Low Flow Periods Research Articles

Assessing Retrospective National Water Model Streamflow with Respect to Droughts and Low Flows in the Colorado River Basin

AbstractStreamflow monitoring in the Colorado River Basin (CRB) is essential to ensure diverse needs are met, especially during periods of drought or low flow. Existing stream gage networks, however, provide a limited record of past and current streamflow. Modeled streamflow products with more complete spatial and temporal coverage (including the National Water Model [NWM]), have primarily focused on flooding, rather than sustained drought or low flow conditions. Objectives of this study are to (1) evaluate historical performance of the NWM streamflow estimates (particularly with respect to droughts and seasonal low flows) and (2) identify characteristics relevant to model inputs and suitability for future applications. Comparisons of retrospective flows from the NWM to observed flows from the United States Geological Survey stream gage network over 22 years in the CRB reveal a tendency for underestimating low flow frequency, locations with low flows, and the number of years with low flows. We found model performance to be more accurate for the Upper CRB and at sites with higher precipitation, snow percent, baseflow index, and elevations. Underestimation of low flows and variable model performance has important implications for future applications: inaccurate evaluations of historical low flows and droughts, and less reliable performance outside of specific watershed/stream conditions. This highlights characteristics on which to focus future model development efforts.

Sources and Mechanisms of Low-Flow River Phosphorus Elevations: A Repeated Synoptic Survey Approach

High-resolution water quality monitoring indicates recurring elevation of stream phosphorus concentrations during low-flow periods. These increased concentrations may exceed Water Framework Directive (WFD) environmental quality standards during ecologically sensitive periods. The objective of this research was to identify source, mobilization, and pathway factors controlling in-stream total reactive phosphorus (TRP) concentrations during low-flow periods. Synoptic surveys were conducted in three agricultural catchments during spring, summer, and autumn. Up to 50 water samples were obtained across each watercourse per sampling round. Samples were analysed for TRP and total phosphorus (TP), along with supplementary parameters (temperature, conductivity, dissolved oxygen, and oxidation reduction potential). Bed sediment was analysed at a subset of locations for Mehlich P, Al, Ca, and Fe. The greatest percentages of water sampling points exceeding WFD threshold of 0.035 mg L−1 TRP occurred during summer (57%, 11%, and 71% for well-drained, well-drained arable, and poorly drained grassland catchments, respectively). These percentages declined during autumn but did not return to spring concentrations, as winter flushing had not yet occurred. Different controls were elucidated for each catchment: diffuse transport through groundwater and lack of dilution in the well-drained grassland, in-stream mobilization in the well-drained arable, and a combination of point sources and cumulative loading in the poorly drained grassland. Diversity in controlling factors necessitates investigative protocols beyond low-spatial and temporal resolution water sampling and must incorporate both repeated survey and complementary understanding of sediment chemistry and anthropogenic phosphorus sources. Despite similarities in elevation of P at low-flow, catchments will require custom solutions depending on their typology, and both legislative deadlines and target baselines standards must acknowledge these inherent differences.

Flow period influence on uranium and trace elements release in water from the waste rock pile of the former La Commanderie uranium mine (France)

Uranium mining activities expose uranium ore and mine tailings to the surface environment, where the release of radionuclides is facilitated by weathering at rates exceeding those typically found in nature. Therefore, close to former uranium mining sites, radionuclides and especially uranium concentrations in water may surpass local background levels. The methodology proposed herein, entails coupling, gamma-ray mapping, water sampling and chemical analyses including DGT (Diffusive Gradient in Thin Film) measurements, provides new insights into describing the environment of the La Commanderie site (France). Gamma-ray mapping allows identifying water seepage, output from a waste rock pile, as a potential pathway for radionuclides into the environment. Water seepage monitoring has shown: a low pH value (4.2), high sulfate content (179 mg.L−1) and high uranium concentrations of up to 436 μg.L−1. These recordings indicate that an acid mining drainage (AMD) process is occurring inside or under the oxidized parts of the waste rock pile. Monitoring data over three flow periods revealed the release of the highest uranium concentrations during a high-flow period downstream of the site, which is compliant with local regulations. The AMD process is also responsible for the release of significant amounts of Fe, Mn and As within the immediate environment in both dissolved and particulate forms. Changes in dissolved oxygen concentration and redox potential during low flow periods, modify the speciation of Fe (in AMD waters) which acts as a scavenger for other elements such as As, Mn and U. The use of DGT under environmental conditions, and specifically AMD waters, seems to be relevant in comparison to filtered spot water sampling strategies. Moreover, based on DGT measurements, the dissolved part of the released uranium is considered as labile with concentrations above the environmental standards for freshwater organisms.

