Representative locations from time series of soil water content using time stability and wavelet analysis

The concept of time stability has been widely used in the design and assessment of monitoring networks of soil moisture, as well as in hydrological studies, because it is as a technique that allows identifying of particular locations having the property of representing mean values of soil moisture in the field. In this work, we assess the effect of time stability calculations as new information is added and how time stability calculations are affected at shorter periods, subsampled from the original time series, containing different amounts of precipitation. In doing so, we defined two experiments to explore the time stability behavior. The first experiment sequentially adds new data to the previous time series to investigate the long-term influence of new data in the results. The second experiment applies a windowing approach, taking sequential subsamples from the entire time series to investigate the influence of short-term changes associated with the precipitation in each window. Our results from an operating network (seven monitoring points equipped with four sensors each in a 2-ha blueberry field) show that as information is added to the time series, there are changes in the location of the most stable point (MSP), and that taking the moving 21-day windows, it is clear that most of the variability of soil water content changes is associated with both the amount and intensity of rainfall. The changes of the MSP over each window depend on the amount of water entering the soil and the previous state of the soil water content. For our case study, the upper strata are proxies for hourly to daily changes in soil water content, while the deeper strata are proxies for medium-range stored water. Thus, different locations and depths are representative of processes at different time scales. This situation must be taken into account when water management depends on soil water content values from fixed locations.     

One century of hydrological monitoring in two small catchments with different forest coverage

Long-term data on precipitation and runoff are essential to draw firm conclusions about the behavior and trends of hydrological catchments that may be influenced by land use and climate change. Here the longest continuous runoff records from small catchments (<1 km(2)) in Switzerland (and possibly worldwide) are reported. The history of the hydrological monitoring in the Sperbel- and Rappengraben (Emmental) is summarized, and inherent uncertainties in the data arising from the operation of the gauges are described. The runoff stations operated safely for more than 90% of the summer months when most of the major flood events occurred. Nevertheless, the absolute values of peak runoff during the largest flood events are subject to considerable uncertainty. The observed differences in average, base, and peak runoff can only partly be attributed to the substantial differences in forest coverage. This treasure trove of data can be used in various ways, exemplified here with an analysis of the generalized extreme value distributions of the two catchments. These distributions, and hence flood return periods, have varied greatly in the course of one century, influenced by the occurrence of single extreme events. The data will be made publicly available for the further analysis of the mechanisms governing the runoff behavior of small catchments, as well as for testing stochastic and deterministic models.     

Hydrology of mountainous areas in the upper Indus Basin, Northern Pakistan with the perspective of climate change

Mountainous areas in the northern Pakistan are blessed by numerous rivers that have great potential in water resources and hydropower production. Many of these rivers are unexploited for their water resource potential. If the potential of these rivers are explored, hydropower production and water supplies in these areas may be improved. The Indus is the main river originating from mountainous area of the Himalayas of Baltistan, Pakistan in which most of the smaller streams drain. In this paper, the hydrology of the mountainous areas in northern Pakistan is studied to estimate flow pattern, long-term trend in river flows, characteristics of the watersheds, and variability in flow and water resource due to impact of climate change. Eight watersheds including Gilgit, Hunza, Shigar, Shyok, Astore, Jhelum, Swat, and Chitral, Pakistan have been studied from 1960 to 2005 to monitor hydrological changes in relation to variability in precipitation, temperature and mean monthly flows, trend of snow melt runoff, analysis of daily hydrographs, water yield and runoff relationship, and flow duration curves. Precipitation from ten meteorological stations in mountainous area of northern Pakistan showed variability in the winter and summer rains and did not indicate a uniform distribution of rains. Review of mean monthly temperature of ten stations suggested that the Upper Indus Basin can be categorized into three hydrological regimes, i.e., high-altitude catchments with large glacierized parts, middle-altitude catchments south of Karakoram, and foothill catchments. Analysis of daily runoff data (1960-2005) of eight watersheds indicated nearly a uniform pattern with much of the runoff in summer (June-August). Impact of climate change on long-term recorded annual runoff of eight watersheds showed fair water flows at the Hunza and Jhelum Rivers while rest of the rivers indicated increased trends in runoff volumes. The study of the water yield availability indicated a minimum trend in Shyok River at Yogo and a maximum trend in Swat River at Kalam. Long-term recorded data used to estimate flow duration curves have shown a uniform trend and are important for hydropower generation for Pakistan which is seriously facing power crisis in last 5 years.     

