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Denitrification in river sediments is the hotspot of nitrogen removal and nosZI gene is essential for reducing nitrous oxide (N

Seawater intrusion in the Minjiang River estuary has gravely endangered the water security of the surrounding area in recent years. Previous studies mainly focused on exploring the mechanism of intrusion, but failed to provide a scheme for suppressing seawater intrusion. The three most relevant determinants to chlorine level, which represented the strength of seawater intrusion, were determined using Pearson correlation analysis as being the daily average discharge, daily maximum tidal range, and daily minimum tidal level. Considering the lower requirement of sample data and the ability to handle high-dimensional data, the random forest algorithm was used to construct a seawater intrusion suppression model and was combined with a genetic algorithm. The critical river discharge for suppressing estuary seawater intrusion determined using this model. The critical river discharge was found to gradually increase with the maximum tidal range, which in three different tide scenarios was 487 m3/s, 493 m3/s, and 531 m3/s. The practicable seawater intrusion suppression scheme was built up with three phases to make it easier to regulate upstream reservoirs. In the scheme, the initial reading of river discharge was 490 m3/s, and it rose to 650 m3/s over six days, from four days before the high tide’s arrival to two days following it, and before falling down to 490 m3/s at the end. Verified with the 16 seawater intrusion events in the five dry years, this scheme could eliminate 75% of the seawater intrusion risk and effectively reduce the chlorine level for the remaining 25% of events.

Hydrologic forecasting serves as an important tool in water resource management to mitigate disasters and managing water infrastructures. In field of hydrology, models for time series forecasting have been developed throughout the years, including the application of data-driven models. In this research, application of Artificial Intelligence (AI) technique in Support Vector Machine (SVM) method is used to forecast monthly discharge in Pemali River Basin, Indonesia. SVM method, optimized with fast messy genetic algorithm (fmGA), is employed for time series forecasting, whereas input data rely solely on previous values. Model performance is assessed with three different performance metrics and against Seasonal Autoregressive Moving Average (SARIMA) method for comparison. Scenarios are constructed with different input pattern for SVM to identify appropriate input data for giving good prediction accuracy. Input data are developed with and without selecting mechanism. Selecting mechanism is done based on assessment in Autocorrelation Function (ACF) coefficient of the time series data. While input data without the selecting mechanism consist of monthly discharge up to lag time 12 months prior (Qt-1, Qt-2,…,Qt-12). The result shows that input data with good correlation can give good prediction accuracy. Involvement of poorly correlated input data series may decrease model performance. However, with proper combination of input data, SVM can have good forecasting accuracy regardless of having the poorly correlated input data. Coherently, appropriate input data combination can reduce the number of support vectors in SVM, thus scaling down the risk of over fitting data.

Lakes have an important role in storing water for drinking, producing hydroelectric power, and environmental, agricultural, and industrial uses. In order to optimize the use of lakes, precise prediction of the lake water level (LWL) is a main issue in water resources management. Due to the existence of nonlinear relations, uncertainty, and characteristics of the time series variables, the exact prediction of the lake water level is difficult. In this study the hybrid support vector regression (SVR) and the grey wolf algorithm (GWO) are used to predict lake water level fluctuations. Also, three types of data preprocessing methods, namely Principal component analysis, Random forest, and Relief algorithm were used for finding the best input variables for prediction LWL by the SVR and SVR-GWO models. Before the LWL simulation on monthly time step using the hybrid model, an evolutionary approach based on different monthly lags was conducted for determining the best mask of the input variables. Results showed that based on the random forest method, the best scenario of the inputs was Xt&minus, 1,&nbsp, Xt&minus, 2,&nbsp, 3,&nbsp, 4 for the SVR-GWO model. Also, the performance of the SVR-GWO model indicated that it could simulate the LWL with acceptable accuracy (with RMSE = 0.08 m, MAE = 0.06 m, and R2 = 0.96).

