VD
V. Diaz Mercado
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17 records found
1
Journal article
(2026)
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Vitali Diaz, Ahmed A. Osman, Gerald A. Corzo Perez, Shreedhar Maskey, D.P. Solomatine
More severe and prolonged droughts observed in recent decades require improved methods to predict impacts on agriculture. Crop-growth models estimate yield and plant development variables and are widely used to assess drought impacts; however, they are not explicit forecasting tools, as their accuracy is constrained by physical assumptions, data availability, and multiple sources of uncertainty. To address these limitations, machine learning (ML) models have been increasingly applied for crop yield prediction, typically using drought indices as input, while spatial drought characteristics remain underexplored. This research develops an ML framework that incorporates the spatial extent of drought to predict seasonal crop yield. The framework combines artificial neural network (ANN) and polynomial regression (PR) models, with PR providing baseline estimates and ANN delivering refined predictions. The approach was tested using 50 years of historical crop yield data and drought areas derived from the Standardised Precipitation Evapotranspiration Index at multiple aggregation periods (1–12 months). Results show ANN models consistently outperform PR models, achieving lower prediction errors, with root mean square error values as low as 48.1 kg/ha in best-performing cases. The results demonstrate that spatiotemporal drought area dynamics and their temporal aggregation provide an effective preprocessing strategy for ML-based drought impact prediction.
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More severe and prolonged droughts observed in recent decades require improved methods to predict impacts on agriculture. Crop-growth models estimate yield and plant development variables and are widely used to assess drought impacts; however, they are not explicit forecasting tools, as their accuracy is constrained by physical assumptions, data availability, and multiple sources of uncertainty. To address these limitations, machine learning (ML) models have been increasingly applied for crop yield prediction, typically using drought indices as input, while spatial drought characteristics remain underexplored. This research develops an ML framework that incorporates the spatial extent of drought to predict seasonal crop yield. The framework combines artificial neural network (ANN) and polynomial regression (PR) models, with PR providing baseline estimates and ANN delivering refined predictions. The approach was tested using 50 years of historical crop yield data and drought areas derived from the Standardised Precipitation Evapotranspiration Index at multiple aggregation periods (1–12 months). Results show ANN models consistently outperform PR models, achieving lower prediction errors, with root mean square error values as low as 48.1 kg/ha in best-performing cases. The results demonstrate that spatiotemporal drought area dynamics and their temporal aggregation provide an effective preprocessing strategy for ML-based drought impact prediction.
Three-Dimensional Clustering in the Characterization of Spatiotemporal Drought Dynamics
Cluster Size Filter and Drought Indicator Threshold Optimization
Book chapter
(2024)
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Vitali Diaz, Gerald A. Corzo Perez, Henny A.J. Van Lanen, Dimitri P. Solomatine
In its three-dimensional (3-D) characterization, drought is an event whose spatial extent changes over time. Each drought event has an onset and end time, a location, a magnitude, and a spatial trajectory. These characteristics help to analyze and describe how drought develops in space and time (i.e., drought dynamics). Methodologies for 3-D characterization of drought include a 3-D clustering technique to extract the drought events from the hydrometeorological data. The application of the clustering method yields small artifact droughts. These small clusters are removed from the analysis with the use of a cluster size filter. However, according to the literature, the filter parameters are usually set arbitrarily, so this study concentrated on a method to calculate the optimal cluster size filter for the 3-D characterization of drought. The effect of different drought indicator thresholds to calculate drought is also analyzed. The approach was tested in South America with data from the Latin American Flood and Drought Monitor for 1950–2017. Analysis of the spatial trajectories and characteristics of the most extreme droughts is also included. Calculated droughts are compared with information reported at a country scale and a reasonably good match is found.
