The FAO 56 Penman–Monteith equation (PM) is considered the most accurate method for estimating reference evapotranspiration (ETo). However, PM requires a large amount of data that is not always available. Thus, the objective of this study is to improve the Hargreaves–Samani (HS) reference evapotranspiration estimates in the Peruvian Altiplano (PA) by calibrating the radiation coefficient K_RS. The results show modified HS (HSM) ETo estimates at validation after K_RS calibration, revealing evident improvements in accuracy with Nash–Sutcliffe efficiency (NSE) between 0.58 and 0.93, percentage bias (PBIAS) between −0.58 and 1.34%, mean absolute error (MAE) between −0.02 and 0.05 mm/d, and root mean square error (RMSE) between 0.14 and 0.25 mm/d. Consequently, the multiple linear regression (MLR) model was used to regionalize the K_RS for the PA. It is concluded that, in the absence of meteorological data, the HSM equation can be used with the new values of K_RS instead of HS for the PA.

La investigación se realizó en la cuenca Ramis, ubicado al norte de la región hidrográfica del Titicaca (lado peruano), teniendo como objetivo validar las precipitaciones estimadas por satélite y su aplicación en la modelación hidrológica del rio Ramis. La validación de precipitaciones estimadas por satélite, se realizó mediante una comparación entre datos observados en ambos casos para el periodo enero de 1998 a diciembre 2013, los cuales fueron evaluados mediante los indicadores estadísticos de sesgo porcentual y coeficiente de determinación. Para el caso de la modelación hidrológica, se efectuó mediante el modelo hidrológico GR2M el cual consistió en las etapas de calibración y validación. La significancia estadística de las simulaciones de caudales fue evaluada mediante los indicadores de eficiencia de Nash-Sutcliffe y el sesgo porcentual. Los resultados obtenidos indican en considerar fiable la lluvia estimada por satélite y se puedan utilizar como alternativa en zonas sin información, dado que el coeficiente de determinación en la validación de precipitaciones es igual a 0.86 con un sesgo porcentual de +21.7% y como datos de entrada en la modelación hidrológica para transformar lluvia en escorrentía, es aceptable con eficiencias de Nash-Sutcliffe igual 0.78 en la etapa de calibración y 0.75 en la validación. Se concluye que el producto de estimación satelital es válido en la cuenca del rio Ramis.

Análisis multi-temporal entre 1975 y 2015 sobre cambios de la cobertura glaciar en los nevados Allin Capac y Chichi Capac, Perú

The Penman-Monteith (PM) equation is considered the most accurate method for estimating reference evapotranspiration (ETo); however, its application requires a large amount of data that is not always available. This study aimed to regionalize the coefficients of the Hargreaves-Samani (HS) equation to estimate ETo in high-altitude areas, specifically the Peruvian Altiplano (PA). The methodology included (1) evaluation of the original HS equation, (2) calibration and validation of the empirical coefficient ($CH$) and empirical exponent ($EH$) at each weather station, and (3) regionalization of the calibrated coefficients using a multiple linear regression approach. The results showed that the original HS equation had NSE values ranging from −0.57 to 0.87, PBIAS from −18.60% to 12.70%, MAE from 0.16 to 0.65 mm/d, and RMSE from 0.20 to 0.67 mm/d. After calibrating $CH$ and $EH$, performance improved significantly, achieving validation values of NSE ranging from 0.67 to 0.94, PBIAS from −0.55% to 1.37%, MAE from 0.01 to 0.05 mm/d, and RMSE from 0.13 to 0.21 mm/d. Finally, the regionalization of 0.859 and 0.744, respectively. These results indicate that the HS equation, with calibrated and regionalized coefficients, is a viable alternative for estimating ETo in regions with limited meteorological data.

Gridded meteorological datasets represent a valuable alternative for hydrometeorological applications, especially in areas with limited ground observations. However, it is important to evaluate these datasets to quantify their accuracy, error, and bias in estimates. The objective of this research was to assess the hydrological performance of gridded meteorological products in Peruvian Altiplano basins. Three evaluation approaches were employed: 1) pixel-to-point, 2) basin-averaged values, and 3) as forcings in hydrological modeling. Using precipitation (P) and potential evapotranspiration (PET) data from AgERA5, ERA5-Land, MERRA-2, and PERSIANN-CDR, comparisons were made with 33 local meteorological stations using statistical metrics such as correlation coefficient (CC), root mean square error (RMSE), and percentage bias (PBIAS). Additionally, the GR4J hydrological model was employed in four watersheds to assess model performance with different data combinations. Results showed that AgERA5 and ERA5-Land excelled in daily precipitation estimation, while MERRA-2 had the lowest PET bias. Using reference data for P and PET as model forcings yielded consistent results across basins. However, the performance declined when using gridded products for both P and PET, revealing significant limitations in replicating hydrological processes and emphasizing the need to enhance these products for areas with limited observational data.

The forecast of river stream flows is of significant importance for the development of early warning systems. Artificial intelligence algorithms have proven to be an effective tool in hydrological modeling data-driven, since they allow establishing relationships between input and output data of a watershed and thus make decisions data-driven. This article investigates the applicability of the k-nearest neighbor (KNN) algorithm for forecasting the mean daily flows of the Ramis river, at the Ramis hydrometric station. As input to the KNN machine learning algorithm, we used a data set of mean basin precipitation and mean daily flow from hydrometeorological stations with various lags. The performance of the KNN algorithm was quantitatively evaluated with hydrological ability metrics such as mean absolute percentage error (MAPE), anomaly correlation coefficient (ACC), Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE') and the spectral angle (SA). The results for forecasting the flows of the Ramis river with the k-nearest neighbor machine learning algorithm reached high levels of reliability with flow lags of one and two days and precipitation with three days. The algorithm used is simple but robust to make short-term flow forecasts and can be integrated as an alternative to strengthen the daily hydrological forecast of the Ramis river.