Overexploitation of water resources at middle reaches has threatened downstream oasis in arid inland river basins. To achieve sustainable development, ecological water conveyance is one effective measure to reallocate water resources between socio-economic and natural systems. A comprehensive impact assessment of ecological water conveyance on groundwater-dependent ecosystems that are common in downstream inland river basins is needed. The present study integrated the technologies of remote sensing analysis, trend detection, and numerical simulation into a technical framework that identifies the spatial response and temporal dynamics of vegetation to groundwater changes induced by ecological water conveyance. The Normalized Difference Vegetation Index (NDVI) was used as a proxy to estimate vegetation. The structure of the framework we used is clear and reasonable. We used remote-sensing data and ground truth information, and a decision tree that incorporates an iterative self-organizing data analysis technique. The decision tree classifies the land cover into affected and unaffected areas, and the results indicate the spatial range of impact. The Mann-Kendall algorithm and Sen's slope detect the tendency in NDVI series that indicates the temporal response of vegetation. The Verhulst logistic function, combined with environment carrying capacity function, constitutes a simplified vegetation dynamic model that can be used to predict potential impact through scenario analysis. An application in the Shiyang River basin in Northwest China evaluated the performance and usefulness of the framework; the accuracy of the results suggested that the framework is effective and practical. Additional case studies are required to assess the reliability and applicability of the framework and identify the factors that affect assessment results beyond our case study in the Shiyang River basin.

Groundwater depth is an important environmental factor affecting vegetation growth and landscape dynamics in arid environments. This study applied a science mapping approach to visualize the development of groundwater-vegetation-related research, synthesized the vegetation response to changes in groundwater depth, and analyzed the change rate of the response curve to identify the groundwater threshold that is essential to conserve the groundwater-dependent terrestrial ecosystems. These ecosystems emerged as a research hotspot due to climate change, groundwater overexploitation, and the recognition of these ecosystems’ importance for sustainable development. There are two main types of response functions of vegetation to changes in groundwater depth—monotone and bell-shaped functions—among which the monotone function includes linear, curvilinear, and stepwise response. The shape of a response curve is mainly determined by the combined effects of oxygen stress, salinization, and water stress; oxygen stress and salinization dominate in shallow groundwater depth, while water stress dominates in deep groundwater depth. On a non-linear vegetation metric—groundwater depth response curve, the change rate analysis method is effective to identify the breakpoint that can be taken as a candidate threshold of groundwater depth. The results will add insight into the intellectual structure of the groundwater-vegetation interactions and provide practical reference for groundwater resource management, ecological conservation, and sustainable development in arid environments.

Groundwater in arid/semiarid regions plays crucial roles in providing drinking water supply, supporting irrigated agriculture, and sustaining important native terrestrial ecosystems. Groundwater depth controls water availability to vegetation and is essential for conserving groundwater-dependent terrestrial ecosystems. Environmental groundwater depth can be defined as a mean depth or a range of depths, satisfying the growth of natural vegetation that is not under stress, either due to lack of water or anoxia or soil salinization. Five methodologies have been reported to estimate environmental groundwater depth: the direct ones rely on response functions that relate vegetation condition, e.g., physiological parameters, appearance frequency, community structure, and remotely sensed physical indexes, to changes in groundwater depth; the indirect one estimates environmental groundwater depth based on the threshold of soil moisture content. To fill a knowledge gap of unique recognized methodology, a conceptual framework was proposed, which involves initial estimation (data collection, response assessment, and estimation) and feedback adjustment (implementation and modification). A key component of the framework is to quantify the linkage between ecological conditions and geohydrological features. This review may provide references for groundwater resources management, ecological conservation, and sustainable development in arid/semiarid regions.