Assessing different sources of uncertainty in hydrological projections of high and low flows: case study for Omerli Basin,Istanbul, Turkey

This study investigates the assessment of uncertainty contribution in projected changes of high and low flows from parameterization of a hydrological model and inputs of ensemble regional climate models (RCM). An ensemble of climate projections including 15 global circulation model (GCM)/RCM combinations and two bias corrections (change factor (CF) and bias correction in mean (BC)) was used to generate streamflow series for a reference and future period using the Hydrologiska Byråns Vattenbalansavdelning (HBV) model with the 25 best-fit parameter sets based on four objective functions. The occurrence time of high flows is also assessed through seasonality index calculation. Results indicated that the inputs of hydrological model from ensemble climate models accounts for greater contribution to the uncertainty related to projected changes in high flows comparing to the contribution from hydrological model parameterization. However, the uncertainty contribution is opposite for low flows, particularly for CF method. Both CF and BC increases the total mean variance of high and low flows. The variability in the occurrence time of high flows through RCMs is greater than the variability resulted from hydrological model parameters with and without statistical downscaling. The CF provides more accurate timing than BC and it shows the most pronounced changes in flood seasonality.     

Testing the MAGIC acid rain model in highly organic,low-conductivity waters using multiple calibrations

Accurate predictions of acid precipitation effects on water resources are important in order to allow a better understanding of various pollution control strategy outcomes. Dynamic geochemical models have been developed to address this need, but have to be tested under a variety of environmental conditions to provide confidence in their predictions. The most commonly used aquatic acidification model in North America and Europe is the model of acidification of groundwater in catchments (MAGIC). Though extensively used, MAGIC has never been tested in catchments with extremely low ionic strength water and high in natural organic acids (NOAs) from wetlands, two conditions which are common in large parts of Canada. We calibrated the model for two catchments located in Nova Scotia, Canada, which had some of the most dilute freshwaters reported in the literature and very high NOA. We also evaluated the variability inherent in calibration data sets by using five separate data sets collected over a 15-year period at the same sites. We show good model simulations for the main cations and anions in catchment waters. However, modeling pH is more difficult in the highly organic waters and requires modification to the acid dissociation constants. Calculated acid neutralization capacity can also be more difficult to model due to the low ion content making small errors more important. In theory, multiple calibrations of a model at a same site should produce identical hindcasts and predictions. In reality, the multiple calibrations produced a series of similar, but not identical outcomes which give a probable range of past values and future outcomes. We feel that this practical approach to validation is a useful addition to the arsenal of model testing tools.     

基于去耦合直接法的兰江流域地表水水质预测方法研究

针对影响因素众多、耦合机制复杂情况下的地表水污染物浓度预测问题,将河道污染物浓度的变化量表示为各种影响因子一阶偏导项和二阶偏导项的线性叠加。其中,一阶偏导项可描述影响因子变化与污染物浓度变化的直接关系,二阶偏导项可描述影响因子之间交互作用对污染物浓度变化的影响。在此基础上,提出了用以模拟地表水污染物浓度的去耦合直接法。采用2014—2016年兰江流域将军岩、低田、半潭、沈村、焦岩5个断面的水文和氨氮、高锰酸盐指数、总氮、总磷4项污染指标逐日实测数据,通过差分法求解了一阶和二阶偏导项,并采用2017—2019年实测数据对模型进行了验证和评价。结果表明：去耦合直接法能够有效预测地表水主要污染物浓度的变化方向和变化量,且模拟结果和实测结果的符合情况较好,4项污染指标模拟值的Nash-Sutcliffe系数为0.479～0.654,模拟值与实测值的偏差为0.070～0.352;汇流区面积增加后,影响因子不确性对污染物浓度的扰动减小,污染物浓度变化的规律性增强,去耦合直接法的模拟精度升高。与SWAT模型的对比分析结果显示,在污染成因不发生显著变化的情况下,去耦合直接法的模拟精度优于SWAT模型。     

Small-scale temporal variation and the effect of urbanisation on extracellular enzyme activity in streams

