Large Watersheds Research Articles

Freshwater fish diversity in three tributary streams in Serayu Basin, Central Java, Indonesia

Abstract. Suryaningsih S, Bhagawati D, Sukmaningrum S, Sugiharto, Puspitasari IGAAR. 2020. Freshwater fish diversity in three tributary streams in Serayu Basin, Central Java, Indonesia. Biodiversitas 21: 5811-5817. Determining the diversity of freshwater fish species in Central Java, Indonesia has been confounded by temporal and spatial limitations in past studies. The Serayu Basin is a large watershed in Central Java that is believed to have high freshwater fish diversity. We aimed to determine the diversity and community structure of freshwater fish species in three tributaries of the Serayu, elucidate the factors influencing this diversity, and determine the extent to which these tributaries contribute to the total freshwater fish species richness for southwest Central Java. We conducted gill net sampling from May to August 2018 on the Tulis, Mrawu, and Kali Sapi Rivers located at Banjanegara district Sampling followed a random group technique in upstream, midstream, and downstream river sections of each tributary. In total, we observed 21 freshwater fish species. Broadly, fish species diversity was relatively low in the study area, and water temperature and dissolved oxygen were important in maintaining fish diversity. These three tributaries house 27–46% of the freshwater fish species reported for southern Central Java.

Performance evaluation of global hydrological models in six large Pan-Arctic watersheds

Global Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds.

River Microbiome Composition Reflects Macroscale Climatic and Geomorphic Differences in Headwater Streams

Maintaining the quality and quantity of water resources in light of complex changes in climate, human land use, and ecosystem composition requires detailed understanding of ecohydrologic function within catchments, yet monitoring relevant upstream characteristics can be challenging. In this study, we investigate how variability in riverine microbial communities can be used to monitor the climate, geomorphology, land-cover, and human development of watersheds. We collected streamwater eDNA and used 16S rRNA sequencing to profile microbiomes from headwaters to outlets of the Willamette and Deschutes basins, two large watersheds prototypical of Pacific Northwest region of the United States. Within each watershed, microbial community composition correlated strongly (mean Mantel test statistic r = 0.24 in Willamette, 0.17 in Deschutes) with development-related characteristics, including percentage impervious area and percentage of low, medium, and high density developed area. In the temperate Willamette basin, percentage of forest and shrublands (r = 0.34) and percentage of medium density developed area (r = 0.30) were among the strongest correlates with microbial community composition. In the arid Deschutes basin, however, geomorphic characteristics were the most strongly correlated to microbial community composition (e.g., r = 0.33 for topographic index, 0.29 for topographic relief). In headwater sub-catchments across both watersheds, microbial community assemblages correlated with catchment-scale climate and geomorphology (r = 0.45 and 0.23, respectively), but these relationships were weaker downstream. Our results build on previous work relating the streamwater microbiome to hydrologic regime and demonstrate that microbial eDNA in headwater streams additionally reflects the structural configuration of landscapes as well as other natural and anthropogenic processes upstream. Our results offer an encouraging indication that streamwater eDNA carries information not only about microbial ecology, but also can be a useful tool for monitoring multiple upstream watershed characteristics.

Assessment of the cascade of uncertainty in future snow depth projections across watersheds of mountainous, foothill, and plain areas in northern latitudes

