Geostatistical modeling of surface water balance (SWB) under variable soil moisture conditions in the Pao river basin, Venezuela

Pérez

2021

DYNA

En este artículo se presenta un novedoso método de predicción espacio-temporal de variables de balance hídrico en el humedal de la cuenca del río Urama, Venezuela. La metodología consta de dos etapas: 1. Compilación de información utilizando datos meteorológicos de precipitación y evaporación de 73 estaciones climáticas; imágenes Landsat y modelo de elevación digital para la cuenca en estudio. 2. Procesamiento de información: a) modelización de predicción estadística espacial; b) modelo de pronóstico; y, c) pronóstico de series temporales de variables de balance hídrico. La ventaja del método está en utilizar datos para pronosticar variables de balance hídrico mediante modelos de predicción estadística espacial seleccionando aquellas con tendencias estacionales que se aproximan a las observaciones. Se obtuvieron coeficientes de correlación mayores que 0.99, indicando predicciones satisfactorias, por lo que el método, es adecuado para reproducir el perfil espacio-temporal de períodos futuros para ser aplicados en gestión sostenible de humedales.

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicción espacio-temporal del balance hídrico en la cuenca del río Urama, Venezuela

Lopez

Pérez

2021

DYNA

“…A geostatistical model classified within the linear statistical models (probabilistic) known as Ordinary Kriging [7] was applied to predict the meteorological variables, precipitation, and evaporation. The experiences of using geostatistical models to predict the water balance have led to the establishment of mathematical functions that satisfactorily approximate the observations of the meteorological variables in the Pao and Urama river basins [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal prediction of water production in basins without records

Pérez

et al. 2022

DYNA

This study carried out the spatio-temporal prediction of water production in two micro-basins without records, located in Venezuela. Geomorphological characteristics of micro-basins were obtained using digital elevation models acquired from ALOS PALSAR satellite. Two models were included for spatio-temporal prediction of hydrometeorological variables. The first model involved deterministic and stochastic components, calibrated using two-time series (TS). TS-1 consisted of records from 227 precipitation stations and 62 evaporation stations managed by the Environment Ministry during 1980-1999. TS-2 included records from 28 precipitation stations and 18 evapotranspiration stations collected by the National Institute of Meteorology and Hydrology during 2015-2018. The second model estimated effective precipitation from TS-2 precipitation maps, runoff coefficient from land use and land cover (LULC) prediction. The LULC was estimated on 59 Landsat 8OLI images for 2015-2018. The validation was carried out with observations of the Hydrological Central Company, resulting in determination coefficients upper to 0.95.

“…Several scenarios are presented for a prospective time where the flood risk is gradually reduced as the predictable life parameter of hydraulic work is increased. By comparing to CIHAM-UC-FR model with that proposed by Farias et al (2020), it has been found that the former takes into account the effect of terrain elevation and slope on the effective precipitation estimate and its associated flood risk introducing an improvement with respect to the latter for mean to high terrain elevations and slopes.…”

Section: Introductionmentioning

confidence: 99%

“…In each case, the goal is the same: forecast the input design flow, run it through the system, and validate if the output is satisfactory. For instance, to make a rational use of the available water, it is necessary to know the amount required for irrigation and the time in which it must be supplied (e.g., using USDA‐FAS Palmer model [Mladenova et al., 2020]), what requires to make decisions supported in the spatio‐temporal forecast of the water balance over the area of interest (Farias et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Approach to a new forecasting method of hydrological variables in tropical basins

Environmental Quality Mgmt

López-Calatayud

Márquez

et al. 2021

In this paper, an approach to a new method for the spatio-temporal forecasting of hydrological variables in basins is proposed termed as CIHAM-UC-SPF-HV. This was demonstrated using as hydrological variables to the precipitation and evaporation measured in 174 and 82 monitoring stations respectively, during period 1960-2000 located in the UTM zone 19 North of Venezuela and the indirect variable estimated by a water balance. This comprises the stages: 1) Collection of hydrological data, 2) Estimation of hydrological variable's spatial prediction, 3) Temporal forecasting of parameters derived from statistical spatial prediction model for hydrological variables, 4) Spatio-Temporal forecasting of hydrological variables, and 5) Validation of the results in the forecasted hydrological variables. The main advantages are: 1) the combination of deterministic and spatially correlated random components, both of these supported by historical records associated to time series of hydrological variables, and 2) multiple mathematical structures contribute to predict the parameters of statistical spatial forecasting model of the hydrological variables, selecting that by which the seasonal pattern and trend to be the closest to the observed values. The method is suitable to reproduce the spatio-temporal pattern between observed and forecasted values below two standard deviation of the mean values.