The activity of six extracellular enzymes involved in the degradation of dissolved organic carbon compounds was measured in two highly urbanised and two minimally impacted streams east of Melbourne, Australia, using 4-methylumbelliferyl-substrates. Small-scale temporal variation in enzyme activity was determined by repeatedly sampling the same point in the water column, while the effect of flow was determined by sampling in regions of higher and lower flow in both stream types. Replicate samples showed that enzyme activity was not significantly different over small (minutes) time scales. On five of six sampling occasions the enzyme activity was unaffected by flow. On one sampling occasion in a minimally disturbed stream, the difference between the high- and low-flow regions was statistically significant (ANOSIM, Global R= 0.78, P= 0.03). Enzyme activity profiles (activities of the suite of enzymes) of the streams in urbanised catchments were different to those in minimally disturbed catchments. The measurements made in four different streams showed high reproducibility over short time periods (minutes) which lends greater credibility to analogous spatial studies. Although these results determined that small-scale temporal variability was not significant, and that the effects of flow were generally minimal, it is recommended that spatial and temporal variability in the stream be at least considered before any studies measuring extracellular enzyme activity in stream waters are carried out. Such an approach will lead to conclusions from measurements that are not likely to be confounded by variables such as flow rate or time.     

Serial Correlation and Inter-annual Variability in Relation to the Statistical Power of Monitoring Schemes to Detect Trends in Fish Populations

We studied the effects of inter-annual variability and serial correlation on the statistical power of monitoring schemes to detect trends in biomass of bream (Abramis brama) in Lake Veluwemeer (The Netherlands). In order to distinguish between 'true' system variability and sampling variability we simulated the development of the bream population, using estimates for population structure and growth, and compared the resulting inter-annual variabilities and serial correlations with those from field data. In all cases the inter-annual variability in the field data was larger than in simulated data (e.g. for total biomass of all assessed bream sigma = 0.45 in field data, and sigma = 0.03-0.14 in simulated data) indicating that sampling variability decreased statistical power for detecting trends. Moreover, sampling variability obscured the inter-annual dependency (and thus the serial correlation) of biomass, which was expected because in this long-lived population biomass changes are buffered by the many year classes present. We did find the expected serial correlation in our simulation results and concluded that good survey data of long-lived fish populations should show low sampling variability and considerable inter-annual serial correlation. Since serial correlation decreases the power for detecting trends, this means that even when sampling variability would be greatly reduced, the number of sampling years to detect a change of 15%.year(-1) in bream populations (corresponding to a halving or doubling in a six-year period) would in most cases be more than six. This would imply that the six-year reporting periods that are required by the Water Framework Directive of the European Union are too short for the existing fish monitoring schemes.     

Manganese in the upper Severn mid-Wales

The concentrations of manganese (Mn) in the Upper River Severn (the Plynlimon catchments) are examined in relation to rainfall, cloud water, throughfall, stemflow and stream water concentrations where there is over 20 years of monitoring data available. Manganese concentrations are particularly low in rainfall and cloud water, with maximum concentrations occurring under low volumes of catch due to atmospheric "washout" of contaminants and dry deposition. There is strong Mn enrichment in throughfall and stemflow and this is probably linked to cycling through the vegetation. Manganese in the streams and groundwaters are primarily supplied from within-catchment sources. The highest concentrations occur within the tree canopy probably due to element cycling and in groundwaters due to mobilisation from the rock. Manganese concentrations in streams are at their lowest during spring and summer following long dry spells, with rapid increases following subsequent rain. There is no clear long-term trend in Mn concentration in the streams although there are increases in Mn concentrations for years when there is extensive felling of spruce plantation forest and in 1995 following a more extensive dry period. New high resolution monitoring picks up the effects of the rising limb of the hydrograph when concentrations rapidly increase, diurnal patterns during summer low-flow periods and contrasting dynamics between moorland and forested catchments.     

Montane Meadows as Indicators of Environmental Change

We used a time series of satellite multispectral imagery for mapping and monitoring six classes of montane meadows arrayed along a moisture gradient (from hydric to mesic to xeric). We hypothesized that mesic meadows would support the highest species diversity of plants, birds, and butterflies because they are more moderate environments. We also hypothesized that mesic meadows would exhibit the greatest seasonal and interannual variability in spectral response across years. Field sampling in each of the meadow types was conducted for plants, birds, and butterflies in 1997 and 1998. Mesic meadows supported the highest plant species diversity, but there was no significant difference in bird or butterfly species diversity among meadow types. These data show that it may be easier to detect significant differences in more species rich taxa (e.g., plants) than taxa that are represented by fewer species (e.g., butterflies and birds). Mesic meadows also showed the greatest seasonal and interannual variability in spectral response. Given the rich biodiversity of mesic montane meadows and their sensitivity to variations in temperature and moisture, they may be important to monitor in the context of environmental change     

POTENTIAL OF EPISODIC FLOWS IN SOME FOUR REPRESENTATIVE NON-PERENNIAL RIVER FLOW CATCHMENTS IN SEMI ARID LAIKIPIA DISTRICT, KENYA

The purpose of this study was to establish the water resources of thenon-perennial streams in providing supplementary water needs inLaikipia district. This district has undergone remarkable land usechanges resulting in water use stress of perennial river abstractions andgroundwater exploitation in the semi arid environment.Over a three year period extending from January 1989 to December1991, hydrological variables were monitored in four non-perennial flowcatchments within the district. These catchments have been shown tohave potential of about 8000 m³/km²/year except forlong dry spells during the observation period and high sediment lossesand evaporation rates calling for proper conservation measures in orderto exploit productively the water resources potential of such catchments.     

