Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain

Water availability is essential for the appropriate analysis of its sustainable management. We performed a comparative study of six hydrological balance models (Témez, ABCD, GR2M, AWBM, GUO-5p, and Thornthwaite-Mather) in several basins with different climatic conditions within Spain in the 1977–2010 period. We applied six statistical indices to compare the results of the models: the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Nash–Sutcliffe model efficiency coefficient (NSE), coefficient of determination (R2), percent bias (PBIAS), and the relative error between observed and simulated run-off volumes (REV). Furthermore, we applied the FITEVAL software to determine the uncertainty of the model. The results show that when the catchments are more humid the obtained results are better. The GR2M model gave the best fit in peninsular Spain in a UNEP aridity index framework above 1, and NSE values above 0.75 in a 95% confidence interval classify GR2M as very good for humid watersheds. The use of REV is also a key index in the assessment of the margin of error. Flow duration curves show good performance in the probabilities of exceedance lower than 80% in wet watersheds and deviations in low streamflows account for less than 5% of the total streamflow.

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Soybean yield in the Matopiba region under climate changes

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v24n1p8-14 ◽

2020 ◽

Vol 24 (1) ◽

pp. 8-14

Author(s):

Vicente de P. R. da Silva ◽

Roberta A. e Silva ◽

Girlene F. Maciel ◽

Enio P. de Souza ◽

Célia C. Braga ◽

...

Keyword(s):

Climate Models ◽

Co2 Concentration ◽

Climatic Conditions ◽

Coefficient Of Determination ◽

Climate Change Scenarios ◽

Efficiency Coefficient ◽

Soybean Yield ◽

Aquacrop Model ◽

Model Efficiency ◽

Rcp 8.5

ABSTRACT The climatic conditions along the cycle are the main factors responsible for the final production of any crop. This study aimed to evaluate the current conditions and the effects of climate change scenarios on the yield of soybean grown in the Matopiba region, located between the states of Tocantins, south and northeast of Maranhão, south of Piauí and west of Bahia, Brazil. The AquaCrop model of FAO, version 5.0, was calibrated with data of 2014 and validated with those of 2016, using climate, soil and crop management parameters collected in two experimental campaigns conducted between June and October in 2014 and 2016 in Palmas, TO, Brazil. The performance of the model was evaluated using the following statistical indicators: prediction error (PE), coefficient of determination (R2), normalized root mean square error (NRMSE), Nash-Sutcliffe model efficiency coefficient (EF) and Willmott’s index of agreement (d). It was verified that the AquaCrop model underestimates soybean grain yield under severe water stress conditions throughout the growing cycle. The increase in CO2 concentration and in the air temperature, projected by the climate models HadGEM2-ES and MIROC5 under the scenario of stabilization (RCP 4.5) and the scenario of progression (RCP 8.5), have contributed to the increase in soybean yield by the end of this century.

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Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽

10.3390/forecast3010004 ◽

2021 ◽

Vol 3 (1) ◽

pp. 39-55

Author(s):

Rodgers Makwinja ◽

Seyoum Mengistou ◽

Emmanuel Kaunda ◽

Tena Alemiew ◽

Titus Bandulo Phiri ◽

...

Keyword(s):

Time Series ◽

Information Criterion ◽

Coefficient Of Determination ◽

Series Data ◽

Efficiency Coefficient ◽

Arima Models ◽

Catch Per Unit Effort ◽

Annual Fish ◽

Unit Effort ◽

The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

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Application of PCSWMM for the 1-D and 1-D–2-D Modeling of Urban Flooding in Damansara Catchment, Malaysia

Applied Sciences ◽

10.3390/app11199300 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9300

Author(s):

Lariyah Mohd Sidek ◽

Lloyd Hock Chye Chua ◽

Aqilah Syasya Mohd Azizi ◽

Hidayah Basri ◽

Aminah Shakirah Jaafar ◽

...

