Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework

Chacon-Hurtado, Juan Carlos; Alfonso, Leonardo; Solomatine, Dimitri

doi:10.5194/hess-2016-368

Cited by 15 publications

(22 citation statements)

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“…Several methods have been proposed to accurately design rain gauge networks. These methods have been classified into two categories: measurement‐free and measurement‐based approaches (Chacon‐Hurtado et al ., ). On one hand, measurement‐free methods rely on technical guidelines or recommendations, which focus on the areal coverage per rain gauge (WMO, ; Baltas and Mimikou, ; Wang et al ., ).…”

Section: Introductionmentioning

confidence: 97%

“…On the other hand, measurement‐based methods are based on prior information provided by existing rain gauge networks. These methods have shown to be the most robust and they are widely used for optimizing (augmentation, relocation and reduction) existing networks (Chacon‐Hurtado et al ., ). Such methods include geostatistics (e.g., Pardo‐Igúzquiza, ; Cheng et al ., ; Adhikary et al ., ), cross‐correlation (e.g., Nazaripour and Daneshvar, ), entropy theory (e.g., Yoo et al ., ; Xu et al ., ), model output error (e.g., Volkmann et al ., ; Xu et al ., ) and hybrid methods (Shaghaghian and Abedini, ; Xu et al ., ).…”

Section: Introductionmentioning

confidence: 97%

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

Contreras

Ballari

Bruin

et al. 2018

Intl Journal of Climatology

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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.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

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

Contreras

Ballari

Bruin

et al. 2018

Intl Journal of Climatology

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“…Characterizing hydrologic similarity between catchments enables a deeper understanding on how heterogeneity of the underlying geomorphic and climatic drivers is propagated through the hydrologic cycle and eventually affects spatial patterns of flow regimes (e.g., Doulatyari et al, ; Schaefli et al, ; Lahaa et al, ). Exploring the spatial structure of streamflows has been suggested to play an important role in a number of fields, ranging from the expansion of existing hydrometric networks (Chacon‐Hurtado et al, ; Messinger & Paybins, ) to the identification of spatial patterns of ecological variables along stream networks (Isaak et al, ; Mc Guire et al, ). Furthermore, it has been shown that streamflow correlation represents a better surrogate than spatial proximity to individuate river sections characterized by analogous flow dynamics (Archfield & Vogel, ).…”

Section: Introductionmentioning

confidence: 99%

What Do They Have in Common? Drivers of Streamflow Spatial Correlation and Prediction of Flow Regimes in Ungauged Locations

Betterle

Radny

Schirmer

et al. 2017

Water Resources Research

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The spatial correlation of daily streamflows represents a statistical index encapsulating the similarity between hydrographs at two arbitrary catchment outlets. In this work, a process‐based analytical framework is utilized to investigate the hydrological drivers of streamflow spatial correlation through an extensive application to 78 pairs of stream gauges belonging to 13 unregulated catchments in the eastern United States. The analysis provides insight on how the observed heterogeneity of the physical processes that control flow dynamics ultimately affect streamflow correlation and spatial patterns of flow regimes. Despite the variability of recession properties across the study catchments, the impact of heterogeneous drainage rates on the streamflow spatial correlation is overwhelmed by the spatial variability of frequency and intensity of effective rainfall events. Overall, model performances are satisfactory, with root mean square errors between modeled and observed streamflow spatial correlation below 10% in most cases. We also propose a method for estimating streamflow correlation in the absence of discharge data, which proves useful to predict streamflow regimes in ungauged areas. The method consists in setting a minimum threshold on the modeled flow correlation to individuate hydrologically similar sites. Catchment outlets that are most correlated ( ρ>0.9) are found to be characterized by analogous streamflow distributions across a broad range of flow regimes.

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“…Network improvements can be made through augmentation or relocation of gauges to improve performance (Barca et al, 2015), while reduction of gauges is often considered to conserve resources (Dong et al, 2005). Coulibaly (2009) andChacon-Hurtado et al (2016) provide detailed literature reviews of hydrometric network design methods. Shannon's information theory (Shannon, 1948) and multi-objective optimization algorithms such as those used in Deb et al (2002) and Kollat et al (2008) have been combined to form methods of maximizing the information content of a network while minimizing redundant information (Alfonso et al, 2010;Li et al, 2012;Samuel et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Mishra and Coulibaly () and Chacon‐Hurtado et al . () provide detailed literature reviews of hydrometric network design methods. Shannon's information theory (Shannon, ) and multi‐objective optimization algorithms such as those used in Deb et al .…”

Section: Introductionmentioning

confidence: 99%

Assessing Spatial Scale Effects on Hydrometric Network Design Using Entropy and Multi‐objective Methods

Werstuck

Coulibaly

2017

J American Water Resour Assoc

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In order to facilitate water resources decisions, it is important that accurate and informative hydrometric data are collected. Combining information theory with multi-objective optimization has led to methods of optimizing the information content provided by hydrometric networks; however, there is no available study on the effects of spatial scale and data limitation on these methods. Herein, a dual entropy multi-objective optimization (DEMO) and a transinformation (TI) analysis were done to recommend optimal locations for additional hydrometric stations in the Madawaska Watershed. This analysis was designed to be comparative to a similar study conducted on the Ottawa River Basin which encompasses the Madawaska Watershed to allow for an investigation of the spatial scale effects in this type of network design. This study concludes that TI analysis is not adversely affected by scaling; however, the DEMO analysis is sensitive to the placement of potential station locations and the size of the study area. This study also examines the benefit of including nearby stations when the area of interest does not have a sufficient number of existing hydrometric stations for analysis. It is shown that these stations can provide useful information because their inclusion in the analysis increased the average TI in the watershed. Recommendations were made as to the ideal locations of additional stations in the Madawaska Watershed hydrometric network.(KEY TERMS: entropy; hydrometric network; multi-objective optimization; network design; water resources; spatial scale.) Werstuck, Connor and Paulin Coulibaly, 2018. Assessing Spatial Scale Effects on Hydrometric Network Design Using Entropy and Multi-objective Methods.

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Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework

Cited by 15 publications

References 0 publications

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

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

What Do They Have in Common? Drivers of Streamflow Spatial Correlation and Prediction of Flow Regimes in Ungauged Locations

Assessing Spatial Scale Effects on Hydrometric Network Design Using Entropy and Multi‐objective Methods

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