Preliminary study on radiocarbon AMS dating of pollen

17 samples were collected from aeolian and lacustrine profiles within the environment sensitive zone of the Loess Plateau, and an experimental method was established which is suitable for pollen extraction from aeolian sediment. A comparative study of pollen dating was carried out using the accelerator mass spectrometry (AMS)¹⁴C dating of known age samples, and then an experiment with the pollen concentrates was performed. The results indicate that pollen that has been deposited simultaneously with sediment in a stable environment can provide reliable ages. This technique will provide a way of improving the chronological framework for the Loess Plateau since the late Pleistocene. The^I4c dating was combined with field investigations, and from the geological record within this zone, evidence was extracted of four major monsoon precipitation changes during the transition from the late Pleistocene to Holocene. Project supported by the National Natural Science Foundation of China (Grant Nos. 49894170, 49725308), Chinese Academy of Sciences (Nos. KZ-951-A1-402& KZ-952-S1-419), and State Committee of Science and Technology of China.     

Effects of biological soil crusts on soil detachment process by overland flow in the Loess Plateau of China

Biological soil crusts (BSCs) cover up to 60 to 70% of the soil surface in grasslands after the ‘Grain for Green’ project was implemented in 1999 to rehabilitate the Loess Plateau. However, few studies exist that quantify the effects of BSCs on the soil detachment process by overland flow in the Loess Plateau. This study investigated the potential effects of BSCs on the soil detachment capacity (D_c), and soil resistance to flowing water erosion reflected by rill erodibility and critical shear stress. Two dominant BSC types that developed in the Loess Plateau (the later successional moss and the early successional cyanobacteria mixed with moss) were tested against natural soil samples collected from two abandoned farmland areas. The samples were subjected to flow scouring under six different shear stresses ranging from 7.15 to 24.08 Pa. The results showed that D_c decreased significantly with crust coverage under both moss and mixed crusts. The mean D_c of bare soil (0.823 kg m^?2 s^?1) was 2.9 to 48.4 times greater than those of moss covered soil (0.017–0.284 kg m^?2 s^?1), while it (3.142 kg m^?2 s^?1) was 4.9 to 149.6 times greater than those of mixed covered soil (0.021–0.641 kg m^?2 s^?1). The relative detachment rate of BSCs compared with bare soils decreased exponentially with increasing BSC coverage for both types of BSCs. The D_c value can be simulated by flow shear stress, cohesion, and BSC coverage using a power function (NSE ≥ 0.59). Rill erodibility also decreased with coverage of both crust types. Rill erodibility of bare soil was 3 to 74 times greater than those of moss covered soil and was 2 to 165 times greater than those of mixed covered soil. Rill erodibility could also be estimated by BSC coverage in the Loess Plateau (NSE ≥ 0.91). The effect of crust coverage on critical shear stress was not significant. Copyright © 2015 John Wiley & Sons, Ltd.     

Development of pedotransfer functions for soil hydraulic properties in the critical zone on the Loess Plateau,China

Soil hydraulic properties (SHPs) including the soil water retention curve and saturated soil hydraulic conductivity (Ks) are crucial input data for simulations of soil water and solute transport in the Earth's critical zone. However, obtaining direct measurements of SHPs at a wide range of scales is time consuming and expensive. Pedotransfer functions (PTFs) are employed as an alternative method for indirectly estimating these parameters based on readily measured soil properties. However, PTFs for SHPs for the deep soil layer in the Earth's critical zone are lacking. In this study, we developed new PTFs in the deep soil profile for Ks and soil water retention curve on the Loess Plateau, China, which were fitted with the van Genuchten equation. In total, 206 data sets comprising the hydraulic and basic soil properties were obtained from three typical sites. Samples were collected from the top of the soil profile to the bedrock by soil core drilling. PTFs were developed between the SHPs and basic soil properties using stepwise multiple linear regression. The PTFs obtained the best predictions for Ks (R_adj² = 0.561) and the worst for van Genuchten α (R_adj² = 0.474). The bulk density and sand content were important input variables for predicting Ks, α, and θs, and bulk density, clay content, and soil organic carbon were important for n. The PTFs developed in this study performed better than existing PTFs. This study contains the first set of PTFs of SHPs to be developed for the deep profile on the Loess Plateau, and they may be applicable to other regions.     

