Topographic Index Model Research Articles

Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model

Efficient analysis of non-susceptibility to landslides targets regions with minimal or zero landslide probability, thereby obviating the need to estimate the likelihood for low-susceptibility zones. This study assesses the effectiveness of the quantile non-linear (QNL) model in delineating the non-susceptibility of landslides in China through a topographic index. The topographic index encompassed slope angle and topographic relief, which are calculated using a 3 × 3 and 15 × 15 square cell moving window, respectively. Additionally, a global landslide susceptibility model established using a comprehensive global landslide database and fuzzy algorithm was employed for comparative analysis, providing a holistic evaluation of the QNL model’s accuracy. The results show that while the overall distribution of the two QNL models for non-susceptible landslide areas was roughly consistent, notable discrepancies were observed in localized regions, especially in the Southwest and Qinghai-Tibet geological environment areas where landslides are prone to occur. The applicability of the QNL model is significantly limited in these areas. In addition, the predicted results of the QNL_CHN model are closer to those based on the global landslide susceptibility model of the fuzzy algorithm. This study provides valuable insights to enhance the QNL model’s applicability, thereby strengthening forest ecosystem management and mitigating ecological disaster risks.

Supervised machine learning for predicting and interpreting dynamic drivers of plantation forest productivity in northern Tasmania, Australia

The capacity of a plantation forest to grow and produce timber is locally constrained by topography, climate, soil conditions, and external factors such as fire and timber harvesting. Accurate estimation of forest productivity supports effective forest management. However, efficiently generating accurate models of forest productivity is hampered by the need to gather, process and integrate large volumes of disparate, high dimensional data that require computationally intensive data analysis processing methods. Recent developments in cloud-based machine learning systems offer a means to address this problem.This research investigates the use of supervised machine learning to model and predict forest productivity across pine (Pinus radiata) plantations in northern Tasmania, Australia. Forest productivity models are generated by integrating 23 predictive features, including multi-temporal LiDAR (Light Detection and Ranging) derived topographic attributes, climate (rainfall and temperature) information, and edaphic conditions (geology and soil). Five machine learning (ML) regression algorithms are compared for this task: Linear Regression (LR), Polynomial Regression (PR), Decision Trees Regression (DT), Random Forests (RF) and Gradient Boosted Decision Trees Regression (GBDT). The best performing algorithm, in terms of the optimal bias-variance trade-off, was RF (RMSE 2.08 and Bias −0.72) followed closely by GBDT (RMSE 2.13 and Bias −0.68) and DT (RMSE 2.94 and Bias −0.68). Tuning Model Complexity was used to provide a clear understanding of the relationship and interactions between input features and forest productivity, resulting in more accurate and interpretable models. In contrast, we conclude that GBDT results in a more reliable predictive performance than RF, and transferability model to unseen data by assessing the spatial autocorrelation. Across the top performing models, rainfall was the most important factor driving forest productivity, followed by geological class, topographic position index (TPI), landscape aspect and Digital Elevation Model (DEM).This work demonstrates the usefulness of supervised machine learning techniques to generate accurate and efficient forest productivity predictions from large and diverse spatial datasets. Moreover, users are afforded the ability to gain insight into changes that affect forest productivity through time, such as the increasing risks of wildfire and climate change by identifying the factors that contribute the most to the tree growth. By delivering a framework to understand complex and dynamic drivers of productivity through a machine learning pipeline enhanced by cloud-based machine learning systems, forest managers are provided with easily accessible tools to maximisation productivity.

