Modelling and testing spatially distributed sediment budgets to relate erosion processes to sediment yields

Identifying the erosion processes contributing to increased basin fine sediment yield is important for reducing downstream impacts on aquatic ecosystems. However, erosion rates are spatially variable, and much eroded sediment is stored within river basins and not delivered downstream. A spatially distributed sediment budget model is described that assesses the primary sources (hillslope soil erosion, gully and riverbank erosion) and sinks (floodplain and reservoir deposition) of fine sediment for each link in a river network. The model performance is evaluated in a 17,000-km² basin in south-east Australia using measured suspended sediment yields from eight catchments within the basin, each 100–700 km² in area. Spatial variations within the basin in yield and area-specific yield were reliably predicted. Observed yields and area-specific yields varied by 17-fold and 15-fold respectively between the catchments, while predictions were generally within a factor of 2 of observations. Model efficiency at predicting variations in area-specific yield was good outside forested areas (0.58), and performance was weakly sensitive to parameter values. Yields from forested areas were under-predicted, and reducing the predicted influence of riparian vegetation on bank erosion improved model performance in those areas. The model provided more accurate and higher resolution predictions than catchment area interpolation of measured yields from neighbouring river basins. The model is suitable for guiding the targeting of remediation measures within river basins to reduce downstream sediment yields.     

Modelling erosion and sediment delivery from unsealed roads in southeast Australia

Unsealed roads and tracks are potentially significant sources of diffuse pollutants, particularly sediment. This paper describes the application and development of a road erosion and sediment transport model in the Moruya-Deua and Tuross River catchments of southeast Australia. An empirical model based on the Washington Road Surface Erosion Model (WARSEM) is applied using typically widely available spatial data sets and field-collected data. The results suggest that approximately 21 kt and 35 kt of sediment respectively are produced annually from road erosion in the Moruya-Deua and Tuross River catchments, but that less than 10% of the sediment is delivered to streams. Surprisingly, about half of the delivered sediment is derived from only 4% of the total road network. Testing of the model shows that the model outputs are likely to overestimate road erosion rates. To address this problem, catchment-specific testing of the factors of the model and improving knowledge of the processes of road to stream sediment transport are required.     

Testing the hillslope erosion model for application in India,New Zealand and Australia

The hillslope erosion model (HEM) was developed to describe erosion and sediment yield on rangelands and is based on mathematical relationships among sediment yield, runoff, hillslope characteristics, and a relative soil erodibility value. It is available on the web site, http://www.eisnr.tucson.ars.ag.gov/HillslopeErosionModel. Currently, HEM has had limited application outside the USA. Our aim was to test the utility of the model with data from (a) a sandy loam at Hyderabad, India; (b) a clay loam at Pukekohe, New Zealand; and (c) a heavy red clay soil in northern Australia. Calibration showed that derived relative soil erodibility values for Indian and Australian locations differed from those determined for the USA datasets, however the default value appeared to be applicable for the New Zealand data with some variability. Our testing suggests that further calibration and analysis are necessary before default values can be identified for all sites. We also suggest however, that cautious use with derived soil erodibilities is possible at these locations, as further model testing occurs.     

Simulating the spatio-temporal dynamics of soil erosion,deposition, and yield using a coupled sediment dynamics and 3D distributed hydrologic model

Since soil erosion is driven by overland flow, it is fair to expect heterogeneity in erosion and deposition in both space and time. In this study, we develop and evaluate an open-source, spatially-explicit, sediment erosion, deposition and transport module for the distributed hydrological model, GEOtop. The model was applied in Dripsey catchment in Ireland, where it captured the total discharge volume and suspended sediment yield (SSY) with a relative bias of −1.2% and −22.4%, respectively. Simulation results suggest that daily SSY per unit rainfall amount was larger when the top soil was near saturation. Simulated erosion and deposition areas, which varied markedly between events, were also found to be directly influenced by spatial patterns of soil saturation. The distinct influence of soil saturation on erosion, deposition and SSY underscores the role of coupled surface-subsurface hydrologic interactions and a need to represent them in models for capturing fine resolution sediment dynamics.     