Modeling aggregate size distribution of eroded sediment resulting from rain-splash and raindrop impacted flow processes

Soil susceptibility to detachment and transport sub-processes of erosion is generally controled by the aggregate breakdown mechanism. Measuring particle size and aggregation to the estimate erodibility potential of soils is important under erosive rainfall conditions. The Aggregate Size Distribution (ASD) is one of the most important determinants of soil structure along with soil organic matter content for describing the efficiency of applied, sustainable management strategies. This study aimed to compare the performances of three different aggregate size distribution models to predict the characteristic aggregate size parameter (median diameter, D50) for eroded sediment from interrill erosion processes of Rain- Splash Transport (RST) and Raindrop Impacted Flow Transport (RIFT). The ASDs of 1143 collected sediment samples from the RST and RIFT processes were measured and modeled by the Log-normal, Fractal, and Weibull approaches. The D50 value, as a characteristic parameter for aggregate size distributions, derived from the cumulative ASD curve was compared for soils from different land use types and different slope and rainfall intensity conditions. The performance of each model was evaluated using the Mean Square Error (MSE) and Coefficient of Determination (R^2). The Weibull approach was the most accurate model showing the best fit with the lowest MSE values (0.0002 ≤MSE≤ 0.0048) and having the greatest R2 values (0.936≤ R^2≤ 0.998) when compared with the Log-normal and Fractal models. Herewith, for semi-arid land use and soil, specific shape and scale parameters for the Weibull distribution, the respective ASDs were successfully re-generated for modeling the eroded sediment of the simulated RST and RIFT interill processes.     

Comparison of five snow water equivalent estimation methods across categories

Snow water equivalent (SWE) is an important indicator used in hydrology, water resources, and climate change impact. There are various methods of estimating SWE (falling in 3 categories: indirect sensors, empirical models, and process‐based models), but few studies that provide comparison across these different categories to help users make decisions on monitoring site design or method selection. Five SWE estimation methods were compared against manual snow course data collected over 2 years (2015–2016) from the Dorset Environmental Science Centre, including the gamma‐radiation‐based CS725 sensor, 3 empirical estimation models (Sexstone snow density model, McCreight & Small snow density model, and a meteorology‐based model), and the University of British Columbia Watershed Model snow energy‐balance model. Snow depth, density, and SWE were measured at the Dorset Environmental Science Centre weather station in south‐central Ontario, on a daily basis over 6 winters from 2011 to 2016. The 2 snow density‐based models, requiring daily snow depth as input, gave the best performance (R² of .92 and .92 for McCreight & Small and Sexstone models, respectively). The CS725 sensor that receives radiation coming from soil penetrating the snowpack provided the same performance (R² = .92), proving that the sensor is an applicable method, although it is expensive. The meteorology‐based empirical model, requiring daily climate data including temperature, precipitation and solar radiation, gave the poorest performance (R² = .77). The energy‐balance‐based University of British Columbia Watershed Model snow module, only requiring climate data, worked better than the empirical meteorology‐based model (R² = .9) but performed worse than the density models or CS725 sensor. Given differences in application objectives, site conditions, and budget, this comparison across SWE estimation methods may help users choose a suitable method. For ongoing and new monitoring sites, installation of a CS725 sensor coupled with intermittent manual snow course measurements (e.g., weekly) is recommended for further SWE method estimation testing and development of a snow density model.     