Snowmelt is a major driver of the hydrological cycle in cold regions, as such, its accurate representation in hydrological models is key to both regional snow depth and streamflow prediction. The choice of a proper method for snowmelt representation is often improvised; however, a thorough characterization of uncertainty in such process representations particularly in the context of climate change has remained essential. To fill this gap, this study revisits and characterizes performance and uncertainty around the two general approaches to snowmelt representation, namely Energy-Balance Modules (EBMs) and Temperature-Index Modules (TIMs). To account for snow depth simulation and projection, two common Snow Density formulations (SNDs) are implemented that map snow water equivalent (SWE) to snow depth. The major research questions we address are two-fold. First, we examine the dominant controls of uncertainty in snow depth and streamflow simulations across scales and in different climates. Second, we evaluate the cascade of uncertainty of snow depth projections resulting from impact model parameters, greenhouse gas emission scenarios, climate models and their internal variability, and downscaling processes. We enable the Soil and Water Assessment Tool (SWAT) by coupling EBM, TIM, and two SND modules for examination of different snowmelt representation methods, and Analysis of Variance (ANOVA) for uncertainty decomposition and attribution. These analyses are implemented in mountainous, foothill, and plain regions in a large snow-dominated watershed in western Canada. Results show, rather counter-intuitively, that the choice of SND is a major control of performance and uncertainty of snow depth simulation rather than the choice between TIMs and EBMs and of their uncertain parameters. Also, analysis of streamflow simulations suggest that EBMs generally overestimate streamflow on main tributaries. Finally, uncertainty decompositions show that parameter uncertainty related to snowmelt modules dominantly controls uncertainty in future snow depth projections under climate change, particularly in mountainous regions. However, in plain regions, the uncertainty contribution of model parameters becomes more variable with time and less dominant compared with the other sources of uncertainty. Overall, it is shown that the hydro-climatic and topographic conditions of different regions, as well as input data availability, have considerable effect on reproduction of snow depth, snowmelt and resulting streamflow, and on the share of different uncertainty sources when projecting regional snow depth.

An empirical modeling approach to predicting pollutant loads and developing cost-effective stormwater treatment strategies for a large urban watershed

Stormwater treatment strategies were evaluated for the upper Ballona Creek Watershed in Los Angeles, CA using an empirical model of stormwater runoff quantity and quality with zeroth-order regularization and a limited memory Broyden-Fletcher-Goldfarb-Shanno Bound constrained optimization routine. The model used landuse based estimation on the runoff volume, event mean concentration (EMC) and pollutant load employing ten different landuses, including highways and local roads. The model was validated by showing that its predictions were in reasonable agreement (r2 ~0.6 to 0.8) with total zinc (Zn), Total Kjeldahl Nitrogen (TKN), and Total Suspended Solids (TSS) loadings measured at the monitoring site at the bottom of the watershed. The developed model was used to demonstrate and quantify the benefits of the stormwater treatment practices (STPs) prioritized at specific landuses with high pollutant mass emission rates. For this demonstration, total Zn was selected as it is one of the most concerning pollutants in an extremely urbanized area such as the Ballona Creek Watershed. Transportation landuse including local roads and highways was found to be the best candidate for the STP applications due to their high percent load contribution per percent area. By focusing STPs for transportation landuse, the water quality goal of total Zn in the study watershed was expected be achieved at approximately 75% less cost.

Modeling Process‐Based Biogeochemical Dynamics in Surface Fresh Waters of Large Watersheds With the IMAGE‐DGNM Framework

AbstractOver the last centuries, human activities have exerted increasing pressures on the environment, leading to drastic alterations in the functioning of freshwater bodies (e.g., eutrophication). Global biogeochemical models have proven crucial to investigate interactions between humans, hydrology, and water quality of surface fresh waters. However, most do not account for high‐resolution spatial and temporal variability within watersheds, and they typically lack any representation of benthic dynamics that can drive pollution legacy effects. We present here the Integrated Model to Assess the Global Environment‐Dynamic Global Nutrient Model (IMAGE‐DGNM), which couples global, spatially explicit hydrology and integrated assessment models with process‐based biogeochemistry in surface fresh waters. The new Dynamic In‐Stream Chemistry (DISC) module calculates advective transport from headwaters to estuaries, processes in the water column and in bed sediments, as well as the exchanges between these two compartments. As application examples of IMAGE‐DGNM, we simulate sediment dynamics and nitrogen cycling in two large river basins. We assess in‐stream concentration time series at specific locations, and identify governing processes in transfers along the aquatic continuum. Results highlight the importance of benthic dynamics in watersheds highly perturbed by damming. The implementation of such dynamics within IMAGE‐DGNM allows for including the temporal effect of pollution legacies in large scale water quality studies and shifts in pollutant speciation along river continua. This new framework therefore incorporates new features for large basin to global scale studies that are crucial to better predict the effects on receiving ecosystems and evaluate future environmental management pathways.