To achieve sustainable development, ecological water diversion has been implemented as a management tool to recover degraded oasis ecosystems in arid endorheic basins. Aiming to explore oasis response and identify appropriate objectives for oasis restoration, this study used a conceptual lumped ecohydrological model. Qingtu Oasis in the Shiyang River basin, Northwest China, was used as a case study to test this model. The model coupled groundwater budget, oasis response, and evapotranspiration dynamic modules. The model structure was clear and reasonable, and the calculation was of low cost. The application in Qingtu Oasis verified the model's effectiveness. The model was able to effectively capture the ecohydrological response attributed to the recovering of Qingtu Oasis in response to ecological water diversions occurring during the last decade. Combined with scenario analysis, the simulation indicated that Qingtu Oasis was fragile and largely dependent on ecological water diversion. Ecological responses were positive and non-linearly correlated with water input, resulting in an optimal water input that produced maximum water benefit by minimum water consumption. The ideal point method was useful in identifying this cost-effective water volume for oasis restoration. For Qingtu Oasis, the optimal water diversion was 45 million m3, which would recover the oasis to 29.16 km2 with an NDVI of 0.41. The current oasis conditions approach the optimal status, and ecological water diversion was suggested to continue and maintain the current oasis status. The volume of 45 million m3 could be taken as an upper threshold of water diversion, because above this threshold, water consumption would not result in water benefits effectively. This study proposed a practical tool for ecohydrological process simulation.

An improved understanding of the response of oasis ecosystems to ecological water diversion is essential for sustainably managing water resources in arid inland river basins. This entropy-based investigation provides new insight into the ecohydrological impacts of ecological water diversions through a case study of Qingtu oasis in the lower Shiyang River basin in Northwest China. Since 2010, an ecological water diversion project was put in place by Water Resources Bureau of Shiyang River Basin to restore the degraded Qingtu oasis. Annual cumulative water releases from this water diversion project had reached 0.25 billion m3 in 2018. This contributed to an increase in shallow groundwater level of 1.2 m. A combination of land cover classification indices and an entropy approach were used to assess the ecohydrological response to water diversion inputs. Land cover was classified as sparsely, lowly, moderately, and highly vegetated areas using Normalized Difference Vegetation Index (NDVI) calculated from Landsat images and the Excess Green minus Excess Red Index that was estimated from unmanned aerial vehicle images. Land cover complexity was evaluated using component complexity and spatial complexity. The component complexity corresponds to land cover composition and was assessed using the Shannon's entropy. The spatial complexity corresponds to spatial configuration of different land cover compositions and was analyzed using spatial entropy methodology. The rise in groundwater level caused by ecological water diversion drove the land cover transition and enhanced the component complexity. Field survey data showed that tendency and randomness co-existed in the spatial pattern of groundwater depth and its relationship with NDVI, which jointly affected spatial complexity (i.e., dependence and independence in spatial co-occurrences of different land cover categories). Results from this study improve understanding of the impacts that ecological water diversions can have on land cover evolution in natural oasis in arid inland river basins.

Two river catchments, the Huangfuchuan and the Hailiutu, located in the same climate zone in the Erdos Plateau, China, have distinctly different flow regimes. This study systematically compared differences between the flow regimes of these two catchments using several statistical methods, and analysed the possible causes. The variations in yearly, monthly and daily mean discharges were found to be much greater in the Huangfuchuan catchment than in the Hailiutu catchment. Preliminary analysis indicated that these differences are not caused by changes in climate, but are instead attributable to differences in geology, geomorphology, hydrological processes and human interventions. In the Hailiutu catchment, the dominant groundwater contribution maintains stationary daily and monthly river discharges, while shifts in yearly mean discharges were closely associated with the expansion or reduction of crop area. In the Huangfuchuan catchment, the dominant direct rainfall–runoff process generates flashier ...

The Hailiutu River basin is a typical area of semi-arid sandy land with relatively flat topography, the surface of which is covered by undulating dunes and the development of the river system is not obvious. The dominant hydrological cycle is precipitation infiltration through dunes to aquifers followed by discharge to rivers. Therefore, the baseflow is an important component of the streamflow in this basin, but few studies for the baseflow characteristics have been conducted. The isotope tracer technique was applied to investigate the contributions of groundwater, soil water, and surface water to streamflow during the flood period. The results showed that the contributions of these components to streamflow were approximately 70%, 27%, and 3% respectively. Several automatic baseflow separation methods including filtering and recursive digital filtering (RDF) techniques were adopted to separate the baseflow from the streamflow and the adaptabilities of these methods were evaluated. All the filtering methods including Hydrograph Separation Program (HYSEP) and UK Institute of Hydrology’s method (UKIH) clearly underestimated the baseflow when compared with the standard baseflow results which were provided by the previous study using the tracer-based method in this basin, while the recursive digital filtering with Eckhardt filter technique (RDF-E) produced better performance. In the nonprecipitation period, the RDF-E method misidentified quick flow values which caused deviations between the separated baseflow and the above standard value. Hence, we proposed a modified automatic baseflow separation method called RDF-M by introducing the precipitation information into RDF-E. In comparison with the above standard, the RDF-M method provided similar baseflow results which were consistent with the actual situation of the Hailiutu River basin.