Treatment of anchor pixels in the METRIC model for improved estimation of sensible and latent heat fluxes

Abstract

Reliable estimation of sensible heat flux (H) is important in energy balance models for quantifying evapotranspiration (ET). This study was conducted to evaluate the value of adding the Priestley-Taylor (PT) equation to the METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model. METRIC was used to estimate energy fluxes for 10 Landsat images from the 2005, 2006 and 2007 crop growing seasons in south-central Nebraska, USA, where each image owing to recent rainfall exhibited high residual moisture content even at the hot pixel. The METRIC model performed satisfactorily for net radiation (R_n ) and soil heat flux (G) estimation with a root mean square error (RMSE) of 52 and 24 W m^-2, respectively. A RMSE of 122 W m^-2 for H indicated the limitation of the METRIC model in estimating H for high residual moisture content of the hot pixel (Alfalfa reference ET fraction, ET _r F > 0.15). The modified METRIC model (wet METRIC or wMETRIC) incorporating the PT equation was applied to calculate H at the anchor pixels (hot and cold) for high residual moisture content of the hot pixel. The α coefficient of the PT equation was locally calibrated using hourly meteorological data from an automatic weather station and R_n and G data from a Bowen ratio flux tower. The mean α coefficient value was 1.14. The wMETRIC model reduced the RMSE of H from 122 to 64 W m^-2 and that of latent heat flux, LE, from 163 to 106 W m^-2. The RMSE of daily ET decreased from 1.7 to 1.1 mm d^-1 with wMETRIC. The results indicate that treatment of anchor pixels for high residual moisture content with the PT approach gives improved estimation of H, LE and daily ET. It is recommended that the wMETRIC model be used for estimating ET if the hot pixel has high residual moisture (i.e. reference ET fraction > 0.15).

Citation Singh, R. K. & Irmak, A. (2011) Treatment of anchor pixels in the METRIC model for improved estimation of sensible and latent heat fluxes. Hydrol. Sci. J. 56(5), 895–906.     

A modification to the Soil Conservation Service curve number method for steep slopes in the Loess Plateau of China

The Soil Conservation Service curve number (CN) method is widely used for predicting direct runoff from rainfall. However, despite the extent of cultivation on hillslope areas, very few attempts have been made to incorporate a slope factor into the CN method. The objectives of this study were (1) to evaluate existing approaches integrating slope in the CN method, and (2) to develop an equation incorporating a slope factor into the CN method for application in the steep slope areas of the Loess Plateau of China. The dataset consisted of 11 years of rainfall and runoff measurements from two experimental sites with slopes ranging from 14 to 140%. The results indicated that the standard CN method underestimated large runoff events and overestimated small events. For our experimental conditions, the optimized and non‐optimized forms of the slope‐modified CN method of the Erosion Productivity Impact Calculator model improved runoff prediction for steep slopes, but large runoff events were still underestimated and small ones overpredicted. Based on relationships between slope and the observed and theoretical CN values, an equation was developed that better predicted runoff depths with an R² of 0·822 and a linear regression slope of 0·807. This slope‐adjusted CN equation appears to be the most appropriate for runoff prediction in the steep areas of the Loess Plateau of China. Copyright © 2005 John Wiley & Sons, Ltd.     

Nondimensional sediment transport capacity of sand soils and its response to parameter in the Loess Plateau of China

Soil erosion is a major contributor to land degradation in the Loess Plateau in China. To clarify the sediment transport capacity of overland flow influenced by hydraulic parameters, such as shear stress, sand shear stress (hydraulic gradient partition method and hydraulic radius partition method), mean flow velocity, Froude number, stream power, and unit stream power, indoor experiments with eight-unit-width flow discharges from 0.0667 × 10⁻³ to 0.3333 × 10⁻³ m²·s⁻¹, six slope gradients from 3.49 to 20.79%, and two kinds of sand soils (d₅₀ = 0.17 and 0.53 mm) were systematically investigated. A nondimensional method was adopted in data processing. Results showed that there was a partition phenomenon of relation curves because of the different median grain diameters. The correlation between the nondimensional stream power and nondimensional sediment transport capacity was the highest, followed by the correlation between the nondimensional unit stream power and nondimensional sediment transport capacity. However, there was a poor correlation between the flow intensity indices of velocity category and nondimensional sediment transport capacity. Nondimensional stream power, nondimensional unit stream power, and nondimensional shear stress could predict sediment transport capacity well. Ignoring the partition phenomenon of the relation curves, stream power could be used to predict sediment transport capacity, with a coefficient of determination of .85. Furthermore, a general flow intensity index was obtained to predict sediment transport capacity of overland flow. Finally, an empirical formula for predicting sediment transport capacity with a coefficient of determination of .90 was established by multiple regression analyses based on the general flow intensity index. During the analysis between measured sediment transport capacities in present study and predicted values based on Zhang model, Mahmoodabadi model, and Wu model, it was found that these three models could not accurately predict sediment transport capacities of this study because different models are estimated on the basis of different experimental conditions.     

