CRITICAL DATA REQUIREMENTS FOR PREDICTION OF EROSION AND SEDIMENTATION IN MOUNTAIN DRAINAGE BASINS1

ABSTRACT: The potential for understanding and, where necessary, managing sedimentation in humid mountain drainage basins increases with awareness of the conditions that lead to shallow landsliding, debris flows, and catastrophic sedimentation in stream channels. Progress in understanding has involved: improved recognition of source areas and the potential for downstream effects of slope failure; improved understanding of hydrological conditions required for failure; and a general theory of slope stability in shallow colluvium, including the role of plants, fires, timber harvest, and other disturbances. The theory acknowledges spatial variability in topographic and geotechnical terrain characteristics, the stochastic nature of climatic triggering events such as forest fires and rainstorms, and the integrating nature of channel networks in modulating the cumulative effects of transient processes within a basin. Anthropogenic fire regimes, road effects, and timber harvest can readily be included. Continued application and modification of the theory over an expanded geographical range require improvements in field data and their systematic storage in spatial databases. Improvements in digital topographic data for mountain basins, systematic network-wide surveys of channel conditions, and new technology for rapid documentation of soil depths in landslide source areas would enhance the prediction of mass failure, its consequences for channel habitat, and the basin-wide or regional distribution of hillslope and channel conditions. Computations of the probabilities of transient effects throughout basins could then form the basis of ecological risk analyses. Large-scale spatial data sets of a few critical variables are required before this next level of understanding can be developed and applied to sedimentation impacts on ecosystems and other resources.     

Modeling Stream Channel Characteristics From Drainage‐Enforced DEMs in Puget Sound,Washington, USA1

Abstract: Mapping stream channels and their geomorphic attributes is an important step in many watershed research and management projects. Often insufficient field data exist to map hydromorphologic attributes across entire drainage basins, necessitating the application of hydrologic modeling tools to digital elevation models (DEMs) via a geographic information system (GIS). In this article, we demonstrate methods for deriving synthetic stream networks via GIS across large and diverse basins using drainage‐enforced DEMs, along with techniques for estimating channel widths and gradient on the reach scale. The two‐step drainage enforcement method we used produced synthetic stream networks that displayed a high degree of positional accuracy relative to the input streams. The accuracies of our estimated channel parameters were assessed with field data, and predictions of bankfull width, wetted width and gradient were strongly correlated with measured values (r² = 0.92, r² = 0.95, r² = 0.88, respectively). Classification accuracies of binned channel attributes were also high. Our methodology allows for the relatively rapid mapping of stream channels and associated morphological attributes across large geographic areas. Although initially developed to provide salmon recovery planners with important salmon habitat information, we suggest these methodologies are relevant to a variety of research and management questions.     

FARM INCOME AND GROUND WATER QUALITY IMPLICATIONS FROM REDUCING SURFACE WATER SEDIMENT DELWERIES1

ABSTRACT: The potential surface water and ground water quality tradeoff implications from the nonpoint source provisions of the 1987 Water Quality Act are investigated in this paper using a national linear programming model developed at Iowa State University and modified by the Economic Research Service and the Leaching Evaluation of Agricultural Chemicals (LEACH) Handbook developed by the U.S. Environmental Protection Agency. The linear programming model is used to maximize net farm revenue using optimal combinations of crop rotations and tillage practices for each region of the United States given natural resource constraints. The LEACH handbook is used to determine the relative potential for pesticides to leach below the root zone for different soil types, hydrologic conditions, pesticides used, and tillage practices. The results indicate that imposing a surface water quality erosion constraint aimed at reducing sediment concentrations results in a larger decrease in farm income than imposing a uniform 5 ton per acre per year erosion constraint. Both constraints could result in regional improvement in ground water quality in some regions of the country while decreasing ground water quality in other regions.     

INTERPLAY BETWEEN PARAMETER UNCERTAINTY AND MODEL AGGREGATION ERROR1

ABSTRACTS: Modeling error can be divided into two basic components: use of an incorrect model and input parameter uncertainty. Incorrect model usage can be further subdivided into inappropriate model selection and inherent modeling error due to process aggregation. Total modeling error is a culmination of these various modeling error components, with overall optimization requiring reductions in all. A technique, utilizing Monte Carlo analysis, is employed to investigate the relative importance of input parameter uncertainty versus process aggregation error. An expanded form of the Streeter-Phelps dissolved oxygen equation is used to demonstrate the application of this technique. A variety of scenarios are analyzed to illustrate the relative obfuscation of each modeling error component. Under certain circumstances an aggregated model performs better than a more complex model, which perfectly simulates the real system. Alternately, process aggregation error dominates total modeling error for other situations. The ability to differentiate modeling error impact is a function of the desired or imposed model performance level (accuracy tolerance).     

安徽省耕地资源动态变化及驱动机制研究

根据安徽省多年的统计资料，运用主成分分析法研究耕地的动态变化特点及动力机制。结果表明，影响安徽省耕地变化的因素有两个方面：人口社会经济因素和土地生产力要素。并在此基础上，建立多元线性回归模型，对安徽省耕地变化的趋势进行科学的预测。     

Mid‐Range Streamflow Forecasts Based on Climate Modeling – Statistical Correction and Evaluation1

Abstract: Mid‐range streamflow predictions are extremely important for managing water resources. The ability to provide mid‐range (three to six months) streamflow forecasts enables considerable improvements in water resources system operations. The skill and economic value of such forecasts are of great interest. In this research, output from a general circulation model (GCM) is used to generate hydrologic input for mid‐range streamflow forecasts. Statistical procedures including: (1) transformation, (2) correction, (3) observation of ensemble average, (4) improvement of forecast, and (5) forecast skill test are conducted to minimize the error associated with different spatial resolution between the large‐scale GCM and the finer‐scale hydrologic model and to improve forecast skills. The accuracy of a streamflow forecast generated using a hydrologic model forced with GCM output for the basin was evaluated by forecast skill scores associated with the set of streamflow forecast values in a categorical forecast. Despite the generally low forecast skill score exhibited by the climate forecasting approach, precipitation forecast skill clearly improves when a conditional forecast is performed during the East Asia summer monsoon, June through August.     

