Fernando González-Leiva

Climate change projections are studied using General Circulation Models (GCMs). GCMs are models that simulate climate on a broad scale, hence they cannot be directly used in local impact studies, such as, for example, hydrological studies. GCMs must go through a process of downscaling, to adjust their results in terms of spatial scale and reduce their bias before being used at the local scale. Quantile Mapping is one of the most widely used approaches for bias correcting GCM climate outputs. However, in its conventional formulation QM assumes a time-invariant correction function, which potentially results in additional biases. This has motivated the development of trend-preserving variations, accounting for a non-stationary correction function and aiming to preserve the raw GCM signal. Unfortunately, choosing which variation to use is not straight-forward. We present the climQMBC package (https://github.com/saedoquililongo/climQMBC or https://doi.org/10.5281/zenodo.18392900) as an easy-to-use tool to compare quantile mapping approaches. climQMBC is available in Python, R and MATLAB, and contains the classic QM method and four trend-preserving variations: Detrended Quantile Mapping (DQM), Quantile Delta Mapping (QDM), Unbiased Quantile Mapping (UQM) and Scaled Distribution Mapping (SDM). This package has a built-in summary report that allows comparing methods in terms of their capability of preserving raw GCM trends. A synthetic exercise showed that the most reliable methods are the UQM and DQM.

Climate change projections are studied using General Circulation Models (GCMs). GCMs are models that simulate climate on a broad scale, hence they cannot be directly used in local impact studies, such as, for example, hydrological studies. GCMs must go through a process of downscaling, to adjust their results in terms of spatial scale and reduce their bias before being used at the local scale. Quantile Mapping is one of the most widely used approaches for bias correcting GCM climate outputs. However, in its conventional formulation QM assumes a time-invariant correction function, which potentially results in additional biases. This has motivated the development of trend-preserving variations, accounting for a non-stationary correction function and aiming to preserve the raw GCM signal. Unfortunately, choosing which variation to use is not straight-forward. We present the climQMBC package (https://github.com/saedoquililongo/climQMBC or https://doi.org/10.5281/zenodo.18392900) as an easy-to-use tool to compare quantile mapping approaches. climQMBC is available in Python, R and MATLAB, and contains the classic QM method and four trend-preserving variations: Detrended Quantile Mapping (DQM), Quantile Delta Mapping (QDM), Unbiased Quantile Mapping (UQM) and Scaled Distribution Mapping (SDM). This package has a built-in summary report that allows comparing methods in terms of their capability of preserving raw GCM trends. A synthetic exercise showed that the most reliable methods are the UQM and DQM.

To develop intensity-duration-frequency (IDF) curves, it is necessary to calculate annual maximum rainfall intensities for different durations. Traditionally, these intensities have been calculated from the analysis of traces recorded by rain gauges on pluviograph strip charts (PSCs). For many years, these charts have been recorded and analyzed by the personnel who operate and maintain the pluviograph gauges, thus the reliability of the observational analysis depends exclusively on the professional experience of the person performing the analysis. Traditionally, the analyzed PSCs are physically stored in data repository centers. After storing rainfall data on aging paper for many years, the risk of losing rainfall records is very high. Therefore, the conversion of PSC records to digital format is crucial to preserve and improve the historical instrumental data base of these records. We conducted the first “Data Rescue Initiative” (DRI) for central Chile using a pluviograph strip charts reader (PSCR), a tool that uses a scanner-type device combined with digital image processing techniques to estimate maximum rainfall intensities for different durations for each paper band (>80,000 paper bands). On the paper bands, common irregularities associated with excess ink, annotations, or blemishes can affect the scanning process; this system was designed with a semi-automatic module that allows users to edit the detected trace to improve the recognition of the data from each PSC. The PSCR’s semi-automatic characteristics were designed to read many PSCs in a short period of time. The tool also allows for the calculation of rainfall intensities in durations ranging between 15 min to 1 h. This capability improves the value of the data for water infrastructure design, since intense storms of shorter duration often have greater impacts than longer but less intense storms. In this study, the validation of the PSCR against records obtained from observational analysis showed no significant differences between maximum rainfall intensities for durations of 1 h, 6 h, and 24 h.

Debido a los eventos de precipitación extrema provocada por el cambio climático y a la alteración acelerada de las cuencas por el crecimiento poblacional, es importante pronosticar los caudales que generan las cuencas por los eventos de precipitación. El objetivo de este estudio fue predecir caudales horarios en la cuenca del río Huaynamota usando el Filtro de Kalman Discreto (DKF) junto con un modelo autorregresivo con entrada exógena (ARX). Al inicio los parámetros del filtro de Kalman se definen y después se recalculan por periodos definidos, es decir los valores de los parámetros del modelo se actualizan constantemente. El pronóstico de caudales se realizó en seis pasos hacia adelante (L=1, 2, 3, 4, 5 y 6 horas). La cuenca de estudio es parte del río Huaynamota, delimitada por la estación hidrométrica Chapalagana, aguas arriba de la presa Aguamilpa, en Nayarit, México. La cuenca del río Huaynamota es un tributario del río Santiago. Series de datos horarias se emplearon para precipitación y caudal, de agosto a septiembre del 2017. El modelo de pronóstico DKF-ARX mostró índices de eficiencia de Nash-Sutcliffe entre 0.99 y 0.85 con L=1 y L=6, respectivamente. Se concluye que es factible obtener un buen pronóstico de caudales horarios con filtro de Kalman discreto.

The lack of reliable continuous rainfall records can exacerbate the negative impact of extreme storm events. The inability to describe the continuous characteristics of rainfall from storm events increases the likelihood that the design of hydraulic structures will be inadequate. To mitigate extreme storm impacts and improve water governance at the catchment scale, it is vital to improve the availability of data and the array of tools used to model and forecast hydrological processes. In this paper, we describe and discuss the implementation of a web-based system for the estimation of intensity–duration–frequency (IDF) curves (WEBSE_IDF) in Chile. The web platform was constructed using records from 47 pluviographic gauges available in central Chile (30–40° S), with at least 15 years of reliable records. IDF curves can be generated for durations ranging from 15 min to 24 h. In addition, the extrapolation of rainfall intensity from pluviograph to pluviometric gauges (i.e., 24-h rainfall accumulation) can be carried out using the storm index (S_I) method. IDF curves can also be generated for any spatial location within central Chile using the ordinary Kriging method. These procedures allow the generation of numerical and graphical displays of IDF curves, for any selected spatial location, and for any combination of probability distribution function (PDF), parameter estimation method, and type of IDF model. One of the major advantages of WEBSE_IDF is the flexibility of its database, which can be easily modified and saved to generate IDF curves under user-defined scenarios, that is, changing climate conditions. The implementation and validation of WEBSE_IDF serves as a decision support system, providing an important tool for improving the ability of the Chilean government to mitigate the impact of extreme hydrologic events in central Chile. The system is freely available for students, researchers, and other relevant professionals, to improve technical decisions of public and private institutions.