Estimation Of The Impact Of Climatic And Anthropogenic Factors On The Formation Of The Extreme Low-Flow Period In The Don River Basin During 2007-2016

The Don River is the largest river in the southwestern part of European Russia and the second largest river system in European Russia. The Don River basin is one of the most water deficient regions in Russia and the long term average water usage in the basin amounts to 45%. The period 2007-2016 was the longest long-term low-flow period observed, with an estimated total water resources deficit of 40.4 km 3 over 8 years. The main reason for this deficit were anomalously warm winters (2-4 degrees over average) with a low degree of soil frost penetration. This resulted in low spring flood volume (37% of the average) due to heavy seepage losses combined with thin snow cover. A similar low-flow situation was observed in 2014, when the drought caused great damage to ecosystem of Tsimlianskoye water reservoir and the River Don. Most of the fish breeding grounds had dried up by May 2014. This caused the number of round fish whitebait to drop 5-10 times below the 2002-2014 average. Inland shipping and hydropower industry also sustained losses of 42 million euro (according to interview from State Shipping company) due to low water level. This study shows that the main reasons for the 2007-2016 extreme hydrological drought are exceptional hydro-climatic conditions and anthropogenic transformations in the watershed, such as urbanisation growth and afforestation. The analysis shows that the main cause in water deficit is associated with the left tributaries of Don – Khoper and Medveditsa, while the flow in Upper Don remained more or less normal. The results can be interpreted as a “warning sign” to reduce water consumption in these sub-basins to avoid similar drought situations in future.

DEVELOPMENT OF A HYDRO-STATISTICAL MODEL OF ESTABLISHING THE LIMITS OF STREAMS AND ITS VERIFICATION (ON THE EXAMPLE OF THE VYATKA-RIVER BASIN)

A new unique method for establishing the shoreline of watercourses has been offered. In accordance with the Russian Water code, the border of rivers and streams is the shoreline with a long-term average annual water level for the ice-free period. That level is unknown in advance: it cannot be directly measured afield; it is not given in hydrological directories, as it is not considered to be characteristic, and on cartographic materials the outlines of water bodies are given at the average water level for the summer-autumn low flow period. To determine the desired level, the authors proposed a new method using the example of the Vyatka River. The method is based on a hydrolytic statistical model: the predicted parameter is the value of ΔH (the excess of the long-term average water level for the ice-free period over the average level of the summer-autumn low flow period), and the predictors (factors) of the model are the conditions of flow formation and passage. These factors are connected with ΔH by a power dependence, which has coefficients established by the least squares method from observations at 17 hydrological stations. The average calculation error ΔH was 5.4 ± 2.2 cm (significance level 0.05). The equation allows for any point or segment of the watercourse to calculate the desired level, and then establish a shoreline using large-scale maps. In this case, hydrometric observations are not needed. Mapping of the shoreline of the Vyatka River within the Kirov city and comparison of its position with the shoreline on a detailed space image received on a date close to the calculated level show that the standard error of the planned coastal position does not exceed 1.3 m, which meets the requirements to the accuracy of land boundaries of the water fund.