Evaluating Shuttle radar and interpolated DEMs for slope gradient and soil erosion estimation in low relief terrain

The error in slope gradient estimates provided by digital elevation models propagates to spatial modelling of erosion and other environmental attributes, potentially impacting land management priorities. This study compared the slope estimates of Shuttle Radar Topographic Mission (SRTM) DEMs with those generated by interpolation of topographic contours, at two grid cell resolutions. The magnitude and spatial patterns of error in DEM slope, and derived erosion estimates using the Revised Universal Soil Loss Equation (RUSLE), were evaluated at three sites in eastern Australia. The sites have low-relief terrain and slope gradients less than 15%, characteristics which dominate the global land surface by area and are often highly utilised. Relative to a reference DEM resampled to the same resolution (a measure of DEM ‘quality’), the 90 m (3-s) SRTM DEM provided the best estimates of slopes, being within 20% for each 5% slope class outside alluvial floodplains where it over-predicted by up to 220%. Relative to a hillslope scale 10 m reference DEM, the 30 m (1-s) SRTM-derived DEM-S, provided slope gradient estimates slightly less biased towards under-prediction than the 90 m SRTM and significantly less biased on alluvial floodplains. In contrast, the 20 m vertical contour intervals underpinning the interpolated DEMs resulted in under-prediction of slope gradient by more than a factor of 5 over large contiguous areas (>1 km²). The 30 m DEM-S product provided the best estimate of hillslope erosion, being 3–4% better than the 90 m SRTM. The slope errors in the interpolated DEMs translated into generally poorer and less consistent erosion estimates than SRTM. From this study it is concluded that the SRTM DEM products, in particular the 30 m SRTM-derived DEM-S, provide estimates of slope gradient and erosion which are more accurate, and more consistent within and between low relief study sites, than interpolated DEMs.     

Evaluation of a non-point source pollution model,AnnAGNPS, in a tropical watershed

Impaired water quality caused by human activity and the spread of invasive plant and animal species has been identified as a major factor of degradation of coastal ecosystems in the tropics. The main goal of this study was to evaluate the performance of AnnAGNPS (Annualized Non-Point Source Pollution Model), in simulating runoff and soil erosion in a 48 km² watershed located on the Island of Kauai, Hawaii. The model was calibrated and validated using 2 years of observed stream flow and sediment load data. Alternative scenarios of spatial rainfall distribution and canopy interception were evaluated. Monthly runoff volumes predicted by AnnAGNPS compared well with the measured data (R² = 0.90, P < 0.05); however, up to 60% difference between the actual and simulated runoff were observed during the driest months (May and July). Prediction of daily runoff was less accurate (R² = 0.55, P < 0.05). Predicted and observed sediment yield on a daily basis was poorly correlated (R² = 0.5, P < 0.05). For the events of small magnitude, the model generally overestimated sediment yield, while the opposite was true for larger events. Total monthly sediment yield varied within 50% of the observed values, except for May 2004. Among the input parameters the model was most sensitive to the values of ground residue cover and canopy cover. It was found that approximately one third of the watershed area had low sediment yield (0–1 t ha⁻¹ y⁻¹), and presented limited erosion threat. However, 5% of the area had sediment yields in excess of 5 t ha⁻¹ y⁻¹. Overall, the model performed reasonably well, and it can be used as a management tool on tropical watersheds to estimate and compare sediment loads, and identify “hot spots” on the landscape.     

Hydrologic connectivity between geographically isolated wetlands and surface water systems: A review of select modeling methods

Geographically isolated wetlands (GIW), depressional landscape features entirely surrounded by upland areas, provide a wide range of ecological functions and ecosystem services for human well-being. Current and future ecosystem management and decision-making rely on a solid scientific understanding of how hydrologic processes affect these important GIW services and functions, and in turn on how GIWs affect downstream surface water systems. Consequently, quantifying the hydrologic connectivity of GIWs to other surface water systems (including streams, rivers, lakes, and other navigable waters) and the processes governing hydrologic connectivity of GIWs at a variety of watershed scales has become an important topic for the scientific and decision-making communities. We review examples of potential mechanistic modeling tools that could be applied to further advance scientific understanding concerning: (1) The extent to which hydrologic connections between GIWs and other surface waters exist, and (2) How these connections affect downstream hydrology at the scale of watersheds. Different modeling approaches involve a variety of domain and process conceptualizations, and numerical approximations for GIW-related questions. We describe select models that require only limited modifications to model the interaction of GIWs and other surface waters. We suggest that coupled surface–subsurface approaches exhibit the most promise for characterizing GIW connectivity under a variety of flow conditions, though we note their complexity and the high level of modeling expertise required to produce reasonable results. We also highlight empirical techniques that will inform mechanistic models that estimate hydrologic connectivity of GIWs for research, policy, and management purposes. Developments in the related disciplines of remote sensing, hillslope and wetland hydrology, empirical modeling, and tracer studies will assist in advancing current mechanistic modeling approaches to most accurately elucidate connectivity of GIWs to other surface waters and the effects of GIWs on downstream systems at the watershed scale.     