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In its three-dimensional (3-D) characterization, drought is an event whose spatial extent changes over time. Each drought event has an onset and end time, a location, a magnitude, and a spatial trajectory. These characteristics help to analyze and describe how drought develops in space and time (i.e., drought dynamics). Methodologies for 3-D characterization of drought include a 3-D clustering technique to extract the drought events from the hydrometeorological data. The application of the clustering method yields small artifact droughts. These small clusters are removed from the analysis with the use of a cluster size filter. However, according to the literature, the filter parameters are usually set arbitrarily, so this study concentrated on a method to calculate the optimal cluster size filter for the 3-D characterization of drought. The effect of different drought indicator thresholds to calculate drought is also analyzed. The approach was tested in South America with data from the Latin American Flood and Drought Monitor for 1950–2017. Analysis of the spatial trajectories and characteristics of the most extreme droughts is also included. Calculated droughts are compared with information reported at a country scale and a reasonably good match is found.
Exploiting big point clouds
Unveiling insights for sustainable development through change detection in the built environment
Conference paper
(2024)
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V. Diaz Mercado, P.J.M. van Oosterom, B.M. Meijers, E. Verbree, Nauman Ahmed, Thijs van Lankveld
Change detection in the built environment is essential for sustainable development practices including Urban Planning and Development, Environmental Monitoring, and Conservation. Change detection provides valuable insights into dynamic processes, facilitates informed decision making, and supports sustainable development initiatives. Point clouds serve as foundational data sources for change detection in built environments, enabling analysts to detect, quantify, and interpret spatial changes with unparalleled accuracy and granularity. By leveraging the inherent characteristics of point clouds, researchers and practitioners can gain valuable insights into dynamic processes, inform decision making, and foster sustainable development in an ever-evolving built environment. We present the preliminary results of cloud-to-cloud (c2c) distance calculations for further change detection analysis of the entire Netherlands. This study utilises point cloud data from AHN2, 3, and 4 (Actueel Hoogtebestand Nederland1, The Netherlands). A method based on a 3D space-filling curve (SFC) was developed to calculate the c2c distances between AHN2, 3, and 4. This SFC method will allow change detection analysis to be carried out for the entire Netherlands. The change detection analysis outcomes can be accessed for future analysis in Potree2, a web-based point cloud rendered for large point clouds. The final implementation will allow the visualisation of AHN point clouds and their attributes, among which is the change in detection-related information. This research contributes to sustainable development practices by offering enhanced spatial insights and informed decision-making tools for further analysis and monitoring of the (built) environment in the Netherlands. [...]
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Change detection in the built environment is essential for sustainable development practices including Urban Planning and Development, Environmental Monitoring, and Conservation. Change detection provides valuable insights into dynamic processes, facilitates informed decision making, and supports sustainable development initiatives. Point clouds serve as foundational data sources for change detection in built environments, enabling analysts to detect, quantify, and interpret spatial changes with unparalleled accuracy and granularity. By leveraging the inherent characteristics of point clouds, researchers and practitioners can gain valuable insights into dynamic processes, inform decision making, and foster sustainable development in an ever-evolving built environment. We present the preliminary results of cloud-to-cloud (c2c) distance calculations for further change detection analysis of the entire Netherlands. This study utilises point cloud data from AHN2, 3, and 4 (Actueel Hoogtebestand Nederland1, The Netherlands). A method based on a 3D space-filling curve (SFC) was developed to calculate the c2c distances between AHN2, 3, and 4. This SFC method will allow change detection analysis to be carried out for the entire Netherlands. The change detection analysis outcomes can be accessed for future analysis in Potree2, a web-based point cloud rendered for large point clouds. The final implementation will allow the visualisation of AHN point clouds and their attributes, among which is the change in detection-related information. This research contributes to sustainable development practices by offering enhanced spatial insights and informed decision-making tools for further analysis and monitoring of the (built) environment in the Netherlands. [...]
