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The conversion of cultivated areas from rainfed to irrigated agriculture alters the watershed’s hydrology and could affect the water quality and quantity. This study examined how streamflow, nitrate load, and nitrate concentration changed after irrigation implementation in a Mediterranean watershed in Navarre, Spain. The Soil Water Assessment Tool (SWAT) model was applied in the Cidacos River watershed to simulate streamflow and nitrate load under rainfed conditions. The simulated outputs were then compared with the post-irrigation observed values from mid-2017 to 2020 at the watershed outlet in Traibuenas to determine the irrigation impact. The model calibration (2000–2010) and validation (2011–2020) results for streamflow (NSE = 0.82/0.83) and nitrate load (NSE = 0.71/0.68) were satisfactory, indicating the model’s suitability for use in the watershed. A comparison of the rainfed and post-irrigation periods showed an average annual increase in streamflow (952.33 m3 ha−1, +18.8%), nitrate load (68.17 kg ha−1, +62.3%), and nitrate concentration (0.89 mg L−1 ha−1, +79%) at the watershed outlet. Irrigation also caused seasonal changes by altering the cropping cycle and increasing the streamflow and nitrate export during the summer and autumn when irrigation was at its peak. The increases in the post-irrigation period were attributed to the added irrigation water for streamflow and increased nitrogen fertilizer application due to changes in cropping for nitrate concentration and export. These findings are useful to farmers and managers in deciding the best nitrate pollution control and management measures to implement. Furthermore, these results could guide future development and expansion of irrigated lands to improve agricultural sustainability.

The present study addresses the problems of mean estimation and nonresponse under the three-stage RRT model. Auxiliary information on an attribute and variable is used to propose a generalized class of exponential ratio-type estimators. Expressions for the bias, mean squared error, and minimum mean squared error for the proposed estimator are derived up to the first degree of approximation. The efficiency of the proposed estimator is studied theoretically and numerically using two real datasets. From the numerical analysis, the proposed generalized class of exponential ratio-type estimators outperforms ordinary mean estimators, usual ratio estimators, and exponential ratio-type estimators. Furthermore, the efficiencies of the mean estimators are observed to decrease with an increase in the sensitivity level of the survey question. As the inverse sampling rate and nonresponse rate go up, so does the efficiency of the mean estimators, which makes them more accurate.

The present study proposes a generalized mean estimator for a sensitive variable using a non-sensitive auxiliary variable in the presence of measurement errors based on the Randomized Response Technique (RRT). Expressions for the bias and mean squared error for the proposed estimator are correctly derived up to the first order of approximation. Furthermore, the optimum conditions and minimum mean squared error for the proposed estimator are determined. The efficiency of the proposed estimator is studied both theoretically and numerically using simulated and real data sets. The numerical study reveals that the use of the Randomized Response Technique (RRT) in a survey contaminated with measurement errors increases the variances and mean squared errors of estimators of the finite population mean.

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