Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon

Abstract

Rain gauge networks are crucial for enhancing the spatio-temporal characterization of precipitation. In tropical regions, scarcity of rain gauge data, climatic variability, and variable spatial accessibility make conventional approaches to design rain gauge networks inadequate and impractical. In this study, we propose the use of conditioned Latin hypercube sampling (cLHS) method with multi-temporal layers of remotely sensed precipitation measurements for capturing the spatio-temporal precipitation patterns in ungauged areas. The study was conducted in the Amazon region of Ecuador, for which monthly precipitation averages were derived based on a 16-year period of Tropical Rainfall Measuring Mission (TRMM 3B43 V7) data which were used as prior information to select representative sampling points through cLHS. Two scenarios for the sampling design were considered and evaluated, one without and one with restrictions on accessible sites according to the proximity to roads and settlements. Results showed that both optimized networks captured the variability of precipitation according to the TRMM climatology. Furthermore, evaluation against an independent satellite precipitation data set showed that the optimized networks support mapping precipitation based on ordinary kriging (OK). Comparison with regular and random sampling methods showed that particularly when a practical scenario is considered, the optimized network provided more reliable results over time, highlighting the suitability of the network to capture temporal changes and map precipitation with high accuracy. The proposed approach could be easily adopted in other ungauged and poorly accessible regions for rain gauge network design as well as to the design of multi-objective monitoring networks.

Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?

“…cHLS is a sampling strategy in which x new sites are chosen such that the multivariate distribution is maximally stratified (Minasny & McBratney, 2006). While cLHS has mostly been used within digital soil mapping (e.g., Chu et al, 2010; Mulder et al, 2013; Silva et al, 2015; Stumpf et al, 2016), some studies have utilized cLHS for other environmental applications (e.g., Contreras et al, 2019; Lin et al, 2009; Villarreal et al, 2019; Yin et al, 2016, 2017). We applied the cLHS algorithm to identify 3000 sites around the globe (because SRDBv5 has >3000 data entries) in locations that maximize the representation of the multivariate space of the previously selected environmental covariates used to model Rs.…”

How to compare sampling designs for mapping?

“…In our case studies, SRS and cLHS were equivalent in terms of map accuracy. Several studies (e.g., Castro‐Franco, Costa, Peralta, & Aparicio, 2015; Chu, Lin, Jang, & Chang, 2010; Contreras, Ballari, De Bruin, & Samaniego, 2019; Domenech, Castro‐Franco, Costa, & Amiotti, 2017; Schmidt et al, 2014) concluded that cLHS in combination with kriging or random forest for mapping gave the most accurate prediction. These studies promote the use of cLHS as an effective sampling design for mapping.…”

Mountain Riverine Floods in Ecuador: Issues, Challenges, and Opportunities

“…Systematic monitoring of flood events is necessary for the validation of flood risk and hazard models that will help in the decision-making process. Since a high spatial and temporal variability of rainfall is expected within the Andean mountain range, efficient distribution of new types of hydrometeorological networks is required [e.g., Contreras et al (2019)] to reduce investment and maintenance costs, but without compromising modeling accuracy (Sucozhañay and Célleri, 2018). In recent years, citizen science has shown an increasing potential for hydrological data collection in remotes areas such as mountains by applying simple downloading procedures from What is required to assess, improve, and innovate flood recovery plans (e.g., "build back better" practices)?…”