首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 31 毫秒
1.
Palsa mires are mire complexes that occur in the Northern Hemisphere, representing one of the most marginal permafrost features at the outer limit of the permafrost zone. A climate‐based spatial model is presented for the distribution of palsa mires in northern Europe. The model is based on an extensive spatial data of palsa mires and climatological variables from 1913 grid cells in an area of c. 240 000 km2. Generalized linear modelling (GLM) with curvilinear and interaction terms is used to derive the palsa mire–climate relationships. The ?nal model correctly classi?ed 77·6 per cent of the palsa mire presence squares. The results indicate a positive association of the distribution of palsa mires with increasing frost number and continentality, whereas precipitation and temperature showed a negative correlation with the distribution of palsa mires. Additionally, interaction of thawing degree days and summer time precipitation showed a negative association. Climatologically, the optimum areas of palsa mires occur in areas of low precipitation (<450 mm) and a mean annual temperature between ?3 °C and ?5 °C. Potential reasons for the performance of the model and the sensitivity of palsa mires to climate change are discussed. The application of a GIS‐based generalized linear modelling as used here provides a versatile method to study the distribution of different geomorphological phenomena across climatological gradients. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

2.
Sasmita Sahoo 《水文研究》2015,29(5):671-691
Groundwater modelling has emerged as a powerful tool to develop a sustainable management plan for efficient groundwater utilization and protection of this vital resource. This study deals with the development of five hybrid artificial neural network (ANN) models and their critical assessment for simulating spatio‐temporal fluctuations of groundwater in an alluvial aquifer system. Unlike past studies, in this study, all the relevant input variables having significant influence on groundwater have been considered, and the hybrid ANN technique [ANN‐cum‐Genetic Algorithm (GA)] has been used to simulate groundwater levels at 17 sites over the study area. The parameters of the ANN models were optimized using a GA optimization technique. The predictive ability of the five hybrid ANN models developed for each of the 17 sites was evaluated using six goodness‐of‐fit criteria and graphical indicators, together with adequate uncertainty analyses. The analysis of the results of this study revealed that the multilayer perceptron Levenberg–Marquardt model is the most efficient in predicting monthly groundwater levels at almost all of the 17 sites, while the radial basis function model is the least efficient. The GA technique was found to be superior to the commonly used trial‐and‐error method for determining optimal ANN architecture and internal parameters. Of the goodness‐of‐fit statistics used in this study, only root‐mean‐squared error, r2 and Nash–Sutcliffe efficiency were found to be more powerful and useful in assessing the performance of the ANN models. It can be concluded that the hybrid ANN modelling approach can be effectively used for predicting spatio‐temporal fluctuations of groundwater at basin or subbasin scales. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

3.
Short‐circuiting flow, commonly experienced in many constructed wetlands, reduces hydraulic retention times in unit wetland cells and decreases the treatment efficiency. A two‐dimensional (2‐D), physically based, distributed modelling approach was used to systematically address the effects of bathymetry and vegetation on short‐circuiting flow, which previously have been neglected or lumped in one‐dimensional wetland flow models. In this study, a 2‐D transient hydrodynamics with advection‐dispersion model was developed using MIKE 21 and calibrated with bromide tracer data collected at the Orlando Easterly Wetland Cell 7. The estimated topographic difference between short‐circuiting flow zone and adjacent area ranged from 0·3 to 0·8 m. A range of the Manning roughness coefficient at the short‐circuiting flow zone was estimated (0·022–0·045 s m?1/3). Sensitivity analysis of topographical and vegetative heterogeneity deduced during model calibration shows that relic ditches or other ditch‐shaped landforms and the associated sparse vegetation along the main flow direction intensify the short‐circuiting pattern, considerably affecting 2‐D solute transport simulation. In terms of hydraulic efficiency, this study indicates that the bathymetry effect on short‐circuiting flow is more important than the vegetation effect. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

