Modeling Nonlinear Monthly Evapotranspiration Using Soft Computing and Data Reconstruction Techniques

The objective of this study is to develop soft computing and data reconstruction techniques for modeling monthly California Irrigation Management Information System (CIMIS) evapotranspiration (ET_o) at two stations, U.C. Riverside and Durham, in California. The nonlinear dynamics of monthly CIMIS ET_o is examined using autocorrelation function, phase space reconstruction, and close returns plot. The generalized regression neural networks and genetic algorithm (GRNN-GA) conjunction model is developed for modeling monthly CIMIS ET_o. Among different input variables considered, solar radiation (RAD) is found to be the most effective variable for modeling monthly CIMIS ET_o using GRNN-GA for both stations. Adding other input variables to the best 1-input combination improves the model performance. The generalized regression neural networks and backpropagation algorithm (GRNN-BP) conjunction model is compared with the results of GRNN-GA for modeling monthly CIMIS ET_o. Two bootstrap resampling methods are implemented to reconstruct the training data. Method 1 (1-BGRNN-GA) employs simple extensions of training data using the bootstrap resampling method. For each training data, method 2 (2-BGRNN-GA) uses individual bootstrap resampling of original training data. Results indicate that Method 2 (2-BGRNN-GA) improves modeling of monthly CIMIS ET_o and is more stable and reliable than are GRNN-GA, GRNN-BP, and Method 1 (1-BGRNN-GA).     

Landscape Scale Assessment of Floodplain Inundation Frequency Using Landsat Imagery

In large river ecosystems, the timing, extent, duration and frequency of floodplain inundation greatly affect the quality of fish and wildlife habitat and the supply of important ecosystem goods and services. Seasonal high flows provide connectivity from the river to the floodplain, and seasonal inundation of the floodplain governs ecosystem structure and function. River regulation and other forms of hydrologic alteration have altered the connectivity of many rivers with their adjacent floodplain – impacting the function of wetlands on the floodplain and in turn, impacting the mainstem river function. Conservation and management of remaining floodplain resources can be improved through a better understanding of the spatial extent and frequency of inundation at scales that are relevant to the species and/or ecological processes of interest. Spatial data products describing dynamic aspects floodplain inundation are, however, not widely available. This study used Landsat imagery to generate multiple observations of inundation extent under varying hydrologic conditions to estimate inundation frequency. Inundation extent was estimated for 50 Landsat scenes and 1334 total images within the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative (GCPO LCC), a conservation science partnership working in a 730 000‐km² region in the south central USA. These data were composited into a landscape mosaic to depict relative inundation frequency over the entire GCPO LCC. An analytical methodology is presented for linking the observed inundation extent and frequency with long‐term gage measurements so that the outcomes may be useful in defining meaningful critical thresholds for a variety of floodplain dependent organisms as well as important ecological processes. Published 2015. This article is a U.S. Government work and is in the public domain in the USA     

Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT

This study applied a time series evapotranspiration (ET) data derived from the remote sensing to evaluate Soil and Water Assessment Tool (SWAT) model calibration, which is a unique method. The SWAT hydrologic model utilized monthly stream flow data from two US Geological Survey (USGS) stations within the Big Sunflower River Watershed (BSRW) in Northwestern, Mississippi. Surface energy balance algorithm for land (SEBAL), which utilized MODerate Resolution Imaging Spectro-radiometer (MODIS) to generate monthly ET time series data images were evaluated with the SWAT model. The SWAT hydrological model was calibrated and validated using monthly stream flow data with the default, flow only, ET only, and flow-ET modeling scenarios. The flow only and ET only modeling scenarios showed equally good model performances with the coefficient of determination (R²) and Nash Sutcliffe Efficiency (NSE) from 0.71 to 0.86 followed by flow-ET only scenario with the R² and NSE from 0.66 to 0.83, and default scenario with R² and NSE from 0.39 to 0.78 during model calibration and validation at Merigold and Sunflower gage stations within the watershed. The SWAT model over-predicted ET when compared with the Modis-based ET. The ET-based ET had the closest ET prediction (~8% over-prediction) as followed by flow-ET-based ET (~16%), default-based ET (~27%) and flow-based ET (~47%). The ET-based modeling scenario demonstrated consistently good model performance on streamflow and ET simulation in this study. The results of this study demonstrated use of Modis-based remote sensing data to evaluate the SWAT model streamflow and ET calibration and validation, which can be applied in watersheds with the lack of meteorological data.     