Spatial and temporal trends in water quality in a Mediterranean temporary river impacted by sewage effluents

This paper analyzes how changes in hydrological conditions can affect the water quality of a temporary river that receives direct inputs of sewage effluents. Data from 12 spatial surveys of the Vène river were examined. Physico-chemical parameters, major ion, and nutrient concentrations were measured. Analyses of variance (ANOVA) and multivariate analyses were performed. ANOVA revealed significant spatial differences for conductivity and major ion but no significant spatial differences for nutrient concentrations even if higher average concentrations were observed at stations located downstream from sewage effluent discharge points. Significant temporal differences were observed among all the parameters. Karstic springs had a marked dilution effect on the direct disposal of sewage effluents. During high-flow periods, nutrient concentrations were high to moderate whereas nutrient concentrations ranged from moderate to bad at stations located downstream from the direct inputs of sewage effluents during low-flow periods. Principal component analysis showed that water quality parameters that explained the water quality of the Vène river were highly dependent on hydrological conditions. Cluster analysis showed that when the karstic springs were flowing, water quality was homogeneous all along the river, whereas when karstic springs were dry, water quality at the monitoring stations was more fragmented. These results underline the importance of considering hydrological conditions when monitoring the water quality of temporary rivers. In view of the pollution observed in the Vène river, “good water chemical status” can probably only be achieved by improving the management of sewage effluents during low-flow periods.     

An urban observatory for quantifying phosphorus and suspended solid loads in combined natural and stormwater conveyances

Water quality in urban streams and stormwater systems is highly dynamic, both spatially and temporally, and can change drastically during storm events. Infrequent grab samples commonly collected for estimating pollutant loadings are insufficient to characterize water quality in many urban water systems. In situ water quality measurements are being used as surrogates for continuous pollutant load estimates; however, relatively few studies have tested the validity of surrogate indicators in urban stormwater conveyances. In this paper, we describe an observatory aimed at demonstrating the infrastructure required for surrogate monitoring in urban water systems and for capturing the dynamic behavior of stormwater-driven pollutant loads. We describe the instrumentation of multiple, autonomous water quality and quantity monitoring sites within an urban observatory. We also describe smart and adaptive sampling procedures implemented to improve data collection for developing surrogate relationships and for capturing the temporal and spatial variability of pollutant loading events in urban watersheds. Results show that the observatory is able to capture short-duration storm events within multiple catchments and, through inter-site communication, sampling efforts can be synchronized across multiple monitoring sites.     

Forecasting of cyanobacterial density in Torrão reservoir using artificial neural networks

The ability of general regression neural networks (GRNN) to forecast the density of cyanobacteria in the Torr?o reservoir (Tamega river, Portugal), in a period of 15 days, based on three years of collected physical and chemical data, was assessed. Several models were developed and 176 were selected based on their correlation values for the verification series. A time lag of 11 was used, equivalent to one sample (periods of 15 days in the summer and 30 days in the winter). Several combinations of the series were used. Input and output data collected from three depths of the reservoir were applied (surface, euphotic zone limit and bottom). The model that presented a higher average correlation value presented the correlations 0.991; 0.843; 0.978 for training, verification and test series. This model had the three series independent in time: first test series, then verification series and, finally, training series. Only six input variables were considered significant to the performance of this model: ammonia, phosphates, dissolved oxygen, water temperature, pH and water evaporation, physical and chemical parameters referring to the three depths of the reservoir. These variables are common to the next four best models produced and, although these included other input variables, their performance was not better than the selected best model.     