An investigation of controlling variables of riverbank erosion in sub-tropical Australia

A large proportion of the uncertainty surrounding catchment sediment budget modelling has been attributed to sediment supplied from riverbank erosion. Some of the variables influencing riverbank erosion are bend curvature, specific streampower, riparian vegetation, and in some instances sand and gravel extraction. The empirical relationship between these variables and observed riverbank erosion across 78 km of the Upper Brisbane River, Australia was investigated. No significant relationship was found between curvature, specific streampower and riverbank erosion. The role of riparian vegetation relative to sediment supply from riverbank erosion varied with spatial location, susceptibility of a reach to erosion, and human disturbance such as sand and gravel extraction. Despite not having data on substrate type the model described approximately 37% of the variation in observed riverbank erosion. It appears that inclusion of a management practice factor in riverbank erosion models is justified, where appropriate, and may improve model performance.     

Spectroscopy of sediments in the Ganges-Brahmaputra delta: Spectral effects of moisture, grain size and lithology

The lithologic composition and grain size distribution of sediments are primary determinants of their inherent reflectance properties. However, moisture content is also known to have a strong influence on reflectances of soils and sediments. If the effects of sediment composition, grain size and moisture content could be distinguished spectrally, it might be possible to map these properties at synoptic scales using hyperspectral, or perhaps even broadband, remote sensing. Mapping the spatiotemporal distribution of sediment composition and moisture content could provide unique constraints on both the processes by which the sediments are deposited as well as the constraints they may impose on subsequent water flow and sediment transport. The Ganges-Brahmaputra delta (GBD) is formed by the convergence of these two great rivers and is superlative in both size and geologic activity. Sediment redistribution and channel migration associated with the annual floods disrupt the lives of hundreds of thousands of people living on the GBD but is also critical for maintaining the delta area fertile and above sea level. The 30+ year archive of Landsat imagery could provide a basis for spatiotemporal analysis of these fluvial dynamics if sediment properties could be inferred or measured from reflectance spectra. However, before confronting the challenge of broadband detection we must understand the spectral properties of the sediments under more controlled laboratory conditions. Bidirectional reflectance spectroscopy of 109 sediment samples from the GBD yields a spectral mixing space that appears to be structured by variations in moisture content, grain size and possibly lithology. Although the individual Empirical Orthogonal Functions of the Principal Components do not correspond to unique absorption features, clustering within the mixing space is clearly influenced by moisture content and grain size. Laboratory spectra of sediment reflectance measured under varying moisture content yield distinct trajectories through the spectral mixing space for different grain size distributions of sieved sediments. These variations in moisture content account for > 98% of spectral variance observed in these samples. Drying trajectories of coarse, fine and mixed sediments are distinct and suggest that moisture and grain size might be spectrally distinguishable. These results are consistent with Angstrom's hypothesis of moisture-driven spectral absorption but more controlled experiments are necessary to test the hypothesis rigorously.     