Sources and variability of CO2 in a prealpine stream gravel bar

AbstractGravel bars (GBs) contribute to carbon dioxide (CO2) emissions from stream corridors, with CO2 concentrations and emissions dependent on prevailing hydraulic, biochemical, and physicochemical conditions. We investigated CO2 concentrations and fluxes across a GB in a prealpine stream over three different discharge‐temperature conditions. By combining field data with a reactive transport groundwater model, we were able to differentiate the most relevant hydrological and biogeochemical processes contributing to CO2 dynamics. GB CO2 concentrations showed significant spatial and temporal variability and were highest under the lowest flow and highest temperature conditions. Further, observed GB surface CO2 evasion fluxes, measured CO2 concentrations, and modelled aerobic respiration were highest at the tail of the GB over all conditions. Modelled CO2 transport via streamwater downwelling contributed the largest fraction of the measured GB CO2 concentrations (31% to 48%). This contribution increased its relative share at higher discharges as a result of a decrease in other sources. Also, it decreased from the GB head to tail across all discharge‐temperature conditions. Aerobic respiration accounted for 17% to 36% of measured surface CO2 concentrations. Zoobenthic respiration was estimated to contribute between 4% and 8%, and direct groundwater CO2 inputs 1% to 23%. Unexplained residuals accounted for 6% to 37% of the observed CO2 concentrations at the GB surface. Overall, we highlight the dynamic role of subsurface aerobic respiration as a driver of spatial and temporal variability of CO2 concentrations and evasion fluxes from a GB. As hydrological regimes in prealpine streams are predicted to change following climatic change, we propose that warming temperatures combined with extended periods of low flow will lead to increased CO2 release via enhanced aerobic respiration in newly exposed GBs in prealpine stream corridors.

Geomorphic change and biogeomorphic feedbacks in a dryland river: The Little Colorado River, Arizona, USA

AbstractThe Little Colorado River in Arizona, United States, has undergone substantial geomorphic change since the late 1800s and early 1900s, consisting of sediment accumulation following an earlier period of likely widespread sediment evacuation. We analyzed hydrologic and geomorphic data at different spatial and temporal scales to determine the primary mechanisms responsible for these changes, and to provide context for periods of sediment evacuation and accumulation in other rivers.Peak-flow magnitude has progressively declined since the 1920s despite the occurrence of four alternating periods of high and low total annual flow. Largely coincident with this hydrologic change, the channel has narrowed between 72% and 88% in some reaches since the 1930s, with increases in sinuosity in wide alluvial valleys causing ∼21%–32% reductions in channel slope. Dense stands of vegetation colonized, and thus stabilized, the newly deposited floodplains. Although large, long-duration floods caused some channel widening, these floods have been too infrequent to offset the progressive narrowing. Channel narrowing, increases in sinuosity, decreases in slope, and increases in vegetative roughness appear to have caused biogeomorphic feedbacks, thereby exacerbating sediment deposition, disrupting flood conveyance, and contributing to decreases in peak-flow magnitude and in sediment transport. The progressive increase in water development in parts of the basin has also likely contributed to progressive declines in peak flow. These results show that biogeomorphic feedback processes combined with human water development may be as important as, if not more important than, changes in climate in driving hydrologic, geomorphic, and sediment-load change in dryland river environments.

Risk factors for progression toward brain death after out-of-hospital cardiac arrest

BackgroundSuccessfully resuscitated out-of-hospital cardiac arrest (OHCA) may lead to brain death (BD) and good-quality transplantable organs. We aimed to determine risk factors for evolution toward BD after OHCA. We analyzed adult patients admitted to an intensive care unit (ICU) who survived at least 24 h after an OHCA between 2005 and 2015. BD was defined according to international guidelines. Multivariate logistic regression was used to identify potential risk factors for BD available 24 h after OHCA.ResultsA total of 214 patients were included (median age 68 years; sex ratio 1.25; non-shockable OHCA: 88%). Among these, 42 (19.6%) developed BD, while 22 (10.3%) were alive at 1 year with a good neurological outcome. Independent risk factors for BD were age (OR per year 0.95; 95% CI [0.92–0.98]), female gender (OR 2.34; 95% CI [1.02–5.35]), neurological cause of OHCA (OR 14.72; 95% CI [3.03–71.37]), duration of the low-flow period > 16 min (OR 2.94, 95% CI [1.21–7.16]) and need of vasoactive drugs at 24 h (OR 6.20, 95% CI [2.41–15.93]).ConclusionsThe study identified, in a population of OHCA with predominantly non-shockable initial rhythms, five simple risk factors independently associated with progression toward BD.