Dams shift microbial community assembly and imprint nitrogen transformation along the Yangtze River

Dams are important for flood control, water storage, irrigation, electric generation, navigation, and have been regarded as the largest anthropogenic disturbance in aquatic ecosystems. However, how dams impact nitrogen transformation on a large watershed scale remained less studied. To explicitly address the impact of dams on nitrogen transformation, we used 16S rRNA gene sequencing to investigate the microbial dynamics and ecological processes under different dam conditions along the Yangtze River, as microbial communities are playing a key role in aquatic nitrogen transformation. Compared with landforms, dams exerted a more significant impact on the distribution patterns of microbial communities along the Yangtze River. The results showed that, by controlling suspended sand concentration, dams filtered keystone species, reshaped distribution of metacommunities, and mediated ecological assembly processes of microbial communities. Moreover, direct causal relationships between dams and nitrogen transformation were chained via microbial communities. To summarize, by combining knowledge in hydrology, microbial ecology, and biogeochemistry, this research exhibited the impact of different dams on the nitrogen transformation along a large river, and the key roles of suspended sand and microbial communities were emphasized. We anticipate a more precise modelling and prediction of nitrogen transformation in large watersheds, which may provide new perspectives for controlling the nitrogen in aquatic environments.

Landscape-scale patterns of nutrient enrichment in a coral reef ecosystem: implications for coral to algae phase shifts.

Nutrient pollution is altering coastal ecosystems worldwide. On coral reefs, excess nutrients can favor the production of algae at the expense of reef-building corals, yet the role of nutrients in driving community changes such as shifts from coral to macroalgae is not well understood. Here we investigate the potential role of anthropogenic nutrient loading in driving recent coral-to-macroalgae phase shifts on reefs in the lagoons surrounding the Pacific island of Moorea, French Polynesia. We use nitrogen (N) tissue content and stable isotopes (δ15 N) in an abundant macroalga (Turbinaria ornata) together with empirical models of nutrient discharge to describe spatial and temporal patterns of nutrient enrichment in the lagoons. We then employ time series data to test whether recent increases in macroalgae are associated with nutrients. Our results revealed that patterns of N enrichment were linked to several factors, including rainfall, wave-driven circulation, and distance from anthropogenic nutrient sources, especially human sewage. Reefs near large watersheds, where inputs of N from sewage and agriculture are high, have been consistently enriched in N for at least the last decade. In many of these areas, corals have decreased and macroalgae have increased, while reefs with lower levels of N input have maintained high cover of coral and low cover of macroalgae. Importantly, these patchy phase shifts to macroalgae have occurred despite substantial island-wide increases in the density and biomass of herbivorous fishes over the time period. Together, these results indicate that nutrient loading may be an important driver of coral-to-macroalgae phase shifts in the lagoons of Moorea even though the reefs harbor an abundant and diverse herbivore assemblage. These results emphasize the important role that bottom-up factors can play in driving coral-to-macroalgae phase shifts and underscore the critical importance of watershed management for reducing inputs of nutrients and other land-based pollutants to coral reef ecosystems.