For social and economic development in China, the rivers are regulated, but its impact on river ecosystem has not evaluated yet. Maintaining a trade-off between social protection and ecological protection of the rivers is very crucial for a country where competition for water is severe. Thus, the main focus of this study is to carry out a comprehensive assessment of river health in relation to water resources management. In this paper, Guijiang, Nenjiang, and lower Yellow- three regulated river’s health statuses were drawn on the river-section scale using Fuzzy Matter Element (FME), Harmony Degree Evaluation (HDE), and Grey Relational Analysis (GRA) methods. The results indicated that the alteration of flow dynamics affected the ‘hydrology’ and ‘water ecology’ attributes of the rivers for which the River Health Index (RHI) of upstream sections of Nenjiang River was poor and downstream sections of lower Yellow River was critical respectively and the overall health statuses of the three rivers in that order, were good, fair to good, and fair. Particularly, Xiaolandi reservoir regulation at lower Yellow River impaired the habitat of benthic macroinvertebrate population that made the ecosystem health critical. We compared the evaluation methods and the results of FME and HDE were found consistent while GRA produced some discrepancies in the outcome. Finally, we proposed a framework for river health assessment for interpretation and communication of results to the managerial level. Our findings suggest that the water resources management of these regulated rivers should be rationalized and special care should be taken for ‘water ecology’ health restoration of the lower Yellow River.

In the original soil and water assessment Tool (SWAT) model (SWAT-O), the contributions of shallow aquifers and deep aquifers to streamflow are simulated using the linear reservoir method. The movement of groundwater was limited in the hydrological response unit which is a minimum calculation unit in the SWAT. However, this computational method may not be suitable for the areas where a groundwater system is complicated, and the river is predominately recharged by groundwater. In this paper, we proposed an enhanced groundwater module which divides shallow aquifers into upper and lower aquifers, integrates all the deep aquifers of a sub-basin into a regional aquifer, and simulates interactive water amount between lower aquifer and deep aquifer using water depth difference. The modified groundwater module was introduced to the original SWAT model, hereby referred to as SWAT-MG. The SWAT-MG and SWAT-O models were applied to the Hailiutu River catchment, which is a semi-arid wind sandy grass shoal catchment. Results showed that both models underestimated streamflow in peak flow, while the simulated streamflow of SWAT-MG was closer the observed values than that of SWAT-O. Three evaluation criteria (NSE, RSR, PBIAS) were applied to evaluate the performance of the models and the results showed that SWAT-MG had a better performance than SWAT-O. The baseflow index of Hailiutu River which was calculated by the results of SWAT-MG was 96.78%, which means the streamflow is predominately recharged by groundwater, and this conforms to the actual situation of Hailiutu River catchment. This indicates that a SWAT model with a modified groundwater module could better represent the groundwater flow behavior in the study area.