山区社会-生态系统转型测度——以三峡典型库区为例

PDF HTML阅读 XML下载 导出引用 引用提醒 山区社会-生态系统转型测度——以三峡典型库区为例 DOI: 10.5846/stxb202111233301 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41661020）；重庆市技术创新与应用发展专项（cstc2019jscx-gksbX0060）；重庆市教育委员会人文社会科学研究项目（19JD026）；重庆师范大学研究生科研创新项目（YKC21037） Measurement of mountain social-ecological system transformation: Taking the typical Three Gorges Reservoir Area as an example Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:三峡库区是集农村和山区为一体的社会-生态系统，研究其系统内部的相互作用和系统间的耦合转型有利于推动库区高质量的可持续发展。通过构建社会-生态系统耦合转型研究框架，采用地形位指数、耦合协调度模型等方法，以库区腹地草堂溪流域为例，揭示其社会-生态系统转型与否、转型路径的多样性及其驱动机制。结果表明：（1）2000-2020年三峡库区社会-生态系统数量-结构-功能均发生了显著变化，表明库区社会-生态系统发生了转型；（2）库区低地形位社会-生态系统表现为由社会生态型向生态经济型转变，中地形位表现为由生态社会型向生态主导、生态经济为辅转变，高地形位则表现为由生态调节型愈加趋向于生态主导型；（3）农户和政府需求变化是驱动社会-生态系统由传统耕地种植系统向新型经果林种植系统转型的根本因素；（4）研究方法证实对山区具有普适性，构建的研究框架较适合刻画山区社会-生态系统转型路径，在其他地方也能同样适用。 Abstract:The Three Gorges Reservoir Area is a social-ecological system integrating rural and mountainous areas. Studying the interaction within the system and the coupling transformation between the systems is conducive to promoting the high-quality sustainable development of the reservoir area. By constructing the research framework of social-ecological system coupling transformation, using the methods of topographic potential index and coupling coordination degree model, so as to reveal whether the social-ecological system transformation is or not, the diversity of transformation paths and its driving mechanism in the Three Gorges Reservoir Area. The results show that:(1) from 2000 to 2020, the quantity, structure and function of the social-ecological system in the Three Gorges Reservoir Area have changed significantly, indicating the transformation of the social-ecological system in the reservoir area. (2) The social-ecological system of the lowland position in the reservoir area changed from social-ecological type to ecological economy type, the median position changed from ecological social type to ecological dominant and ecological economy supplemented, and the highland changed from ecological regulation type to ecological dominant type. (3) The change of farmers' and government's demand is the fundamental factor driving the transformation of social-ecological system from traditionally cultivated land planting system to new economic fruit forest planting system. (4) The research method of this paper proves that it is universal to mountainous areas, and the research framework is more suitable to describe the transformation path of social-ecological system in mountainous areas, which can also be applied in other places. 参考文献 相似文献 引证文献

Determination of the relationship among compound Topographic Index (CTI), soil properties and land-use in karst ecosystems

ABSTRACT Topography is a key factor that affects edaphic, climatic, and biotic factors directly or indirectly. Relationships between Compound Topographic Index (CTI) model and soil properties were investigated in order to determine the ecological potential of the area in order to plan the sustainability of the fertile lands and the rehabilitation of the degraded areas in the study area. Totally 84 soil samples (0–20 cm) collected from different land use types (forest, cropland, rangeland) in Karst Mountain, Andırın-Kahramanmaras, Turkey. The CTI distributions are generated from digital elevation data of 12 m resolution obtained from AlosPalsar Satellite images. Although CTI values changed to Forest> Cropland> Rangeland, the highest correlation was found between CTI and soil moisture content in rangeland (R2 = 0.81). Lower correlations were found with other land uses (R2 = 0.63 in Cropland and R2 = 0.31 in Forest). CEC and Clay content were found to have a significant (p < 0.01) positive correlation with Curvature. Despite a distinctive topographic structure of karst ecosystems, these correlations verified the influence of topography on soil properties. CTI, which has strong correlations with soil moisture content can be used in planning reclamation processes of degraded karst ecosystems.