Deciphering centurial anthropogenic pollution processes in large lakes dominated by socio-economic impacts

Human activities have greatly increased the influx of pollutants into global freshwater ecosystems. Understanding the pollution patterns at multi-decadal scales, and how the socio-economic development and public policy have shaped them, are crucial to assess the current status of environmental quality and develop sound strategies for watershed management. This study takes the test case of large Taihu Lake (∼2,338 km2) in rapidly developing regions of China, to examine the temporal-spatial patterns and driving factors of centurial trace metal and metalloid contamination. Using comparative sedimentary records, we applied multiple geochemical indices and positive matrix factorization modelling to identify the anthropogenic versus natural sources and their contributions. Results identified cadmium, antimony and lead as typical pollutants, with high anthropogenic contribution rates of 58–79 %, 54–62 % and 28–36 %, respectively. Contamination histories were asynchronous across the lake, starting from the 1940s (North) to mid-1990s (East), following the gradient of socio-economic stressors of the sub-basins. Specifically, industrial and agricultural inputs through runoff and river networks were main sources of metal in the northern and western lake parts, accounting for ≥ 70 % since the 1980s. In contrast, aquaculture and agriculture related sources (≥ 80 %) dominated in Eastern Taihu Lake over the last decade. Moreover, atmospheric metal deposition from nearby industrial cities significantly affected the northern parts of the lake. The stationary and/or slightly decreased contamination trends, especially after 2000 AD, shed light into the effectiveness of recent environmental regulation. Our findings suggest, however, that agricultural non-point sources and legacy effects probably delayed reductions in the input of pollutants. Sustainable management of similar large watersheds worldwide, therefore, would benefit by highlighting the role of multiple anthropogenic sources, legacy contamination and lake-specific response.

The Effects of Restoration Practices on a Small Watershed in China’s Loess Plateau: A Case Study of the Qiaozigou Watershed

Soil erosion and restoration affect the structure and function of ecosystems and society, and have attracted worldwide attention. Changes in runoff and sediment transport after restoration practices in China’s Loess Plateau have been widely studied and many valuable results have been reported. However, this research was mainly conducted in large watersheds, and quantified the effects of restoration practices through the restoration period. In this study, we compared two adjacent watersheds (one restored and the other natural) in a hill and gully region of China’s Loess Plateau to reveal the impacts of restoration practices. We collected annual rainfall, runoff, and sediment transport data from 1988 to 2018, then investigated temporal variation of runoff and sediment transport to examine their relationships with rainfall. We also calculated the retention rate of soil and water under the restoration practices. The restored watershed showed a significantly decreased sediment modulus (the amount per unit area); the natural watershed showed no significant change. In addition, the restored watershed had lower runoff and sediment modulus values than the natural watershed, with greater effectiveness as rainfall increased. Revegetation and terrace construction contributed more to the retention of soil and water (65.6 and 69.7%, respectively) than check dams (<10%). These results improve our understanding of the effects of restoration practices, and provide guidance on ways to preserve soil and water through restoration in a small watershed in the Loess Plateau.

Activity characteristics and enlightenment of the debris flow triggered by the rainstorm on 20 August 2019 in Wenchuan County, China

Debris flow activity increased after the Wenchuan earthquake, and many debris flows continue to occur. Recently, a catastrophic debris flow event was triggered by strong rainfall on 20 August 2019 in Wenchuan County, China. The main purposes of this article are to analyse the activity characteristics of this disaster and further to analyse the long-term activity of debris flow in Wenchuan County based on the analysis results of this disaster. According to a field survey and remote sensing image interpretation, this strong rainfall triggered 15 debris flows that occurred simultaneously in the large watershed in northern Wenchuan County, and they were characterised by strong channel erosion and a disaster chain effect. The activity and sediment supply capacity of landslides have decreased significantly, and the landslides that provided materials for this debris flow event are mainly distributed on both banks of tributaries. The amounts of triggering rainfall and antecedent accumulated rainfall for this debris flow event were 13.7 mm and 47.1 mm, respectively. In addition, the channel runoff formed by strong short-duration rainfall eroded the channel deposits, and temporary check dams failed due to slope failures; these are the main causes for debris flows in the next period. Analyses of the debris flow volume versus watershed area and the maximum erosion modulus prove that the debris flow activity in Wenchuan County is decreasing with time, but more than 5~10 years will be required to recover to the pre-earthquake level.

