Spatial and temporal changes in the structure of groundwater nitrate concentration time series (1935–1999) as demonstrated by autoregressive modelling

Autoregressive modelling is used to investigate the internal structure of long-term (1935–1999) records of nitrate concentration for five karst springs in the Mendip Hills. There is a significant short term (1–2 months) positive autocorrelation at three of the five springs due to the availability of sufficient nitrate within the soil store to maintain concentrations in winter recharge for several months. The absence of short term (1–2 months) positive autocorrelation in the other two springs is due to the marked contrast in land use between the limestone and swallet parts of the catchment, rapid concentrated recharge from the latter causing short term switching in the dominant water source at the spring and thus fluctuating nitrate concentrations. Significant negative autocorrelation is evident at lags varying from 4 to 7 months through to 14–22 months for individual springs, with positive autocorrelation at 19–20 months at one site. This variable timing is explained by moderation of the exhaustion effect in the soil by groundwater storage, which gives longer residence times in large catchments and those with a dominance of diffuse flow. The lags derived from autoregressive modelling may therefore provide an indication of average groundwater residence times. Significant differences in the structure of the autocorrelation function for successive 10-year periods are evident at Cheddar Spring, and are explained by the effect the ploughing up of grasslands during the Second World War and increased fertiliser usage on available nitrogen in the soil store. This effect is moderated by the influence of summer temperatures on rates of mineralization, and of both summer and winter rainfall on the timing and magnitude of nitrate leaching. The pattern of nitrate leaching also appears to have been perturbed by the 1976 drought.     

Flowpath controls on high-spatial-resolution water-chemistry profiles in headwater streams

Stream water chemistry is routinely measured over time at fixed and sparse sites, which provides a coarse image of spatial variability. Here, we measured nitrate, dissolved organic carbon (DOC) and several chemical proxies for water flowpaths, catchment residence time and biogeochemical transformations, every 50–100 m along 13 km of streams in six agricultural headwater catchments (1.1–3.5km²). The objective was to examine controls on longitudinal nitrate profiles at a high spatial resolution during four seasons: rewetting of the catchments in autumn, winter high-flow, spring recession and summer low-flow. Our results showed monotonic trends in longitudinal profiles for nitrate and DOC, which were opposite for the two solutes. Spatial trends in water-chemistry profiles persisted across seasons, which suggests time-invariant controls on the spatial variations in concentrations. Four catchments exhibited decreasing nitrate and increasing DOC from upstream to downstream, while two catchments exhibited increasing nitrate and decreasing DOC. These smooth gradients did not reflect a longitudinal land-use gradient, but rather an increase in the proportion of groundwater inflows when moving downstream, as suggested by the chemical proxies and punctual discharge measurements. Water chemistry also changed abruptly at confluences, at a farm point source and at a localized groundwater inflow zone.     

Seasonal and interannual variations of nitrate and chloride in stream waters related to spatial and temporal patterns of groundwater concentrations in agricultural catchments

Nitrate concentrations in streamwater of agricultural catchments often exhibit interannual variations, which are supposed to result from land‐use changes, as well as seasonal variations mainly explained by the effect of hydrological and biogeochemical cycles. In catchments on impervious bedrock, seasonal variations of nitrate concentrations in streamwater are usually characterized by higher nitrate concentrations in winter than in summer. However, intermediate or inverse cycles with higher concentrations in summer are sometimes observed. An experimental study was carried out to assess the mechanisms that determine the seasonal cycles of streamwater nitrate concentrations in intensive agricultural catchments. Temporal and spatial patterns of groundwater concentrations were investigated in two adjacent catchments located in south‐western Brittany (France), characterized by different seasonal variations of streamwater nitrate concentrations. Wells were drilled across the hillslope at depths ranging from 1·5 to 20 m. Dynamics of the water table were monitored and the groundwater nitrate and chloride concentrations were measured weekly over 2 years. Results highlighted that groundwater was partitioned into downslope domains, where denitrification induced lower nitrate concentrations than into mid‐slope and upslope domains. For one catchment, high subsurface flow with high nitrate concentrations during high water periods and active denitrification during low water periods explained the higher streamwater nitrate concentrations in winter than in summer. For the other catchment, the high contribution of groundwater with high nitrate concentrations smoothed or inverted this trend. Increasing bromide/chloride ratio and nitrate concentrations with depth argued for an effect of past agricultural pressure on this catchment. The relative contribution of flows in time and correlatively the spatial origin of waters, function of the depth and the location on the hillslope, and their chemical characteristics control seasonal cycles of streamwater nitrate concentrations and can influence their interannual trends. Copyright © 2004 John Wiley & Sons, Ltd.     