Interannual Improvement in Sea Lettuce Blooms in an Agricultural Catchment

Riverine nutrient loading from agriculture is one of the most prominent pressures in the second cycle of river basin management planning for the European Union (EU) Water Framework Directive (WFD). Better farmyard nutrient management planning is the measure most likely to reduce agricultural nutrient loading to catchment watercourses and coastal receiving waters. The adjoining Argideen Estuary and Courtmacsherry Bay in the south west of Ireland drain a 150km2 catchment comprising mainly agricultural land. The receiving waters were allocated Poor ecological status under the WFD at the most recent appraisal. Subhourly water quality monitoring in the Timoleague River has been carried out by the Teagasc Agricultural Catchments Programme to track the changes in nutrient loading to the Argideen Estuary in response to improved farming practice. A biophysical model of the adjoining Argideen Estuary and Courtmacsherry Bay was calibrated and Six nutrient load scenarios were simulated to determine their impact upon macroalgae and phytoplankton bloom magnitude. In addition, nutrient flow-load relationships were derived for summers 2010 and 2016 to elucidate the improvements induced by better catchment management practice. Flow-load relationships for dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) for each year were applied to the flow data for the other year, to query the outcome if 1) there had been no change in farm management practice between 2010 and 2016, or 2) the improved farm management practice in place by 2016 had been implemented by 2010. The difference between expediting and delaying improvement in farm nutrient management practice was a 5% increase in DIN loading and a 233% increase in DIP load. Application of this higher estimated load to the calibrated bio-physical model projected an increase in 2016 Ulva bloom magnitude from 381t to 1,391t. Although phosphorus retention within the catchment has improved in recent years, with an attendant improvement in Ulva bloom magnitudes, flow connectivity in the catchment still facilitates a higher phosphorus transfer during large rainfall events. A high amount of phosphorus is stored within the catchment, while point source pressures continue to contribute to phosphorus transfer to streams during periods of low flow.

Real-time river monitoring supports community management of low-flow periods

Increasing pressure on river catchment water resources has heightened the need for real-time monitoring data for water management agencies and users. This study investigates the capability to develop a technical basis for water co-management at the catchment scale. A web application (dashboard) was created that displayed real-time data for weather, soil water, river flow and water quality at high spatial and temporal resolutions, including streamflow and rainfall forecasts. A method to predict stream water temperature was developed and tested using an interactive table for visualisation of complex data. The dashboard was used to provide information and alerting to water users in an agricultural catchment, through the Ringarooma Water Users Group (RWUG) in NE Tasmania, Australia. The dashboard was updated several times per day using telemetry and data platform services, and accessed by RWUG members mainly during periods of low flow when water extraction restrictions can be applied. Irrigators gained an improved understanding of streamflow behaviour in response to weather variability, and water extractions and releases, and developed abilities to voluntarily manage the water resource as three sub-groups. The Department of Primary Industries, Parks, Water and Environment (DPIPWE) collaborated with the RWUG in this adaptive management approach, as enabled by the Ringarooma River Catchment Water Management Plan, and later informed a new Ministerial Policy for ‘Water Resource Management During Extreme Dry Conditions’. Group management was assisted by using alerts sent through the existing DPIPWE text messaging service. Results showed that stakeholder understanding of technical information and scenario implications using the sensor-based dashboard increased capacity of irrigators to make decisions that improve production and social outcomes, while considering environmental values. This study demonstrates that the information provided through the dashboard enabled a high level of community and inter-agency co-operation, demonstrating potential to apply this management approach in other catchments.