GIP-SWMM: A new Green Infrastructure Placement Tool coupled with SWMM

A new Green Infrastructure Placement Tool coupled with Storm Water Management Model (GIP-SWMM) is developed for selection and strategic placement of Green Infrastructure (GI) practices. The tool supports GI placement at multiple scales - from a few city blocks to large watersheds. GI is a multi-benefit option for stormwater management and can revitalize communities while reducing sewage overflows and improving runoff quality. However, cost-effective planning and placement of GI to achieve management goals remains a challenge and requires an integrated watershed approach. An optimization approach was developed coupled with the SWMM to find optimal combination and placement of GI to meet target flow and pollutant load reduction while minimizing cost at a watershed scale. The tool includes 13 GI types and selection and placement of GI within a watershed is based on their capital cost and effectiveness. The tool generates cost-effectiveness curves (cost vs size of GIs) for discharge and pollutants. GIP-SWMM provides a means for objective analysis of managing alternatives among multiple interacting and competing options. The desired outcome from the system application is a thorough, practical, and informative assessment considering economic, and engineering factors. The performance of the tool was evaluated in the Meade-Hawthorne drainage basin in Rapid City, South Dakota. GI placement options were assessed for multiple target levels for discharge, TSS, E coli, and Fecal coliform. Cost-effectiveness curves were developed for discharge, TSS, E coli, and Fecal coliform. Total GI cost increased as the target discharge, TSS, E coli, and Fecal coliform concentration at the outlet of the watershed was reduced. The tool placed a larger percentage of the GIs at the locations where most of the discharge and pollutant loads originated. This case study demonstrates that GIP-SWMM is a planning level decision support framework that allows for optimization of GIs and is adaptable for use in addressing regulatory compliance and practices across the U.S.

Developing the hydrological dependency structure between streamgage and reservoir networks

Reliable operation of physical infrastructures such as reservoirs, dikes, nuclear power plants positioned along a river network depends on monitoring riverine conditions and infrastructure interdependency with the river network, especially during hydrologic extremes. Developing this cascading interdependency between the riverine conditions and infrastructures for a large watershed is challenging, as conventional tools (e.g., watershed delineation) do not provide the relative topographic information on infrastructures along the river network. Here, we present a generic geo-processing tool that systematically combines three geospatial layers: topographic information from the National Hydrographic Dataset (NHDPlusV2), streamgages from the USGS National Water Information System, and reservoirs from the National Inventory of Dams, to develop the interdependency between reservoirs and streamgages along the river network for upper and lower Colorado River Basin (CRB) resulting in River and Infrastructure Connectivity Network (RICON) that shows the said interdependency as a concise edge list for the CRB. Another contribution of this study is an algorithm for developing the cascading interdependency between infrastructure and riverine networks to support their management and operation.

Climatic influences on forest fire and mountain pine beetle outbreaks and resulting runoff effects in large watersheds in British Columbia, Canada

AbstractMany studies have defined the interrelationships between climate, forest disturbance, and runoff at small scales (<100 km2), but few have translated these relationships to large watersheds (>500 km2). In this study, we explore the relationship between climate, extreme forest fire seasons, mountain pine beetle (MPB) outbreaks, and runoff in eight large watersheds within the Fraser and Peace drainage basins in British Columbia (BC), Canada from 1981–2019. Using a climate index based on precipitation and air temperature anomalies, we find extreme forest fire seasons (those that burned >5% of a watershed's area) are most likely to occur when a warm/dry summer is preceded by multiple seasons of cool/wet conditions. Using the climate suitability class (CSC) model to explore the relationship between climate and MPB outbreaks, we validate previous findings that lower‐than‐average precipitation, warm growing season temperatures, and lack of extremely cold temperatures during winter are connected to MPB outbreaks within central BC. However, the CSC model needs improvements to accurately assess MPB suitability in northern watersheds that are located outside the model's calibration region, either through weighted variables or lower degree day thresholds. Minimal runoff response occurs from these forest disturbances, with the most prominent runoff change being related to the 2014 fire season in the Osilinka and Mesilinka watersheds. The limited effects of forest disturbance on annual runoff are likely related to large watershed sizes, low percentages of disturbed area in some study watersheds and post‐MPB forest dynamics. These results provide valuable insight into the interrelationships of climate, forest disturbance and runoff in large Canadian boreal forested watersheds.