Does the complexity in temporal precipitation disaggregation matter for a lumped hydrological model?

ABSTRACT

Flood peaks and volumes are essential design variables and can be simulated by precipitation–runoff (P–R) modelling. The high-resolution precipitation time series that are often required for this purpose can be generated by various temporal disaggregation methods. Here, we compare a simple method (M1, one parameter), focusing on the effective precipitation duration for flood simulations, with a multiplicative cascade model (M2, 32/36 parameters). While M2 aims at generating realistic characteristics of precipitation time series, M1 aims only at accurately reproducing flood variables by P–R modelling. Both disaggregation methods were tested on precipitation time series of nine Swiss mesoscale catchments. The generated high-resolution time series served as input for P–R modelling using a lumped HBV model. The results indicate that differences identified in precipitation characteristics of disaggregated time series vanish when introduced into the lumped hydrological model. Moreover, flood peaks were more sensitive than flood volumes to the choice of disaggregation method.     

Application of the Takagi–Sugeno fuzzy system for combination forecasting of river flow in semiarid mountain regions

Because of high spatial heterogeneity and the degree of uncertainty about hydrological processes in large‐scale catchments of semiarid mountain areas, satisfactory forecasting of daily discharge is seldom available using a single model in many practical cases. In this paper the Takagi–Sugeno fuzzy system (TS) and the simple average method (SAM) are applied to combine forecasts of three individual models, namely, the simple linear model (SLM), the seasonally based linear perturbation model (LPM) and the nearest neighbour linear perturbation model (NNLPM) for modelling daily discharge, and the performance of modelling results is compared in five catchments of semiarid areas. It is found that the TS combination model gives good predictions. The results confirm that better prediction accuracy can be obtained by combining the forecasts of different models with the Takagi–Sugeno fuzzy system in semi‐arid mountain areas. Copyright © 2009 John Wiley & Sons, Ltd.     

A longer-term perspective on soil moisture,groundwater and stream flow response to the 2018 drought in an experimental catchment in the Scottish Highlands

The drought of summer 2018, which affected much of Northern Europe, resulted in low river flows, biodiversity loss and threats to water supplies. In some regions, like the Scottish Highlands, the summer drought followed two consecutive, anomalously dry, winter periods. Here, we examine how the drought, and its antecedent conditions, affected soil moisture, groundwater storage, and low flows in the Bruntland Burn; a sub-catchment of the Girnock Burn long-term observatory in the Scottish Cairngorm Mountains. Fifty years of rainfall-runoff observations and long-term modelling studies in the Girnock provided unique contextualisation of this extreme event in relation to more usual summer storage dynamics. Whilst summer precipitation in 2018 was only 63% of the long-term mean, soil moisture storage across much of the catchment were less than half of their summer average and seasonal groundwater levels were 0.5 m lower than normal. Hydrometric and isotopic observations showed that ~100 mm of river flows during the summer (May-Sept) were sustained almost entirely by groundwater drainage, representing ~30% of evapotranspiration that occurred over the same period. A key reason that the summer drought was so severe was because the preceding two winters were also dry and failed to adequately replenish catchment soil moisture and groundwater stores. As a result, the drought had the biggest catchment storage deficits for over a decade, and likely since 1975–1976. Despite this, recovery was rapid in autumn/winter 2018, with soil and groundwater stores returning to normal winter values, along with stream flows. The study emphasizes how long-term data from experimental sites are key to understanding the non-linear flux-storage interactions in catchments and the “memory effects” that govern the evolution of, and recovery from, droughts. This is invaluable both in terms of (a) giving insights into hydrological behaviours that will become more common water resource management problems in the future under climate change and (b) providing extreme data to challenge hydrological models.     