Abstract
(2024)
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Vitali Diaz, Peter van Oosterom, Martijn Meijers, Edward Verbree, Nauman Ahmed, Thijs van Lankveld
The advantages of using point clouds for change detection analysis include comprehensive spatial and temporal representation, as well as high precision and accuracy in the calculations. These benefits make point clouds a powerful data type for spatio-temporal analysis. Nevertheless, most current change detection methods have been specifically designed and utilized for raster data. This research aims to identify the most suitable cloud-to-cloud (c2c) distance calculation algorithm for further implementation in change detection for spatio-temporal point clouds. Eight different methods, varying in complexity and execution time, are compared without converting the point cloud data into rasters. Hourly point cloud data from monitoring a beach-dune system's dynamics is used to carry out the comparison. The c2c distance methods are (1) the nearest neighbor, (2) least squares plane, (3) linear interpolation, (4) quadratic (height function), (5) 2.5D triangulation, (6) natural neighbor interpolation (NNI), (7) inverse distance weight (IDW) and (8) multiscale model to model cloud comparison (M3C2). We evaluate these algorithms, considering both the accuracy of the calculated distance and the execution time. The results can be valuable for analyzing and monitoring the (build) environment with spatio-temporal point cloud data.
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The advantages of using point clouds for change detection analysis include comprehensive spatial and temporal representation, as well as high precision and accuracy in the calculations. These benefits make point clouds a powerful data type for spatio-temporal analysis. Nevertheless, most current change detection methods have been specifically designed and utilized for raster data. This research aims to identify the most suitable cloud-to-cloud (c2c) distance calculation algorithm for further implementation in change detection for spatio-temporal point clouds. Eight different methods, varying in complexity and execution time, are compared without converting the point cloud data into rasters. Hourly point cloud data from monitoring a beach-dune system's dynamics is used to carry out the comparison. The c2c distance methods are (1) the nearest neighbor, (2) least squares plane, (3) linear interpolation, (4) quadratic (height function), (5) 2.5D triangulation, (6) natural neighbor interpolation (NNI), (7) inverse distance weight (IDW) and (8) multiscale model to model cloud comparison (M3C2). We evaluate these algorithms, considering both the accuracy of the calculated distance and the execution time. The results can be valuable for analyzing and monitoring the (build) environment with spatio-temporal point cloud data.
Comparison of Cloud-to-Cloud Distance Calculation Methods
Is the Most Complex Always the Most Suitable?
Conference paper
(2024)
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Vitali Diaz, Peter van Oosterom, B.M. Meijers, Edward Verbree, Nauman Ahmed, Thijs van Lankveld
Cloud-to-cloud (C2C) distance calculations are frequently performed as an initial stage in change detection and spatiotemporal analysis with point clouds. There are various methods for calculating C2C distance, also called inter-point distance, which refers to the distance between two corresponding point clouds captured at different epochs. These methods can be classified from simple to complex, with more steps and calculations required for the latter. Generally, it is assumed that a more complex method will result in a more precise calculation of inter-point distance, but this assumption is rarely evaluated. This paper compares eight commonly used methods for calculating the inter-point distance. The results indicate that the accuracy of distance calculations depends on the chosen method and a characteristic related to the point density, the intra-point distance, which refers to the distance between points within the same point cloud. The results are helpful for applications that analyze spatiotemporal point clouds for change detection. The findings will be helpful in future applications, including analyzing spatiotemporal point clouds for change detection.
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Cloud-to-cloud (C2C) distance calculations are frequently performed as an initial stage in change detection and spatiotemporal analysis with point clouds. There are various methods for calculating C2C distance, also called inter-point distance, which refers to the distance between two corresponding point clouds captured at different epochs. These methods can be classified from simple to complex, with more steps and calculations required for the latter. Generally, it is assumed that a more complex method will result in a more precise calculation of inter-point distance, but this assumption is rarely evaluated. This paper compares eight commonly used methods for calculating the inter-point distance. The results indicate that the accuracy of distance calculations depends on the chosen method and a characteristic related to the point density, the intra-point distance, which refers to the distance between points within the same point cloud. The results are helpful for applications that analyze spatiotemporal point clouds for change detection. The findings will be helpful in future applications, including analyzing spatiotemporal point clouds for change detection.