Modeling a dynamically varying mixed sediment bed with erosion,deposition, bioturbation,consolidation, and armoring

Erosion and deposition of bottom sediments reflect a continual, dynamic adjustment between the fluid forces applied to a sediment bed and the condition of the bed itself. Erosion of fine and mixed sediment beds depends on their composition, their vertical structure, their disturbance/recovery history, and the biota that inhabit them. This paper presents a new one-dimensional (1D), multi-layer sediment bed model for simulating erosion and deposition of fine and mixed sediments subject to consolidation, armoring, and bioturbation. The distinguishing characteristics of this model are a greatly simplified first-order relaxation treatment for consolidation, a mud erosion formulation that adapts to both Type I and II erosion behavior and is based directly on observations, a continuous deposition formulation for mud that can mimic exclusive erosion and deposition behavior, and straightforward inclusion of bioturbation effects. Very good agreement with two laboratory data sets on consolidation effects is achieved by adjusting only the first-order consolidation rate r_c. Full model simulations of three idealized cases based on upper Chesapeake Bay, USA observations are presented. In the mud only case, fluid stresses match mud critical stresses at maximum erosion. A consolidation lag results in higher suspended sediment concentrations after erosional events. Erosion occurs only during accelerating currents and deposition does not occur until just before slack water. In the mixed mud and sand case without bioturbation, distinct layers of high and low sand content form and mud suspension is strongly limited by sand armoring. In the mixed mud and sand case with bioturbation, suspended mud concentrations are greater than or equal to either of the other cases. Low surface critical stresses are mixed down into the bed, constrained by the tendency to return towards equilibrium. Sand layers and the potential for armoring of the bed develop briefly, but mix rapidly. This model offers a relatively simple and robust tool for simulating the complex interactions that can affect muddy and mixed sediment bed erodibility.     

Toward automatic detection of brain responses to emotional music through analysis of EEG effective connectivity

The purpose of this study was to investigate the effective brain networks associated with joyful, melancholic, and neutral music. Connectivity patterns among EEG electrodes in different frequency bands were extracted by multivariate autoregressive modeling while 19 nonmusicians listened to selected classical and Iranian musical excerpts. Musical selections were categorized according to the participants' average self-assessment results. Connectivity matrices were analyzed to identify distinct variations in the connectivity indices related to the categorized excerpts. We studied the correlation of inter-/intra-regional connectivity patterns with the self-reported evaluations of the musical selections. The perceived valence was positively correlated with the frontal inter-hemispheric flow, but negatively correlated with the parietal bilateral connectivity. Using the connectivity indices between different cortical areas and a support vector machine, we sought to distinguish trials in terms of the self-reported valence of perceived emotions and the familiarity of the musical genres. For 16 participants, the average classification accuracies in discriminating joyful from neutral, joyful from melancholic and familiar from unfamiliar trials were 93.7% ± 1.06%, 80.43% ± 1.74%, and 83.04% ± 1.47, respectively. Integration of different cortical areas is required for music perception and emotional processing. Thus, by studying the connectivity of brain regions, we may be able to develop a noninvasive assessment tool for investigating musical emotions.     

Estimation of relative evapotranspiration from NOAA PAL to derive growth characteristics in India

Clear‐sky dekadal relative evapotranspiration (RET) was derived using the surface energy‐balance approach applied to 10‐day composite NOAA PAL (8 km×8 km) datasets over the Indian landmass. This was further used to differentiate between growth characteristics for an irrigated intensive agriculture over a northern India state (e.g. Punjab) and a rainfed ill‐posed agriculture over a central India state (e.g. Madhya Pradesh) using time‐series data sets for five growing years (June–April): 1996–1997, 1997–1998, 1998–1999, 1999–2000, and 2000–2001. The triangular scatter between RET and normalized difference vegetation index (NDVI) showed that the minimum RET increases linearly with NDVI producing a ‘basal line’ that represents relative canopy transpiration only. A clear distinction in scatter was found between the two contrasting agro‐ecosystems showing a higher RET or root zone wetness in irrigated than rainfed systems. In rainfed rice‐growing regions, an inverse correlation (0.6–0.75) was found between RET and the Keetch–Byram meteorological drought index (KBDI), and a substantial reduction in RET was also found in a sub‐normal (2000) compared with a normal (1999) monsoon season. RET estimates were found to be most sensitive to atmospheric transmissivity followed by other land‐surface radiation budget inputs, such as NDVI, LST, and albedo. Error propagation due to three surface parameters is the opposite of that for transmissivity. The maximum possible error in clear‐sky NOAA PAL RET was estimated to be 12–15%. This test study would be helpful in deriving RET using optical and thermal data from a suite of current and future Indian geostationary satellite sensors for monitoring growing conditions.     

