moreover, NAPL entrapment and remobilization were evaluated using force analysis to develop capillary desaturation curve (CDC) and predict the onset of remobilization and complete removal of entrapped NAPL blobs. The corner diffusion mechanism was also applied in the modeling of interphase mass transfer to represent NAPL dissolution as the dominant mass transfer process. Our model showed that although surfactants enhance NAPL recovery during two-phase flow, surfactant-enhanced remediation of residual NAPL through dissolution is highly dependent on surfactant type. When sodium dodecyl sulfate (SDS), as a surfactant with high critical micelle concentration (CMC) and low micelle partition coefficient ( ) was injected into a NAPL contaminated site, reduction in mass transfer rate coefficient (due to considerable changes in interface chemical potentials) significantly reduced NAPL recovery after the end of two-phase flow. However, Triton X-100 (with low CMC and high ) improved NAPL recovery. This is because by enhancing solubility at surfactant concentrations greater than CMC, Triton X-100 overcompensates the interphase mass transfer reduction.

Mangrove plants are cyclically exposed to variations in salinity. However, high salinity for long periods can significantly alter their metabolism. Here, we studied the effect of contrasting interstitial salinities (45 ppt vs. 70 ppt) on leaf morpho-physiological traits in adult Avicennia germinans L. trees in the dry and rainy seasons in Tampamachoco lagoon, Mexico. In the dry season, there was low stomatal conductance and low water potential. Plants under 70 ppt of salinity had significantly lower leaf Ca and Mg concentrations than those at 45 ppt. The metabolomics results revealed that plants produced different organic compounds based on the salinity they were exposed to. The specific leaf area was significantly lower under 70 ppt of salinity (12.94 ± 0.87 g cm -2) compared to 45 ppt (19.57 ± 1.52 g cm -2) may as a result of the leaf stomatal conductance responses. Salt glands and trichome density were significantly higher in the leaves of trees found at the more saline site. Although mangroves are exposed to freshwater availability, saline, and tidal variation, prolonged exposure to high salinity results in morphophysiological and biochemical changes in leaves which facilitates their survival, even under extremely salt conditions.

The projected climate change for Norway through the 21st century predicts that the temperature will increase by 4.5 OC. Events with heavy rainfall will be more intense and occur more frequently. Rainfloods will increase in magnitude and also occur more frequently. Extreme flooding and heavy rain will significantly impact the sediment dynamics in rivers. In the mountain areas, floods are often associated with erosion, transport and deposition of coarse sediment along the streams. These processes are related to bed load transport and pose a hazard in addition to the elevated water discharge and have to be included in management plans for river basins. This paper studies the bed load delivery from sources that contribute the most to the sediment budget in the Gudbrandsdalslågen river basin during the large magnitude floods in 2011 and 2013. More than 100 debris slides and debris flow were triggered in the tributary river Veikleåi by the heavy rain and snowmelt during these floods. The volume of the contribution from debris flows and erosion and deposition of the river bed was determined by subtracting digital elevation models acquired during repeated airborne LIDAR surveys. In the river Dørja the supply of sediment from a number of debris flows caused extensive aggradation and channel changes. In their new position, lateral erosion by these channels triggered slides on the adjacent slopes. The contributing volumes of debris flows, lateral erosion and river-bed erosion and deposition were determined from the LIDAR surveys. Relations obtained from studies of sediment transport in modern glacier rivers were used to obtain estimates of the ratio of bed load vs suspended load derived from the Pleistocene moraine deposits. Several monitoring stations using conventional methods for measuring bed load and suspended load recorded very large volumes of sediment delivery during both of the extreme floods. Implications for the future development of mitigation are discussed.

The establishment of biological soil crusts is widely perceived as a main method to control ecological environment in arid and semi-arid regions. However, artificial biocrusts are insufficient to face with some stress from environment by using traditional established methods. Hence in this study, kaolin, a common clay mineral, was introduced as a stabilizer by mixing with Microcoleus steenstruppi of different mass ratios for inoculating onto sand to establish artificial biocrust. The results showed that the addition of kaolin exhibited a significantly positive effect on promoting biocrust formation, and accelerating the biocrust development. Moreover, the artificial biocrust from 1:500 (algae:kaolin) inoculant achieved the best performances with coverage of 98%, and thickness of 5.62 mm after 86 days of incubation. The highest contents of chlorophyll a, exopolysaccharides, and soluble protein were also observed in 1:500 mass ratio of algae:kaolin throughout the biocrust development process. As for the water retention performances, the results of contact angle, water drop penetration time (WDPT), and repellency index (RI) illustrated that biocrusts improve water utilization in kaolin-treated groups by delaying the time of water infiltration, especially in 1:500 group. After 86 days post inoculation, a series of common bacteria appeared in the biocrusts such as actinobacteria and acidobacteria and decomposed metabolites from cyanobacteria as energy source to supply their own life activities. This study gains new insights on clay minerals on biocrust development and puts forward a new approach for rapid artificial biocrust establishment to reverse desertification.