Evapotranspiration Modeling Using Different Tree Based Ensembled Machine Learning Algorithm

Water Resources Management - The present study investigates and evaluate the scope and potential of modern computing tools and techniques such as ensembled machine learning methods in estimating...     

Performance Evaluation of Reference Evapotranspiration Estimation Using Climate Based Methods and Artificial Neural Networks

This study is an attempt to find best alternative method to estimate reference evapotranspiration (ET_o) for the Mahanadi reservoir project (MRP) command area located at Raipur (Chhattisgarh) in India, when input climatic parameters are insufficient to apply standard Food and Agriculture Organization (FAO) of the United Nations Penman–Monteith (P–M) method. To identify the best alternative climatic based method that yield results closest to the P–M method, performances of four climate based methods namely Blaney–Criddle, Radiation, Modified Penman and Pan evaporation were compared with the FAO-56 Penman–Monteith method. Performances were evaluated using the statistical indices. The statistical indices used in the analysis were the standard error of estimate (SEE), raw standard error of estimate (RSEE) and the model efficiency. Study was extended to identify the ability of Artificial Neural Networks (ANNs) for estimation of ET_o in comparison to climatic based methods. The networks, using varied input combinations of climatic variables have been trained using the backpropagation with variable learning rate training algorithm. ANN models were performed better than the climatic based methods in all performance indices. The analyses of results of ANN model suggest that the ET_o can be estimated from maximum and minimum temperature using ANN approach in MPR area.     

非充分灌溉条件下水稻蒸发蒸腾量估算方法研究

如何估算非充分灌溉条件下作物蒸发蒸腾量,这是目前节水灌溉工作中经常会碰到的问题。根据连续4 a的灌溉试验田间实测,获得水稻从充分灌溉到多种亏水水平的蒸发蒸腾量和田间土壤水分变化过程等资料,在此基础上,研究建立了非充分灌溉条件下水稻蒸发蒸腾量估算模型。作为经验模型,与估算相对产量等模型结合运用,可获得广东省范围水稻非充分灌溉、无灌溉条件下的水分生产率、灌溉水利用效率等节水灌溉各项设计指标和评价指标。该文以梅县为例,对该模型的应用方法进行了介绍。     

Framework for Standardizing Less Data-Intensive Methods of Reference Evapotranspiration Estimation

Evapotranspiration is one of the vital components of water cycle and its accurate estimation is the key to sustainable management of irrigation water. The FAO Penman-Monteith (FAO-PM) method is recommended as the standard method for computing reference evapotranspiration (ET_o) as well as for evaluating other indirect methods. However, due to the lack of weather data such as radiation, relative humidity and wind speed in many regions of the world, especially in developing countries, the FAO-PM method is difficult to use. To address this issue, a fairly robust methodology is proposed in this study to standardize two popular less data-intensive (temperature-based) ET_o methods, viz., Hargreaves-Samani (HS) and Penman-Monteith Temperature (PMT) against the FAO-PM method. To achieve this goal, the daily and monthly biases of these two methods were adjusted using the weather data of 14 locations for the 1979–2003 period. Subsequently, the performance of the standardized (de-biased) less data-intensive methods were verified using salient statistical and graphical indicators for the 2004–2013 period. The results indicated that the HS and PMT methods underestimate ET_o on a monthly time step by 9.62 and 14.77%, respectively. However, the performances of these methods significantly improve after the standardization. The estimates of ET_o by the standardized less data-intensive methods were found to be in close agreement with those by the standard FAO-PM method, thereby suggesting the usefulness and applicability of the proposed framework in data-scarce situations irrespective of agro-climatic conditions.     

Optimizing the Runoff Estimation with HEC-HMS Model Using Spatial Evapotranspiration by the SEBS Model

Most of the commonly used hydrological models do not account for the actual evapotranspiration (ETa) as a key contributor to water loss in semi-arid/arid regions. In this study, the HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) model was calibrated, modified, and its performance in simulating runoff resulting from short-duration rainfall events was evaluated. The model modifications included integrating spatially distributed ETa, calculated using the surface energy balance system (SEBS), into the model. Evaluating the model’s performance in simulating runoff showed that the default HEC-HMS model underestimated the runoff with root mean squared error (RMSE) of 0.14 m³/s (R²?=?0.92) while incorporating SEBS ETa into the model reduced RMSE to 0.01 m³/s (R²?=?0.99). The integration of HECHMS and SEBS resulted in smaller and more realistic latent heat flux estimates translated into a lower water loss rate and a higher magnitude of runoff simulated by the HECHMS model. The difference between runoff simulations using the default and modified model translated into an average of 95,000 m³ runoff per rainfall event (equal to seasonal water requirement of ten-hectare winter wheat) that could be planned and triggered for agricultural purposes, flood harvesting, and groundwater recharge in the region. The effect of ETa on the simulated runoff volume is expected to be more pronounced during high evaporative demand periods, longer rainfall events, and larger catchments. The outcome of this study signifies the importance of implementing accurate estimates of evapotranspiration into a hydrological model.