Comparison of streamflow recession between plantations and native forests in small catchments in Central-Southern Chile

In central Chile, many communities rely on water obtained from small catchments in the coastal mountains. Water security for these communities is most vulnerable during the summer dry season and, from 2010 to 2017, rainfall during the dry season was between 20% and 40% below the long-term average. The rate of decrease in stream flow after a rainfall event is a good measure of the risk of flow decreasing below a critical threshold. This risk of low flow can be quantified using a recession coefficient (α) that is the slope of an exponential decay function relating flow to time since rainfall. A mathematical model was used to estimate the recession coefficient (α) for 142 rainstorm events (64 in summer; 78 in winter) in eight monitored catchments between 2008 and 2017. These catchments all have a similar geology and extend from 35 to 39 degrees of latitude south in the coastal range of south-central Chile. A hierarchical cluster analysis was used to test for differences between the mean value of α for different regions and forest types in winter and summer. The value of α did not differ (p < 0.05) between catchments in winter. Some differences were observed during summer and these were attributed to morphological differences between catchments and, in the northernmost catchments, the effect of land cover (native forest and plantation). Moreover, α for catchments with native forest was similar to those with pine plantations, although there was no difference (p < 0.05) between these and Eucalyptus plantations. The recession constant is a well-established method for understanding the effect of climate and disturbance on low flows and baseflows and can enhance local and regional analyses of hydrological processes. Understanding the recession of flow after rainfall in small headwater catchments, especially during summer, is vital for water resources management in areas where the establishment of plantations has occurred in a drying climate.     

BIBLIOGRAPHIE

Abstract

Time series modelling approaches are useful tools for simulating and forecasting hydrological variables and their change through time. Although linear time series models are common in hydrology, the nonlinear time series model, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, has rarely been used in hydrology and water resources engineering. The GARCH model considers the conditional variance remaining in the residuals of the linear time series models, such as an ARMA or an ARIMA model. In the present study, the advantages of a GARCH model against a linear ARIMA model are investigated using three classes of the GARCH approach, namely Power GARCH, Threshold GARCH and Exponential GARCH models. A daily streamflow time series of the Matapedia River, Quebec, Canada, is selected for this study. It is shown that the ARIMA (13,1,4) model is adequate for modelling streamflow time series of Matapedia River, but the Engle test shows the existence of heteroscedasticity in the residuals of the ARIMA model. Therefore, an ARIMA (13,1,4)-GARCH (3,1) error model is fitted to the data. The residuals of this model are examined for the existence of heteroscedasticity. The Engle test indicates that the GARCH model has considerably reduced the heteroscedasticity of the residuals. However, the Exponential GARCH model seems to completely remove the heteroscedasticity from the residuals. The multi-criteria evaluation for model performance also proves that the Exponential GARCH model is the best model among ARIMA and GARCH models. Therefore, the application of a GARCH model is strongly suggested for hydrological time series modelling as the conditional variance of the residuals of the linear models can be removed and the efficiency of the model will be improved.

Editor D. Koutsoyiannis; Associate editor C. Onof

Citation Modarres, R. and Ouarda, T.B.M.J., 2013. Modelling heteroscedasticty of streamflow times series. Hydrological Sciences Journal, 58 (1), 1–11.     

REPORT ON THE ACTIVITIES OF THE INTERNATIONAL COMMISSION ON WATER RESOURCES RELATIONS AND SYSTEMS FOR THE PERIOD 1971–1975

Abstract

This paper describes the development of a field-scale model that simulates the nitrogen (N) cycle in grazed grassland within a catchment-scale management model which can predict the loading and concentration of nitrate in rivers. The development is comprised of the addition of two sub-models of nitrate transport: one relating the amount of soil nitrate to its concentration in drainage water for different types of soil, and the second accounting for the proportion of permeable rock underlying the catchment. The sub-model that calculates the supply and transport of soil nitrate has been made sensitive to annual patterns of weather according to a classification based on the maximum soil water deficit. The model predictions were tested against best estimates of annual load and peak concentration of nitrate in rivers draining 11 small, predominantly grassland, catchments in the UK during the period 1974–1987.     