The spatial distribution of vegetation pattern and vegetation cover fraction (VCF) was quantified with remote sensing data in the Hailiutu River basin, a semiarid area in North China. The moderate resolution imaging spectroradiometer normalized different vegetation index (NDVI) values for 4 years from 2008 to 2011 and field observation data were used to assess the impact of climate factors, landform and depth to water table on vegetation distribution at large scale. In the VCF map, 74 % of the study area is covered with low and low–medium density vegetation, 24 % of the catchment is occupied by medium–high and high-density vegetation, and 2 % of area is bare soil. The relationship between NDVI and climate factors indicated that NDVI is correlated with relative humidity and precipitation. In the river catchment, NDVI increases gradually from landform of sand dune, eolian sand soil to river valley; 92.4 % of low NDVI from 0.15 to 0.3 is mostly distributed in sand dunes and the vegetation type is shrubs. Crops, shrubs and some dry willows dominate in eolian sand soil and 82.5 % of the NDVI varies between 0.2 and 0.35. In the river valley, 70.4 % of NDVI ranges between 0.25 and 0.4, and grass, dry willow and some crops are the main plants. Shrubs development of Korshinsk peashrub and Salix psammophila are dependent on groundwater by analyzing NDVI response to groundwater depth. However, NDVI of Artemisia desertorum had little sensitivity to groundwater.

To investigate the groundwater levels changes effects on the transpiration of Salix psammophila (S. psammophila) bush, systemic measurements of meteorological conditions, sap flow of S. psammophila, soil water contents and groundwater levels were conducted in the Hailiutu River catchment, NW of China. Based on the collected field data, Hydrus-1D software package was used to calibrate water movement for root uptake in the saturated–unsaturated zone. The soil hydraulic parameters and root uptake function parameters were calibrated. The simulated results of soil water contents and sap flow fitted well with the observed ones. Based on the calibrated hydraulic parameters, different groundwater levels were imposed at the low boundary to simulate the groundwater levels changes effects on the transpiration. The relationship between ratio of actual transpiration and potential (T/T p) and groundwater water table depth was established. The results shows that the ratio of actual transpiration and potential transpiration decreases with groundwater table depth increase as inverse ‘S’ shape. And the turn point is corresponding to the extinct depth, i.e., no groundwater contribution to S. psammophila transpiration. To further verify this phenomenon is universal, the soil hydraulic parameters were replaced with the other five groups in the calibrated forward model. The results confirmed the similar changes of T a/T p with the groundwater levels changed, i.e. inverse ‘S’ shape. However, the extinction depth is different corresponding to different soil hydraulic parameters. So, the research results indicated the relationship between transpiration and groundwater levels is non-linear function.

The Xinanjiang rainfall–runoff model has been successfully applied in many humid and sub-humid areas in China since 1973. The wide application is due to the simple model structure, the clear physical meaning of the parameters and the well-defined model calibration procedure. However, due to a data scarcity problem and short runoff concentration time, its applications to small drainage basins are difficult. Therefore, we investigate the model application in Lianghui, a small drainage basin of Zhejiang province in China. By using generalized likelihood uncertainty estimation (GLUE) methodology, the sensitivity of parameters of Xinanjiang model was investigated. The data clearly showed that equifinality phenomenon was evident in both water balance parameter calibration and runoff routing parameter calibration procedures. The results showed that K (evapotranspiration conversion coefficient), C s (recession constant in channel system) and S m (areal free water storage capacity of surface soil) are the most sensitive parameters for the water balance parameter calibration while C s , S m and W m (mean area tension water capacity) are the most sensitive parameters for runoff routing parameter calibration. The conclusion is favourable for understanding parameters of Xinanjiang model in order to provide valuable scientific information for simulating hydrological processes in small drainage basins.

With the continuous growth of economic water consumption in arid regions, many endorheic rivers and terminal lakes have desiccated. As an important ecological engineering measure, water transport in arid regions has vital ecological significance for protecting the regional ecological environment and delaying desertification. In this study, Qingtu Lake, the terminal lake of Shiyang River, was selected to analyze the ecological effects of water transport by means of remote sensing interpretations and current year field investigations. The results demonstrated that, in July 2018, the water surface had formed and recovered to 5.68 km2. Additionally, Qingtu Lake formed a spatial gradient distribution in groundwater depth. The depth increased in gradient from the waterside to the desert edge. There was a significant increase in the overall regional vegetation coverage, which mainly occurred in the water areas because of the extensive growth in Phragmites australis, which reached 10.54 km2 in area in 2018. Furthermore, the regional vegetation formed a gradient distribution, which transitioned from hygrophytes to xerophytes. This study can provide guidelines for the protection and restoration of lakes in arid regions.