Drought impact on vegetation productivity in the Lower Mekong Basin

The Lower Mekong Basin (LMB) has a typical monsoon climate, with high temperatures and an uneven distribution of precipitation throughout the year. This climate, combined with the geographic position of the LMB, has led to an increase in the frequency of extreme weather events over last decade. However, few previous studies have used remote-sensing data to investigate the impact of such weather events, particularly severe droughts, on biological productivity in the LMB. To address this, we assessed the impact of drought on vegetation productivity in the LMB during 2000–2011 using MOD17 products. Several drought events were identified during this period. Of these, the most severe occurred during 2005 and 2010, although the 2005 drought was both more extensive and more intense. Net primary productivity (NPP) exhibited considerable variation during 2000–2011: the droughts in 2005 and 2010 reduced NPP by 14.7% and 8.4%, respectively. The impact of drought on NPP in 2005 was much greater than that in 2010, likely owing to the longer duration and larger deficit of precipitation in 2005 (which lasted from winter 2004 to spring 2005). Our results demonstrate that severe drought had a greater impact on NPP than mild drought, especially for forests, woodlands, and shrublands. Comparatively, little variation in NPP was found for croplands, even under drought conditions, which were attributed to the wide use of irrigation and the exploitation of water sources during drought periods. Moreover, multi-season croplands in Vietnam experienced only a small reduction in gross primary productivity (GPP) in 2005 compared to one-season croplands in Cambodia, which can be related to the shorter growing periods of the former impacted by droughts.     

Assimilating satellite imagery and visible–near infrared spectroscopy to model and map soil loss by water erosion in Australia

Soil loss causes environmental degradation and reduces agricultural productivity over large areas of the world. Here, we use the latest earth observation data and soil visible–near infrared (vis–NIR) spectroscopy to estimate the factors of the Revised Universal Soil Loss Equation (RUSLE) and to model soil loss by water erosion in Australia. We estimate rainfall erosivity (R) using the Tropical Rainfall Measuring Mission (TRMM); slope length and steepness (L and S) using a 3-arcsec Shuttle Radar Topography Mission (SRTM) digital elevation model; cover management (C) and control practice (P) using the national dynamic land cover dataset (DLCD) of Australia derived from the moderate-resolution imaging spectroradiometer (MODIS); and soil erodibility (K) using vis–NIR estimates of the contents of sand, silt, clay and organic carbon in Australian soil. We model K using a machine-learning algorithm with environmental predictors selected to best capture the factors that influence erodibility and produced a digital map of K. We use the derived RUSLE factors to estimate soil loss at 1-km resolution across the whole of Australia. We found that the potential gross average soil loss by water erosion in Australian is 1.86 t ha⁻¹ y⁻¹ (95% confidence intervals of 1.78 and 1.93 t ha⁻¹ y⁻¹), equivalent to a total of 1242 × 10⁶ tonnes of soil lost annually (95% confidence intervals of 1195 and 1293 t × 10⁶ y⁻¹). Our estimates of erosion are generally smaller than previous continental estimates using the RUSLE, but particularly in croplands, which might indicate that soil conservation practices effectively reduced erosion in Australia. However we also identify localized regions with large erosion in northern Australia and northeastern Queensland. Erosion in these areas carries sediments laden with nitrogen, phosphorus and pollutants from agricultural production into the sea, negatively affecting marine ecosystems. We used the best available data and our results provide better estimates compared to previous assessments. Our approach will be valuable for other large, sparsely sampled areas of the world where assessments of soil erosion are needed.     

Zimbabwe's Drought Relief Programme in the 1990s: A Re‐Assessment Using Nationwide Household Survey Data

Zimbabwe's Drought Relief Programme was hailed in the 1980s and 1990s as an effective response to a food crisis in a poor country. International observers in particular credited the Programme with preventing famine and protecting livelihoods. Even before the current political turmoil and the ensuing politicisation of Drought Relief that have afflicted Zimbabwe since 2000, Zimbabwean authors were more sceptical about the effectiveness of Drought Relief. Both sides in the debate, however, failed to substantiate their arguments with national household survey data on who got what kind of assistance from Drought Relief, but rather relied on administrative data, qualitative interviews or sub‐national surveys. Drawing its inspiration from WHO's minimum evaluation procedure, this article uses data from four nationwide household surveys in 1992–1993 and 1995–1996 and various definitions of poverty to ask whether Drought Relief provided poor people with relevant, timely and adequate assistance in the 1990s. The analysis suggests that Drought Relief was effective in supporting drought‐affected smallholders during the 1990s. Drought Relief generally had a slight pro‐poor bias. Unfortunately, Drought Relief since 2000 has a very different character.     