Drought directly impacts the living organisms and environment, and thereby, its assessment is essential. Different drought indices require different data, which can be obtained based on models or in-situ measurements, demanding a significant amount of effort. Using remotely sensed (RS) data from satellites can facilitate this data acquisition. Nowadays, more and more satellite techniques are rising, highlighting the need to assess the accuracy of their data and the reliability of the results obtained by employing them. The Wet-environment Evapotranspiration Precipitation Standardized Index (WEPSI) has shown good performance in drought monitoring and assessment, especially for agricultural purposes. This chapter employs the Global Land Evaporation Amsterdam Model (GLEAM) data to investigate its applicability in the Lempa River basin drought assessment using WEPSI. In this order, evaluated data obtained from the Water Evaluation and Planning system (WEAP) were used as the basis for comparison. Precisely, a comparison was made with GLEAM and WEAP-based data as well as WEPSI time series based on these two datasets. The results show a relatively high similarity between these two datasets and calculated WEPSI drought indices. This validates the good performance of GLEAM-based data in drought monitoring and assessment based on WEPSI.
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Drought directly impacts the living organisms and environment, and thereby, its assessment is essential. Different drought indices require different data, which can be obtained based on models or in-situ measurements, demanding a significant amount of effort. Using remotely sensed (RS) data from satellites can facilitate this data acquisition. Nowadays, more and more satellite techniques are rising, highlighting the need to assess the accuracy of their data and the reliability of the results obtained by employing them. The Wet-environment Evapotranspiration Precipitation Standardized Index (WEPSI) has shown good performance in drought monitoring and assessment, especially for agricultural purposes. This chapter employs the Global Land Evaporation Amsterdam Model (GLEAM) data to investigate its applicability in the Lempa River basin drought assessment using WEPSI. In this order, evaluated data obtained from the Water Evaluation and Planning system (WEAP) were used as the basis for comparison. Precisely, a comparison was made with GLEAM and WEAP-based data as well as WEPSI time series based on these two datasets. The results show a relatively high similarity between these two datasets and calculated WEPSI drought indices. This validates the good performance of GLEAM-based data in drought monitoring and assessment based on WEPSI.
Journal article
(2023)
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Carmelo Cammalleri, Juan Camilo Acosta Navarro, Davide Bavera, Vitali Diaz, Chiara Di Ciollo, Willem Maetens, Diego Magni, Dario Masante, Jonathan Spinoni, Andrea Toreti
Droughts evolve in space and time without following borders or pre-determined temporal constraints. Here, we present a new database of drought events built with a three-dimensional density-based clustering algorithm. The chosen approach is able to identify and characterize the spatio-temporal evolution of drought events, and it was tuned with a supervised approach against a set of past global droughts characterized independently by multiple drought experts. About 200 events were detected over Europein the period 1981-2020 using SPI-3 (3-month cumulated Standardized Precipitation Index) maps derived from the ECMWF (European Centre for Medium-range Weather Forecasts) 5th generation reanalysis (ERA5) precipitation. The largest European meteorological droughts during this period occurred in 1996, 2003, 2002 and 2018. A general agreement between the major events identified by the algorithm and drought impact records was found, as well as with previous datasets based on pre-defined regions.
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Droughts evolve in space and time without following borders or pre-determined temporal constraints. Here, we present a new database of drought events built with a three-dimensional density-based clustering algorithm. The chosen approach is able to identify and characterize the spatio-temporal evolution of drought events, and it was tuned with a supervised approach against a set of past global droughts characterized independently by multiple drought experts. About 200 events were detected over Europein the period 1981-2020 using SPI-3 (3-month cumulated Standardized Precipitation Index) maps derived from the ECMWF (European Centre for Medium-range Weather Forecasts) 5th generation reanalysis (ERA5) precipitation. The largest European meteorological droughts during this period occurred in 1996, 2003, 2002 and 2018. A general agreement between the major events identified by the algorithm and drought impact records was found, as well as with previous datasets based on pre-defined regions.