Drinking Locally: A Water 87Sr/86Sr Isoscape for Geolocation of Archeological Samples in the Peruvian Andes

The analysis of 87Sr/86Sr has become a robust tool for identifying non-local individuals at archaeological sites. The 87Sr/86Sr in human bioapatite reflects the geological signature of food and water consumed during tissue development. Modeling relationships between 87Sr/86Sr in human environments, food webs, and archaeological human tissues is critical for moving from identifying non-locals to determining their likely provenience. In the Andes, obstacles to sample geolocation include overlapping 87Sr/86Sr of distant geographies and a poor understanding of mixed strontium sources in food and drink. Here, water is investigated as a proxy for bioavailable strontium in archaeological human skeletal and dental tissues. This study develops a water 87Sr/86Sr isoscape from 262 samples (220 new and 42 published samples), testing the model with published archaeological human skeletal 87Sr/86Sr trimmed of probable non-locals. Water 87Sr/86Sr and prediction error between the predicted and measured 87Sr/86Sr for the archaeological test set are compared by elevation, underlying geology, and watershed size. Across the Peruvian Andes, water 87Sr/86Sr ranges from 0.7049 to 0.7227 (M = 0.7081, SD = 0.0027). Water 87Sr/86Sr is higher in the highlands, in areas overlying older bedrock, and in larger watersheds, characteristics which are geographically correlated. Spatial outliers identified are from canals, wells, and one stream, suggesting those sources could show non-representative 87Sr/86Sr. The best-fit water 87Sr/86Sr isoscape achieves prediction errors for archaeological samples ranging from 0.0017 – 0.0031 (M = 0.0012, n = 493). The water isoscape explains only 7.0% of the variation in archaeological skeletal 87Sr/86Sr (R2 = 0.07), but 90.0% of archaeological skeleton 87Sr/86Sr fall within the site isoscape prediction ± site prediction standard error. Due to lower sampling density and higher geological variability in the highlands, the water 87Sr/86Sr isoscape is more useful for ruling out geographic origins for lowland dwellers than for highlanders. Baseline studies are especially needed in the highlands and poorly-sampled regions. Because the results demonstrate that a geostatistical water model is insufficient for fully predicting human 87Sr/86Sr variation, future work will incorporate additional substrates like plants, fauna, soils, and dust, aiming to eventually generate a regression and process-based mixing model for probabilistic geolocation of Andean samples.

Using Sentinel-2 and canopy height models to derive a landscape-level biomass map covering multiple vegetation types