基于高阶最小生成树脑网络的多特征融合分类方法

现有的基于脑疾病的分类方法的研究使用的都是传统的低阶功能连接网络。低阶功能连接网络可能会忽略复杂的大脑区域之间动态的相互作用的模式。高阶功能连接网络能够反映网络中包含的丰富的动态时间信息,但原有的高阶功能连接网络使用聚类的方法降低了数据维度,使得构建的网络无法进行有效的神经学解释;其次,高阶功能连接网络由于规模较大,在利用复杂网络或图理论计算一些拓扑指标时消耗较大。基于此,提出了一种高阶最小生成树网络的构建方法,然后计算了传统的可量化网络指标(度和离心率)并结合频繁子图挖掘技术来挖掘具有判别能力的子网络,最后采用多核支持向量机进行分类。实验结果表明所提方法的分类精确度高达97.54%,获得了很好的分类性能。     

Snowmelt runoff modelling in a Himalayan basin with the aid of satellite data

The Himalayan basins have runoff contributions from rainfall as well as from snow and ice. In the present study a snowmelt runoff model (SRM) was applied to estimate the streamflow for Satluj basin located in the western Himalayan region. This model uses the direct input of remotely sensed snow-cover area (SCA) data for calibration and simulation. The SCA in the basin was determined using remote sensing data from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectrometer (MODIS) onboard the Terra-Aqua satellite. In addition, daily precipitation and temperature data, as well as a Shuttle Radar Topography Mission digital elevation model (SRTM-DEM), were used to prepare the area elevation curves. The model was calibrated using the dataset for a period of 3 years (1996–1997, 1997–1998 and 1998–1999) and model parameters for streamflow routing were optimized. Using the optimized parameters, streamflow simulations were made for four years of data (i.e. 2000–2003 and 2004–2005). The accuracy of the streamflow verification was determined using different criteria such as shape of the outflow hydrograph, efficiency and difference in volume. The seasonal temperature lapse rates (TLRs) estimated from land surface temperature (LST) maps were used in the model and considerable improvement in simulation was observed. It was found that the overall efficiency increased when using varying TLRs.     

Component connectivity of generalized Petersen graphs

Let G be a simple non-complete graph of order n. The r-component edge connectivity of G denoted as λ_r (G) is the minimum number of edges that must be removed from G in order to obtain a graph with (at least) r connected components. The concept of r-component edge connectivity generalizes that of edge connectivity by taking into account the number of components of the resulting graph. In this paper we establish bounds of the r component edge connectivity of an important family of interconnection network models, the generalized Petersen graphs GP(n, k) in which n and k are relatively prime integers.     

Distance-Ranked Connectivity Compression of Triangle Meshes

We present a new, single-rate method for compressing the connectivity information of a connected 2-manifold triangle mesh with or without boundary. Traditional compression schemes interleave geometry and connectivity coding, and are thus typically unable to utilize information from vertices (mesh regions) they have not yet processed. With the advent of competitive point cloud compression schemes, it has become feasible to develop separate connectivity encoding schemes that can exploit complete, global vertex position information to improve performance. Our scheme demonstrates the utility of this separation of vertex and connectivity coding. By traversing the mesh edges in a consistent fashion, and using global vertex information, we can predict the position of the vertex that completes the unprocessed triangle attached to a given edge. We then rank the vertices in the neighborhood of this predicted position by their Euclidean distance. The distance rank of the correct closing vertex is stored. Typically, these rank values are small, and the set of rank values thus possesses low entropy and compresses very well. The sequence of rank values is all that is required to represent the mesh connectivity—no special split or merge codes are necessary. Results indicate improvements over traditional valence-based schemes for more regular triangulations. Highly irregular triangulations or those containing a large number of slivers are not well modelled by our current set of predictors and may yield poorer connectivity compression rates than those provided by the best valence-based schemes.