Grid order prediction of ephemeral gully head cut position: Regional scale application

Predicting ephemeral gully (EG) location is essential for many land management decisions and for accurate application of widely used soil erosion models. Topographic indexes are widely used for identifying EG head locations, even though the calibration of the topographic index models limits their use for broad scales. The objective of this research was to test the accuracy and usability of grid order in predicting EG head locations at the regional scale compared to other topographic indexes commonly used for this purpose. Three hundred fifty-three EGs in eight watersheds located within different landforms (Major Land Resource Areas) in Iowa, USA, were visually digitized, georeferenced, and analyzed using 3-meter LiDAR-derived elevation datasets and orthoimages. The location prediction accuracy, spatial location prediction error stability, and the variation of grid order critical threshold (CT) values were compared to predictions from four commonly used topographic indexes, specific contributing drainage area (As), Compound Topographic Index (CTI), Stream Power Index (SPI), and a variation of Stream Power Index (AS2). Results indicate that using As, CTI, SPI, and AS2 to identify flow length upstream from predicted EG head locations would require careful calibration for each watershed or substantial errors in prediction accuracy would occur. In contrast, the accuracy of using grid order was acceptable at the regional scale even without model calibration with Nash Sutcliff Efficiency estimates ranging from 0.29 to 0.59; the distance between surveyed and predicted EG head locations was within 20 m for all studied EGs. The grid order value of 4 was the best CT value when the slope gradient was less than 10%, and 3 for steeper slopes. These results could be easily applied for different landforms across wider areas. The result of this research supports the prediction of EGs using automated systems without calibration at a broad scale where quality DEMs are available.

Optimal spatial priority scheme of urban LID-BMPs under different investment periods

The optimal spatial layout of low impact development best management practices (LID-BMPs) can be used to inform LID-BMP construction to prioritize resources to achieve the highest comprehensive benefits. An objective decomposition algorithm is designed to decompose the control objectives of total rainwater runoff, peak discharge, pollution reduction and rainwater utilization for each pixel of a raster image. A type selection algorithm is then designed to optimize the types of LID-BMPs deployed in each pixel. The most suitable positions for the construction of LID-BMPs are identified using a topographic index model to determine spatial priority level of LID-BMP construction sequence. A multi-objective model, including the maximum average reduction ratios of runoff and pollution and minimum total cost, is constructed. An adaptive differential evolution algorithm (ADEA) is designed to solve the multi-objective optimization problem. The optimal grid-based priority scheme is obtained for high-resolution spatial planning. It is shown that the most suitable positions for implementing LID-BMPs are located in regions with negative soil moisture deficits. Optimal total cost and reduction ratios of runoff and pollution to construct LID-BMPs in Yinchuan, China are $49,463,516, and 0.46 and 0.38 in the first priority scheme; $50,920,496, and 0.74 and 0.62 in the second priority scheme; and $99,207,806, and 0.92 and 0.86 in the third priority scheme. The optimal spatial priority schemes of LID-BMPs obtained from the ADEA can maintain the maximum runoff and pollution controls under different investment periods.

Improvement of forest canopy height estimation model by combining Geoscience Laser Altimetry System full waveform and multispectral remote sensing data over sloping terrain

Light detection and ranging (LiDAR) technology can provide accurate data for the vertical structure of a forest. Such data are fundamental for the study of aboveground biomass in the global carbon cycle. Most studies to date have used Geoscience Laser Altimetry System (GLAS) waveforms and terrain parameters as raw data in creating forest canopy height estimation models. Whether other parameters can be introduced to improve the accuracy of these models remain to be studied. In this study, we introduce parameters for forest texture to augment the topographic index model. Texture parameters were combined with field observations of canopy height to increase the accuracy of canopy height estimates over sloping terrain in the Lushuihe Forest District, a typical forest in the Changbai Mountains, Jilin Province, China. The study area consists mainly of broadleaf forest, coniferous forest, and mixed forest. Fifty-five sample plots covering different terrain reliefs and forest types were selected. The study areas were divided into high and low relief areas (i.e., hills and plains) using four different terrain relief thresholds (5, 10, 15, and 20 m) to select the best partition value. The results showed that the most accurate estimate of canopy height was given by the improved model using a 20-m threshold. Compared with the baseline topographic index model, R2 for leave-one-out cross-validation between the estimates given by the improved model and observed data increased from 0.777 to 0.952 and RMSE decreased from 2.90 to 1.35 m. The increased accuracy of canopy height estimates is due to the correction of GLAS waveform parameters by introducing texture parameters to incorporate changes in canopy structure because of forest aging and different forest types.