Assessment of a lumped coupled flow‐isotope model in data scarce Boreal catchments

Quantifying streamflow sources within remote, data scarce, Boreal catchments remains a significant challenge because of limited accessibility and complex, flat topography. The coupled use of hydrometric and isotopic data has previously been shown to facilitate quantification of streamflow sources, but application has generally been limited to small basins and short time scales. A lumped flow‐isotope model was used to estimate contributing streamflow sources (soil, ground, and wetland water) over a four‐year period in two large nested headwater catchments (Sapochi and Odei Rivers) in northern Manitoba, Canada. On average, the primary streamflow source was estimated as soil water (60%) in the Sapochi River, and groundwater (54%) in the Odei River. A strong seasonal influence was observed: soil water was the primary streamflow source in summer, changing to groundwater and wetlands during the winter. Interannual variability in streamflow sources was strongly linked to the presence or absence of late summer rainfall. The greatest uncertainties in source quantification were identified during the spring freshets and high precipitation events, and hence, simulations may be improved through explicit representation of the soil freeze/thaw process and data collection during this period. Assessment of primary streamflow components and qualitative uncertainty estimation using coupled isotope‐flow modelling is an effective method for first‐order identification of streamflow sources in data sparse remote headwaters. Copyright © 2016 John Wiley & Sons, Ltd.     

Prediction of climate change effects on the runoff regime of a forested catchment in northern Iran

ABSTRACT

The southern coast of the Caspian Sea in northern Iran is bordered by a mountain range with forested catchments which are susceptible to droughts and floods. This paper examines possible changes to runoff patterns from one of these catchments in response to climate change scenarios. The HEC-HMS rainfall–runoff model was used with downscaled future rainfall and temperature data from 13 global circulation models, and meteorological and hydrometrical data from the Casilian (or “Kassilian”) Catchment. Annual and seasonal predictions of runoff change for three future emissions scenarios were obtained, which suggest significantly higher spring rainfall with increased risk of flooding and significantly lower summer rainfall leading to a higher probability of drought. Flash floods arising from extreme rainfall may become more frequent, occurring at any time of year. These findings indicate a need for strategic planning of water resource management and mitigation measures for increasing flood hazards.

EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned     

Estimation of rainfall elasticity of streamflow in Australia

Abstract

Estimates of rainfall elasticity of streamflow in 219 catchments across Australia are presented. The rainfall elasticity of streamflow is defined here as the proportional change in mean annual streamflow divided by the proportional change in mean annual rainfall. The elasticity is therefore a simple estimate of the sensitivity of long-term streamflow to changes in long-term rainfall, and is particularly useful as an initial estimate of climate change impact in land and water resources projects. The rainfall elasticity of streamflow is estimated here using a hydrological modelling approach and a nonparametric estimator. The results indicate that the rainfall elasticity of streamflow (? _P ) in Australia is about 2.0–3.5 (observed in about 70% of the catchments), that is, a 1% change in mean annual rainfall results in a 2.0–3.5% change in mean annual streamflow. The rainfall elasticity of streamflow is strongly correlated to runoff coefficient and mean annual rainfall and streamflow, where streamflow is more sensitive to rainfall in drier catchments, and those with low runoff coefficients. There is a clear relation-ship between the ? _P values estimated using the hydrological modelling approach and those estimated using the nonparametric estimator for the 219 catchments, although the values estimated by the hydrological modelling approach are, on average, slightly higher. The modelling approach is useful where a detailed study is required and where there are sufficient data to reliably develop and calibrate a hydrological model. The nonparametric estimator is useful where consistent estimates of the sensitivity of long-term streamflow to climate are required, because it is simple to use and estimates the elasticity directly from the historical data. The nonparametric method, being model independent, can also be easily applied in comparative studies to data sets from many catchments across large regions.     