The forthcoming Surface Water and Ocean Topography (SWOT) satellite and AirSWOT airborne instrument are the first imaging radar-altimeters designed with near-nadir, 35.75 GHz Ka-band InSAR for mapping terrestrial water storage variability. Remotely sensed surface water extents are crucial for assessing such variability, but are confounded by emergent and inundated vegetation along shorelines. However, because SWOT-like measurements are novel, there remains some uncertainty in the ability to detect certain land and water classes. We study the likelihood of misclassification between 15 land cover types and develop the Ka-band Phenomenology Scattering (KaPS) scattering model to simulate changes to radar backscatter as a result of changing surface water fraction and roughness. Using a separability metric, we find that water is five times more distinct compared with dry land classes, but has the potential to be confused with littoral zone and wet soil cover types. The KaPS scattering model simulates AirSWOT backscatter for incidence angles 1-27°, identifying the conditions under which open water is likely to be confused with littoral zone and wet soil cover types. A comparison of KaPS simulated backscatter with AirSWOT observed backscatter shows good overall agreement across the 15 classes (median r2=0.76). KaPS characterization of the sensitivity of near-nadir, Ka-band SAR to small changes in both wet area fraction and surface roughness enables more nuanced classification of inundation area. These results provide additional confidence in the ability of SWOT to classify water inundation extent, and open the door for novel hydrological and ecological applications of future Ka-band SAR missions.

Beavers influence hydrology by constructing woody dams. Using a before after control impact experimental design, we quantified the effects of a beaver dam sequence on the flow regime of a stream in SW England. Building upon our previous research (Puttock et al., 2021), we consider the mechanisms that underpin flow attenuation in beaver wetlands. Rainfall-driven hydrological events were extracted between 2009 and 2020, for the impacted (n=612) and control (n=634) catchments, capturing events seven years before and three years after beaver occupancy, at the impacted site. General additive models were used to describe average hydrograph geometry across all events. After beaver occupancy, Lag times increased by 55.9% and declined by 17.5% in impacted and control catchments, respectively. Flow duration curve analysis showed a larger reduction in frequency of high flows, following beaver dam construction, with declines of Q5 exceedance levels of 33% and 15% for impact and control catchments, respectively. Using event total rainfall to predict peak flow, five generalised linear models were fitted to test the hypothesis that beaver dams attenuate flow, to a greater degree, with larger storm magnitude. The best performing model showed we can have high confidence that beaver dams attenuated peak flows, with increasing magnitude, up to between 0.5-2.5 m 3 s -1 for the 94 th percentile of event total rainfall; but we cannot confidently detect attenuation beyond the 97 th percentile. Increasing flow attenuation, with event magnitude, is attributed to transient floodplain storage in low gradient/profile floodplain valleys. These findings support the assertion that beaver dams restore attenuated flows. However, with long-term datasets of extreme hydrological events lacking, it is challenging to predict the effect of beaver dams during extreme events with high precision. Beaver dams will have spatially variable impacts on hydrological processes, requiring further investigation to quantify responses to dams across differing landscapes and scales.

Complementarity has become an essential concept in energy supply systems. Although there are some other metrics, most studies use correlation coefficients to quantify complementarity. The standard interpretation is that a high negative correlation indicates a high degree of complementarity. However, we show that the correlation is not an entirely satisfactory measure of complementarity. As an alternative, we propose a new index based on the mathematical concept of the total variation. For two time series, the new index φ is one minus the ratio of the total variation of the sum to the sum of the two series’ total variation. We apply the index first to an auto-regressive (AR) process and then to various Colombian electric system series. The AR case clearly illustrates the limitations of the correlation coefficient as a measure of complementarity. We then evaluate complementarity across various space-time scales in the Colombian power sectors, considering hydro and wind projects. The complementarity assessment on a broad temporal and geographical scale helps analyze large power systems with different energy sources. The case study of the Colombian hydropower systems suggests that φ is better than ρ because (i) it considers scale, whereas ρ, being non-dimensional, is insensitive to the scale and even to the physical dimensions of the variables; (ii) one can apply φ to more than two resources; and (iii) ρ tends to overestimate complementarity.

