Groundwater is the main source of water in the semi-arid Calera watershed, located in the State of Zacatecas, Mexico. Due to increasing population, rapid industrial growth, and increased irrigation to meet growing food demand, groundwater extraction in the Calera watershed are exceeding recharge rates. Therefore, development and evaluation of alternative water management strategies are needed for sustainable development of the region. The Soil and Water Assessment Tool (SWAT) model was selected for this purpose as it has been used to simulate a wide range of agricultural production, the extensive testing and application in diverse watersheds worldwide, and the potential for future linkage of this model to groundwater models. However, crucial flow data which are commonly used for calibrating hydrologic models are not available in this watershed. This paper describes a novel calibration methodology that uses biomass and water balance approach which has potential for calibration of hydrologic models in ungauged or data-scarce watersheds, which are prevalent in many parts of the world. Estimated long-term annual average actual evapotranspiration (AET), and deep aquifer recharge rates and plant biomass values based on the expert knowledge of researchers and managers in the watershed were used as targets for calibration. The model performance was assessed using the Nash-Sutcliffe efficiency coefficient (NSE), coefficient of determination (R 2 ), and percent bias (PBIAS, %) statistics. On average, the calibrated SWAT model yielded annual Nash-Sutcliffe efficiency coefficient values of 0.95, 0.99, and 0.85 for AET, recharge, and biomass, respectively. The coefficient of determination, values for AET, recharge, and biomass were 0.95, 0.94, and 0.99 respectively. The percent bias values of ±2.21%, ±0.18%, and ±0.96% for AET, recharge, and biomass, respectively, indicated that the model reproduced the calibration target values of the three water budget variables within an acceptable value of ±10.0%. Therefore, it is concluded that the calibrated SWAT model can be used in evaluating alternative water management scenarios for the Calera watershed without further validation. Considering the relative ease in developing calibration data and excellent performance statistics, the calibration methodology proposed in this study may have the potential to be used for ungauged or data-scare agricultural watersheds that are prevalent in many parts of the world.
Assessment of water resources requires reliable rainfall data, and rain gauge networks may not provide adequate spatial representation due to limited point measurements. The Tropical Rainfall Measuring Mission (TRMM) provides rainfall data at global scale, and has been used with good results. However, TRMM data are an indirect measurement of rainfall, and therefore must be validated for its proper use. In this work, a validation scheme was designed and implemented to compare the TRMM Version 7 (V7) monthly rainfall product at different time frames with data measured in two hydrologic subregions of the Santiago River Basin (SRB) in Mexico: Río Alto Santiago and Río Bajo Santiago (RBS). Additionally, three physio‐climatic regions provide an assessment of the interplay of topography, distance from coastal regions, and seasonal weather patterns on the correspondence between both datasets. The TRMM V7 rainfall product exhibited good agreement with the rain gauge data particularly for the RBS and for the whole SRB during wettest summer and autumn seasons. However, strong regional dependence was observed due to differences in climate and topography. Overall, in spite of some noted underestimations, the monthly TRMM V7 rainfall product was found to provide useful information that can be used to complement limited monitoring as is the case of RBS. An improved combined rainfall product could be generated and thus gaining the most benefits from both data sources.
Collection, processing, and analysis of GIS and satellite data were performed in this work to estimate temporal groundwater recharge changes, which are needed as input in numerical groundwater-flow models. Layers of geological alignments, land use, drainage network, lithology, topography, and precipitation were collected. This information was spatialized, and then layer importance was calculated using an analytic hierarchy process (AHP) based on infiltration capacity to define potential recharge (PR) regions. A water budget equation was used to calculate PR volumes. The analysis was done every 5 years from 1970 to 2019, considering average urban area changes. For all study periods, an increase in urban area was calculated from 16 to 28% of the total study area, while potential recharge decreased from 23 to 19% of the mean precipitation values for each 5-year period. The most significant urban expansion was from 1980 to 1994 and 2010 to 2019, which match periods of potential recharge decrease. However, a slight increase in PR from 2000 to 2009, unrelated to urban area change, may be due to temperature variations. The results account for the spatial and temporal dynamics of the recharge in the study area and can be used as input data to calibrate the actual recharge in a groundwater numerical model.
Accurate hydraulic conductivity estimates are vital for groundwater evaluation. Usually, interpolations of hydraulic conductivity data are needed to obtain spatial estimates over larger areas, but the results present a high uncertainty which can be reduced by adding a secondary variable in the estimation. In this paper, the influence of the number and spatial configuration of hydraulic conductivity (K) and hydraulic head (HH) data on the estimation of K is evaluated using univariate and bivariate geostatistical-Kalman filter approaches (similar to kriging and cokriging, respectively). A synthetic case based on a transient groundwater flow model is used to generate different numbers, spatial arrays, and data. With these data, variogram models for the univariate and bivariate cases were fitted and used to calculate the corresponding covariance matrices for the Kalman filter. The results show that K estimates are more reliable when HH data is added than when only K is used, independently of the number and distribution of the data, since there is a better agreement between the calculated errors and estimate error variances. HH data provides valuable information only where K is not sampled. This evaluation could support the design of optimal sampling strategies to obtain reliable K estimates.
Abstract. Measured rainfall data are very important in agriculture and environmental science. However, in many cases, the information gathered by existing rain gauges is insufficient for characterizing climatic variation within a study area. Thus, the use of interpolation techniques is necessary to predict values to unsampled sites. In this work, the performances of geostatistical algorithms, such as ordinary kriging and ordinary cokriging, and a proposed Kalman filter method were compared for mapping rainfall. The analysis was performed using both univariate and bivariate approaches. Natural terrain elevation was taken as the auxiliary variable for the bivariate case. The analysis was conducted for specific months of the dry and wet seasons in the Santiago River basin in Mexico. After comparison of the statistical errors, it was established that the geostatistical methods provided excellent results (especially cokriging) for the wet season months, with good correlation of 0.7 or above between rainfall and elevation, but not for the dry season months. Nevertheless, good results were achieved for the dry season months using the proposed Kalman filter methodology, due to the high normality and spatial dependence of the sample in this period.
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