4.
Forecasting river flow is important to water resources management and planning. In this study, an artificial neural network (ANN) model was successfully developed to forecast river flow in Apalachicola River. The model used a feed‐forward, back‐propagation network structure with an optimized conjugated training algorithm. Using long‐term observations of rainfall and river flow during 1939–2000, the ANN model was satisfactorily trained and verified. Model predictions of river flow match well with the observations. The correlation coefficients between forecasting and observation for daily, monthly, quarterly and yearly flow forecasting are 0·98, 0·95, 0·91 and 0·83, respectively. Results of the forecasted flow rates from the ANN model were compared with those from a traditional autoregressive integrated moving average (ARIMA) forecasting model. Results indicate that the ANN model provides better accuracy in forecasting river flow than does the ARIMA model. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

5.
It is important to evaluate the impacts of grasses on soil erosion process so as to use them effectively to control soil and water losses on the Loess Plateau. Laboratory-simulated rainfall experiments were conducted to investigate the runoff and sediment processes on sloped loess surfaces with and without the aboveground parts of grasses and moss (GAM: grass and moss; NGAM: no grass and moss) under slope gradients of 5°, 10°, 15°, 20°, 25° and 30°. The results show that runoff from GAM and NGAM plots increased up to a slope gradient of 10° and decreased thereafter, whereas the runoff coefficients increased with gradient. The average runoff rates and runoff coefficients of NGAM plots were less than those of GAM plots except for the 5° slope. This behaviour may be due to the reduction in water infiltration under moss. The difference between GAM and NGAM plots in average runoff rates varied from 1·4 to 8%. At the same gradients, NGAM plots yielded significantly (α = 0·05) more sediment than GAM plots. Average sediment deliveries for different slopes varied from 0·119 to 3·794 g m−2 min−1 from GAM plots, and from 0·765 to 16·128 g m−2 min−1 from NGAM plots. Sediment yields from GAM plots were reduced by 45 to 85%, compared with those from the NGAM plots. Plots at 30° yielded significantly higher sediments than at the other gradients. Total sediments S increased with slope gradients G in a linear form, i.e. S = 9·25G − 39·6 with R2 = 0·77*, for the GAM plots, and in an exponential model, i.e. S = 40·4 exp(0·1042G) with R2 = 0·93**, for the NGAM plots. In all cases, sediment deliveries decreased with time, and reached a relative steady state at a rainfall duration of 14 min. Compared with NGAM plots, the final percentage reductions in sediment delivery from GAM plots were higher than those at the initial time of rainfall at all slopes. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

6.
Among the numerous environmental factors affecting plant communities in alpine ecosystems, the influence of geomorphic processes and landforms has been minimally investigated. Subjected to persistent climate warming, it is vital to understand how these factors affect vegetation properties. Here, we studied 72 vegetation plots across three sites located in the Western Swiss Alps, characterized by high geomorphological variability and plant diversity. For each plot, vascular plant species were inventoried and ground surface temperature, soil moisture, topographic variables, earth surface processes (ESPs) and landform morphodynamics were assessed. The relationships between plant communities and environmental variables were analysed using non-metric multi-dimensional scaling (NMDS) and multivariate regression techniques (generalized linear model, GLM, and generalized additive model, GAM). Landform morphodynamics, growing degree days (sum of degree days above 5°C) and mean ground surface temperature were the most important explanatory variables of plant community composition. Furthermore, the regression models for species cover and species richness were significantly improved by adding a morphodynamics variable. This study provides complementary support that landform morphodynamics is a key factor, combined with growing degree days, to explain alpine plant distribution and community composition. © 2019 John Wiley & Sons, Ltd. © 2019 John Wiley & Sons, Ltd.  相似文献   