SfM-MVS Photogrammetry for Splash Erosion Monitoring under Natural Rainfall

An understanding of splash erosion is the basis to describe the impact of rain characteristics on soil disturbance. In typical splash cup experiments, splashed soil is collected, filtered, and weighed. As a way to collect additional data, our experiments have been supplemented by a photogrammetric approach. A total of three soils were tested across three sites, one in the Czech Republic and two in Austria, all equipped with rain gauges and disdrometers to measure rainfall parameters. The structure from motion multiview stereo (SfM-MVS) photogrammetric method was used to measure the raindrops impact on the soil surface. The images were processed using Agisoft PhotoScan, resulting in orthophotos and digital elevation models (DEMs) with a resolution of 0.1 mm/pix. The surface statistics included the mean surface height (whose standard deviation was used as a measure of surface roughness), slope, and other parameters. These parameters were evaluated depending on soil texture and rainfall parameters. The results show a linear correlation between consolidation and splash erosion with a coefficient of determination (R²) of approximately 0.65 for all three soils. When comparing the change in soil volume with rainfall parameters, the best correlation was found with the maximum 30-minintensity (I₃₀), resulting in R² values of 0.48 (soil A, silt loam, 26% clay), 0.59 (soil B, silt loam, 18% clay), and 0.68 (soil C, loamy sand, 12% clay). The initial increase in the sample volume for the lowest splashed mass corresponds with the increase in the clay content of each of the soils. Soil A swells the most. Soil B swells less. Soil C does not swell at all and consolidates the most. We derived the relationship between the photogrammetrically measured change in surface height and the splash erosion (measured by weight) by accounting for the effect of the clay content.     

Temporal variation in soil detachment under different land uses in the Loess Plateau of China

Measurements of temporal variations in soil detachability under different land uses are badly needed to develop new algorithms or evaluate the existing ones for temporal adjustment of soil detachability in continuous soil erosion models. Few studies have been conducted in the Loess Plateau to quantify temporal variations in detachment rate of runoff under different land uses. The objectives of this study were to investigate the temporal variations of soil detachment rate under different land uses and to further identify the potential factors causing the change in detachment rate in the Loess Plateau. Undisturbed soil samples were collected in the fields of arable land (millet, soybean, corn, and potato), grassland, shrub land, wasteland, and woodland and tested in a laboratory flume under a constant hydraulic condition. The measurements started in mid‐April and ended in early October, 2006. The results showed that soil detachment rate of each land use fluctuated considerably over time. Distinctive temporal variation in detachment rate was found throughout the summer growing season of measurement in each land use. The maximum detachment rates of different land uses varied from 0·019 to 0·490 kg m^–2 s^–1 and the minimum detachment rates ranged from 0·004 to 0·092 kg m^–2 s^–1. Statistical analysis using a paired‐samples t‐test indicated that variations in soil detachment rate differed significantly at the 0·05 level between land uses in most cases. The major factors responsible for the temporal variation of soil detachment were tillage operations (such as planting, ploughing, weeding, harvesting), soil consolidation, and root growth. The influence of tillage operations on soil detachment depended on the degree of soil disturbance caused by the operations. The consolidation of the topsoil over time after tillage was reflected by increases in soil bulk density and soil cohesion. As soil bulk density and cohesion increased, detachment rate decreased. The impact of root density was inconclusive in this study. Further studies are needed to quantify the effects of root density on temporal variations of soil detachment. This work provides useful information for developing temporal adjustments to soil detachment rate in continuous soil erosion models in the Loess Plateau. Copyright © 2009 John Wiley & Sons, Ltd.     