Accuracy of topographic index models at identifying ephemeral gully trajectories on agricultural fields

Topographic index (TI) models have been widely used to predict trajectories and initiation points of ephemeral gullies (EGs) in agricultural landscapes. Prediction of EGs strongly relies on the selected value of critical TI threshold, and the accuracy depends on topographic features, agricultural management, and datasets of observed EGs. This study statistically evaluated the predictions by TI models in two paired watersheds in Central Kansas that had different levels of structural disturbances due to implemented conservation practices. Four TI models with sole dependency on topographic factors of slope, contributing area, and planform curvature were used in this study. The observed EGs were obtained by field reconnaissance and through the process of hydrological reconditioning of digital elevation models (DEMs). The Kernel Density Estimation analysis was used to evaluate TI distribution within a 10-m buffer of the observed EG trajectories. The EG occurrence within catchments was analyzed using kappa statistics of the error matrix approach, while the lengths of predicted EGs were compared with the observed dataset using the Nash–Sutcliffe Efficiency (NSE) statistics. The TI frequency analysis produced bi-modal distribution of topographic indexes with the pixels within the EG trajectory having a higher peak. The graphs of kappa and NSE versus critical TI threshold showed similar profile for all four TI models and both watersheds with the maximum value representing the best comparison with the observed data. The Compound Topographic Index (CTI) model presented the overall best accuracy with NSE of 0.55 and kappa of 0.32. The statistics for the disturbed watershed showed higher best critical TI threshold values than for the undisturbed watershed. Structural conservation practices implemented in the disturbed watershed reduced ephemeral channels in headwater catchments, thus producing less variability in catchments with EGs. The variation in critical thresholds for all TI models suggested that TI models tend to predict EG occurrence and length over a range of thresholds rather than find a single best value.

Identifying Potential Locations for Grassed Waterways using Terrain Attributes and Precision Conservation Technologies

Grassed waterways (GWWs) are an effective conservation practice for preventing ephemeral gully erosion resulting from channelized surface runoff in agricultural fields. However, field reconnaissance to identify areas of channelized erosion within a watershed can be time-consuming and labor-intensive. Recent advances in precision conservation and light detection and ranging (LiDAR) technologies can provide valuable information on environmentally sensitive areas that cause soil degradation. The objective of this study was to demonstrate that a compound topographic index (CTI) model supplemented with LiDAR data can be used to identify potential GWW locations and inform design recommendations. A LiDAR digital elevation model with a spatial resolution of 3 m was used to derive terrain attributes (slope, drainage area, and plan curvature). The GWW identification and design process was automated in the ArcGIS Python environment. The plan curvature identified erosion channels, but discontinuity in the model output was observed. The CTI model was calibrated to a field with GWWs installed under USDA-NRCS guidelines, which yielded a CTI threshold of 30. The calibrated model (CTI ≥ 30) was able to identify all 14 existing GWWs in the watershed. Field surveys were conducted in the watershed, and areas exhibiting evidence of channelized erosion were identified by the model for GWW placement. Furthermore, the CTI model overestimated (PBIAS = -23.34%) the lengths of predicted GWWs, suggesting a need to further extend the existing GWWs. The total surface area of the predicted GWWs was 29.3 ha in the study watershed, with depth of GWWs reaching 0.3 m. The design process provides an estimate of land to be set aside for conservation practices. The terrain analysis was effective in targeting conservation practice placement and improves the accuracy of field assessments.