Keyword(s):

River Flow ◽

Flood Hazard ◽

Developed Countries ◽

Coefficient Of Determination ◽

Efficiency Coefficient ◽

Urban Flood ◽

Storm Water Management Model ◽

Flood Depth ◽

Comparison Results ◽

Model Efficiency

Coupled with climate change, the urbanization-driven increase in the frequency and intensity of floods can be seen in both developing and developed countries, and Malaysia is no exemption. As part of flood hazard mitigation, this study aimed to simulate the urban flood scenarios in Malaysia’s urbanized catchments. The flood simulation was performed using the Personal Computer Storm Water Management Model (PCSWMM) modeling of the Damansara catchment as a case study. An integrated hydrologic-hydraulic model was developed for the 1-D river flow modeling and 1-D–2-D drainage overflow modeling. The reliability of the 1-D river flow model was confirmed through the calibration and validation, in which the water level in TTDI Jaya was satisfactorily predicted, supported by the coefficient of determination (R2), Nash–Sutcliffe model efficiency coefficient (NSE), and relative error (RE). The performance of the 1-D–2-D model was further demonstrated based on the flood depth, extent, and risk caused by the drainage overflow. Two scenarios were tested, and the comparison results showed that the current drainage effectively reduced the drainage overflow due to the increased size of drains compared to the historic drainage in 2015. The procedure and findings of this study could serve as references for the application in flood mitigation planning worldwide, especially for developing countries.

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Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

Remote Sensing ◽

10.3390/rs13091716 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1716

Author(s):

Ankur Srivastava ◽

Jose F. Rodriguez ◽

Patricia M. Saco ◽

Nikul Kumari ◽

Omer Yetemen

Keyword(s):

Cloud Cover ◽

Climatic Factors ◽

Global Analysis ◽

Empirical Relationship ◽

Aridity Index ◽

Global Scale ◽

Empirical Models ◽

Coefficient Of Determination ◽

Crop Modeling ◽

Atmospheric Transmissivity

Atmospheric transmissivity (τ) is a critical factor in climatology, which affects surface energy balance, measured at a limited number of meteorological stations worldwide. With the limited availability of meteorological datasets in remote areas across different climatic regions, estimation of τ is becoming a challenging task for adequate hydrological, climatic, and crop modeling studies. The availability of solar radiation data is comparatively less accessible on a global scale than the temperature and precipitation datasets, which makes it necessary to develop methods to estimate τ. Most of the previous studies provided region specific datasets of τ, which usually provide local assessments. Hence, there is a necessity to give the empirical models for τ estimation on a global scale that can be easily assessed. This study presents the analysis of the τ relationship with varying geographic features and climatic factors like latitude, aridity index, cloud cover, precipitation, temperature, diurnal temperature range, and elevation. In addition to these factors, the applicability of these relationships was evaluated for different climate types. Thus, empirical models have been proposed for each climate type to estimate τ by using the most effective factors such as cloud cover and aridity index. The cloud cover is an important yet often overlooked factor that can be used to determine the global atmospheric transmissivity. The empirical relationship and statistical indicator provided the best performance in equatorial climates as the coefficient of determination (r2) was 0.88 relatively higher than the warm temperate (r2 = 0.74) and arid regions (r2 = 0.46). According to the results, it is believed that the analysis presented in this work is applicable for estimating the τ in different ecosystems across the globe.

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Effects of air quality on the health of Mediterranean forests

10.5194/egusphere-egu21-16171 ◽

2021 ◽

Author(s):

Adrián García Bruzón ◽

Patricia Arrogante Funes ◽

Laura Muñoz Moral

Keyword(s):

Climate Change ◽

Machine Learning ◽

Random Forest ◽

Aridity Index ◽

Plant Health ◽

Mediterranean Forests ◽

Random Forest Algorithm ◽

The Mediterranean ◽

Heterogeneous Variables ◽

Peninsular Spain

The climate change has turned out to be a determining factor in the development of forest in Spain. Production systems have emitted polluting gases and other particles into the atmosphere, for which some plants have not yet developed adaptation systems. Among the most harmful pollutants for the environment are gases such as nitrous oxides, ozone, particulate matter.However, this condition is not the same in Peninsular Spain, and the Balearic Islands since the plant compositions differ in the territory and the bioclimatic, topographic, and anthropic characteristics. Monitoring the vegetation with sufficient spatial and temporal resolution, studying variables conditioning plant health is a challenge from the nature of the variables and the amount of data to be handled.&#160;The Mediterranean forest is one of the most ecosystem affected by climate change because of usually experimented long periods of drought that, in combination with increased temperatures, can drastically reduce the photosynthetic activity of trees and therefore the biomass of forests.That is why the application of environmental technologies based on Remote Sensing (which provide plant health indices from passive sensors on satellite platforms and other variables of interest), Geographic Information Systems (to integrate, process, analyze spatial and temporal data) and machine learning models (which facilitate the extraction of relationships between variables, conditioning factors and predict patterns).&#160;In this regard, this work's objective is to evaluate the possible effect that different pollutants have on the health of the vegetation, measured from the annual values of the Normalized Difference Vegetation Index (NDVI), in the Mediterranean forests of Peninsular Spain. To achieve this, we are used machine learning techniques using the Random Forest algorithm. The study has also been done with various climatic, topographic, and anthropic variables that characterize the forest to carry it out.&#160;The results showed that certain variables such as the aridity index had generated the NDVI values and therefore plant development, while others are limiting factors such as the concentration of certain pollutants and the direct relationship between them particulates and NOx. This study can verify how the Random Forest algorithm offers reliable results, even when working with heterogeneous variables.&#160;