Assessing climate change impacts on the ecohydrology of the Jinghe River basin in the Loess Plateau,China

Abstract

Quantifying the impacts of climate change on the hydrology and ecosystem is important in the study of the Loess Plateau, China, which is well known for its high erosion rates and ecosystem sensitivity to global change. A distributed ecohydrological model was developed and applied in the Jinghe River basin of the Loess Plateau. This model couples the vegetation model, BIOME BioGeochemicalCycles (BIOME-BGC) and the distributed hydrological model, Water and Energy transfer Process in Large river basins (WEP-L). The WEP-L model provided hydro-meteorological data to BIOME-BGC, and the vegetation parameters of WEP-L were updated at a daily time step by BIOME-BGC. The model validation results show good agreement with field observation data and literature values of leaf area index (LAI), net primary productivity (NPP) and river discharge. Average climate projections of 23 global climate models (GCMs), based on three emissions scenarios, were used in simulations to assess future ecohydrological responses in the Jinghe River basin. The results show that global warming impacts would decrease annual discharge and flood season discharge, increase annual NPP and decrease annual net ecosystem productivity (NEP). Increasing evapotranspiration (ET) due to air temperature increase, as well as increases in precipitation and LAI, are the main reasons for the decreasing discharge. The increase in annual NPP is caused by a greater increase in gross primary productivity (GPP) than in plant respiration, whilst the decrease in NEP is caused by a larger increase in heterotrophic respiration than in NPP. Both the air temperature increase and the precipitation increase may affect the changes in NPP and NEP. These results present a serious challenge for water and land management in the basin, where mitigation/adaption measures for climate change are desired.

Editor Z.W. Kundzewicz; Associate editor D. Yang

Citation Peng, H., Jia, Y.W., Qiu, Y.Q., and Niu, C.W., 2013. Assessing climate change impacts on the ecohydrology of the Jinghe River basin in the Loess Plateau, China. Hydrological Sciences Journal, 58 (3), 651–670.     

Solar radiation,longwave radiation and emergent wetland evapotranspiration estimates from satellite data in Florida,USA / Estimations à partir de données satellitales du rayonnement solaire,du rayonnement de grande longueur d’onde et de l’évapotranspiration d’une zone humide de Floride (EUA)

Abstract

Abstract Routine estimates of daily incoming solar radiation from the GOES-8 satellite were compared to locally measured values in Florida. Longwave radiation estimates corrected using GOES-derived cloud amount and cloud top temperature products improved net radiation estimates as compared to a clear sky longwave approach. The Penman-Monteith, Turc, Hargreaves and Makkink models were applied using GOES-derived estimates of solar radiation and net radiation to predict daily evapotranspiration and were compared to evapotranspiration measured with an eddy-correlation system in an emergent wetland experimental site in north-central Florida under unstressed conditions. While the Penman-Monteith model provided the best estimates of evapotranspiration (R ² = 0.92), the empirical Makkink method demonstrated nearly comparable agreement (R ² = 0.90) using only the GOES solar radiation and measured temperature. The results show that it is possible to generate spatially distributed daily potential evapotranspiration estimates using GOES-derived solar radiation and net radiation with limited additional surface measurements.     

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

This study challenges the use of three nature‐inspired algorithms as learning frameworks of the adaptive‐neuro‐fuzzy inference system (ANFIS) machine learning model for short‐term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography‐based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R² parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R²), root‐mean‐square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all‐nature‐inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS‐PSO and ANFIS‐BOA provide slightly better results than the other ANFIS models.     