7.
Fish habitat and aquatic life in rivers are highly dependent on water temperature. Therefore, it is important to understand andto be able to predict river water temperatures using models. Such models can increase our knowledge of river thermal regimes as well as provide tools for environmental impact assessments. In this study, artificial neural networks (ANNs) will be used to develop models for predicting both the mean and maximum daily water temperature. The study was conducted within Catamaran Brook, a small drainage basin tributary to the Miramichi River (New Brunswick, Canada). In total, eight ANN models were investigated using a variety of input parameters. Of these models, four predicted mean daily water temperature and four predicted maximum daily water temperature. The best model for mean daily temperature had eight input parameters: minimum, maximum and mean air temperatures of the current day and those of the preceding day, the day of year and the water level. This model had an overall root‐mean‐square error (RMSE) of 0·96 °C, a bias of 0·26 °C and a coefficient of determination R2 = 0·971. The model that best predicted maximum daily water temperature was similar to the first model but excluded mean daily air temperature. Good results were obtained for maximum water temperatures with an overall RMSE of 1·18 °C, a bias of 0·15 °C and R2 = 0·961. The results of ANN models were similar to and/or better than those observed from the literature. The advantages of artificial neural networks models in modelling river water temperature lie in their simplicity of use, their low data requirement and their good performance, as well as their flexibility in allowing many input and output parameters. Copyright © 2008 Crown in the right of Canada and John Wiley & Sons, Ltd.  相似文献   

8.
Robust models of geomorphic process–environment relationships are important to advance theoretical knowledge of geomorphic systems. Here, we examined a generalized additive modeling (GAM) based approach to provide new theoretical insights into process–environment relationships. More precisely, we (i) simulated the shapes of the relationships between geomorphic processes and environmental variables based on GAM and (ii) compared the shapes of the simulated response curves to (a) the hypothetical curves based on theory and (b) the response curves produced by generalized linear modeling (GLM). Hitherto, GLM was the most common technique to study the relationships between environmental gradients and geomorphic processes. The study is based on empirical cryoturbation and solifluction data and environmental variables from subarctic Finland. Our results showed that non‐linear relationships were more common than linear responses and the simulated GAM based response curves coincided more closely with the hypothetical response curves than did the response curves derived from GLM. The simulated response curves showed high potential in geomorphic hypothesis testing. In conclusion, our findings indicate that careful examination of the response curves may provide new insights into theoretical debates in the earth sciences. Copyright © 2010 John Wiley and Sons, Ltd.  相似文献   

9.
One of the basic limitations to the use of geomorphological maps is their coarse resolution relative to the needs of pure and applied geomorphological research. In response to this, attempts have been made to ‘downscale’ geomorphological information to finer spatial resolutions. However, the potential of statistical downscaling in geomorphology has been insufficiently examined. We downscaled four different periglacial features (wind deflation, palsa mire, earth hummock and sorted solifluction sheet) from a 100 ha grid to a 1 ha grid resolution utilizing two different techniques: point sampling (PSA) and direct (DA) approaches. We assessed the predictive accuracy of the models with the area under the curve (AUC) of a receiver operating characteristic plot using independent evaluation data. The PSA technique yielded encouraging results with a mean accuracy of 0·81, whereas the performance of DA was poorer. The predictive performance of the palsa mire and solifluction sheet models was excellent (AUC values from 0·89 to 0·96), whereas the AUC values of deflation and earth hummock models were lower (AUC = 0·57–0·81). The application of a point sampling approach as used here provides an efficient method to translate geomorphological information to finer resolution. However, further testing of the downscaling approaches is required before they can be applied to real‐world situations. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

10.
Groundwater is sensitive to the climate change and agricultural activities in arid and semi‐arid areas. Over the past several decades, human activities, such as groundwater extraction for irrigation, have resulted in aquifer overdraft and disrupted the natural equilibrium in these areas. Regional groundwater simulation is important to determine appropriate groundwater management policies, and numerical simulation has become the most popular method. However, most groundwater models were developed with static boundary conditions. In this research, the Minqin oasis, an arid region located in northwest China, was selected as the study area. An artificial neural network (ANN) was developed to simulate effects of weather conditions, agricultural activities and surface water on groundwater level in a dynamic boundary of the domain. Subsequently, a groundwater numerical model, named ANN‐FEFLOW model, was developed, with a dynamic boundary condition defined by the ANN model. The verifying results showed that the model has higher precision, with a root mean square error (RMSE) of 0·71 m, relative error (RE) of 17·96% and R2 of 0·84 relative to the great groundwater change. Furthermore, the groundwater model has higher precision than the conventional groundwater model with static boundary condition, particularly in the area near the dynamic boundary. This study demonstrated that dynamic boundaries can improve the precision of the regional groundwater model in an arid area and that ANN can provide higher accuracy prediction capability for groundwater levels with dynamic boundary. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