Predicting location and length of ephemeral gullies with a process-based Topographic Index model

Abstract. Ephemeral gully (EG) erosion can be a major source of sediment in agricultural watersheds and predicting the location and length of EGs is important to assess sediment source areas. Topographic index (TI) models utilize topographic-only factors to locate concentrated flow paths on agricultural fields. In this paper, the TI-model is expanded by incorporating an overland flow equation that describes surface excess rate, roughness, and soil critical shear stress. An un-calibrated Process-Based (PB) topographic index model was applied to an agricultural watershed in Central Kansas and the results were compared with a calibrated Slope-Area (SA) model. The analysis showed that the PB model slightly over-predicted topographic properties of ephemeral gullies compared to the observed data. Accuracy of the SA model depended on the selected threshold value.

Simulation and comparative study of two types of Topographic Index model for a homogeneous mountain catchment

In order to expand the application range of the classic Topographic Index model (TOPMODEL) and develop a more appropriate submodel of hydrological processes for use in the land surface model, two types of TOPMODEL are investigated, one with saturated hydraulic conductivity change with depth obeying exponential law (classical e-TOPMODEL or e-TOPMODEL for short) and the other obeying general power law (general p-TOPMODEL or p-TOPMODEL for short). Using observation date in the Suomo River catchment located in the upper reaches of the Yangtze River, the sensitivity study of the p-TOPMODEL was conducted and the simulated results from the model were examined and evaluated first, and then the results were compared with the results from the e-TOPMODEL to find the similarities and differences between the two types of models. The main conclusions obtained from the above studies are (1) topographic index and its distribution derived from the p-TOPPMODEL for the Suomo Basin are sensitive to changes of parameter n and m; (2) changes of n and m have impacts on the simulation results of various hydrological components (such as daily runoff, monthly averaged runoff, monthly averaged surface runoff and subsurface runoff), but have the weaker impacts on forty-year averaged total runoff; and (3) for the same value of m, the simulated results of e-TOPMODEL display higher surface runoff and lower subsurface runoff than the general p-TOPMODEL does but multi-year averaged total runoffs produced from the two types of TOPMODEL show insignificant difference. The differences between the two types of models indicate that it is necessary to pay close attention to correct selection from different hydrological models for use in land surface model development. The result mentioned above is useful to provide some referential information for the model selection.

Evaluating ephemeral gullies with a process-based topographic index model

Soil conservation practices have been implemented to control soil degradation from sheet and rill erosion, but excessive sediment in runoff remains among the most prevalent water quality problems in the world. Ephemeral gully (EG) erosion has been recognized as a major source of sediment loss in agricultural watersheds; thus, predicting location and length of EGs is important to assess sediment contribution from EG erosion. Geomorphological models are based on topographic information and ignore other important factors such as precipitation, soil, topography, and land use/land management practices, whereas physically based models are complex, require detailed input information, and are difficult to apply to larger areas. In this study, an approach was developed to incorporate a process-based Overland Flow-Turbulent (OFT) EG model that contained factors accounting for drainage area, surface roughness, slope, soil critical shear stress, and surface runoff in the ArcGIS environment. Two hydrologic models, Soil Water Assessment Tool (SWAT) and ArcCN-Runoff (ACR), were adopted to simulate precipitation excess in Goose Creek watershed in central Kansas, USA. These two realizations of the OFT model were compared with the Slope-Area (SA) topographic index model for accuracy of EG location identification and length calculation. The critical threshold index in the SA model was calibrated in a single field in the watershed prior to EG identification whereas the OFT models were uncalibrated. Results demonstrated overall similar performance between calibrated SA model and uncalibrated OFT-SWAT model, and both outperformed the uncalibrated OFT-ACR model. In simulation of EG location, the OFT-SWAT model resulted in 12% fewer false negatives but 8% more false positives than the SA model, compared with 19% fewer false positives and 6% more false negatives than the OFT-ACR model. Greater errors in runoff estimation by ACR translated directly into errors in EG simulation. All models over-predicted EG lengths compared with observed data, though OFT-SWAT and SA models did so with better-fit exceedance probability curves, about zero Nash–Sutcliffe model efficiency and ≤40% bias compared to −3 model efficiency and >100% bias for OFT-ACR. Success of the uncalibrated OFT-SWAT model in producing satisfactory predictions of EG location and EG length shows promise for process-based EG simulation. The OFT-SWAT model used data and parameters also commonly used for SWAT model development, which should simplify its adoption to other watersheds and regions. Further testing is needed to determine the robustness of the OFT-SWAT model to dissimilar field and hydrologic conditions. It is expected that inclusion of more site-specific physical properties in OFT-SWAT would improve model performance in predicting location and length of EGs, which is essential for accurate estimation of EG sediment erosion rates.