The Hailiutu River basin is a typical semi-arid wind sandy grass shoal watershed in northwest China. Climate and land use have changed significantly during the period 1970&ndash, 2014. These changes are expected to impact hydrological processes in the basin. The Mann&ndash, Kendall (MK) test and sequential t-test analysis of the regime shift method were used to detect the trend and shifts of the hydrometeorological time series. Based on the analyzed results, seven scenarios were developed by combining different land use and/or climate situations. The Soil Water Assessment Tool (SWAT) model was applied to analyze the impacts of climate variability and land use change on the values of the hydrological components. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) was applied to enhance the spatial expressiveness of precipitation data in the study area during the period 2008&ndash, 2014. Rather than solely using observed precipitation or CMADS precipitation, the precipitation values of CMADS and the observed precipitation values were combined to drive the SWAT model for better simulation results. From the trend analysis, the annual streamflow and wind speed showed a significant downward trend. No significant trend was found for the annual precipitation series, however, the temperature series showed upward trends. With the change point analysis, the whole study period was divided into three sub-periods (1970&ndash, 1985, 1986&ndash, 2000, and 2001&ndash, 2014). The annual precipitation, mean wind speed, and average temperature values were 316 mm, 2.62 m/s, and 7.9 &deg, C, respectively, for the sub-period 1970&ndash, 1985, 272 mm, 2.58 m/s, and 8.4 &deg, C, respectively, for the sub-period 1986&ndash, 2000, and 391 mm, 2.2 m/s, and 9.35 &deg, C, respectively, for the sub-period 2001&ndash, 2014. The simulated mean annual streamflow was 35.09 mm/year during the period 1970&ndash, 1985. Considering the impact of the climate variability, the simulated mean annual streamflow values were 32.94 mm/year (1986&ndash, 2000) and 36.78 mm/year (2001&ndash, 2014). Compared to the period 1970&ndash, 1985, the simulated mean annual streamflow reduced by 2.15 mm/year for the period 1986&ndash, 2000 and increased by 1.69 mm/year for the period 2001&ndash, 2014. The main variations of land use from 1970 to 2014 were the increased area of shrub and grass land and decreased area of sandy land. In the simulation it was shown that these changes caused the mean annual streamflow to decrease by 0.23 mm/year and 0.68 mm/year during the periods 1986&ndash, 2000 and 2001&ndash, 2014, respectively. Thus, the impact of climate variability on the streamflow was more profound than that of land use change. Under the impact of coupled climate variability and land use change, the mean annual streamflow decreased by 2.45 mm/year during the period 1986&ndash, 2000, and the contribution of this variation to the decrease in observed streamflow was 27.8%. For the period 2001&ndash, 2014, the combined climate variability and land use change resulted in an increase of 0.84 mm/year in annual streamflow. The results obtained in this study could provide guidance for water resource management and planning in the Erdos plateau.

The impact of the El Nino-Southern Oscillation (ENSO) phenomenon within the Huangfuchuan watershed, one of the major first- order sub-basins in th e middle region of the Yellow River, has not clearly been established. Consequently, the co-varying relationships between rainfall and El Nino/La Nina spanning the period 1954 to 2010 are investigated. Trends and step changes in annual rainfall are investigated with the Mann-Kendall and the distribution free cumulative sum (CUSUM) tests. Wavelet transfor ms are employed to perform spectral analysis of temporal variations in rainfall rates within the watershed. Cross wavelet and wavelet coherence transforms are used to study localized co-varying relationships between rainfall and ENSO index. Results from statistical tests indicate that rainfall in the Huangfuchuan watershed is declining, although not significantly. In addition, wavelet coherency and cross wavelet analysis, and comparison of the extracted dominant annual rainfall and 2–7 year ENSO signals demonstrate that ENSO events impact Huangfuchuan rainfall with El Nino corresponding to rainfall de cline and La Nina to rainfall increment with a semiannual to annual lag.