Comparison of the multiple imputation approaches for imputing rainfall data series and their applications to watershed models

Rainfall data scarcity has caused enormous problems in hydrologic and non-point pollution (H/NPS) predictions, as rainfall data represent the key input to watershed models. In this study, the effects of different imputation methods such as the data augmentation (DA) and the expectation maximization with bootstrap (EMB) algorithms on rainfall data scarcity were compared. The effects of different data scarcity rates and periods on model performance and prediction uncertainty were then quantified. Finally, the effects of different imputed data sets on H/NPS results were evaluated with the soil and water assessment tool (SWAT). A real case study in the Daning River watershed, Three Gorges Reservoir Region, China, was evaluated. The results indicated that rainfall data scarcity during low flow periods would result in poorer model performance and larger prediction uncertainty, especially to some minimum values, and the time when the maximum values are more susceptible is the rainfall data scarcity during high flow periods. The repair of rainfall data and the H/NPS model performance obtained by the EMB algorithm are superior to the traditional DA and weather generator performances. This advantage of the EMB algorithm would be more definitive if a specific threshold of data scarcity is reached. It is noted that even if the best algorithm is used, the imputed value is always lower than the peak observed value. This paper reports important implications for the choice of imputation methods and the use of H/NPS models for solving data scarcity problems for watershed studies.

Diel changes in aquatic biogeochemistry of a pristine stream receiving untreated urban sewage at Brazilian rainforest.

Freshwater bodies experience diel variations in aquatic chemistry, driven by natural processes. However, changes in land use, like urbanization, can modify the natural dynamics of such systems. This article describes changes in biogeochemistry of a pristine stream after receiving untreated sewage of an urban nucleus. Water samples were collected and field parameters measured, during low flow period. Temperature, pH, dissolved oxygen, electric conductivity, turbidity, total suspended solids, silicate, N-NO3, N-NO2, N-NH4, dissolved and particulate organic nitrogen, PO4, and dissolved and particulate organic phosphorus were measured hourly during a diel cycle. Upstream hydrochemistry resembles pristine watersheds in tropical rainforest, and results are restricted to a narrow range of values during the entire sampling period. Conversely, values downstream varied widely and, for some analytes, presented differences between day and night. Dissolved oxygen and electric conductivity showed the effect of the urban pulse, varying according to the routine of the population. Other field parameters did no presents a pattern that could distinguish up- and downstream stations. All the nutrients, but silicate, increased in concentration downstream. Particulate organic phosphorus, N-NO2, and N-NH4 were the nutrients that highlight the magnitude of the changes driven by urban effluents. These three nutrient species, and dissolved organic phosphorus, revealed a remarkably pattern that reflects the routine of the urban population, with low concentrations at night and a progressive increase starting at the early morning.

Analyses on the Temporal and Spatial Characteristics of Water Quality in a Seagoing River Using Multivariate Statistical Techniques: A Case Study in the Duliujian River, China.

In the Duliujian River, 12 water environmental parameters corresponding to 45 sampling sites were analyzed over four seasons. With a statistics test (Spearman correlation coefficient) and multivariate statistical methods, including cluster analysis (CA) and principal components analysis (PCA), the river water quality temporal and spatial patterns were analyzed to evaluate the pollution status and identify the potential pollution sources along the river. CA and PCA results on spatial scale revealed that the upstream was slightly polluted by domestic sewage, while the upper-middle reach was highly polluted due to the sewage from feed mills, furniture and pharmaceutical factories. The middle-lower reach, moderately polluted by sewage from textile, pharmaceutical, petroleum and oil refinery factories as well as fisheries and livestock activities, demonstrated the water purification role of wetland reserves. Seawater intrusion caused serious water pollution in the estuary. Through temporal CA, the four seasons were grouped into three clusters consistent with the hydrological mean, high and low flow periods. The temporal PCA results suggested that nutrient control was the primary task in mean flow period and the monitoring of effluents from feed mills, petrochemical and pharmaceutical factories is more important in the high flow period, while the wastewater from domestic and livestock should be monitored carefully in low flow periods. The results may provide some guidance or inspiration for environmental management.