The carbon isotope composition of C<Superscript>3</Superscript> herbaceous plants in loess area of northern China

¹³C of 367 C₃ herbaceous plants was measured in loess area in northern China. Their δ¹³C values vary between −21.7%. and −30.0%., with a mean of −26.7%.. In the center of Loess Plateau (semimoist area) with annual precipitation of 400–600 mm, the δ¹³ C values of C₃ herbaceous plants range from −24.4%. to −28.5%., with a mean of −27.5%.. In the west of Loess Plateau (semiarid and arid area) with annual precipitation less than 400 mm, they range between −21.7%. and −30.0%., with a mean of −26.2%.. Annual precipitation is the main factor that makes δ¹³C values of C₃ herbaceous plants in the west greater than those in the central Loess Plateau. The composition of δ¹³C in C₃ plants increases with deceasing annual precipitation, and the mean change is −49%./100 mm.     

Runoff responses to afforestation in a watershed of the Loess Plateau,China

Afforestation has been suggested as a means of improving soil and water conservation in north‐western China, especially on the Loess Plateau. Understanding of the hydrological responses to afforestation will help us develop sustainable watershed management strategies. A study was conducted during the period of 1956 to 1980 to evaluate runoff responses to afforestation in a watershed on the Loess Plateau with an area of 1·15 km², using a paired watershed approach. Deciduous trees, including locust (locusta L.), apricot (praecox L.) and elm (ulmus L.), were planted on about 80% of a treated watershed, while a natural grassland watershed remained unchanged. It was estimated that cumulative runoff yield in the treated watershed was reduced by 32% as a result of afforestation. A significant trend was also observed that shows annual runoff reduction increases with the age of the trees planted. Reduction in monthly runoff occurred mainly from June to September, which was ascribed to greater rainfall and utilization by trees during this period. Afforestation also resulted in reduction in the volume and peak flow of storm runoff events in the treated watershed with greater reduction in peak flow. Copyright © 2003 John Wiley & Sons, Ltd.     

Bi‐Solute Sorption of Estrogens at Low Concentration: Two‐ and Three‐Parametric Analysis

Competitive sorption of estriol (E3) and 17α‐ethinylestradiol (EE2) was studied on activated charcoal. Better sorption of E3 (88.9%) and EE2 (69.5%) was observed with single‐solute sorption system than with bi‐solute sorption system. Single‐solute sorption kinetics of E3 and EE2 were evaluated with two (Langmuir and Freundlich) and three (dual mode and Song) parameter models. Freundlich model (R², 0.9915 (E3); 0.9875 (EE2)) showed good prediction compared to other models for single‐solute sorption. Adsorption capacity documented reduced efficacy (86.4% (E3); 65.9% (EE2)) due to induced competitive behavior between the estrogens in aqueous phase. Bi‐solute adsorption kinetics of E3 and EE2 were described by IAST with two and three parameter models. Among these models, IAST‐Freundlich model (R², 0.9765 (E3); 0.9985 (EE2)) was best in predicting bi‐solute sorption of E3 and EE2 by activated charcoal. All these models showed favorable representation of both single‐ and bi‐solute sorption behaviors.     

Landscape influences on aluminium and dissolved organic carbon in streams draining the Hubbard Brook valley,New Hampshire,USA