11.
Measurements of sap flow, meteorological parameters, soil water content and tension were made for 4 months in a young cashew (Anacardium occidentale L.) plantation during the 2002 rainy season in Ejura, Ghana. This experiment was part of a sustainable water management project in West Africa. The Granier system was used to measure half‐hourly whole‐tree sap flow. Weather variables were observed with an automatic weather station, whereas soil moisture and tension were measured with a Delta‐T profile probe and tensiometers respectively. Clearness index (CI), a measure of the sky condition, was significantly correlated with tree transpiration (r2 = 0·73) and potential evaporation (r2 = 0·86). Both diurnal and daily stomata conductance were poorly correlated with the climatic variables. Estimated daily canopy conductance gc ranged from 4·0 to 21·2 mm s−1, with a mean value of 8·0 ± 3·3 mm s−1. Water flux variation was related to a range of environmental variables: soil water content, air temperature, solar radiation, relative humidity and vapour pressure deficit. Linear and non‐linear regression models, as well as a modified Priestley–Taylor formula, were fitted with transpiration, and the well‐correlated variables, using half‐hourly measurements. Measured and predicted transpiration using these regression models were in good agreement, with r2 ranging from 0·71 to 0·84. The computed measure of accuracy δ indicated that a non‐linear model is better than its corresponding linear one. Furthermore, solar radiation, CI, clouds and rain were found to influence tree water flux. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

12.
This study was conducted under the USDA‐Conservation Effects Assessment Project (CEAP) in the Cheney Lake watershed in south‐central Kansas. The Cheney Lake watershed has been identified as ‘impaired waters’ under Section 303(d) of the Federal Clean Water Act for sediments and total phosphorus. The USDA‐CEAP seeks to quantify environmental benefits of conservation programmes on water quality by monitoring and modelling. Two of the most widely used USDA watershed‐scale models are Annualized AGricultural Non‐Point Source (AnnAGNPS) and Soil and Water Assessment Tool (SWAT). The objectives of this study were to compare hydrology, sediment, and total phosphorus simulation results from AnnAGNPS and SWAT in separate calibration and validation watersheds. Models were calibrated in Red Rock Creek watershed and validated in Goose Creek watershed, both sub‐watersheds of the Cheney Lake watershed. Forty‐five months (January 1997 to September 2000) of monthly measured flow and water quality data were used to evaluate the two models. Both models generally provided from fair to very good correlation and model efficiency for simulating surface runoff and sediment yield during calibration and validation (correlation coefficient; R2, from 0·50 to 0·89, Nash Sutcliffe efficiency index, E, from 0·47 to 0·73, root mean square error, RMSE, from 0·25 to 0·45 m3 s?1 for flow, from 158 to 312 Mg for sediment yield). Total phosphorus predictions from calibration and validation of SWAT indicated good correlation and model efficiency (R2 from 0·60 to 0·70, E from 0·63 to 0·68) while total phosphorus predictions from validation of AnnAGNPS were from unsatisfactory to very good (R2 from 0·60 to 0·77, E from ? 2·38 to 0·32). The root mean square error–observations standard deviation ratio (RSR) was estimated as excellent (from 0·08 to 0·25) for the all model simulated parameters during the calibration and validation study. The percentage bias (PBIAS) of the model simulated parameters varied from unsatisfactory to excellent (from 128 to 3). This study determined SWAT to be the most appropriate model for this watershed based on calibration and validation results. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