A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

Algal blooms often occur in the tidal freshwater (TF) of the James River estuary, a tributary of the Chesapeake Bay. The timing of algal blooms correlates highly to a summer low-flow period when residence time is long and nutrients are available. Because of complex interactions between physical transport and algal dynamics, it is challenging to predict interannual variations of bloom correctly using a complex eutrophication model without having a high-resolution model grid to resolve complex geometry and an accurate estimate of nutrient loading to drive the model. In this study, an approach using long-term observational data (from 1990 to 2013) and the Support vector machine (LS-SVM) for simulating algal blooms was applied. The Empirical Orthogonal Function was used to reduce the data dimension that enables the algal bloom dynamics for the entire TF to be modeled by one model. The model results indicate that the data-driven model is capable of simulating interannual algal blooms with good predictive skills and is capable of forecasting algal blooms responding to the change of nutrient loadings and environmental conditions. This study provides a link between a conceptual model and a dynamic model, and demonstrates that the data-driven model is a good approach for simulating algal blooms in this complex environment of the James River. The method is very efficient and can be applied to other estuaries as well.

Bivariate low flow return period analysis in the Mahanadi River basin, India using copula

ABSTRACTAssessments of low flows of major river of India are essential for effective management of drought and irrigation water demand/supply mechanism. In the present work, bivariate low flow frequency analysis of Mahanadi basin, that had significant variations in hydrological behaviour from upstream to downstream, was done for two major low flow characteristics, namely, severity and occurrence . Three Copulas have been tested for their applicability to estimate the joint return period of low flow at 2, 5, 10, 20, 50 years return periods. According to the Akaike information criterion (AIC) value, the Clayton Copula and the Frank Copula were found to be the most suitable for the stations in Basantapur and Kantamal, respectively. Further, the conditional low flow return periods were estimated and were found useful for low flow management and ecological analysis in the Mahanadi basin.

Improved understanding of snowmelt runoff from the headwaters of China's Yangtze River using remotely sensed snow products and hydrological modeling

As a crucial source of runoff in headwater regions, seasonal snowmelt plays an important role in ensuring water availability downstream, particularly during low flow periods. As the major constituent of China's water towers, the headwater region of the Yangtze River (HRYR) provides water to hundreds of millions of people downstream. Therefore, accurately simulating snowmelt is critical to developing a better understanding of the hydrological processes, which would, in turn, benefit water supply management, irrigation, hydropower generation, and ecological integrity over the HRYR and its lower reaches. However, it is a considerable challenge to conduct hydrological modeling for ungauged and poorly gauged headwater regions, owing to a lack of in situ measurements to appropriately constrain the model and evaluate its results. Satellite remote sensing provides an unprecedented opportunity to capture hydrological state variables globally, such as snow cover area (SCA) based on optical remote sensing and snow water equivalent (SWE) based on passive microwave remote sensing. This study simulates snow and glacier meltwater of the HRYR (above the Zhimenda gauging station), and quantifies proportional meltwater contributions to total runoff using multisource remote sensing data and a distributed hydrological model. We, for the first time ever, evaluate the snowmelt simulations based on the hydrological consistency among precipitation, air and land surface temperatures, and remotely sensed SWE/SCA. Results show that the snowmelt simulations using either SWE or SCA as a reference for calibrating parameters of the hydrological model are highly consistent, with snow and glacier meltwater contributing ~7% and ~5%, respectively, to the total runoff during 2003–2014. This study serves as a basis to simulate snowmelt to understand runoff generation and evolution under climate change across ungauged and poorly gauged headwater regions using multisource remote sensing data.

Application of Entropy Spectral Method for Streamflow Forecasting in Northwest China.