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The use of the nonlinear models in the growth of pears of ‘Shinseiki’ cultivar

Ciência Rural ◽

10.1590/0103-8478cr20161097 ◽

2017 ◽

Vol 48 (1) ◽

Cited By ~ 4

Author(s):

Thais Destefani Ribeiro ◽

Taciana Villela Savian ◽

Tales Jesus Fernandes ◽

Joel Augusto Muniz

Keyword(s):

Logistic Model ◽

Least Squares Method ◽

Nonlinear Models ◽

Logistic Models ◽

Information Criterion ◽

Fruit Weight ◽

Coefficient Of Determination ◽

Pear Fruit ◽

Asian Pear ◽

Residual Correlations

ABSTRACT: The goal of this study was to elucidate the growth and development of the Asian pear fruit, on the grounds of length, diameter and fresh weight determined over time, using the non-linear Gompertz and Logistic models. The specifications of the models were assessed utilizing the R statistical software, via the least squares method and iterative Gauss-Newton process (DRAPER & SMITH, 2014). The residual standard deviation, adjusted coefficient of determination and the Akaike information criterion were used to compare the models. The residual correlations, observed in the data for length and diameter, were modeled using the second-order regression process to render the residuals independent. The logistic model was highly suitable in demonstrating the data, revealing the Asian pear fruit growth to be sigmoid in shape, showing remarkable development for three variables. It showed an average of up to 125 days for length and diameter and 140 days for fresh fruit weight, with values of 72mm length, 80mm diameter and 224g heavy fat.

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Nonlinear modeling growth body weight of Mangalarga Marchador horses

Ciência Rural ◽

10.1590/0103-8478cr20160636 ◽

2017 ◽

Vol 47 (4) ◽

Cited By ~ 2

Author(s):

Felipe Amorim Caetano Souza ◽

Tales Jesus Fernandes ◽

Raquel Silva de Moura ◽

Sarah Laguna Conceição Meirelles ◽

Rafaela Aparecida Ribeiro ◽

...

Keyword(s):

Body Weight ◽

Linear Models ◽

Nonlinear Modeling ◽

Growth Curves ◽

Live Weight ◽

Information Criterion ◽

Coefficient Of Determination ◽

Cross Sectional ◽

Adult Body Weight ◽

The Cross

ABSTRACT: The analysis of the growth and development of various species has been done using the growth curves of the specific animal based on non-linear models. The objective of the current study was to evaluate the fit of the Brody, Gompertz, Logistic and von Bertalanffy models to the cross-sectional data of the live weight of the MangalargaMarchador horses to identify the best model and make accurate predictions regarding the growth and maturity in the males and females of this breed. The study involved recording the weight of 214 horses, of which 94 were males and 120 were non-pregnant females, between 6 and 153 months of age. The parameters of the model were estimated by employing the method of least squares, using the iteratively regularized Gauss-Newton method and the R software package. Comparison of the models was done based on the following criteria: coefficient of determination (R²); Residual Standard Deviation (RSD); corrected Akaike Information Criterion (AICc). The estimated weight of the adult horses by the models ranged between 431kg and 439kg for males and between 416kg and 420kg for females. The growth curves were studied using the cross-sectional data collection method. For males the von Bertalanffymodel was found to be the most effective in expressing growth, while in females the Brody model was more suitable. The MangalargaMarchador females achieve adult body weight earlier than the males.

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A NON-DESTRUCTIVE METHOD FOR ESTIMATING LEAF AREA OF Ceiba glaziovii (Kuntze) K. Schum.