The effect of atmospheric pressure on CH4 and CO2 emission from a closed landfill site in Manchester, UK

A time series study was conducted to ascertain the effect of barometric pressure on the variability of CH₄ and CO₂ concentrations in a closed landfill site. An in situ data of methane/carbon dioxide concentrations and environmental parameters were collected by means of an in-borehole gas monitor, the GasClam (Ion Science, UK). Linear regression analysis was used to determine the strength of the correlation between ground-gas concentrations and barometric pressure. The result shows CH₄ and CO₂ concentrations to be variable with weak negative correlations of 0.2691 and 0.2773, respectively, with barometric pressure over the entire monitoring period. Although the R ² was slightly improved by considering their concentration over single periods of rising and falling pressure, single periods of rising pressure and single periods of falling pressure, their correlations remained insignificant at 95 % confidence level. The result revealed that atmospheric pressure—the acclaimed major control on the variability of ground-gas concentration—is not always so. A case was made for the determination of other possible controls such as changes in temperature, soil permeability, landfill water depth, season, and geology of the borehole and also how much of control each factor would have on the variability/migration of CH₄ and CO₂ concentrations from the studied landfill.     

指标动态权重对湖泊水生态系统健康评价影响研究

利用2014年和2018年鄱阳湖丰〖CD*2〗涨〖CD*2〗枯〖CD*2〗退4个水文时期的监测数据,引入可拓评价法对鄱阳湖水生态系统健康进行评价并探讨指标动态权重的影响。结果表明：鄱阳湖水生态系统健康状态不同水文时期差异显著,退水期最优,枯水期最差,涨水期稍优于丰水期。鄱阳湖水生态系统健康评价指标权重具有动态变化特征,指标权重值会随着指标具体取值的不同而发生变化,即便指标值相同,各指标间关系不同,指标权重也不同,从而影响鄱阳湖水生态系统健康的评价结果。鄱阳湖不同时期生态系统功能和结构差异较大,采用动态的权重对其水生态系统健康评价相对更加合理。     

Characteristics and origin of the hydrogen sulphide spring water from the Split spa (Southern Croatia)

The objective of this paper is to study the hydrological characteristics and origin of the hydrogen sulphide spring water from the Split spa in Southern Croatia in 1987, 1988 and 2003. This paper presents the results of monitoring the content of chlorides and hydrogen sulphide as well as the temperature of the hydrogen sulphide spring water. Since the hydrogen sulphide content during the dry periods significantly differs from the hydrogen sulphide content during rainy periods, this paper also compares the results obtained for those two periods. Under the influence of great quantities of rainfall during cold periods (winter and the beginning of spring) the ratio between seawater and surface water changes and thus the reduction of chlorides and other minerals occurs. The lowered temperature also reduces hydrogen sulphide which can disappear completely. The concentration of 12‰ chlorides (76–94.4% days/year) and 12 mg/L hydrogen sulphide (66.7–88.9% days/year) has been taken as a limit value between water with a normal typical content and water in cold rainy periods. According to the monitoring results it can be concluded that hydrogen sulphide spring water consists of seawater and hydrogencarbonate surface water with a fairly constant content during dry periods while the hydrogencarbonate content increases during rainy periods.     

Methodological uncertainties in multi-regression analyses of middle-atmospheric data series

Multi-regression analyses have often been used recently to detect trends, in particular in ozone or temperature data sets in the stratosphere. The confidence in detecting trends depends on a number of factors which generate uncertainties. Part of these uncertainties comes from the random variability and these are what is usually considered. They can be statistically estimated from residual deviations between the data and the fitting model. However, interferences between different sources of variability affecting the data set, such as the Quasi-Biennal Oscillation (QBO), volcanic aerosols, solar flux variability and the trend can also be a critical source of errors. This type of error has hitherto not been well quantified. In this work an artificial data series has been generated to carry out such estimates. The sources of errors considered here are: the length of the data series, the dependence on the choice of parameters used in the fitting model and the time evolution of the trend in the data series. Curves provided here, will permit future studies to test the magnitude of the methodological bias expected for a given case, as shown in several real examples. It is found that, if the data series is shorter than a decade, the uncertainties are very large, whatever factors are chosen to identify the source of the variability. However the errors can be limited when dealing with natural variability, if a sufficient number of periods (for periodic forcings) are covered by the analysed dataset. However when analysing the trend, the response to volcanic eruption induces a bias, whatever the length of the data series. The signal to noise ratio is a key factor: doubling the noise increases the period for which data is required in order to obtain an error smaller than 10%, from 1 to 3-4 decades. Moreover, if non-linear trends are superimposed on the data, and if the length of the series is longer than five years, a non-linear function has to be used to estimate trends. When applied to real data series, and when a breakpoint in the series occurs, the study reveals that data extending over 5 years are needed to detect a significant change in the slope of the ozone trends at mid-latitudes.