13.
The growing concern for health‐related problems deriving from pollutants leaching is driving national and international administrations to support the development of tools for evaluating the effects of alternate management scenarios and identifying vulnerable areas. Cropping systems models are powerful tools for evaluating leachates under different environmental, social, and management conditions. As percolating water is the transport vehicle for pollutants transport in soil, a reliable evaluation of water balance models is a fundamental prerequisite for investigating pesticides and nitrate fate. As specific approaches for the evaluation of multi‐layer evolution of state variables are missing, we propose a fuzzy‐based, integrated indicator (ISWC: 0, best; 1, worst) for a comprehensive evaluation of soil water content (SWC) simulations. We aggregated error metrics with others quantifying the homogeneity of errors across different soil layers, the capability of models to reproduce complex dynamics function of both time and soil depth, and model complexity. We tested ISWC on a sample dataset where the models CropSyst and CERES‐Wheat were used to simulate SWC for winter wheat systems. ISWC revealed that, in the explored conditions, the global assessment of the two models' performances allowed identification of CropSyst as the best (average ISWC = 0·441, with a value of 0·537 obtained by CERES‐Wheat), although each model prevailed for some of the metrics. CropSyst presented the highest accuracy (average agreement module = 0·400), whereas CERES‐Wheat's accuracy was slightly worse, although achieved with a simplified modelling approach (average Akaike Information Criterion = − 230·44), thereby favouring large‐area applicability. The non‐univocal scores achieved by the models for the different metrics support the use of multi‐metric evaluation approaches for quantifying the different aspects of water balance model performances. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

14.
Flow diversion terraces (FDT) are commonly used beneficial management practice (BMP) for soil conservation on sloped terrain susceptible to water erosion. A simple GIS‐based soil erosion model was designed to assess the effectiveness of the FDT system under different climatic, topographic, and soil conditions at a sub‐basin level. The model was used to estimate the soil conservation support practice factor (P‐factor), which inherently considered two major outcomes with its implementation, namely (1) reduced slope length, and (2) sediment deposition in terraced channels. A benchmark site, the agriculture‐dominated watershed in northwestern New Brunswick (NB), was selected to test the performance of the model and estimated P‐factors. The estimated P‐factors ranged from 0·38–1·0 for soil conservation planning objectives and ranged from 0·001 to 0·45 in sediment yield calculations for water‐quality assessment. The model estimated that the average annual sediment yield was 773 kg ha?1 yr ?1 compared with a measured value of 641 kg ha?1 yr?1. The P‐factors estimated in this study were comparable with predicted values obtained with the revised universal soil loss equation (RUSLE2). The P‐factors from this study have the potential to be directly used as input in hydrological models, such as the soil and water assessment tool (SWAT), or in soil conservation planning where only conventional digital elevation models (DEMs) are available. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

15.
This paper reports on an evaluation of the use of artificial neural network (ANN) models to forecast daily flows at multiple gauging stations in Eucha Watershed, an agricultural watershed located in north‐west Arkansas and north‐east Oklahoma. Two different neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBFNN), were developed and their abilities to predict stream flow at four gauging stations were compared. Different scenarios using various combinations of data sets such as rainfall and stream flow at various lags were developed and compared for their ability to make flow predictions at four gauging stations. The input vector selection for both models involved quantification of the statistical properties such as cross‐, auto‐ and partial autocorrelation of the data series that best represented the hydrologic response of the watershed. Measured data with 739 patterns of input–output vector were divided into two sets: 492 patterns for training, and the remaining 247 patterns for testing. The best performance based on the RMSE, R2 and CE was achieved by the MLP model with current and antecedent precipitation and antecedent flow as model inputs. The MLP model testing resulted in R2 values of 0·86, 0·86, 0·81, and 0·79 at the four gauging stations. Similarly, the testing R2 values for the RBFNN model were 0·60, 0·57, 0·58, and 0·56 for the four gauging stations. Both models performed satisfactorily for flow predictions at multiple gauging stations, however, the MLP model outperformed the RBFNN model. The training time was in the range 1–2 min for MLP, and 5–10 s for RBFNN on a Pentium IV processor running at 2·8 GHz with 1 MB of RAM. The difference in model training time occurred because of the clustering methods used in the RBFNN model. The RBFNN uses a fuzzy min‐max network to perform the clustering to construct the neural network which takes considerably less time than the MLP model. Results show that ANN models are useful tools for forecasting the hydrologic response at multiple points of interest in agricultural watersheds. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