Predicting Ephemeral Gully Location and Length Using Topographic Index Models

Abstract. Ephemeral gullies (EGs) are incised channels resulting from concentrated overland flow that often form in a similar location every year. These erosional features add to producers’ management efforts and costs. Locating EGs and predicting their length is crucial for estimating sediment load and planning conservation strategies. Since topography plays an important role in the formation of EGs, this study investigated the prediction of EG location and length in two agricultural areas (S1 and S2) in two different physiographic regions using four topographic index models: compound topographic index (CTI), slope area (SA), wetness topographic index (WTI), and slope area power (SAP). The impacts of digital elevation model (DEM) resolution, agricultural land use mask data source, and topographic model critical thresholds were also evaluated. Automated geospatial models were developed to locate and derive EG length. Results show that the SA model predicted EG occurrence and length better than other models tested. The SA and CTI model predictions had similar patterns in terms of locating EG trajectory; however, the CTI model had greater discontinuity along the trajectory. The method developed to derive length in this study was sensitive to discontinuity, so the performance of the CTI model was poor. Finer-resolution DEMs (2 m) predicted EG location and lengths better than coarser-resolution DEMs (10 m or greater). Use of actual field-level reconnaissance data instead of NASS data for agricultural land use masking decreased false negative classification by 16% or more for all models. Detailed calibration of the SA model yielded different optimal thresholds for the two study regions: TSA = 30 for S1 and TSA = 50 for S2. Topographic index models were found to be useful in locating EGs and estimating expected lengths, but site-specific calibration of the topographic index model threshold was required, which might limit the general utility of these methods.

Predicting Ephemeral Gully Location and Length Using Topographic Index Models

<abstract><title><italic>Abstract.</italic></title> Ephemeral gullies (EGs) are incised channels resulting from concentrated overland flow that often form in a similar location every year. These erosional features add to producers’ management efforts and costs. Locating EGs and predicting their length is crucial for estimating sediment load and planning conservation strategies. Since topography plays an important role in the formation of EGs, this study investigated the prediction of EG location and length in two agricultural areas (S1 and S2) in two different physiographic regions using four topographic index models: compound topographic index (CTI), slope area (SA), wetness topographic index (WTI), and slope area power (SAP). The impacts of digital elevation model (DEM) resolution, agricultural land use mask data source, and topographic model critical thresholds were also evaluated. Automated geospatial models were developed to locate and derive EG length. Results show that the SA model predicted EG occurrence and length better than other models tested. The SA and CTI model predictions had similar patterns in terms of locating EG trajectory; however, the CTI model had greater discontinuity along the trajectory. The method developed to derive length in this study was sensitive to discontinuity, so the performance of the CTI model was poor. Finer-resolution DEMs (2 m) predicted EG location and lengths better than coarser-resolution DEMs (10 m or greater). Use of actual field-level reconnaissance data instead of NASS data for agricultural land use masking decreased false negative classification by 16% or more for all models. Detailed calibration of the SA model yielded different optimal thresholds for the two study regions: T_SA = 30 for S1 and T_SA = 50 for S2. Topographic index models were found to be useful in locating EGs and estimating expected lengths, but site-specific calibration of the topographic index model threshold was required, which might limit the general utility of these methods.