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

This study was conducted at Algadarif State Area, east of the Sudan latitudes 12ᵒ 17/, longitudes 34ᵒ 36/ E, which aimed to build a database of the morphometric of 26 properties from a 176 basin, this done through analyzing a digital elevation model ( DEM ) by using a group of geographical data systems programs, which integrated to result in a large number of morphometric variances and measurements. They are represented in the programs ArcMap 10.4.1 as basic programs and other supportive programs like excel. The study was done for the purpose to understand its hydrologic significances and consequently understanding the water movement on the surface of the base. The study depended on the data of the digital elevation model accurately 30 m in addition to a group of maps and satellite images. Adoption of Algadarif State upon automatic agriculture who leads to needing to know a lot about conditions, nature and description runoff water for the rain to know the different characteristics for basins to draw the water map of the State, recognition of cadastral characteristics and formal properties, identify the histological properties and water drainage network characteristics. Arc gis was installed on a windows 10 computer and loaded the digital elevation model for the experiment site from earth explorer, the DEM file was only used. Work was done by Arc Hydro Tools within the Arc GIS.

This work aims to study the streamflow statistic patterns in the Sapucaí River watershed, state of Minas Gerais, Brazil. This study embraces the streamflow probabilistic modeling to determine the reference streamflow and, later, the streamflow regionalization to improve the water resources management. A 26-year-data series (1989 - 2014) of maximum, average, and minimum streamflow were used. Probability density functions were applied to the maximum and minimum daily streamflow to determine the recurrence periods. Long-term average annual and monthly streamflow were also calculated. Linear and non-linear regressions were adjusted for the streamflow regionalization. The drainage area and the streamflow equivalent to the total rainfall (with and without abstractions) were used as predictor variables. The probability density functions that best adjusted the maximum streamflow data set were the Generalized Extreme Values, and for the minimum streamflow was the normal distribution. Linear and non-linear regressions were efficient (R²> 0.90 and d Willmott> 0.97) in the regionalization process regardless of the predictor variables. However, a small statistical advantage was found for the adjustment of non-linear regressions that used the predictor variables drainage area and the streamflow equivalent to the total rainfall (without abstractions).

Water resource management in ungauged catchments is complex due to the uncertainties around the hydrological parameters that dominate the streamflow behaviour. These parameters are usually defined by regionalization approaches in which hydrological response patterns are transferred from gauged to ungauged basins. Regression-based methods using physical properties derived from cartographic data sources are widely used. The current remote sensing techniques offer us new standpoints in regionalisation processing since the hydrological response depends on the physical attributes related to the spectral responses of the territory. Moreover, machine learning approaches have not been specifically applied to the regionalization of hydrologic parameters. This work studies the capability of a catchment’s spectral response based on Sentinel-1 and Sentinel-2 data to address a regression-based regionalization of hydrological parameters using a machine learning approach. Hydrological modelling was conducted by the HBV-light model. We tested the random forest algorithm in several regionalization scenarios: the new approach using the catchments’ spectral signature, the traditional method using physical properties and a fusion of them. The calibration results were excellent (median KGE = 0.83), and the regionalized parameters obtained with the random forest algorithm achieved good performance in which the three scenarios showed almost the same goodness of fit (median KGE = 0.45 to 0.50). We found that the effectiveness depends on the climatic environment and that predictions in humid catchments exhibited better performance than those in the driest catchments. The physical approach (median KGE= 0.71) exhibited better performance than the spectral approach (median KGE= 0.64) in humid catchments, whereas spectral regionalization (median KGE= 0.33) outperformed the physical scenario in the driest catchments (median KGE= 0.25). Herein, our results confirm that regionalization is still challenging in Mediterranean climate variants where the new spectral approach showed promising results and time series of satellite data could improve seasonal regionalization methodologies.

