Linking El Niño Southern Oscillation for early drought detection in tropical climates: The Ecuadorian coast
Graphical abstract
Introduction
The tropical atmosphere is characterized by high temperatures and humidity (Hastenrath, 1985). Rain is probably the most varying element in tropical climates. Three general sub-climatic regions can be distinguished: humid (>1800 mm), humid-dry (700–1800 mm) and dry (<700 mm). Humid tropical climates are characterized by temperatures ranging from 24 to 30 °C, with annual fluctuations of approximately 3 °C, whereas dry tropical climates have higher temperatures as a consequence of the intense radiation on the earth surface. Three typical humid-dry climatic regions in South America are: El Cerrado, Llanos and Chaco. Geographically, tropical regions are more or less demarcated by the Tropics of Cancer (23° 27′N) and Capricorn (23° 27′S). Sun angles are high, and therefore, there is only a diurnal variation of 12 to 13 h. Solar radiation affects the hydrological cycle, more directly in the tropics than in other regions of the planet (Latrubesse et al., 2005).
El Niño Southern Oscillation (ENSO) events are usually linked to major flood and drought episodes (Barlow et al., 2001). These extreme hydrometeorological events have the potential to cause devastating impacts on humans and the environment (Pielke and Landsea, 1999; Gómez-Martínez et al., 2018). Several studies have been carried out to find the relationship between ENSO events and droughts, for example, (Wang and Kumar, 2015) found a strong relationship between the southwestern US drought and La Niña during the period 1948–1977, and between southwestern precipitations and El Niño during the period 1978–1999, reaching the conclusion that ENSO can alter precipitation patterns, and therefore, affect southwestern droughts in terms of frequency and intensity. A study carried out in the Sonoran desert, from May to September 2000–2015 (Zolotokrylin et al., 2016), indicates that the probability of wet conditions between May–September was incremented following El Niño and La Niña or the setting of La Niña conditions. Likewise Verbist et al. (2010) concluded that the Coquimbo region, located to the North of Chile, is under a strong influence of El Niño, with a reported precipitation increase during hot weather episodes. However, most of the influence of ENSO on droughts is caused by a decrease in precipitation associated with La Niña (Meza, 2013).
A drought is a natural phenomenon that affects humans much more severely than any other natural events (FAO, 2013). It is ranked first among the natural dangers affecting agriculture, water resources, ecology, and society (Mishra and Singh, 2010). The effects of droughts can vary on an extensive scale, depending on the type of drought and people's vulnerability (Wong et al., 2013). Drought frequency in many countries reduces GDP growth and threatens social development goals (Shiferaw et al., 2014). Over the last decades, Ecuador has incurred losses of more than US$ 4 billion due to drought events (World Food Programme, 2011). In November 2009, dry climatic conditions were reported in the coastal regions of Ecuador (Baringer et al., 2010), mainly affecting the province of Manabí and causing substantial losses in the agricultural sector. Losses have been estimated to have reached US$ 262 million, without taking into account the livestock sector due to the lack of information (Climate, Energy and Tenure Division, Deputy Directory-General Natural Resources, 2010).
The drought early detection helps to implement drought mitigation strategies and measures before they occur (Barua et al., 2015). Reassignment of water resources in a reasonable way allow mitigate the loss of agricultural products due to the drought (Haile, 2005).
Impacts associated with drought have contributed to trend toward improved drought preparedness and policy development (Wilhite et al., 2005). Drought preparedness, and the policies which facilitate its implementation, can increase adaptive capacity and resilience of water resources management (Engle, 2013). The characteristics that constitute effective drought preparedness are different depending on the scale at which one is evaluating it, as well as the sector of interest (Gutiérrez et al., 2014). A methodology developed by (Wilhite, 1991) and revised to incorporate greater emphasis on risk management (Wilhite et al., 2000a, Wilhite et al., 2000b) has provided a set of guidelines or a checklist of the key elements to facilitating the preparation of drought contingency plans which they can be adapted to any level of government. Drought preparedness include three basic categories; monitoring and early warning, vulnerability and impact assessments, and mitigation and response planning and measures (Wilhite et al., 2005). Estrela and Vargas (2012), are highlight the link between the national drought indicator system and the actions to be taken in the drought management plans developed in Spain, show that, they represent strategic tools with positive results in drought warning and impact mitigation respectively.
To limit the adverse impacts of drought are useful structural and non-structural measures e.g., strategic, tactical and emergency measures are use in Spain (CHJ, 2007). Strategic measures are long-term actions of an institutional and infrastructural nature, tactical measures are short-term actions and are intended to conserve resources through improvements in management, joint use of surface and groundwater and voluntary savings in large consumer units. Emergency measures are activated in a state of emergency and are intended to extend the available resources as long as possible, so it is necessary to establish restrictions to lower priority uses and even generalize restrictions in advanced phases. Among structural measures is useful to use e.g., rain-fed water cisterns construction, building dams, aqueducts and pumping stations (Gutiérrez et al., 2014), floodplain storages (De Martino et al., 2012), stormwater capture tanks (De Paola et al., 2013). Freshwater systems much of the world will experience significant stress as a result of climate change, (Kundzewicz et al., 2007) to cope with this, is useful to use measures e.g., De Paola et al. (2015).
