Evaluation of Early Action Mechanisms in Peru Regarding Preparedness for El Niño

Abstract

In this article, we provide an impact evaluation of an intervention in Peru regarding preparedness for El Niño impacts in Picsi District of Chiclayo Province in Peru's northwestern coastal Lambayeque region. This effort involved the provision of special kits that reduce the potential damage to homes as a consequence of rainfall and floods associated with an El Niño-Southern Oscillation event. Information was collected in 2016 when this Forecast-based Financing early action was activated by an El Niño forecast, and after a coastal El Niño actually struck in 2017. This dual database permits us to estimate the impact of the intervention on the damage level of homes by comparing those homes supported by the program with those homes not receiving pilot-program support. This comparison is achieved by using propensity score matching techniques, which identify the most comparable homes to the ones that were supported by the intervention. The main findings of the study suggest a positive impact of the program in terms of its effectiveness in mitigating the damage caused by the 2017 El Niño. These results suggest a drop in the scale of house damage (less damage) by around 63% for a home that received the modular kit treatment. When considering other specifications of the model, the decrease in the scale of house damage improves up to approximately 66%.

Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness

“…Empirical evidence demonstrates that actions taken in advance of a disaster can reduce loss of life and result in cost savings for relief organizations (Aguirre et al, 2019;Braman et al, 2013;Golnaraghi, 2012;Gros et al, 2019).…”

Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness in Peru

“…While no strict definition for FbA exists, the term generally refers to initiatives that provide assistance and allocation of resources for preparation in advance of disasters based on hydro-climate forecasts (Wilkinson et al, 2018). Empirical evidence demonstrates that actions taken in advance of a disaster can reduce loss of life and result in cost savings for relief organizations (Aguirre et al, 2019;Braman et al, 2013;Golnaraghi, 2012;Gros et al, 2019).…”

“…Because these methods are prone to multicollinearity due to the overlapping signals present in many hydroclimate variables, techniques such as principal component regression (PCR; a combination of principal component analysis and MLR) and partial least-squares regression (e.g., Lala et al, 2020) are employed to address this challenge. More recently, machine learning techniques, adept at capturing nonlinear relationships between predictors and a predictand, have been successfully applied to hydroclimate forecasting, including artificial neural networks (Zealand et al, 1999), random forest classification (Ali et al, 2020;Lala et al, 2020), and support-vector machines (Asefa et al, 2006;Shabri and Suhartono, 2012). There is also increasing recognition that hybrid approaches combining statistical and dynamical techniques can offer greater accuracy than even state-of-the-art dynamical models (Cohen et al, 2019).…”

ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru

“…Peru is one of the most affected countries, given its coastal location and exposure [3,[7][8][9][10][11]. Between 1980 and 2008 there were 495,000 people affected by floods (equal to 15% of the total impacted by 2 of 34 natural disasters) and at least 23% of the Peruvian population lives in flood-prone areas.…”

“…Various studies associate "El Niño events" with "anomalous" rains that occurred in Lima and its hydrological basin over the past four and a half centuries [18][19][20][21][22][23]. The effect due to ENSO sums up with other different predisposing factors (natural, geological and anthropogenic) and, consequentially, flooding risk is of great concern [9,11,[24][25][26][27][28]. Villacorta et al [28] conducted a spatial analysis of the geological hazards in the metropolitan area of Lima and the Callao region, aiming to interpret the evolution of the geomorphological landscape, identify processes that can cause disasters, and propose prevention and mitigation measures.…”