Vegetation biomass is a globally important climate-relevant terrestrial carbon pool and also drives local hydrological systems via evapotranspiration. Vegetation biomass of individual vegetation types has been successfully estimated from active and passive remote sensing data. However, for many tasks, landscape-level biomass maps across several vegetation types are more suitable than biomass maps of individual vegetation types. For example, the validation of ecohydrological models and carbon budgeting typically requires spatially continuous biomass estimates, independent from vegetation type. Studies that derive biomass estimates across multiple vegetation or land-cover types to merge them into a single landscape-level biomass map are still scarce, and corresponding workflows must be developed. Here, we present a workflow to derive biomass estimates on landscape-level for a large watershed in central Chile. Our workflow has three steps: First, we combine field plot-based biomass estimates with spectral and structural information collected from Sentinel-2, TanDEM-X and airborne LiDAR data to map grassland, shrubland, native forests and pine plantation biomass using random forest regressions with an automatic feature selection. Second, we predict all models to the entire landscape. Third, we derive a land-cover map including the four considered vegetation types. We then use this land-cover map to assign the correct vegetation type-specific biomass estimate to each pixel according to one of the four considered vegetation types. Using a single repeatable workflow, we obtained biomass predictions comparable to earlier studies focusing on only one of the four vegetation types (Spearman correlation between 0.80 and 0.84; normalized-RMSE below 16 % for all vegetation types). For all woody vegetation types, height metrics were amongst the selected predictors, while for grasslands, only Sentinel-2 bands were selected. The land-cover was also mapped with high accuracy (OA = 83.1 %). The final landscape-level biomass map spatially agrees well with the known biomass distribution patterns in the watershed. Progressing from vegetation-type specific maps towards landscape-level biomass maps is an essential step towards integrating remote-sensing based biomass estimates into models for water and carbon management.

Determination of sediment sources in a mixed watershed within the Appalachian-St. Lawrence Lowland Regions of southern Quebec using sediment fingerprinting.

This paper identifies the main sediment sources to the Beaudet Reservoir in Quebec (Canada) using sediment fingerprinting. The reservoir, which is built on the Bulstrode River and provides drinking water to Victoriaville, has decreased in capacity by 35% in the past 35 years. This study provides new data on fingerprinting in large and complex watersheds, a first in the province of Quebec. Nine sampling sites on the Bulstrode River and its three main tributaries were selected and five sampling campaigns were conducted. Samples from river bank profiles and adjacent fields, along with suspended sediments, were collected. All samples were sieved to 2 mm and analyzed for 137Caesium, 15 geochemical elements and sieved to 63 μm for color analysis. Source classification, based on an ANOVA test to verify the independence hypothesis and iterative linear discriminant analysis to optimize the ratio of inter-group/within-group variability, resulted in four sample classes: agricultural soils, forested soils, stream bank bottom and stream bank top. A Kruskal-Wallis H test then identified 21 out of the 32 tracers with p value < 0.05. The linear discriminant analysis led to a set of 14 tracers, namely 137Cs and 13 color coefficients with a discriminating result of 94%. That combination of 137Cs and color coefficients proved to be a cost-effective fingerprint. Based on MixSIAR modeling results, this sediment fingerprinting study has demonstrated that the main sediment sources varied within the watershed but, generally, forested soil particles dominated (33 to 49%), then agricultural soils (43 to 50%) reflecting the land use changes, followed by stream bank bottoms (82%) at the Beaudet Reservoir.

Energy and carbon fluxes from an oil sands pit lake

Currently, post-mining landscape plans in the Athabasca Oil Sand Region include large watersheds terminating in pit lakes. In 2012, Base Mine Lake (BML), was constructed with the aim of demonstrating technologies associated with lake reclamation in the region. This paper examines the first 6.5 years of lake-atmosphere energy and carbon exchange. Energetically, BML behaved similar to other northern lakes, storing large quantities of heat in the spring and releasing it in the fall as sensible and latent heat fluxes. At various times a hydrocarbon sheen formed on the lake, which may have suppressed evaporation. However, simple linear relationships failed to statistically quantify the impacts and more comprehensive modelling of the variability may be required. At daily scales, variability in evaporation was well explained by the product of vapour pressure deficit and wind speed as well as the available energy (R2 = 0.74), while sensible heat was explained by the product of wind speed and the difference in air and surface temperature as well as available energy (R2 = 0.85). Spring CH4 fluxes were high, particularly around ice melt, with a maximum flux of 3.3 g m−2 day−1. Otherwise fluxes were low, except during irregular periods. The peak flux of these periods occurred following ~58 h of continuously falling pressure, relating cyclone activity to these large periods of methane emissions. Annually, CO2 and CH4 fluxes were initially high, with median fluxes of 231 mg CO2 m−2 h−1 and 23 mg CH4 m−2 h−1 in 2014. However, the median fluxes reduced quickly and over the least three years of the study (2017 through 2019) the median fluxes declined to 36 mg CO2 m−2 h−1 and 10 mg CH4 m−2 h−1. Overall, BML behaves similar to other boreal lake ecosystems with above average carbon fluxes compared to other constructed reservoirs.