Concentrations of both aluminium (Al) and dissolved organic carbon (DOC) in stream waters are likely to be regulated by factors that influence water flowpaths and residence times, and by the nature of the soil horizons through which waters flow. In order to investigate landscape‐scale spatial patterns in streamwater Al and DOC, we sampled seven streams draining the Hubbard Brook valley in central New Hampshire. We observed considerable variation in stream chemistry both within and between headwater watersheds. Across the valley, concentrations of total monomeric aluminium (Al_m) ranged from below detection limits (<0·7 µmol l⁻¹) to 22·3 µmol l⁻¹. In general, concentrations of Al_m decreased as pH increased downslope. There was a strong relationship between organic monomeric aluminium (Al_o) and DOC concentrations (R² = 0·92). We observed the highest Al_m concentrations in: (i) a watershed characterized by a steep narrow drainage basin and shallow soils and (ii) a watershed characterized by exceptionally deep forest floor soils and high concentrations of DOC. Forest floor depth and drainage area together explained much of the variation in ln Al_m (R² = 0·79; N = 45) and ln DOC (R² = 0·87; N = 45). Linear regression models were moderately successful in predicting ln Al_m and ln DOC in streams that were not included in model building. However, when back‐transformed, predicted DOC concentrations were as much as 72% adrift from observed DOC concentrations and Al_m concentrations were up to 51% off. This geographic approach to modelling Al and DOC is useful for general prediction, but for more detailed predictions, process‐level biogeochemical models are required. Copyright © 2005 John Wiley & Sons, Ltd.     

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul,Turkey

Tropospheric (ground‐level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1‐h average ozone concentrations in Istanbul were predicted using multi‐layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross‐validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d₂) for the testing data were 0.90, 8.78 µg/m³, 11.15 µg/m³, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM₁₀, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d₂ did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non‐linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul.     

Effects of long-term wastewater irrigation on soil physical properties and performance of selected infiltration models in a semi-arid region

ABSTRACT

In many arid and semi-arid countries, wastewater irrigation is becoming a common practice in agriculture. In this study, the effect of long-term (40 years) wastewater irrigation on selected physical and hydraulic properties of soil in different parts of a landscape was investigated. The performance of some infiltration models, including Philip (Ph), Kostiakov (Kos), Kostiakov-Lewis (Kos-L), Horton (Ho), Huggins and Monke (Hug-M), and linear and nonlinear Smith-Parlange (S-P(L) and S-P(NL)), was compared. This study was performed in the Urmia region, Iran, where flooding wastewater irrigation has been practised for at least 40 years. Five paired sites, each of which contained a measurement location at the wastewater-irrigated (WWI) and adjacent control area were studied. Accuracy of the infiltration models was evaluated using several statistical criteria, including root mean square error (RMSE) and Akaike information criterion (AIC). The models were classified into groups using cluster analysis based on level of similarity in their performance. The cumulative water infiltration into soils after 1 h (I_1h) was calculated using the selected most accurate models and introduced so as to use only one term to compare the infiltration behaviour of soils. Based on RMSE and AIC, the performance of the Ph, Ho, Kos and Kos-L models was considerably better than that of Hug-M, S-P(L) and S-P(NL). The ranking of the models in terms of their AIC values was: Kos-L > Ho > Kos > Ph > S-P(L) > Hug-M > S-P(NL). The models were classified into two distinct groups. The similarity among Ph, Ho, Kos and Kos-L models was more than 80% and for Hug-M, S-P(L), and S-P(NL) models, it was more than 79%. However, the similarity between these two groups of models was less than 58%.

Editor M.C. Acreman; Associate editor not assigned     

分季节的太湖悬浮物遥感估测模型研究

根据1996-2002年无锡太湖监测站的水质资料分析,太湖悬浮物具有季节性特征,因而分季节的悬浮物估测模型比单一的模型可能更加适合用来估测太湖全年的悬浮物浓度.在分析太湖水体光谱特征的基础上,根据太湖悬浮物的季节性分布特征,使用春夏秋冬四季的Landsat TM/ETM图像和准同步的水质采样数据,建立了太湖分季节的悬浮物估算模型.结果表明:估测因子(B2 B3)/(B2/B3)在春、秋、冬三季都能很好地估测出悬浮物的浓度(R2＞0.52).夏季由于叶绿素的干扰性较大,悬浮物的估测效果不理想.冬季的估测效果最好(R2=0.81),模型为lnSS=14.656×(B2 B3)/(B2/B3) 1.661,其中,ln SS表示悬浮物取自然对数后的值,B2、B3为TM/ETM图像经过6S大气校正、3×3低通滤波后第2、3波段的反射率值.