Morphotectonic and lithostratigraphic analysis of intermontane Karewa Basin of Kashmir Himalayas, India

Morpho-tectonic study plays an important role in deciphering the effects of tectonic activity in the geomorphic evolution of the drainage basins. Romushi watershed forms one of the major watersheds of the intermontane Karewa Basin of Kashmir Valley. The Karewa sediments are characterized by glacio-fluvio-lacustrine deposits capped by the aeolian loess. The geomorphic, morphometric and lithostratigraphic studies of these cap deposits have been carried out to elucidate the effect of tectonics on the geomorphic evolution of Romushi Watershed. Geomorphic mapping was carried out using GPS measurements, DEM at 30m resolution, Topographic Position Index (TPI) model, topographic maps, LANDSAT TM Imagery and field data. Morphometric and morphotectonic analyses in GIS environment were used to calculate various geomorphic indices (Mountain Front Sinuosity Index, Bifurcation Ratio, Asymmetry Factor, River Profile, etc). These indices reveal that the tectonic uplift observed in the region due to Himalayan orogeny coupled with mass movement and aeolian deposition have dominated the landscape evolution of intermontane Karewa Basin of Kashmir throughout the Late Quaternary Period. Additional data from lithostratigraphic measurements were analyzed to understand the geomorphic evolution of intermontane Karewa Basin. The data revealed that the basin has experienced differential uplift and erosion rates from time to time in the geological past. This was corroborated by the results from the morphometric and morphotectonic analysis.

Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey

BackgroundThe burden of malaria has decreased dramatically within the past several years in parts of sub-Saharan Africa. Further malaria control will require targeted control strategies based on evidence of risk. The objective of this study was to identify environmental risk factors for malaria transmission using remote sensing technologies to guide malaria control interventions in a region of declining burden of malaria.MethodsSatellite images were used to construct a sampling frame for the random selection of households enrolled in prospective longitudinal and cross-sectional surveys of malaria parasitaemia in Southern Province, Zambia. A digital elevation model (DEM) was derived from the Shuttle Radar Topography Mission version 3 DEM and used for landscape characterization, including landforms, elevation, aspect, slope, topographic wetness, topographic position index and hydrological models of stream networks.ResultsA total of 768 individuals from 128 randomly selected households were enrolled over 21 months, from the end of the rainy season in April 2007 through December 2008. Of the 768 individuals tested, 117 (15.2%) were positive by malaria rapid diagnostic test (RDT). Individuals residing within 3.75 km of a third order stream were at increased risk of malaria. Households at elevations above the baseline elevation for the region were at decreasing risk of having RDT-positive residents. Households where new infections occurred were overlaid on a risk map of RDT positive households and incident infections were more likely to be located in high-risk areas derived from prevalence data. Based on the spatial risk map, targeting households in the top 80th percentile of malaria risk would require malaria control interventions directed to only 24% of the households.ConclusionsRemote sensing technologies can be used to target malaria control interventions in a region of declining malaria transmission in southern Zambia, enabling a more efficient use of resources for malaria elimination.