Drought does not appear to have a universal definition. (Mishra and Singh, 2010) provide a thorough review on the concept of drought. Various indices have been developed to quantify droughts, some examples of such indices are: The Palmer Drought Severity Index PDSI (Palmer, 1965a, Palmer, 1965b); Rainfall Anomaly Index (RAI) (Van Rooy, 1965); Deciles (Gibbs and Maher, 1967); Crop Moisture Index (CMI) (Palmer, 1968); Bhalme and Mooly drought index (BMDI) (Bhalme and Mooley, 1980); Surface Water Supply Index (SWSI) (Shafer and Dezman, 1982); National Rainfall Index (NRI) (Gommes and Petrassi, 1996). Standardized Precipitation Index (SPI) (McKee et al., 1995); and Reclamation Drought Index (RDI) (Weghorst, 1996); The Soil Moisture Drought Index (SMDI) (Hollinger et al., 1993); and Crop-Specific Drought Index (CSDI) (Meyer and Hubbard, 1995) appeared after CMI. Furthermore, CSDI is divided into a Corn Drought Index (CDI) (Meyer and Pulliam, 1992) and Soybean Drought Index (SDI) (Meyer and Hubbard, 1995); Vegetation Condition Index (VCI) (Liu and Kogan, 1996). In addition to the aforementioned indicators, indices of Penman, 1948; Thornthwaite, 1948; and Keetch and Byram, 1968, have been used in limited cases (Hayes, 1996). A new index has been developed for evaluating drought: The Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano et al., 2009). The self-calibrating Palmer Drought Severity Index (scPDSI) was likewise developed for comparing dry regions with varying drought conditions (Wells et al., 2004). In Spain, an index called Status Index (SI) has been adopted for monitoring operational droughts in hydrological systems (CHJ, 2007; Ortega-Gómez et al., 2018). The most common drought indices are: PDSI, Deciles, CMI, SWSI, SPI or RDI (Pedro-Monzonís et al., 2015).
There is a significant interest in using climate indices for long-term forecasting of regional droughts (Vicente-Serrano et al., 2016). The objective of this research was, therefore, to establish a drought forecasting system, based on ENSO and Drought Indicators in the tropical climate of Manabí River Basin District (MRBD). To characterize the drought was using the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Indices values were validated through press releases addressing droughts in the study area. We correlated the Southern Oscillation Index (SOI), Oceanic Niño Index (ONI), Sea Surface Temperature (SST), and the drought indices SPI and PDSI for estimating the relationship between ENSO events and drought occurrence in the MRBD.
The paper is structured in: Section 2, describing the methodology and data set used in the study; Section 3 presenting the results and discussion, with a special emphasis on the early drought detection based on ENSO and drought indicators; followed by the conclusions in Section 4.
Section snippets
Methodology and dataset
In this study, we used the historical precipitation and temperature series (Oct. 1964–Sep. 2012) to obtain the historical series of drought indices SPI and PDSI and to identify drought occurrence in the MRBD during the same period. Index results were validated with the historical press releases shown in Table 1, available in the Disaster Inventory System (DesInventar in Spanish) from OSSO Corporation – Colombia (https://online.desinventar.org). DesInventar is a conceptual and methodological
Index evaluation and validation
The drought analysis for the MRBD contemplated the period Oct./64–Sep.t/12. SPI was calculated for 3, 6, 9, 12 and 24 months; in a distributed and aggregate form for MRBD (Fig. 4) and CZ, respectively (Fig. 5).
SPI-1, SPI-3 and SPI-6 showed normal conditions for Sep./82 in the NZ and CZ, and reflected drought events in the SZ (Fig. 4), whereas SPI-12 and SPI-24 showed droughts in the CZ and SZ, and normal conditions in the NZ. SPI-1 and SPI-3 showed droughts in the NZ and SZ in Sep.t/01, whereas
Conclusions
The manuscript presents a drought forecasting system, based on ENSO and Drought Indicators in the tropical climate of Manabí River Basin District. The main ENSO climate indices (ONI, SOI, and SST) for this area are used and two drought indices (SPI–PDSI) are developed of this river basin.
The PDSI calculation is based on the results obtained from a calibrated water balance model for this river basin, instead the original water balance model defined by Palmer. The results about drought occurrence
Acknowledgment
The authors thank the Secretariat of Higher Education, Science, Technology and Innovation of Ecuador (Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, SENESCYT) for funding this research in the scholarship program: “CONVOCATORIA ABIERTA 2012, 2ª FASE” (contract 323-2012).
We would also like to express our gratitude to the National Institute of Meteorology and Hydrology (Instituto Nacional de Meteorología e Hidrología – INAMHI) and Geographic Military Institute (Instituto
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