Pesticide transport through the soil profile at the field scale is notoriously difficult to predict because of a lack of appropriate field-scale models and model parameters that take adequate account of heterogeneity in local flow rates and parameters. It is generally impossible to measure all the parameters required to describe that heterogeneity. A way of approximating pesticide transport at the field scale, given uncertainty in the representation of both local characteristics and field-scale distributions, is presented. The methodology allows prior estimates of field-scale distribution parameters to be conditioned on uncertain column-scale measurements. It is shown how this conditioning can drastically constrain steady-state flow predictions of atrazine (2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine) transport to groundwater for a site in the Rhone Valley, Switzerland. Some of the simplifying assumptions of the current analysis, in particular, to allow for transient simulations, can be relaxed in the future as more computer power becomes available. The methodology, however, should remain valid.

This study aims to investigate trends in streamflow and precipitation in the period 1954–2010 in a semiarid region of the Yellow River watershed, Huangfuchuan basin, China. The combination of the wavelet transform and different Mann-Kendall (MK) tests were employed to figure out the basic trends structure in streamflow and precipitation and what time scales are affecting the observed trends. The comparative analysis with five MK test methods showed that the modified MK tests with full serial correlation structure performed better when significant autocorrelations exhibited for more than one lag. Three criteria were used to determine the optimal smooth mother wavelet, the decomposition level and the extension mode used in the discrete wavelet transform (DWT) procedure. The first criteria referred to the relative error of the wavelet approximated component and the original series. The second one was the relative error of MK Z-values of approximation component and the original series. Additionally, a new criterion (Er), based on the relative error of energy between the approximate component and the original series, was proposed in this study, with better performance than the previous two criteria. Further, a new powerful index, the energy of the hydrological time series, was proposed to verify the dominant periodic components for the observed trends. The analysis indicated that all monthly, seasonal and annual streamflow showed significant decreasing trends, while no significant trends were found in precipitation. Results from the DWT and MK tests revealed that the main factors influencing the trends in the monthly and seasonal series in Huangfuchuan watershed are intra-annual cycles, while the leading factors affecting the trends in the annual series are decadal events. Different driving factors (e.g., seasonal cycles, solar activities, etc.) related to the periodicities identified in these data types resulted in this discrepancy.

Sorption and degradation behaviors of atrazine were investigated with packed and intact soil columns. The measured breakthrough curves from columns were fitted with CXTFIT model program. The high mobility and long persistence of atrazine indicated by experiment results confirm that atrazine application may cause potential groundwater pollution. It was found that the obtained sorption and degradation parameters from the packed column experiments were quite similar to those obtained from the intact column experiments. These results suggest that sorption and degradation behaviors in both experiments can be used jointly to model herbicide transport at a large scale if the soil is relatively homogeneous.

A two dimensional(2D) numerical model based on the stream tube concept is developed not only to quantitatively examine the physical behavior in open channels, but also for examining the physical processes release of pollutants upstream. It is also intended to examine the numerical property advection scheme combined with the flux limiter. The proposed numerical model is examined by comparing with both the analytical solution and measurements. The model performance is discussed and the results are analyzed. Both the numerical test and the model application show that the proposed model is capable for modeling two dimensional contaminant transports in open channel flows. The advantage of the proposed model is that it requires less hydraulic data and is ease of use. Therefore it is of great practical interest for engineering use.

The problems of calibrating soil hydraulic and transport parameters are well documented, particularly when data are limited. Programs such as CXTFIT, UUCODE and PEST, based on well established principles of statistical inference, will often provide good fits to limited observations giving the impression that a useful model of a particular soil system has been obtained. This may be the case, but such an approach may grossly underestimate the uncertainties associated with future predictions of the system and resulting dependent variables. In this paper, this is illustrated by an application of CXTFIT within the generalised likelihood uncertainty estimation (GLUE) approach to model calibration which is based on a quite different philosophy. CXTFIT gives very good fits to the observed breakthrough curves for several different model formulations, resulting in very small parameter uncertainty estimates. The application of GLUE, however, shows that much wider ranges of parameter values can provide acceptable fits to the data. The wider range of potential outcomes should be more robust in model prediction, especially when used to constrain field scale models.