Adoption and economic impacts of laser land leveling in the irrigated rice-wheat system in Haryana,India using endogenous switching regression

This study assesses the factors affecting the adoption of laser land leveling (LLL) and its impact on crop yields and net returns. It uses household survey data collected from 621 randomly selected farmers in Karnal District of Haryana, India, and applies endogenous switching regression models. Unbiased model results show that the adoption of LLL has significant positive impacts on yields (rice +549 kg ha⁻¹; wheat +471 kg ha⁻¹) and net returns (an aggregate increase of US$230/ha) in the rice-wheat production system, thereby raising farmers' income substantially. Our results show that LLL adoption at the farm level is influenced by land size and quality, tenure system, availability of farm machinery (tractor), access to finance and farm cooperatives, gender of household head, level of education and training and access to extension services. Therefore, LLL scaling strategies need to consider these bio-physical and socio-economic parameters to reach adoption at scale and generate large social, economic, and environmental benefits.     

Interparticle diffusion and thermodynamic modeling studies for the removal of heavy metals using mixed adsorbents in industrial effluents

Pollution of water, air, and soil by industrial effluents is a major problem nowadays. A variety of contaminants are too responsible for changing the physicochemical properties of the receiving body. There are practical treatment solutions available to clean up contaminants from various resources. The term “adsorption” refers to one of them. The purpose of the research work is to remove heavy metals from industrial effluent. Mixed adsorbents prepared from activated charcoal and bone charcoals were used to remove the copper and cadmium ions. The experiment carried out in a batch operation and modeling of these data for intraparticle diffusion and thermodynamic calculations were reported in this research work. At optimum operating condition pH 6; metal ion concentration 50 mg/L; dose 5 g/L; agitation 180 rpm and temperature 40°C maximum 99.41% copper ions and 88.12% cadmium ion removal was achieved. Cadmium ions were well fitted in the thermodynamic model compared to copper ions, as demonstrated by the higher correlation coefficient R² (0.9824) value. Intra particle diffusion demonstrated that film diffusion was a rate-limiting step at the start of the reaction, while microporous intraparticle diffusion was the rate-determining phase later on. A Fourier transformation infrared spectroscopy, X-ray diffraction, and scanning electron micrography analysis confirmed the suitability of mixed adsorbents for the removal of cadmium and copper metal ions.     

Assessment of the benefits of climate model weights for ensemble analysis in three urban precipitation frequency studies

In hydrology, projected climate change impact assessment studies typically rely on ensembles of downscaled climate model outputs. Due to large modeling uncertainties, the ensembles are often averaged to provide a basis for studying the effects of climate change. A key issue when analyzing averages of a climate model ensemble is whether to weight all models in the ensemble equally, often referred to as the equal-weights or unweighted approach, or to use a weighted approach, where, in general, each model would have a different weight. Many studies have advocated for the latter, based on the assumption that models that are better at simulating the past, that is, the models with higher hindcast accuracy, will give more accurate forecasts for the future and thus should receive higher weights. To examine this issue, observed and modeled daily precipitation frequency (PF) estimates for three urban areas in the United States, namely Boston, Massachusetts; Houston, Texas; and Chicago, Illinois, were analyzed. The comparison used the raw output of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. The PFs from these models were compared with the observed PFs for a specific historical training period to determine model weights for each area. The unweighted and weighted averaged model PFs from a more recent testing period were then compared with their corresponding observed PFs to determine if weights improved the estimates. These comparisons indeed showed that the weighted averages were closer to the observed values than the unweighted averages in nearly all cases. The study also demonstrated how weights can help reduce model spread in future climate projections by comparing the unweighted and weighted ensemble standard deviations in these projections. In all studied scenarios, the weights actually reduced the standard deviations compared to the equal-weights approach. Finally, an analysis of the results' sensitivity to the areal reduction factor used to allow comparisons between point station measurements and grid-box averages is provided.     

Assessing Linkages in Stream Habitat,Geomorphic Condition,and Biological Integrity Using a Generalized Regression Neural Network

Watershed managers often use physical geomorphic and habitat assessments in making decisions about the biological integrity of a stream, and to reduce the cost and time for identifying stream stressors and developing mitigation strategies. Such analysis is difficult since the complex linkages between reach‐scale geomorphic and habitat conditions, and biological integrity are not fully understood. We evaluate the effectiveness of a generalized regression neural network (GRNN) to predict biological integrity using physical (i.e., geomorphic and habitat) stream‐reach assessment data. The method is first tested using geomorphic assessments to predict habitat condition for 1,292 stream reaches from the Vermont Agency of Natural Resources. The GRNN methodology outperforms linear regression (69% vs. 40% classified correctly) and improves slightly (70% correct) with additional data on channel evolution. Analysis of a subset of the reaches where physical assessments are used to predict biological integrity shows no significant linear correlation, however the GRNN predicted 48% of the fish health data and 23% of macroinvertebrate health. Although the GRNN is superior to linear regression, these results show linking physical and biological health remains challenging. Reasons for lack of agreement, including spatial and temporal scale differences, are discussed. We show the GRNN to be a data‐driven tool that can assist watershed managers with large quantities of complex, nonlinear data.