16.
In this paper two models are presented for calculating the hourly evapotranspiration λE (W m?2) using the Penman–Monteith equation. These models were tested on four irrigated crops (grass, soya bean, sweet sorghum and vineyard), with heights between 0·1 and 2·2 m at the adult growth stage. In the first model (Katerji N, Perrier A. 1983. Modélisation de l'évapotranspiration réelle ETR d'une parcelle de luzerne : rôle d'un coefficient cultural. Agronomie 3(6): 513–521, KP model), the canopy resistance rc is parameterized by a semi‐empirical approach. In the second model (Todorovic M. 1999. Single‐layer evapotranspiration model with variable canopy resistance. Journal of Irrigation and Drainage Engineering—ASCE 125: 235–245, TD model), the resistance rc is parameterized by a mechanistic model. These two approaches are critically analysed with respect to the underlying hypotheses and the limitations of their practical application. In the case of the KP model, the mean slope between measured and calculated values of λE was 1·01 ± 0·6 and the relative correlation coefficients r2 ranged between 0·8 and 0·93. The observed differences in slopes, between 0·96 and 1·07, were not associated with the crop height. This model seemed to be applicable to all the crops examined. In the case of the TD model, the observed slope between measured and calculated values of λE for the grass canopy was 0·79. For the other crops, it varied between 1·24 and 1·34. In all the situations examined, the values of r2 ranged between 0·73 and 0·92. The TD model underestimated λE in the case of grass and overestimated it in the cases of the other three crops. The under‐ or overestimation of λE in the TD model were due: (i) to some inaccuracies in the theory of this model, (ii) to not taking into account the effect of aerodynamic resistance ra in the canopy resistance modelling. Therefore, the values of rc were under‐ or overestimated in consequence of mismatching the crop height. The high value of air vapour pressure deficit also contributed to the overestimation of λE, mainly for the tallest crop. The results clarify aspects of the scientific controversy in the literature about the mechanistic and semi‐empirical approaches for estimating λE. From the practical point of view the results also present ways for identifying the most appropriate approach for the experimental situations encountered. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

17.
A simple modelling framework for assessing the response of ungauged catchments to land use change in South‐Western Australia is presented. The framework uses knowledge of transpiration losses from native vegetation and pasture and then partitions the ‘excess’ water (resulting from reduced transpiration after land use change) between runoff and deep storage. The simple partitioning is achieved by using soft information (satellite imagery, previous mapping and field assessment) to delimit the spread of the permanently saturated area close to the stream. Runoff is then assumed to increase in proportion to the saturated area, with the residual difference going to deep storage. The model parameters to describe the annual water yield are obtained a priori and no calibration to streamflow is required. We tested the model using gauged records over 25 years from paired catchment experiments in South‐Western Australia. Very good estimates of runoff were obtained from high rainfall (>1100 mm yr−1) catchments (R2 > 0·9) and for low rainfall (<900 mm yr−1) catchments after clearing (R2 = 0·96) but results were poorer (R2 = 0·55) for an uncleared low rainfall catchment, although overall balances were reasonable. In the drier uncleared catchments, the within‐year distributions of rainfall may exert a substantial influence on runoff response that is not completely captured by the presented model. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