FLORESTA ◽

10.5380/rf.v50i1.61088 ◽

2019 ◽

Vol 50 (1) ◽

pp. 1063

Author(s):

João Everthon da Silva Ribeiro ◽

Francisco Romário Andrade Figueiredo ◽

Ester Dos Santos Coêlho ◽

Walter Esfrain Pereira ◽

Manoel Bandeira de Albuquerque

Keyword(s):

Leaf Area ◽

Regression Models ◽

Information Criterion ◽

Coefficient Of Determination ◽

Linear Measurements ◽

Maximum Width ◽

Bias Index ◽

Different Shapes ◽

Non Destructive

The determination of leaf area is of fundamental importance in studies involving ecological and ecophysiological aspects of forest species. The objective of this research was to adjust an equation to determine the leaf area of Ceiba glaziovii as a function of linear measurements of leaves. Six hundred healthy leaf limbs were collected in different matrices, with different shapes and sizes, in the Mata do Pau-Ferro State Park, Areia, Paraíba state, Northeast Brazil. The maximum length (L), maximum width (W), product between length and width (L.W), and leaf area of the leaf limbs were calculated. The regression models used to construct equations were: linear, linear without intercept, quadratic, cubic, power and exponential. The criteria for choosing the best equation were based on the coefficient of determination (R²), Akaike information criterion (AIC), root mean square error (RMSE), Willmott concordance index (d) and BIAS index. All the proposed equations satisfactorily estimate the leaf area of C. glaziovii, due to their high determination coefficients (R² ≥ 0.851). The linear model without intercept, using the product between length and width (L.W), presented the best criteria to estimate the leaf area of the species, using the equation 0.4549*LW.

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Estimation of leaf area of Erythroxylum citrifolium from linear leaf dimensions

Bioscience Journal ◽

10.14393/bj-v35n6a2019-41743 ◽

2019 ◽

Vol 35 (6) ◽

Author(s):

João Everthon da Silva Ribeiro ◽

Ester dos Santos Coêlho ◽

Francisco Romário Andrade Figueiredo ◽

Sérgio de Faria Lopes ◽

Manoel Bandeira de Albuquerque

Keyword(s):

Leaf Area ◽

Leaf Blade ◽

Information Criterion ◽

Coefficient Of Determination ◽

Concordance Index ◽

R And D ◽

Linear Dimensions ◽

Bias Index ◽

Linear Leaf ◽

Leaf Dimensions

Erythroxylum citrifolium is a neotropical plant species recorded in all regions of Brazil. Determining leaf area is of fundamental importance to studies related to plant propagation and growth. The objective was to obtain an equation to estimate the leaf area of E. citrifolium from linear dimensions of the leaf blade (length and width). A total of 200 leaf blades were collected in Parque Estadual Mata do Pau-Ferro in the municipality of Areia, state of Paraíba, Northeast Brazil. The models evaluated were: linear, linear without intercept, quadratic, cubic, power and exponential. The best model was determined by the criteria of: high coefficient of determination (R²), low root mean square error (RMSE), low Akaike information criterion (AIC), high Willmott concordance index (d) and a BIAS index close to zero. All of the models constructed satisfactorily estimated the leaf area of E. citrifolium, with coefficients of determination above 0.9050, but the power model using the product between length and width (L*W) ŷ = 0.5966 * LW1.0181 was the best, with the highest values of R² and d, low values of RMSE and AIC, and a BIAS index closest to zero.

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Comparison of different efficiency criteria for hydrological model assessment

Advances in Geosciences ◽

10.5194/adgeo-5-89-2005 ◽

2005 ◽

Vol 5 ◽

pp. 89-97 ◽

Cited By ~ 1307

Author(s):

P. Krause ◽

D. P. Boyle ◽

F. Bäse

Keyword(s):

Hydrological Model ◽

Objective Assessment ◽

Hydrologic Model ◽

Coefficient Of Determination ◽

Evaluation Procedure ◽

Efficiency Coefficient ◽

Model Assessment ◽

Different Types ◽

Index Of Agreement ◽

And Performance

Abstract. The evaluation of hydrologic model behaviour and performance is commonly made and reported through comparisons of simulated and observed variables. Frequently, comparisons are made between simulated and measured streamflow at the catchment outlet. In distributed hydrological modelling approaches, additional comparisons of simulated and observed measurements for multi-response validation may be integrated into the evaluation procedure to assess overall modelling performance. In both approaches, single and multi-response, efficiency criteria are commonly used by hydrologists to provide an objective assessment of the "closeness" of the simulated behaviour to the observed measurements. While there are a few efficiency criteria such as the Nash-Sutcliffe efficiency, coefficient of determination, and index of agreement that are frequently used in hydrologic modeling studies and reported in the literature, there are a large number of other efficiency criteria to choose from. The selection and use of specific efficiency criteria and the interpretation of the results can be a challenge for even the most experienced hydrologist since each criterion may place different emphasis on different types of simulated and observed behaviours. In this paper, the utility of several efficiency criteria is investigated in three examples using a simple observed streamflow hydrograph.

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