Extension of TOPMODEL applications to the heterogeneous land surface

At present, the Topographic Index Model (TOPMODEL) has been recommended for integration in Land Surface Models (LSMs). But, the applicable scope of the original TOPMODEL (OTOP) is limited because the OTOP derivation relies on three fundamental but unrealistic assumptions. In this paper, several versions of a generalized TOPMODEL (GTOP), which relax some unrealistic assumptions involved in OTOP, are presented, and the theoretical derivationsn to obtain these modifications are demonstrated in detail. Specifically, the extension for the OTOP applicability comes down to following three basic cases: (1) Give up the assumption of spatially uniform recharge rate to the groundwater and let the rate be spatially varying, (2) Keep same original exponential distribution profile of hydraulic conductivity used in OTOP but change the saturated hydraulic conductivity and effective soil depth from spatial constants in OTOP to spatially variable quantities; and (3) Extend the original exponential distribution profile of hydraulic conductivity to more general power law distribution profile of hydraulic conductivity together with spatially variable saturated hydraulic conductivity and effective soil depth. Finally, a brief numerical sensitivity study based on one version of GTOP using an exponential distribution profile for soil hydraulic conductivity is conducted. This shows the heterogeneous effects of the effective soil depth, saturated hydraulic conductivity, at ground surface and groundwater recharge rate on hydrological processes and serves as an example application of GTOP to a heterogeneous catchment.

Identifying critical source areas for water quality: 1. Mapping and validating transport areas in three headwater catchments in Otago, New Zealand

Summary Validity of five empirical to process-based, hydrological models described by Srinivasan and McDowell (2007) in mapping transport areas was tested in the Invermay and Glenomaru headwater catchments in Otago, New Zealand. These transport areas together with contaminant source areas form critical source areas (CSAs), where the majority of contaminant loss occurs and therefore represent areas where mitigation potential would be most efficient. Rainfall and 15-min instantaneous surface flows at the catchment outlets and the shallow water table ( 10 m from the stream during storm events. There appeared to be an active subsurface (shallow) flow system transferring flows from land to streams. However, during many rainfall events, semi-pervious areas like fence lines, animal tracks and gateways were connected to the stream via infiltration-excess surface runoff, as measured by surface runoff samplers. This may be a significant for contaminant transfer given the amount of time spent by animals on these areas and deposition of contaminants (e.g., in dung) and is explored in a companion paper. Of the approaches used to delineate transport areas during storms, generally the empirically-based curve number model and Phosphorus Index over-predicted stormflow areas. The drainage density approach predicted stormflow areas well, but under-predicted flow volumes by 55–82%. The physically-based topographic index (TI) model and a model combining TI model and surface runoff from semi-pervious areas predicted flow volumes within 6% of that observed, but the extent of predicted transport areas greatly differed from those observed. Among the five approaches, the process-based approaches were identified to be more applicable and expandable for future prediction of CSAs for contaminant loss. In contrast, the other approaches offered little flexibility in representing the dynamic hydrology of these catchments. However, improved input data (elevation, soil properties) may improve the applicability these models.

GIS‐Based Predictive Models of Hillslope Runoff Generation Processes1

Abstract: Successful nonpoint source pollution control using best management practice placement is a complex process that requires in‐depth knowledge of the locations of runoff source areas in a watershed. Currently, very few simulation tools are capable of identifying critical runoff source areas on hillslopes and those available are not directly applicable under all runoff conditions. In this paper, a comparison of two geographic information system (GIS)‐based approaches: a topographic index model and a likelihood indicator model is presented, in predicting likely locations of saturation excess and infiltration excess runoff source areas in a hillslope of the Savoy Experimental Watershed located in northwest Arkansas. Based on intensive data collected from a two‐year field study, the spatial distributions of hydrologic variables were processed using GIS software to develop the models. The likelihood indicator model was used to produce probability surfaces that indicated the likelihood of location of both saturation and infiltration excess runoff mechanisms on the hillslope. Overall accuracies of the likelihood indicator model predictions varied between 81 and 87% for the infiltration excess and saturation excess runoff locations respectively. On the basis of accuracy of prediction, the likelihood indicator models were found to be superior (accuracy 81‐87%) to the predications made by the topographic index model (accuracy 69.5%). By combining statistics with GIS, runoff source areas on a hillslope can be identified by incorporating easily determined hydrologic measurements (such as bulk density, porosity, slope, depth to bed rock, depth to water table) and could serve as a watershed management tool for identifying critical runoff source areas in locations where the topographic index or other similar methods do not provide reliable results.