Coarse sediment dynamics in a large glaciated river system: Holocene history and storage dynamics dictate contemporary climate sensitivity

Abstract The gravel-bedded White River drains a 1279 km2 basin in Washington State, with lowlands sculpted by continental glaciation and headwaters on an actively glaciated stratovolcano. Chronic aggradation along an alluvial fan near the river’s mouth has progressively reduced flood conveyance. In order to better understand how forecasted climate change may influence coarse sediment delivery and aggradation rates in this lowland depositional setting, we assessed the contemporary delivery and routing of coarse sediment through the watershed; this assessment was based on a rich set of topographic, sedimentologic, and hydrologic data from the past century, with a focus on repeat high-resolution topographic surveys from the past decade. We found that most of the lower river’s contemporary bed-load flux originates from persistent erosion of alluvial deposits in the lower watershed. This erosion is a response to a drop in local base level caused by a major avulsion across the fan in 1906 and then augmented by subsequent dredging. The 1906 avulsion and modern disequilibrium valley profiles reflect landscape conditioning by continental glaciation and a massive mid-Holocene lahar. In the proglacial headwaters, infrequent large sediment pulses have accomplished most of the observed coarse sediment export, with exported material blanketing downstream valley floors; during typical floods, transported bed material is largely sourced from erosion of these valley floor floods. Throughout the watershed, we observe decadal-scale coarse sediment dynamics strongly related to the filling or emptying of valley-scale sediment storage over 102–104 yr time scales, often in response to major disturbances that either emplace large deposits or influence their redistribution. Paraglacial responses in large watersheds are suggested to be inherently complicated and punctuated as a result of internal landform interactions and stochastic/threshold-dependent events. We argue that, in combination, Holocene disturbance, storage dynamics, and human flow modification make coarse sediment fluxes in the lower White River relatively insensitive to decadal climate variability. Results highlight the degree to which river sensitivity to contemporary disturbance, climatic or otherwise, may be contingent on local and idiosyncratic watershed histories, underscoring the need to unpack those histories while demonstrating the utility of watershed-scale high-resolution topography toward that end.

Dynamic Scaling of the Generalized Complementary Relationship Improves Long-term Tendency Estimates in Land Evaporation

Most large-scale evapotranspiration (ET) estimation methods require detailed information of land use, land cover, and/or soil type on top of various atmospheric measurements. The complementary relationship of evaporation (CR) takes advantage of the inherent dynamic feedback mechanisms found in the soil-vegetation-atmosphere interface for its estimation of ET rates without the need of such biogeophysical data. ET estimates over the conterminous United States by a new, globally calibrated, static scaling (GCR-stat) of the generalized complementary relationship (GCR) of evaporation were compared to similar estimates of an existing, calibration-free version (GCR-dyn) of the GCR that employs a temporally varying dynamic scaling. Simplified annual water balances of 327 medium and 18 large watersheds served as ground-truth ET values. With long-term monthly mean forcing, GCR-stat (also utilizing precipitation measurements) outperforms GCR-dyn as the latter cannot fully take advantage of its dynamic scaling with such data of reduced temporal variability. However, in a continuous monthly simulation, GCR-dyn is on a par with GCR-stat, and especially excels in reproducing long-term tendencies in annual catchment ET rates even though it does not require precipitation information. The same GCR-dyn estimates were also compared to similar estimates of eight other popular ET products and they generally outperform all of them. For this reason, a dynamic scaling of the GCR is recommended over a static one for modeling long-term behavior of terrestrial ET.