Modelling the hydrological characteristics of watershed is a method of understanding behavior and simulating the water balance components of watershed for planning and development of integrated water resources management. The soil and water assessment tool (SWAT) physically based hydrological modelling was used for modelling hydrologic characteristics of the Hangar watershed. The data used for this study were digital elevation model (DEM), land use land cover data, soil map, climatological and hydrological data. The model calibrated and validated using measured streamflow data of 13 years (1990-2002) and 9 years (2003-2011) respectively including warm-up period. The SWAT model performs well for both calibration (R2 = 0.87, NSE = 0.82 and PBIAS = +1.4) and validation (R2 = 0.89, NSE = 0.88 and PBIAS = +1.2). The sensitivity analysis, which was carried out using 18 SWAT parameters, identified the 13 most sensitive parameters controlling the output variable and with which goodness-of-fit was reached. The analysis results indicated that the watershed receives around, 9.6%, 59.9%, and 30.5% precipitation during dry, wet and short rainy seasons respectively. The received precipitation was lost by 9.6 %, 40.5%, and 41.3% in the form of evapotranspiration for each seasons correspondingly. The surface runoff contribution to the Watershed were 3.8%, and 79.2% during dry and wet seasons respectively, whereas, it contributes by 17.0% during short rainy seasons.

Runoff coefficient (C) values are tabulated and enshrined in hydrological engineering. Its values are considered to be constant although it may not correspond to reality. In the same catchment, they can vary according to the intensity, temporal and spatial distribution of precipitation events, humidity conditions, soils and land uses. This study had the objective of analyzing extreme events of precipitation and their corresponding flows to obtain experimental runoff coefficients (C) and compare them with the tabulated values. The study was conducted in five experimental catchments in the state of São Paulo, Brazil, with different land uses. The runoff coefficients (C) were obtained from the analysis of hydrograms and using a digital filter, which allowed the separation of the direct runoff, of the total flow. We observed a variation of the flow coefficient values between catchments different from those obtained from the tables. The runoff coefficients had a high correlation with land use. In the catchments with original vegetation cover, such as cerrado and forest, it varied little among the events analyzed, differently from the catchments where land use is diversified, with predominantly agricultural and urban occupation.

Changes in land use/land cover (LULC) may result in water shortages, flood risk and soil erosion, contributing to the degradation of living conditions. Recognition of the impacts of LULC changes on water resources is a crucial aspect of watershed management. Thus, this paper aims to determine how LULC change affects runoff and other hydrological components including: groundwater, water yield, procolation and evaportranspiration in Upper Prek Thnot watershed from 2006 to 2018 by using SWAT modelling. The result indicates that LULC of Upper Prek Thnot watershed experienced such significant changes during these 13 years. Conversion of forest area into agricultural land was the main modification in the study area, which accounts for 39%. This followed by an increase of rubber plantation, built-up area, barren land and water bodies and a decrease of the wood shrub. These changes resulted in a corresponding increase in annual average surface runoff (36%) and water yield (2%), and a decrease of groundwater (24%), percolation (8%) and evapotranspiration (1%). In particular, if the forest area is converted to agricultural land, especially if the conversion takes place in large numbers, the hydrological elements will be significantly affected. Consequently, due to a noticeable alteration of LULC in the study area, a sound strategic management plan should be applied considerably to ensure the sustainability of ecosystem services.

The Jordan River Basin, and its seven sub-catchments of the Central Wasatch Mountains immediately east of Salt Lake City, UT, are home to an array of research infrastructrure that collectively form the Wasatch Environmental Observatory (WEO). Each sub-catchment is comprised of a wildland to urban land use gradient that spans an elevation range of over 2000 m in a linear distance of ~25km. Geology varies across the sub-catchments, ranging from granitic, intrusive to mixed sedimentary rocks in uplands that drain to the alluvial or colluvial sediments of the former Lake Bonneville. Vegetation varies by elevation, aspect, distance to stream channels, and land use.  The sharp elevation gradient results in a range of precipitation from 700 to 1200 mm/yr (roughly 2/3 as snow) and mean annual temperature from 3.5 o to 6.8o C. Spring snowmelt dominates annual discharge. Although climate is relatively similar across the catchments, annual water yield varies spatially by more than a factor of 3, ranging from 0.18 to 0.63. With historical strengths in ecohydrology, water supply, and social-ecological research, current infrastructure supports both basic and applied research in meteorology, climate, atmospheric chemistry, hydrology, ecology, biogeochemistry, resource management, sustainable systems, and urban redesign. Climate and discharge data span over a century for the seven sub-catchments of the larger basin. These data sets, combined with multiple decades of hydrochemistry, isotopes, ecological data sets, social survey data sets, and high-resolution LiDAR topography and vegetation structure, provide a baseline for long-term data collected by NEON, public agencies, and individual research projects. The combination of long-term data with active state of the art observing facilities allows WEO to serve as a unique natural laboratory for addressing research questions facing rapidly growing, seasonally snow-covered, semi-arid regions worldwide and an excellent facility for providing student education and research training.

