Predicting future urban impervious surface distribution using cellular automata and regression analysis

Urban impervious surfaces are considered as key indicator of urbanization intensity and environmental quality. Due to their significant impact on surface runoff, flood frequency, and water quality, impervious surfaces have been identified as an important indicator for examining the hydrological impact of urbanization. The amount and distribution of impervious surfaces have been estimated using remote sensing and geographic information system (GIS) techniques. Little research, however, has been conducted to predict future impervious surface distributions. To address this problem, we developed an integrated residential/commercial growth and impervious surface distribution model to predict urban impervious surface distribution. Taking Milwaukee River Basin, Wisconsin as a case study, we simulated future residential and commercial developments using a CA model. Further, we developed a linear regression model to predict impervious surface distributions in residential and commercial land uses. Analysis of results suggests that the proposed model performs significantly better than the traditional approaches.     

Assessing the impacts of land use changes on watershed hydrology using MIKE SHE

A fully distributed, physically-based hydrologic modeling system, MIKE SHE, was used in this study to investigate whole-watershed hydrologic response to land use changes within the Gyeongancheon watershed in Korea. A grid of 200 × 200 m was established to represent spatial variations in geology, soil, and land use. Initial model performance was evaluated by comparing observed and simulated streamflow from 1988 to 1991. Results indicated that the calibrated MIKE SHE model was able to predict streamflow well during the calibration and validation periods. Proportional changes in five classes of land use within the watershed were derived from multi-temporal Landsat TM imageries taken in 1980, 1990 and 2000. These imageries revealed that the watershed experienced conversion of approximately 10% non-urban area to urban area between 1980 and 2000. The calibrated MIKE SHE model was then programmed to repeatedly analyze an artificial dataset under the various land use proportions identified in the Landsat TM imageries. The analysis was made to quantitatively assess the impact of land use changes (predominantly urbanization) on watershed hydrology. There were increases in total runoff (5.5%) and overland flow (24.8%) as a response to the land use change.     

Assessing distributed groundwater recharge rate using integrated surface water-groundwater modelling: application to Mihocheon watershed, South Korea

A method of estimating groundwater recharge, based on water-balance components using the SWAT-MODFLOW model (an integrated surface water-groundwater model), is described. A multi-reservoir storage routing module is suggested instead of a single storage routing module in SWAT; this represents a more realistic delay in the travel of water through the vadose zone. By using this module, the parameter related to the delay time can be optimized by checking the correlation between simulated recharge and observed groundwater levels. The final step of this procedure is to compare simulated groundwater levels as well as the simulated watershed stream flow with the observed groundwater levels and watershed stream flow. This method is applied to the Mihocheon watershed in South Korea to estimate spatio-temporal groundwater recharge distribution. The computed annual recharge rate is compared with the independently estimated recharge rate using BFLOW. The hydrologic modelling results show that the annual average recharge rate should be estimated by a long-term continuous simulation with a distributed hydrologic modelling technique.     

城市下垫面空间特征对地表产汇流过程的影响研究综述

快速城市化显著改变了城市下垫面空间特征,对地表产汇流过程产生了重要影响。综述城市下垫面空间特征对地表产汇流过程的影响研究进展,以下垫面空间特征对产汇流过程的影响为主线,聚焦不透水面和微地形两类空间特征,总结城市下垫面空间特征及其表征方法;从不透水率与不透水面空间变化、数值模拟与物理实验等方面,分别梳理不透水面和微地形等空间特征对地表产汇流过程的影响研究成果。展望未来城市下垫面空间特征对地表产汇流过程的影响研究重点和发展方向,主要包括影响机理解析、主控空间特征参数识别与敏感性分析、城市水文效应适应性应对等,以期为快速城市化下城市水文效应解析和城市洪涝灾害防治提供一定参考。     

Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation

Soil erosion is one of most widespread process of degradation. The erodibility of a soil is a measure of its susceptibility to erosion and depends on many soil properties. Soil erodibility factor varies greatly over space and is commonly estimated using the revised universal soil loss equation. Neglecting information about estimation uncertainty may lead to improper decision-making. One geostatistical approach to spatial analysis is sequential Gaussian simulation, which draws alternative, equally probable, joint realizations of a regionalised variable. Differences between the realizations provide a measure of spatial uncertainty and allow us to carry out an error analysis. The objective of this paper was to assess the model output error of soil erodibility resulting from the uncertainties in the input attributes (texture and organic matter). The study area covers about 30 km² (Calabria, southern Italy). Topsoil samples were collected at 175 locations within the study area in 2006 and the main chemical and physical soil properties were determined. As soil textural size fractions are compositional data, the additive-logratio (alr) transformation was used to remove the non-negativity and constant-sum constraints on compositional variables. A Monte Carlo analysis was performed, which consisted of drawing a large number (500) of identically distributed input attributes from the multivariable joint probability distribution function. We incorporated spatial cross-correlation information through joint sequential Gaussian simulation, because model inputs were spatially correlated. The erodibility model was then estimated for each set of the 500 joint realisations of the input variables and the ensemble of the model outputs was used to infer the erodibility probability distribution function. This approach has also allowed for delineating the areas characterised by greater uncertainty and then to suggest efficient supplementary sampling strategies for further improving the precision of K value predictions.     