     

Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration

The study investigates accuracy of a new modeling scheme, subset adaptive neuro fuzzy inference system (subset ANFIS), in estimating the daily reference evapotranspiration (ET₀). Daily weather data of relative humidity, solar radiation, air temperature, and wind speed from three stations in Central Anatolian Region of Turkey were utilized as input to the applied models. The input data set for modeling the ET₀ was divided to several subsets to calibrate the local data using a local modeling-based ANFIS. The estimates obtained from subset ANFIS models were compared with those of the M5 model tree (M5Tree), ANFIS models and ANN. Mean absolute error (MAE), root mean square error (RMSE), and model efficiency factor criteria were applied for analysis of models. The accuracy of M5Tree (from 15.3% to 32.5% in RMSE, from 14.4% to 24.2% in MAE), ANN (from 24.3% to 65.3% in RMSE, from 34.1% to 47% in MAE) and ANFIS (from 17.4% to 35.4% in RMSE, from 10.8% to 28.3% in MAE) models was significantly increased using subset ANFIS for estimating da ily ET_0.     

Multitemporal Monitoring of Land Degradation Risk Due to Soil Loss in a Fire-Prone Mediterranean Landscape Using Multi-decadal Landsat Imagery

Water Resources Management - Natural, as well as human-induced, landscape changes may have profound effects on soil-loss rates in Mediterranean countries. Knowledge of the spatial and temporal...     

Performance of Recalibrated Equations for the Estimation of Daily Reference Evapotranspiration

An accurate estimation of reference evapotranspiration (ET₀) is of paramount importance for many studies such as hydrologic water balance, irrigation system design and management, crop yield simulation, and water resources planning and management. In the present study, Blaney-Criddle, Jensen-Haise and Hargreaves (temperature based), Priestley-Taylor, Radiation and Makkink (radiation based) and, Pan Evaporation and Christiansen (pan evaporation based) methods have been evaluated and recalibrated with respect to FAO-56 Penman-Monteith method for estimating daily ET₀ in the semi-arid Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar sites of Andhra Pradesh, India. Recalibrated Blaney-Criddle (temperature based), Radiation (Radiation based) and Christiansen (Pan evaporation based) methods showed a satisfactory performance at the sites. Further, recalibrated Blaney- Criddle method showed relatively better performance than Radiation and Christiansen methods in the daily ET₀ estimation. Recalibrated Blaney- Criddle method may therefore be adopted at the sites selected for the present study and also at the sites with similar climatic conditions for satisfactory daily ET₀ estimation.     

新疆和田-若羌地区区域蒸散量估算

基于中等分辨率的MODIS卫星遥感数据及气象数据,应用表面能量平衡系统,对新疆和田-若羌地区2015年的区域蒸散量进行了估算。结果表明:研究区2015年的区域蒸散量为366.22 mm,夏季蒸散量较大,冬季蒸散量较小。最大蒸散量发生在6月份,为88.44 mm,最小蒸散量在1月份,只有0.10 mm。蒸散量随着气温和降水的增加而增大。不同用地类型的蒸散量也不一样,水体和耕地的蒸散量较大,沙地和戈壁的蒸散量最小。     

Estimation of Monthly Mean Reference Evapotranspiration in Turkey

Monthly mean reference evapotranspiration (ET ₀ ) is estimated using adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN) models. Various combinations of long-term average monthly climatic data of wind speed, air temperature, relative humidity, and solar radiation, recorded at stations in Turkey, are used as inputs to the ANFIS and ANN models so as to calculate ET ₀ given by the FAO-56 PM (Penman-Monteith) equation. First, a comparison is made among the estimates provided by the ANFIS and ANN models and those by the empirical methods of Hargreaves and Ritchie. Next, the empirical models are calibrated using the ET ₀ values given by FAO-56 PM, and the estimates by the ANFIS and ANN techniques are compared with those of the calibrated models. Mean square error, mean absolute error, and determination coefficient statistics are used as comparison criteria for evaluation of performances of all the models considered. Based on these evaluations, it is found that the ANFIS and ANN schemes can be employed successfully in modeling the monthly mean ET ₀ , because both approaches yield better estimates than the classical methods, and yet ANFIS being slightly more successful than ANN.     