Climate change impacts on bedload transport in alpine drainage basins with hydropower exploitation

Sediment transport is known to have a significant impact on hydropower infrastructures and changes in sediment transport rates are important for sediment management measures and hydroelectricity production. In this study, we present how climate change may affect bedload transport in 66 high alpine catchments used by hydropower companies in the Valais, Switzerland. Future sediment yield is estimated with a runoff‐based sediment transport model for the two future 30 year time periods 2021–2050 and 2070–2099. The analysis is integrated into a modelling chain in which error‐corrected and downscaled climate scenarios generated in the framework of the ENSEMBLES project are coupled to the hydrological model PREVAH, glacier retreat and bedload transport. To calibrate the sediment transport model, we used the observed sediment volumes in water intakes and reservoirs if such data were available. The results obtained show on average a decrease of sediment yield due to the reduced runoff generation during summer, especially for the scenario period 2070–2099. A shift of the seasonal sediment transport regime with a current maximum during July and August to earlier months in the year is predicted. Projections of future sediment yield rely on the accuracy of the individual modeling chain elements. The different sources of uncertainty are discussed qualitatively. Copyright © 2015 John Wiley & Sons, Ltd.     

Time series analysis of long‐term river dissolved organic carbon records

Two long records of dissolved organic carbon (DOC) concentrations in river water were examined by a detailed time series analysis in order to shed light on the mechanisms generating observed increases in DOC concentrations across the UK. The records date back as far as 1962 and come from catchments 589 and 818 km² in area. The DOC records were compared with others taken simultaneously for flow, pH, alkalinity, air temperature and rainfall, and in one of the catchments also for turbidity and conductivity. All records were examined by the seasonal Kendall test; frequency distributions of daily DOC measurements were examined; annual cycles were calculated, Autoregressive and impulse functions were derived for DOC against flow records. The time series analysis shows that: (i) DOC trends cannot be readily explained by trends in flow, pH, alkalinity, turbidity or conductivity; (ii) there is a significant increase in carbon flux from these catchments; (iii) maximum and minimum components of the annual distribution of daily readings both show increases in DOC, implying that DOC flux is increasing for differing hydrological pathways; (iv) increases in DOC concentrations coincide with increases in temperature, though the biggest increases in temperature are in the winter months when such increases might be expected to have less effect on DOC production; (v) change in trend, and therefore flux, was observed to occur after a severe drought in 1976. The study suggests that there are real, significant increases in carbon loss from upland peat catchments and that climate is a major driver, especially a severe drought. Severe drought triggering changes in the DOC flux might be attributed to enzymic latch mechanisms. Copyright © 2004 John Wiley & Sons, Ltd.     

Understanding hydrological processes of glacierized catchments in the western Himalayas by a multi-year tracer-based hydrograph separation analysis

Snow and glacier melt are significant contributors to streamflow in Himalayan catchments, and their increasing contributions serve as key indicators of climate change. Consequently, the quantification of these streamflow components holds significant importance for effective water resource management. In this study, we utilized the spatio-temporal variability of isotopic signatures in stream water, rainfall, winter fresh snow, snowpack, glaciers, springs, and wells, in conjunction with hydrometeorological observations and Snow Cover Area (SCA) data, to identify water sources and develop a conceptual understanding of streamflow dynamics in three catchments (Lidder, Sindh, and Vishow) within the western Himalayas. The following results were obtained: (a) endmember contributions to the streamflow exhibit significant spatial and seasonal variability across the three catchments during 2018–2020; (b) snowmelt dominates streamflow, with average contributions across the entire catchment varying: 59% ± 9%, 55% ± 4%, 56% ± 6%, and 55% ± 9% in Lidder, 43% ± 6%, 38% ± 6%, 32% ± 4%, and 33% ± 5% in Sindh and 45% ± 8%, 40% ± 6%, 39% ± 6%, and 32% ± 5% in Vishow during spring, summer, autumn, and winter seasons, respectively; (c) glacier melt contributions can reach ~30% to streamflow near the source regions during peak summer; (d) The primary uncertainties in streamflow components are attributed to the spatiotemporal variability of tracer signatures of winter fresh snow/snowpack (±1.9% to ±20%); (e)regarding future streamflow components, if the glacier contribution were to disappear completely, the annual average streamflow in Lidder and Sindh could decrease up to ~20%. The depletion of the cryosphere in the region has led to a rapid increase in runoff (1980–1900), but it has also resulted in a significant streamflow reduction due to glacier mass loss and changes in peak streamflow over the past three decades (1990–2020). The findings highlight the significance of environmental isotope analysis, which provides insights into water resources and offers a critical indication of the streamflow response to glacier loss under a changing climate.     