Conference paper
(2022)
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Vitali Diaz, Haicheng Liu, Peter van Oosterom, Martijn Meijers, Edward Verbree, Fedor Baart, Maarten Pronk, Thijs van Lankveld
Point cloud is made up of a multitude of three-dimensional (3D) points with one or more attributes attached. Point cloud is the third data paradigm in addition to the well-established object (vector) and gridded (raster) representations, since point cloud data can be directly collected, computed, stored, and analyzed without converting to other types. Modern ways of data acquisition, including laser scanning from airborne, mobile, or static platforms, multi-beam echo-sounding, and dense image matching from photos, generate millions to trillions of 3D points with attached attributes. If the collection is carried out in different periods, one of the essential attributes is precisely time, allowing spatiotemporal analysis to be performed. Its use is widespread in some fields such as metrology and quality inspection, virtual reality, indoor/outdoor navigation, object detection, vegetation monitoring, building modeling, cultural heritage, and diverse visualization applications. There are some examples in fields related to hydroinformatics, mainly related to terrain modeling. Due to its nature of big data, over the past decades, a series of developments have been carried out in the different processing chains for the optimal use of point cloud. This research seeks to introduce the various point cloud developments from which the hydroinformatics community and research could benefit. A review of recent advances is made, mainly including the analysis and visualization of point cloud for dealing with water-related problems. Potential areas of application and development in hydroinformatics are identified. These include, for example, the topics of coastal monitoring, coastal erosion, shallow water assessment, ice sheet change analysis, sea-level rise assessment, monitoring of levels in water bodies, crop and vegetation monitoring, analysis of the effects of groundwater depletion, detail tracing of basins and channels, analysis of floods with detailed terrain models, and drought monitoring in crops and forests. The challenges to overcome and ongoing developments regarding point cloud application in hydroinformatics are also discussed.
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Point cloud is made up of a multitude of three-dimensional (3D) points with one or more attributes attached. Point cloud is the third data paradigm in addition to the well-established object (vector) and gridded (raster) representations, since point cloud data can be directly collected, computed, stored, and analyzed without converting to other types. Modern ways of data acquisition, including laser scanning from airborne, mobile, or static platforms, multi-beam echo-sounding, and dense image matching from photos, generate millions to trillions of 3D points with attached attributes. If the collection is carried out in different periods, one of the essential attributes is precisely time, allowing spatiotemporal analysis to be performed. Its use is widespread in some fields such as metrology and quality inspection, virtual reality, indoor/outdoor navigation, object detection, vegetation monitoring, building modeling, cultural heritage, and diverse visualization applications. There are some examples in fields related to hydroinformatics, mainly related to terrain modeling. Due to its nature of big data, over the past decades, a series of developments have been carried out in the different processing chains for the optimal use of point cloud. This research seeks to introduce the various point cloud developments from which the hydroinformatics community and research could benefit. A review of recent advances is made, mainly including the analysis and visualization of point cloud for dealing with water-related problems. Potential areas of application and development in hydroinformatics are identified. These include, for example, the topics of coastal monitoring, coastal erosion, shallow water assessment, ice sheet change analysis, sea-level rise assessment, monitoring of levels in water bodies, crop and vegetation monitoring, analysis of the effects of groundwater depletion, detail tracing of basins and channels, analysis of floods with detailed terrain models, and drought monitoring in crops and forests. The challenges to overcome and ongoing developments regarding point cloud application in hydroinformatics are also discussed.
Journal article
(2021)
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Emmanouil A. Varouchakis, Dionissios T. Hristopulos, George P. Karatzas, Gerald A. Corzo Perez, Vitali Diaz
Precipitation data are useful for the management of water resources as well as flood and drought events. However, precipitation monitoring is sparse and often unreliable in regions with complicated geomorphology. Subsequently, the spatial variability of the precipitation distribution is frequently represented incorrectly. Satellite precipitation data provide an attractive supplement to ground observations. However, satellite data involve errors due to the complexity of the retrieval algorithms and/or the presence of obstacles that affect the infrared observation capability. This work presents a methodology that combines satellite and ground observations leading to improved spatiotemporal mapping and analysis of precipitation. The applied methodology is based on space-time regression kriging. The case study refers to the island of Crete, Greece, for the time period of 2010-2018. Precipitation data from 53 stations are used in combination with satellite images for the reference period. This work introduces an improved spatiotemporal approach for precipitation mapping.