Modeling River Mixing Mechanism Using Data Driven Model

Modeling river mixing mechanism in terms of pollution transmission in rivers is an important subject in environmental studies. Dispersion coefficient is an important parameter in river mixing problem. In this study, to model and predict the longitudinal dispersion coefficient (D _L ) in natural streams, two soft computing techniques including multivariate adaptive regression splines (MARS) as a new approach to study hydrologic phenomena and multi-layer perceptron neural network as a common type of neural network model were prepared. To this end, related dataset were collected from literature and used for developing them. Performance of MARS model was compared with MLP and the empirical formula was proposed to calculate D _L . To define the most effective parameters on D _L structure of obtained formula from MARS model and more accurate formula was evaluated. Calculation of error indices including coefficient of determination (R²) and root mean square error (RMSE) for the results of MARS model showed that MARS model with R²?=?0.98 and RMSE?=?0.89 in testing stage has suitable performance for modeling D _L . Comparing the performance of empirical formulas, ANN and MARS showed that MARS model is more accurate compared to others. Attention to the structure of developed MARS and the most accurate empirical formulas model showed that flow velocity, depth of flow (H) and shear velocity are the most influential parameters on D _L .     

Multi-Criteria Process-Based Calibration Using Functional Data Analysis to Improve Hydrological Model Realism

It has been argued that rainfall-runoff model calibration based solely on streamflow is not sufficient to evaluate the realism of a hydrological model to represent the internal fluxes. Therefore, model calibration has evolved to evaluating model performance using a number of hydrological signatures that link the model to the underlying processes. However, this approach uses goodness-of-fit measures, unable to describe the entire dynamic of time series, to evaluate model consistency and to simulate hydrological signatures. The present paper develops a stepwise multicriteria calibration using hydrograph partitioning and calibration criteria defined on the basis of Functional Data Analysis (FDA), a statistical tool that conserves all important features of the hydrograph by approximating times series as a single function. The aim of this approach is to improve model realism by scrutinizing model components and by evaluating its ability to reproduce the entire flow dynamic. The proposed approach is compared to a calibration against daily streamflow only. The stepwise calibration improved the estimation of the flood curve, the annual peak volume as well as the performance of the model at sites other than the calibration station.     

A Modified SEBAL Modeling Approach for Estimating Crop Evapotranspiration in Semi-arid Conditions

Remote sensing methods are becoming attractive to estimate crop evapotranspiration, as they cover large areas and can provide accurate and reliable estimations; intensive field monitoring is also not required, although some ground-truth measurements can be helpful in interpreting satellite images. For the purposes of this paper, modeling and remote sensing techniques were integrated for estimating actual evapotranspiration of groundnuts (Arachishypogaea, L.) that is cultivated near Mandria Village in Paphos District of Cyprus. The Surface Energy Balance Algorithm for Land (SEBAL) was adopted for the first time in Cyprus, employing the essential adaptations for local soil and meteorological conditions. Landsat-5 TM and 7 ETM+ images were used to retrieve the needed spectral data. The SEBAL model is enhanced with empirical equations determined as part of the present study, regarding crop canopy factors, in order to increase its accuracy. Maps of ET_a were created using the SEBAL modified model (CYSEBAL) for the area of interest. The results have been compared to the measurements from an evaporation pan (which was used as a reference) and those of the original SEBAL model. The statistical comparison has shown that the modified SEBAL yields results that are comparable to those of the evaporation pan. T-test application has revealed that the statistical difference between SEBAL and CYSEBAL is significant and quite crucial, especially in a place with limited surface and underground water resources.     

Bivariate Simulation of Potential Evapotranspiration Using Copula-GARCH Model

Developing statistical period and simulating the required values in case of data shortage increases certainty and reliability of simulations and statistical analyses, which is very important in studies on hydrology and water resources. Therefore, in this study, for simulating values of potential evapotranspiration at Birjand Station located in eastern Iran, contemporaneous autoregressive moving average (CARMA), CARMA-generalized autoregressive conditional heteroskedasticity (GARCH), and Copula-GARCH models were used in statistical period of 1984–2019. The potential evapotranspiration and relative humidity time series were simulated using these three models. CARMA model has acceptable accuracy for simulating potential evapotranspiration values due to the effect of the second parameter on simulations. Nash–Sutcliffe efficiency (NSE) coefficient of CARMA model for simulating potential evapotranspiration values was estimated as 0.85. NSE coefficient of CARMA-GARCH model was obtained as 0.87 through extracting residuals of CARMA model and simulating variance of data using GARCH model. Comparing the CARMA and CARMA-GARCH models with each other, it was concluded that a combination of two linear and non-linear time series models increases simulation accuracy to some extent. Using Clayton copula (the selected copula from the studied copulas), the mentioned values were simulated by Copula-GARCH model. The results showed that among the three models used, Copula-GARCH model reduced root mean square error of bivariate simulation compared to CARMA and CARMA-GARCH models by 15 and 13%, respectively. The results also showed that the proposed model simulates the average, first, and third quarters and range of changes in the data by 5 and 95% better than the two CARMA and CARMA-GARCH models.

