Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination: The Bilate cropland sub watershed.

Nitrate contamination in surface and groundwater remains a widespread problem in agricultural watersheds is primarily associated to high levels of percolation or leakage from fertilized soil, which allows easy infiltration from soil into groundwater. This study was aimed to predict canopy water content to determine the nitrate contamination index resulting from nitrogen fertilizer loss in surface and groundwater. The study used Geographically Weighted Regression (GWR) model using MODIS 006 MOD13Q1-EVI Earth observation data, crop information and rainfall data. Satellite data collection was synchronized with regional crop calendars and calibrated to plant biomass. The average plant biomass during observed plant growth stages was between 0.19 kg/m2 at the minimum and 0.57 kg/m2 at the maximum. These values are based on the growth stages of crops and provide a solid basis for monitoring and validating crop water productivity data. The simulation results were validated with a high correlation coefficient (R2 = 0.996, P < 0.0005) for the observed rainfall in the growing zone compared to the predicted canopy water content. The nitrate contamination index assessment was conducted in 2004, 2008, 2009, 2010, 2011, 2013, 2014, 2015, 2018 and 2020. Canopy water content and root zone seasonal water content were measured in (%) per portion as indicators of the NO-3-N-nitrate contamination index in these years (0.391, 0.316, 0.298, 0.389, 0.380, 0.339, 0.242, 0.342 and 0.356).

Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks.

The success of many projects linked to the management and planning of water resources depends mainly on the quality of the climatic and hydrological data that is provided. Nevertheless, the missing data are frequently found in hydroclimatic variables due to measuring instrument failures, observation recording errors, meteorological extremes, and the challenges associated with accessing measurement areas. Hence, it is necessary to apply an appropriate fill of missing data before any analysis. This paper is intended to present the filling of missing data of monthly rainfall of 45 gauge stations located in southwestern Colombia. The series analyzed covers 34 years of observations between 1983 and 2016, available from the Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM). The estimation of missing data was done using Non-linear Principal Component Analysis (NLPCA); a non-linear generalization of the standard Principal Component Analysis Method via an Artificial Neural Networks (ANN) approach. The best result was obtained using a network with a [45-44-45] architecture. The estimated mean squared error in the imputation of missing data was approximately 9.8 mm. month-1, showing that the NLPCA approach constitutes a powerful methodology in the imputation of missing rainfall data. The estimated rainfall dataset helps reduce uncertainty for further studies related to homogeneity analyses, conglomerates, trends, multivariate statistics and meteorological forecasts in regions with information deficits such as southwestern Colombia.

Cluster analysis identified rainfall homogeneous regions in Tocantins state, Brazil / Análises de agrupamento identificaram regiões pluviometricamente homogêneas no estado de Tocantis, Brasil

Identifying homogeneous regions regarding the monthly rainfall is relevant in agricultural planning, especially in relation to the installation of crops. Thus, the aim of this study was to apply the cluster analysis using Ward's algorithm to identify homogeneous regions in Tocantins State. Rainfall database of 34 stations (sites) of Tocantins, from 1976 to 2012, it were obtained of the Database of the Agência Nacional de Águas, Instituto Nacional de Meteorologia and Serviço Meteorológico do Brasil. Thus, were formed 408 time series (12 months × 34 sites) wherein AA technique was applied in conjunction with Ward's algorithm. Three homogeneous regions in relation to monthly rainfall were identified. Group 1, located in the extreme north of the state, has intermediate average values (135.58 mm) to the other groups. Group 2 showed the greatest variance (13,543.68 mm²) and higher average (162.19 mm) for the studied period. On the other hand, group 3 has the lowest average rainfall (117.93 mm) among the homogeneous groups. Separating the groups followed a north-south alignment which suggests that latitude is the physiographic factor that most influences the occurrence of monthly rainfall in the State of Tocantins.

A identificação de regiões homogêneas quanto a chuva mensal é relevante no planejamento agrícola, sobretudo no que diz respeito à instalação de culturas. Assim, o objetivo deste estudo foi aplicar a análise de agrupamento (AA) em conjunto com o algoritmo de Ward para identificar regiões homogêneas no Estado do Tocantins. Dados de precipitação mensal de 34 estações (locais) do Tocantins, de 1976 a 2012, foram obtidos do banco de dados da Agência Nacional de Águas, Instituto Nacional de Meteorologia e Serviço Meteorológico do Brasil. Assim, foram formadas 408 séries temporais (12 meses x 34 locais) em que foi aplicada a técnica AA em conjunto com o algoritmo de Ward. Foram identificadas três regiões homogenias quanto a precipitação mensal. O grupo 1, localizado no extremo-norte do estado, possui valores médios intermediários (135.58 mm) aos outros grupos. O grupo 2 apresentou a maior variância (13,543.68 mm²) e maior média (162.19 mm) para o período de estudo. Por outro lado, o grupo 3 possui a menor média pluviométrica (117,93 mm) dentre os grupos homogêneos. A separação dos grupos seguiu um alinhamento Norte-Sul, o que sugere que a latitute é o fator fisiográfico que mais influência na ocorrência da precipitação mensal no Estado do Tocantins.