Showing papers by "Juan B. Valdés published in 2018"
TL;DR: In this paper, the authors analyzed low-frequency patterns of precipitation in Chile (>30 years), and their relationship to global Sea Surface Temperatures (SSTs), with special focus on associations with the Pacific Decadal Oscillation (PDO) and the Atlantic Multi-decal Oscillations (AMO) indices.
45 citations
TL;DR: In this article, the impacts of a range of short-term climate change scenarios (2020-2050) on the hydrology of the Mara River Basin in East Africa using a new high-resolution (025°) daily climate dataset was assessed.
12 citations
TL;DR: The implementation and validation of WEBSEIDF 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.
Abstract: 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 (WEBSEIDF) 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 (SI) 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 WEBSEIDF 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 WEBSEIDF 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.
7 citations
Journal Article•
TL;DR: In this paper, the authors used the Discrete Kalman Filter Algorithm (DKF) to forecast the SPI and SPEI drought indices for 14 meteorological stations in the Fuerte River watershed in northwest Mexico.
Abstract: The monitoring and forecasting of droughts are important to evaluate risks, take decisions, as well as undertake effective and timely actions to avoid and reduce
their negative effects. Therefore, the objective of this study was to forecast the SPI (Standard Precipitation Index) and SPEI (Standard Precipitation Evapotranspiration Index) drought
indices for 14 meteorological stations in the Fuerte River watershed in northwest Mexico. Our hypothesis was that it is possible to achieve such objective through the implementation of the
Discrete Kalman filter algorithm (DKF). The Fuerte River watershed, Sinaloa, Mexico, is important for its agricultural production and generation of hydroelectric power. We did the forecast of
the SPI and SPEI drought indices for time scales (drought durations) of 3, 6, 12 and 24 months, during the period 1961-2011, and with 1, 2, 3 and 4 months in advance. Two models were
implemented using the Discrete Kalman filter: a second-order autoregressive (DKF-AR2), and a second-order autoregressive with exogenous input (DKF-ARX). The climatic variables tested as
exogenous were precipitation (Pt), maximum and minimum temperatures (Tmax and Tmin) and reference evapotranspiration (ET0 ); the exogenous variable precipitation, Pt, recorded better results.
The DKF-AR2 methodology presented the best result in the forecast of the indices for six stations located in the upper part of the watershed, with predominance of temperate and semi-cold
climates. The DKF-ARX-Pt methodology proved better in the remaining eight stations of the middle and lower parts, located in warm climates. The best forecasts were obtained for scales
(drought durations) of 12 and 24 months, and the SPEI forecast was better than that of SPI. The Nash-Sutcliffe indices (E) for 12 and 24 months reached up to 0.92 and 0.96; in the case of 3
and 6 months, the NashSutcliffe indices were approximately 0.5. The anticipation of the prognosis was better for 1 and 2 months.
1 citations