     

Multiobjective Calibration of Reservoir Water Quality Modeling Using Multiobjective Particle Swarm Optimization (MOPSO)

Water resource management encounters large variety of multi objective problems that require powerful optimization tools in order to fully characterize the existing tradeoffs between various objectives that can be minimizing difference between forecasted physical, chemical, and biological behaviors of model and measured data. Calibration of complex water quality models for river and reservoir systems may include conflicting objectives addressed by various combinations of interacting calibration parameters. Calibration of the two dimensional CE-QUAL-W2 water quality and hydrodynamic model is an excellent example where the model must be calibrated for both hydrodynamic and water quality behavior. The aim of the present study is to show how multiobjective particle swarm optimization (MOPSO) can be implemented for automatic calibration of water quality and hydrodynamic parameters of a 2-dimensional, hydrodynamic, and water quality models (CEQUAL-W2) to predict physical, chemical, and biological behaviors of a water body, and then focus on a relevant case study. So MOPSO is utilized to generate Pareto optimal solutions for two conflicting calibration objectives. A combined measure of thermal and reservoir water level is considered as the first calibration objective. The second objective is formulated to forecast the best physical, chemical, and biological behavior of the model. Realizing the strong interactions between water quality and hydrodynamic issues of water bodies and their dependencies on the same set of calibration parameters, the proposed multiobjective approach may provide a wide version of all possible calibration solutions for better decision making to select best solution from pareto front.     

山东省陆面实际蒸发量估算及变化特征分析

选取互补相关蒸发模型估算了山东省17个气象站1961—2008年的陆面实际蒸发量,采用统计分析、相关分析和Mann-Kendall非参数检验等方法分析了该时段实际蒸发量的变化特征。结果表明:山东省多年平均陆面实际蒸发量为586.7 mm,年内呈单峰型变化,峰值出现在7月份;年际变化呈稳定下降趋势,在1991年左右发生突变,之后下降速度加快,该趋势与夏、秋季变化特征一致,而与春、冬季变化特征相反;就空间而言,山东省陆面实际蒸发量由东南沿海向西北内陆逐步减少,中部地区存在一条高蒸发带。     

Cross Comparison of Empirical Equations for Calculating Potential Evapotranspiration with Data from Switzerland

Earlier studies (Singh and Xu, 1997; Xu and Singh, 2000, 2001) have evaluated and compared various popular empirical evapotranspiration equations that belonged to three categories:(1) mass-transfer based methods, (2) radiation based methods, and(3) temperature-based methods; and the best and worst equations of each category were determined for the study regions. In this study a cross comparison of the best or representative equation forms selected from each category was made. Five representativeempirical potential evapotranspiration equations selected from the three categories, namely: Hargreaves and Blaney-Criddle (temperature-based), Makkink and Priestley-Taylor (radiation-based) and Rohwer (mass-transfer-based) were evaluatedand compared with the Penman-Monteith equation using daily meteorological data from the Changins station in Switzerland.The calculations of the Penman-Monteith equation followed theprocedure recommended by FAO (Allen et al., 1998). Thecomparison was first made using the original constant valuesinvolved in each empirical equation and then made using therecalibrated constant values. The study showed that: (1) theoriginal constant values involved in each empirical equationworked quite well for the study region, except that the valueof = 1.26 in Priestley-Taylor was found to be too high and therecalibration gave a value of = 0.90 for the region.(2) Improvement was achieved for the Blaney-Criddle method by addinga transition period in determining the parameter k. (3) The differences of performance between the best equation forms selected from each category are smaller than the differences between different equations within each category as reportedin earlier studies (Xu and Singh, 2000, 2001). Further examinationof the performance resulted in the following rank of accuracy ascompared with the Penman-Monteith estimates: Priestley-Taylor andMakkink (Radiation-based), Hargreaves and Blaney-Criddle (temperature-based) and Rohwer (Mass-transfer).