Seasonal controls on DOC dynamics in nested upland catchments in NE Scotland

Spatial and temporal variability of hydrological responses affecting surface water dissolved organic carbon (DOC) concentrations are important for determining upscaling patterns of DOC export within larger catchments. Annual and intra‐annual variations in DOC concentrations and fluxes were assessed over 2 years at 12 sites (3·40–1837 km²) within the River Dee basin in NE Scotland. Mean annual DOC fluxes, primarily correlated with catchment soil coverage, ranged from 3·41 to 9·48 g m^?2 yr^?1. Periods of seasonal (summer–autumn and winter–spring) DOC concentrations (production) were delineated and related to discharge. Although antecedent temperature mainly determined the timing of switchover between periods of high DOC in the summer‐autumn and low DOC in winter‐spring, inter‐annual variability of export within the same season was largely dependent on its associated water flux. DOC fluxes ranged from 1·39 to 4·80 g m^?2 season^?1 during summer–autumn and 1·43 to 4·15 g m^?2 season^?1 in winter–spring.Relationships between DOC areal fluxes and catchment scale indicated that mainstem fluxes reflect the averaging of highly heterogeneous inputs from contrasting headwater catchments, leading to convergent DOC fluxes at catchment sizes of ca 100 km². However, during summer–autumn periods, in contrast to winter–spring, longitudinal mainstem DOC fluxes continue to decrease, most likely because of increasing biological processes. This highlights the importance of considering seasonal as well as annual changes in DOC fluxes with catchment scale. This study increases our understanding of the temporal variability of DOC upscaling patterns reflecting cumulative changes across different catchment scales and aids modelling of carbon budgets at different stages of riverine systems. Copyright © 2010 John Wiley & Sons, Ltd.     

Assessing the results of scenarios of climate and land use changes on the hydrology of an Italian catchment: modelling study

Hydrological models are recognized as valid scientific tools to study water quantity and quality and provide support for the integrated management and planning of water resources at different scales. In common with many catchments in the Mediterranean, the study catchment has many problems such as the increasing gap between water demand and supply, water quality deterioration, scarcity of available data, lack of measurements and specific information. The application of hydrological models to investigate hydrological processes in this type of catchments is of particular relevance for water planning strategies to address the possible impact of climate and land use changes on water resources. The distributed catchment scale model (DiCaSM) was selected to study the impact of climate and land use changes on the hydrological cycle and the water balance components in the Apulia region, southern Italy, specifically in the Candelaro catchment (1780 km²). The results obtained from this investigation proved the ability of DiCaSM to quantify the different components of the catchment water balance and to successfully simulate the stream flows. In addition, the model was run with the climate change scenarios for southern Italy, i.e. reduced winter rainfall by 5–10%, reduced summer rainfall by 15–20%, winter temperature rise by 1·25–1·5 °C and summer temperature rise by 1·5–1·75 °C. The results indicated that by 2050, groundwater recharge in the Candelaro catchment would decrease by 21–31% and stream flows by 16–23%. The model results also showed that the projected durum wheat yield up to 2050 is likely to decrease between 2·2% and 10·4% due to the future reduction in rainfall and increase in temperature. In the current study, the reliability of the DiCaSM was assessed when applied to the Candelaro catchment; those parameters that may cause uncertainty in model output were investigated using a generalized likelihood uncertainty estimation (GLUE) methodology. The results showed that DiCaSM provided a small level of uncertainty and subsequently, a higher confidence level. Copyright © 2010 John Wiley & Sons, Ltd.     