Assessment of impervious surface growth in urban environment through remote sensing estimates

The fast growth in population and expansion of urban built area has led to the transformation of the natural landscape into impervious surfaces. Remote sensing-based estimate of impervious surface area (ISA) has emerged as an important indicator for the assessment of water resources depletion in urban areas and developed a correlation between land-use change and their potential impact on urban hydrology. In the present work, a remote sensing-based Impervious Surface Area (ISA) was carried out for New Okhla Industrial Development Authority (NOIDA) city, one of the fastest growing cities in National Capital Region (NCR) of India. The impervious surface area (ISA) of NOIDA was calculated for the years 2001, 2007 and 2014 using multi-temporal LANDSAT thermal data by applying linear spectral mixing analysis (LSMA) techniques to monitor the growth rate of impervious surface. The results observed by analysis of multi-temporal satellite images show an extreme temporal change in the growth of ISA in the city. The ISA observed for the year 2001 is 28 sq.km; in 2007, its increase was 48 sq.km and was 132 in 2014. The results were observed from this work through the use of satellite data which is very important for water resource management, planning and prediction of ISA impact on hydrology.     

Assessing the impact of flood inundation dynamics on an urban environment

Natural Hazards - This study attempted to examine the complex impact of dynamic inundation process of extreme events on flood hazard assessment (FHA) for the affected urban settings around a local...     

HSPF模型在流域水文与水环境研究中的进展

HSPF(Hydrological Simulation Program-Fortran)模型是在Stanford水文模型的基础上发展起来的.作为半分布式水文水质模型的优秀代表,HSPF模型能够综合模拟河道水力、流域径流、土壤流失、污染物迁移等过程,从而被广泛应用于流域水文与水环境研究中.通过概述HSPF模型研发与整合...     

基于随机森林模型的城市不透水面提取研究 ——以呼和浩特市为例

城市不透水面信息对于城市生态环境动态演化过程研究具有重要意义。以Landsat 8遥感影像为数据源,以呼和浩特市为实证区域,进行了随机森林模型应用于城市不透水面的提取研究,并与目前应用广泛的支持向量机模型进行了对比分析。研究表明：在不同的抽样比例训练样本条件下,随机森林模型对于城市不透水面的提取精度均优于支持向量机的提取精度;对于随机森林模型和支持向量机模型,70%的训练样本比例均为最佳训练样本抽样比例。在该抽样比例下,随机森林模型提取城市不透水面的总体分类精度为93.29%,Kappa系数为0.9051,支持向量机模型的总体分类精度为91.26%,Kappa系数为0.8757;随机森林模型对于城市裸土的识别度较高,能更好地将城市裸土和不透水面进行区分,而支持向量机模型对于城市裸土、不透水面和绿地的区分能力均弱于随机森林模型。综合而言,随机森林模型对城市不透水面的提取精度优于支持向量机模型,随机森林模型可以有效应用于城市不透水面提取领域,进一步丰富了城市不透水面提取方法体系构成。     

面向对象的GIS水文水资源数据模型设计与实现

分析和总结了地理信息系统(GIS)在水文水资源应用中存在的问题与挑战。在ArcHydro数据模型框架的基础上,对面向对象的GIS水文水资源数据模型的结构与实现进行了深入研究与探讨。面向对象的GIS水文水资源数据模型能够很好地表达水文对象的空间位置、特征属性、时间属性以及对象间的拓扑关系,能完整地描述水文现象在地理空间的运动过程,并在江西省中部清丰山流域地理信息系统建设中应用。应用结果表明,面向对象的GIS水文水资源数据模型为设计水文空间数据库提供了一个通用的结构框架,有助于推动GIS在水文学研究与水资源管理的深入应用。     

Exploring the effects of urban and agricultural land use on surface water chemistry,across a regional watershed,using multivariate statistics