Local community and research interest to better understand regional climate change impacts has led to the establishment of a long-term soil moisture and weather observation network in the Roaring Fork catchment of the Colorado River Headwaters. This catchment-wide suite of 10 stations collects frequent and continuous data on soil moisture, soil temperature, rain, air temperature, relative humidity, and (at some stations) snow across an elevational gradient from 1,800m to 3,680m in elevation. We demonstrate how this effort can support research on mountain hydrology with applications for resource management and climate change adaptation decision making. We also share perspectives on the value and opportunities a community science approach can bring to catchment studies moving forward. All data from this project are publicly available.

The Niwot Ridge and Green Lakes Valley (NWT) long-term ecological research (LTER) site collects environmental observations spanning both alpine and subalpine regimes. The first observations began in 1952 and have since expanded to nearly 300 available datasets over an area of 99 km2 within the north-central Colorado Rocky Mountains that include hydrological (n = 101), biological (n = 79), biogeochemical (n = 62), and geographical (n = 56) observations. The NWT LTER database is well suited to support hydrologic investigations that require long-term and interdisciplinary data sets. Experimentation and data collection at the NWT LTER are designed to characterize ecological responses of high-mountain environments to changes in climate, nutrients, and water availability. In addition to the continuation of the many legacy NWT datasets, expansion of the breadth and utility of the NWT LTER database is driven by new initiatives including (a) a catchment-scale sensor network of soil moisture, temperature, humidity, and snow-depth observations to understand hydrologic connectivity and (b) snow-albedo alteration experiments using black carbon to evaluate the effects of snow-disappearance on ecosystems. Together, these observational and experimental datasets provide a substantial foundation for hydrologic studies seeking to understand and predict changes to catchment and local-scale process interactions.

In this study, we introduce datasets that include both hydrological and meteorological records at the Nučice experimental catchment (0.53 km2) which is representative for an intensively farmed landscape in the Czech Republic. The Nučice experimental catchment was established in 2011 for the observation of rainfall-runoff processes, soil erosion processes, and water balance of a cultivated landscape. The average altitude is 401 m a.s.l., the mean land slope is 3.9%, and the climate is humid continental (mean annual temperature 7.9 °C, annual precipitation 630 mm). The catchment is drained by an artificially straightened stream and consists of three fields covering over 95 % of the area which are managed by two different farmers. The typical crops are winter wheat, rapeseed, and alfalfa. The installed equipment includes a standard meteorological station, several rain gauges distributed across the basin, and an H-flume that monitors stream discharge, water turbidity, and basic water quality indicators. Additionally, the groundwater level and soil water content at various depths near the stream are recorded. Recently, large-scale soil moisture monitoring efforts have been introduced with the installation of two cosmic-ray soil moisture sensors. The datasets consist of measured precipitation, air temperature, stream discharge, and soil moisture and are available online for public use. The cross seasonal, open access runoff generation datasets at this small-scale agricultural catchment will benefit not only hydrologists but also local farmers.

The 20 km² Galabre catchment belongs to the French network of critical zone observatories. It is representative of the sedimentary geology and meteorological forcing found in Mediterranean and mountainous areas. Due to the presence of highly erodible and sloping badlands of various lithologies, the site was instrumented in 2007 to understand the dynamics of suspended sediments (SS) in such areas. Two meteorological stations including measurements of air temperature, wind speed and direction, air moisture, rainfall intensity, raindrop size and velocity distribution are installed both in the upper and lower part of the catchment. At the catchment outlet, a gauging station records the water level, temperature and the turbidity (10 min. time-step). Water and sediment samples are collected automatically to estimate SS concentration-turbidity relationships, providing SS fluxes quantifications with known uncertainties. The sediment samples are further characterized by measuring their particle size distributions (PSD) and by applying a low-cost sediment fingerprinting approach using spectrocolorimetric tracers. Thus, the contributions of badlands on different lithologies to total SS flux are quantified at a high temporal resolution providing the opportunity to better analyze the links between meteorological forcing variability and watershed hydrosedimentary response. The set of measurements was extended to the dissolved phase in 2017. Both the river electrical conductivity and its major ion concentrations are measured each week and every three hours during storm events. This allows progress in understanding both the origin of the water during the events and the partitioning between particulate and dissolved fluxes in the critical zone.