Streamflow forecasting is vital for reservoir operation, flood control, power generation, river ecological restoration, irrigation and navigation. Although monthly streamflow time series are statistic, they also exhibit seasonal and periodic patterns. Using maximum Burg entropy, maximum configurational entropy and minimum relative entropy, the forecasting models for monthly streamflow series were constructed for five hydrological stations in northwest China. The evaluation criteria of average relative error (RE), root mean square error (RMSE), correlation coefficient (R) and determination coefficient (DC) were selected as performance metrics. Results indicated that the RESA model had the highest forecasting accuracy, followed by the CESA model. However, the BESA model had the highest forecasting accuracy in a low-flow period, and the prediction accuracies of RESA and CESA models in the flood season were relatively higher. In future research, these entropy spectral analysis methods can further be applied to other rivers to verify the applicability in the forecasting of monthly streamflow in China.

The influence of multiple controls on structural and functional characteristics of macroinvertebrate community in a regulated Alpine river

AbstractIn the past 30 years, several studies have proved that the river flow regime is fundamental in structuring biotic communities. The condition of the ecosystem results not only from the occurrence of extreme events, such as floods and droughts but also from habitat availability and from its temporal variation. Describing the relationships between macroinvertebrate community characteristics and environmental gradients of flow, temperature, nutrient supplies, and habitat conditions is fundamental to understand ecological dynamics. This is the basis for predicting changes within the communities and in ecosystem functions and ultimately to properly manage and conserve the riverine ecosystems. Seven sites, along a 20‐km river sector, were surveyed for macroinvertebrates and water chemistry seasonally, from 2012 to 2016. Habitat conditions were assessed along a 500‐m stretch in each site. The river discharge was continuously monitored by two water level recorders and used to reconstruct various hydrological indices specific for each sampling location. During the sampling period, numerous high flow events and some prolonged periods of low flow were observed. Quantile regression was used to describe the effects of potential limiting factors on macroinvertebrate community, which were primarily driven by antecedent flow conditions and season whereas habitat conditions and water chemistry played only a minor role. Quantitative models have been developed to predict structural and functional characteristics of macroinvertebrate community as a function of antecedent flow conditions, habitat, and physicochemical water characteristics. Those models allow to identify the main drivers and predict the effect of different water management strategies to riverine ecosystem.

Statistical approaches for identification of low-flow drivers: temporal aspects

Abstract. The characteristics of low-flow periods, especially regarding their low temporal dynamics, suggest that the dimensions of the metrics related to these periods may be easily related to their meteorological drivers using simplified statistical model approaches. In this study, linear statistical models based on multiple linear regressions (MLRs) are proposed. The study area chosen is the German federal state of Lower Saxony with 28 available gauges used for analysis. A number of regression approaches are evaluated. An approach using principal components of local meteorological indices as input appeared to show the best performance. In a second analysis it was assessed whether the formulated models may be eligible for application in climate change impact analysis. The models were therefore applied to a climate model ensemble based on the RCP8.5 scenario. Analyses in the baseline period revealed that some of the meteorological indices needed for model input could not be fully reproduced by the climate models. The predictions for the future show an overall increase in the lowest average 7-day flow (NM7Q), projected by the majority of ensemble members and for the majority of stations.

Spatial and temporal distribution characteristics of different forms of inorganic nitrogen in three types of rivers around Lake Taihu, China

In order to control nitrogen (N) pollution of Lake Taihu, China, we studied the spatial and temporal distribution characteristics of inorganic N in inflowing rivers polluted by industry, agriculture, and domestic sewage during low, moderate, and high flow periods. The results showed that dissolved total nitrogen (DTN) was the main fraction of total nitrogen (TN) input from these rivers. Inflowing rivers had distinct impacts on TN, DTN, ammonium N (NH4+), and nitrate N (NO3-) concentrations of Lake Taihu during the low flow period. Particulate nitrogen (PN) had an impact on Lake Taihu during the three flow periods and all the three types of rivers would increase PN concentration in the lake. Rivers polluted by agriculture had the greatest impact on Lake Taihu's TN, DTN, NO3-, and dissolved inorganic N (DIN) concentrations, while rivers polluted by industry had the greatest impact on NH4+ concentration. Therefore, agriculture and industry should be key targets for nutrient reductions. The in-lake N concentrations were higher than those of inflowing rivers during moderate and high flow periods.