In this paper, the influence of anthropogenic activity on surface water chemistry is investigated. Base flow samples from dominant land use streams in the Muskegon River Watershed, Michigan, USA, were analyzed for nutrients, major ions, and trace elements. Principal component and hierarchical cluster analysis were used to investigate the processes controlling the effects of land use on the biogeochemistry of streams in this Watershed. Biogeochemical fingerprints of land use were developed based on the results of the cluster analysis. Spider diagrams which referenced the natural environment aided the identification of the individual contributions of urban and agricultural land uses to surface water chemistry. The biogeochemical fingerprints quantified support previous findings (Urb: Na, K, Cl/Ag: Ca, Mg) and produced new insights into the effects of land use on the behavior of nutrients (Ag: N and Urb: N, P) and trace elements (Urb: V, Cr, Co, Cu, Se, Rb, Mo, Sr, Cd, Pb and Ba/Ag: U and As). The higher correlations of urban than agricultural land uses with nutrients, specifically P, were not expected and may reflect the effects of fertilization and wastewater or the season the samples were taken. More study in different geologic and urban settings is needed to help refine these fingerprints, but it is becoming apparent that despite obscuring factors, land use produces consistent, quantifiable associations between biogeochemical analytes.     

Spatial evaluation of impacts of increase in impervious surface area on SCS-CN and runoff in Nagpur urban watersheds,India

The changing land use due to rapid urbanization has profound impact on the runoff in urban watersheds. The spatial analysis in urban watersheds is felt necessary for management of surface and subsurface water regimes. Significant increase in impervious zones was observed in Nagpur urban watersheds between 2000 and 2012 having impacts on runoff, and even flash floods were observed. This study presents spatial and temporal impacts of change in urban built-up area on curve number (CN) and runoff during the years 2000 and 2012. The study also analyzes effect of slope on CN values and shows that CN increases with slope. High-resolution satellite images were used to map impervious surface areas (ISAs) which show an increase of 0.9 to 34 % during 2000–2012. Spearman’s and Pearson’s coefficients have been generated to establish relationship between runoff, impervious surface areas, vegetation index, slope, and runoff coefficient. It has been hypothetically assumed that if 100, 50, and 25 % rooftop rainwater harvesting is considered, the estimated runoff reduces in 2012 as compared to the year 2000. The study suggests that increase in impervious areas within urban watersheds can be utilized for groundwater augmentation adopting rooftop rainwater-harvesting techniques and to prevent flash floods.     

Assessing the organic pollution of surface water of Medjerda watershed (NE Algeria)

Water quality in the Northern part of Mellegue-Medjerda watershed (East Algeria) has been adversely affected by important pollutants discharged into the Medjerda wadi without, in most cases, any treatment. Chemical and physical degradation are due to agricultural and industrial practices and domestic wastewaters. Over a three-month period, a study of the low-flow water quality characteristics throughout Medjerda wadi was undertaken. Longitudinal profiles of water quality were constructed using data from fourteen sites. All sewage, agricultural, and industrial inputs were included. Analyzed properties were nutrients (NO₃ ⁻, NO₂ ⁻, NH₄ ⁺, and PO₄ ³⁻), Biochemical oxygen demand after five days (BOD₅), chemical oxygen demand (COD), and dissolved oxygen (DO). Along Medjerda wadi, all values change because of conditions specific to each sampling station. Nitrate was the most important form of nitrogen-element load (94%). Its concentration reached 34.3 mg L⁻¹ at OM₄ point, downstream of domestic wastewater discharges. The spatial evolution of the organic pollution index (OPI) shows that the wastewater effluent constitutes the main source of pollution. Indeed, water quality goes from a moderate pollution state at some sampling stations not or slightly affected by wastewaters discharges to a very strong pollution state (OPI of about 1.75) downstream of the domestic effluents inputs of Souk-Ahras city.     

城市下垫面空间格局对社区尺度内涝过程的影响模拟

为研究城市下垫面空间格局对社区尺度内涝过程的影响, 构建社区尺度下的8种不同城市下垫面空间格局, 并建立相应的城市内涝数值模型, 模拟6种不同重现期(2 a, 5 a, 10 a, 20 a, 50 a, 100 a)设计降雨条件下内涝过程; 基于不同下垫面和降雨情景下的内涝数值模拟结果, 分别从内涝淹没特征值、时空变化过程、水动力特性等方面开展分析。结果表明: ①城市下垫面空间格局对社区尺度内涝淹没特征值、淹没时空变化过程和内涝积水流速分布均有一定影响; ②设计降雨重现期为2 a、5 a、50 a和100 a时, 不同城市下垫面空间格局间的积水总量峰值、积水面积峰值、区域最大积水深差别显著, 重现期为10 a和20 a时, 无明显差别; ③内涝积水较大流速分布主要集中在道路交汇处, 在本研究构建的情景中, 最大流速的差值占比为31.9%;④在8种不同城市下垫面空间格局中, 环形放射型格局在应对内涝方面更具弹性。本研究可为城市下垫面空间管控和城市内涝形成机理研究提供科学参考。     