Quantifying seasonal export and retention of nutrients in West European lowland rivers at catchment scale

To set accurate critical values for the protection of lakes and coastal areas, it is crucial to know the seasonal variation of nutrient exports from rivers. This article presents an improved method for estimating export and in‐stream nutrient retention and its seasonal variation. For 13 lowland river catchments in Western Europe, inputs to surface water and exports were calculated on a monthly basis. The catchments varied in size (21 to 486 km²), while annual in‐stream retention ranged from 23 to 84% for N and 39 to 72% for P. A novel calculation method is presented that quantifies monthly exports from lowland rivers based on an annual load to the river system. Inputs in the calculation are annual emission to the surface waters, average monthly river discharge, average monthly water temperature and fraction of surface water area in the catchment. The method accounts for both seasonal variation of emission to the surface water and seasonal in‐stream retention. The agreement between calculated values and calibration data was high (N: r² = 0·93; p < 0·001 and P: r² = 0·81; p < 0·001). Validation of the model also showed good results with model efficiencies for the separate catchments ranging from 31 to 95% (average 76%). This indicates that exports of nitrogen and phosphorus on a monthly basis can be calculated with few input data for a range of West European lowland rivers. Further analysis showed that retention in summer is higher than that in winter, resulting in lower summer nutrient concentrations than that calculated with an average annual input. This implies that accurate evaluation of critical thresholds for eutrophication effects must account for seasonal variation in hydrology and nutrient loading. Our quantification method thus may improve the modelling of eutrophication effects in standing waters. Copyright © 2011 John Wiley & Sons, Ltd.     

Conceptual modelling to assess the influence of hydro-climatic variability on runoff processes in data scarce semi-arid Andean catchments

Using hydro-meteorological time series of 50 years and in situ measurements, the dominant runoff processes in perennial Andean headwater catchments in Chile were determined using the hydrological model HBV light. First, cluster analysis was used to identify dry, wet and intermediate years. From these, sub-periods were identified with contrasting seasonal climatic influences on streamflow. By calibrating the model across different periods, impacts on model performance, parameter sensitivity and identifiability were investigated, providing insights into differences in hydrological processes. The modelling approach suggested that, independently of a dry or wet period of calibration, the streamflow response is mostly consistent with flux from groundwater storage, while only a small fraction comes from direct routing of snowmelt. The variation of model parameters, such as the groundwater rate coefficient, was found to be consistent with differing recharge in wet and dry years. The resulting snowmelt–groundwater model is a realistic hypothesis of the hydrological operation of such complex, data scarce and semi-arid Andean catchments. This model may also be a useful tool for predictions of seasonal water availability and a basis for further field studies.     

ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO)

In the present study, a stationary stochastic ARMA/ARIMA [Autoregressive Moving (Integrated) Average] modelling approach has been adapted to forecast daily mean ambient air pollutants (O₃, CO, NO and NO₂) concentration at an urban traffic site (ITO) of Delhi, India. Suitable variance stabilizing transformation has been applied to each time series in order to make them covariance stationary in a consistent way. A combination of different information-criterions, namely, AIC (Akaike Information Criterion), HIC (Hannon–Quinn Information Criterion), BIC (Bayesian Information criterion), and FPE (Final Prediction Error) in addition to ACF (autocorrelation function) and PACF (partial autocorrelation function) inspection, has been tried out to obtain suitable orders of autoregressive (p) and moving average (q) parameters for the ARMA(p,q)/ARIMA(p,d,q) models. Forecasting performance of the selected ARMA(p,q)/ARIMA(p,d,q) models has been evaluated on the basis of MAPE (mean absolute percentage error), MAE (mean absolute error) and RMSE (root mean square error) indicators. For 20 out of sample forecasts, one step (i.e., one day) ahead MAPE for CO, NO₂, NO and O₃, have been found to be 13.6, 12.1, 21.8 and 24.1%, respectively. Given the stochastic nature of air pollutants data and in the light of earlier reported studies regarding air pollutants forecasts, the forecasting performance of the present approach is satisfactory and the suggested forecasting procedure can be effectively utilized for short term air quality forewarning purposes.