18.
The emergence of regional and global satellite‐based rainfall products with high spatial and temporal resolution has opened up new large‐scale hydrological applications in data‐sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage and distributed nature of satellite‐based rainfall estimates (SRFE). In this study, five SRFEs with temporal resolution of 24 h and spatial resolution between 8 and 27 km have been evaluated through their predictive capability in a distributed hydrological model of the Senegal River basin in West Africa. The main advantage of this evaluation methodology is the integration of the rainfall model input in time and space when evaluated at the sub‐catchment scale. An initial data analysis revealed significant biases in the SRFE products and large variations in rainfall amounts between SRFEs, although the spatial patterns were similar. The results showed that the Climate Prediction Center/Famine Early Warning System (CPC‐FEWS) and cold cloud duration (CCD) products, which are partly based on rain gauge data and produced specifically for the African continent, performed better in the modelling context than the global SRFEs, Climate Prediction Center MORPHing technique (CMORPH), Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The best performing SRFE, CPC‐FEWS, produced good results with values of R2NS between 0·84 and 0·87 after bias correction and model recalibration. This was comparable to model simulations based on traditional rain gauge data. The study highlights the need for input specific calibration of hydrological models, since major differences were observed in model performances even when all SRFEs were scaled to the same mean rainfall amounts. This is mainly attributed to differences in temporal dynamics between products. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

19.
Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, an artificial neural network (ANN) model for reference evapotranspiration (ET0) calculation was investigated. ANNs were trained and tested for arid (west), semi‐arid (middle) and sub‐humid (east) areas of the Inner Mongolia district of China. Three or four climate factors, i.e. air temperature (T), relative humidity (RH), wind speed (U) and duration of sunshine (N) from 135 meteorological stations distributed throughout the study area, were used as the inputs of the ANNs. A comparison was conducted between the estimates provided by the ANNs and by multilinear regression (MLR). The results showed that ANNs using the climatic data successfully estimated ET0 and the ANNs simulated ET0 better than the MLRs. The ANNs with four inputs were more accurate than those with three inputs. The errors of the ANNs with four inputs were lower (with RMSE of 0·130 mm d?1, RE of 2·7% and R2 of 0·986) in the semi‐arid area than in the other two areas, but the errors of the ANNs with three inputs were lower in the sub‐humid area (with RMSE of 0·21 mm d?1, RE of 5·2% and R2 of 0·961. For the different seasons, the results indicated that the highest errors occurred in September and the lowest in April for the ANNs with four inputs. Similarly, the errors were higher in September for the ANNs with three inputs. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

20.
Verification of distributed hydrologic models is rare owing to the lack of spatially detailed field measurements and a common mismatch between the scale at which soil hydraulic properties are measured and the scale of a single modelling unit. In this study, two of the most commonly calibrated parameters, i.e. soil depth and the vertical distribution of lateral saturated hydraulic conductivity Ks, were eliminated by a spatially detailed soil characterization and results of a hillslope‐scale field experiment. The soil moisture routing (SMR) model, a geographic information system‐based hydrologic model, was modified to represent the dominant hydrologic processes for the Palouse region of northern Idaho. Modifications included Ks as a double exponential function of depth in a single soil layer, a snow accumulation and melt algorithm, and a simple relationship between storage and perched water depth (PWD) using the drainable porosity. The model was applied to a 2 ha catchment without calibration to measured data. Distributed responses were compared with observed PWD over a 3‐year period on a 10 m × 15 m grid. Integrated responses were compared with observed surface runoff at the catchment outlet. The modified SMR model simulated the PWD fluctuations remarkably well, especially considering the shallow soils in this catchment: a 0·20 m error in PWD is equivalent to only a 1·6% error in predicted soil moisture content. Simulations also captured PWD fluctuations during a year with high spatial variability of snow accumulation and snowmelt rates at upslope, mid‐slope, and toe slope positions with errors as low as 0·09 m, 0·12 m, and 0·12 m respectively. Errors in distributed and integrated model simulations were attributed mostly to misrepresentation of rain events and snowmelt timing problems. In one location in the catchment, simulated PWD was consistently greater than observed PWD, indicating a localized recharge zone, which was not identified by the soil morphological survey. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号