Intermittent reservoir daily-inflow prediction using lumped and distributed data multi-linear regression models

In this study, multi-linear regression (MLR) approach is used to construct intermittent reservoir daily inflow forecasting system. To illustrate the applicability and effect of using lumped and distributed input data in MLR approach, Koyna river watershed in Maharashtra, India is chosen as a case study. The results are also compared with autoregressive integrated moving average (ARIMA) models. MLR attempts to model the relationship between two or more independent variables over a dependent variable by fitting a linear regression equation. The main aim of the present study is to see the consequences of development and applicability of simple models, when sufficient data length is available. Out of 47 years of daily historical rainfall and reservoir inflow data, 33 years of data is used for building the model and 14 years of data is used for validating the model. Based on the observed daily rainfall and reservoir inflow, various types of time-series, cause-effect and combined models are developed using lumped and distributed input data. Model performance was evaluated using various performance criteria and it was found that as in the present case, of well correlated input data, both lumped and distributed MLR models perform equally well. For the present case study considered, both MLR and ARIMA models performed equally sound due to availability of large dataset.     

Assessing the impact of weir construction on recent sedimentation using cesium-137

Ten cores were obtained from a marsh developed along Mad Island Slough, Texas, USA, upstream of a weir constructed in 1948. The cores were analyzed for cesium-137 to identify time-stratigraphic marker horizons and calculate recent sedimentation rates. The cesium-137 analysis provided a 1954 marker horizon in nine of the ten cores. A second marker horizon, present in all ten cores, consisted of an abrupt downcore change in lithology from dark organic-rich muds to grey organic-poor sands. This transition was tentatively identified as coinciding with 1948 and the beginning of marsh sedimentation. Resulting sedimentation rates show that surprisingly little sedimentation has occurred behind the weir, averaging only 27 cm in almost 50 years. Sedimentation rates in the marsh declined from an average of 2.4 cm yr^–1 in 1948–1954 to 0.32 cm yr^–1 in 1954–1994. A similar trend of declining sedimentation has been documented for adjoining Mad Island Lake, suggesting that land-use changes in the lake's watershed have reduced the sediment supply in recent decades. The results also suggest that the weir is not a very efficient sediment trap in this watershed.     

Pattern recognition techniques for evaluating the spatial impact of copper mining on surface waters

In this study, multivariate statistical approaches, namely hierarchical cluster analysis (CA) and principal component analysis (PCA), were employed to understand the impact of copper mining on surface waters located in Central-East India. The data set generated consisted of nine parameters, namely pH, dissolved oxygen (DO), alkalinity, total dissolved solids, copper, iron, manganese, zinc and fluoride, collected in forty sampling points covering all seasons. As delineated by CA, the entire data set for both the surface waters was bifurcated into groups, namely Banjar River inclusion of seepage points (BRISP) and Banjar River exclusion of seepage points (BRESP), Son River inclusion of seepage points (SRISP) and Son River exclusion of seepage points (SRESP). Four latent factors were identified, namely copper, iron, fluoride and manganese, explaining 84.7 % of variance for BRISP, 71.9 % of variance for BRESP, 66.7 % of variance for SRISP and 68 % of variance for SRESP. The extensive application of PCA on BRISP, BRESP, SRISP and SRESP reveals that the main stream of both the rivers remains unaffected by mining operations when seepage points were excluded. Additionally, iron content is considerably significant throughout the stream due to the geogenic sources and it is considered as a major factor for the depletion of DO level in the streams. This study reveals the level of contamination in the studied surface waters and the effectiveness of multivariate statistical techniques for evaluation and interpretation of complex data matrix in understanding the spatial variations and identification of pollution sources.     

Explorative data analysis of heavy metal contaminated soil using multidimensional spatial regression

To obtain data on heavy metal contaminated soil requires laborious and time-consuming data sampling and analysis. Not only has the contamination to be measured, but also additional data characterizing the soil and the boundary conditions of the site, such as pH, land use, and soil fertility. For an integrative approach, combining the analysis of spatial distribution, and of factors influencing the contamination, and its treatment, the Mollifier interpolation was used, which is a non-parametric kernel density regression. The Mollifier was capable of including additional independent variables (beyond the spatial dimensions x and y) in the spatial interpolation and hence explored the combined influence of spatial and other variables, such as land use, on the heavy metal distribution. The Mollifier could also represent the interdependence between different heavy metal concentrations and additional site characteristics. Although the uncertainty measure supplied by the Mollifier at first seems somewhat unusual, it is a valuable feature and supplements the geostatistical uncertainty assessment.