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Precipitation data are useful for the management of water resources as well as flood and drought events. However, precipitation monitoring is sparse and often unreliable in regions with complicated geomorphology. Subsequently, the spatial variability of the precipitation distribution is frequently represented incorrectly. Satellite precipitation data provide an attractive supplement to ground observations. However, satellite data involve errors due to the complexity of the retrieval algorithms and/or the presence of obstacles that affect the infrared observation capability. This work presents a methodology that combines satellite and ground observations leading to improved spatiotemporal mapping and analysis of precipitation. The applied methodology is based on space-time regression kriging. The case study refers to the island of Crete, Greece, for the time period of 2010-2018. Precipitation data from 53 stations are used in combination with satellite images for the reference period. This work introduces an improved spatiotemporal approach for precipitation mapping.
Spatio-Temporal Characterisation of Drought
Data Analytics, Modelling, Tracking, Impact and Prediction
Studies of drought have increased in light of new data availability and advances in spatio-temporal analysis. However, the following gaps still need to be filled: 1) methods to characterise drought that explicitly consider its spatio-temporal features, such as spatial extent (area) and pathway; 2) methods to monitor and predict drought that include the above-mentioned characteristics and 3) approaches for visualising and analysing drought characteristics to facilitate interpretation of its variation. This research aims to explore, analyse and propose improvements to the spatio-temporal characterisation of drought. Outcomes provide new perspectives towards better prediction.
The following objectives were proposed. 1) Improve the methodology for characterising drought based on the phenomenon’s spatial features. 2) Develop a visual approach to analysing drought variations. 3) Develop a methodology for spatial drought tracking. 4) Explore machine learning (ML) techniques to predict crop-yield responses to drought. The four objectives were addressed and results are presented.
Finally, a scope was formulated for integrating ML and the spatio-temporal analysis of drought. Proposed scope opens a new area of potential for drought prediction (i.e. predicting spatial drought tracks and areas). It is expected that the drought tracking and prediction method will help populations cope with drought and its severe impacts.
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Studies of drought have increased in light of new data availability and advances in spatio-temporal analysis. However, the following gaps still need to be filled: 1) methods to characterise drought that explicitly consider its spatio-temporal features, such as spatial extent (area) and pathway; 2) methods to monitor and predict drought that include the above-mentioned characteristics and 3) approaches for visualising and analysing drought characteristics to facilitate interpretation of its variation. This research aims to explore, analyse and propose improvements to the spatio-temporal characterisation of drought. Outcomes provide new perspectives towards better prediction.
The following objectives were proposed. 1) Improve the methodology for characterising drought based on the phenomenon’s spatial features. 2) Develop a visual approach to analysing drought variations. 3) Develop a methodology for spatial drought tracking. 4) Explore machine learning (ML) techniques to predict crop-yield responses to drought. The four objectives were addressed and results are presented.
Finally, a scope was formulated for integrating ML and the spatio-temporal analysis of drought. Proposed scope opens a new area of potential for drought prediction (i.e. predicting spatial drought tracks and areas). It is expected that the drought tracking and prediction method will help populations cope with drought and its severe impacts.
Spatiotemporal drought risk assessment considering resilience and heterogeneous vulnerability factors
Lempa transboundary river basin in the central american dry corridor
Drought characterization and risk assessment are of great significance due to drought’s negative impact on human health, economy, and ecosystem. This paper investigates drought characterization and risk assessment in the Lempa River basin in Central America. We applied the Standardized Evapotranspiration Deficit Index (SEDI) for drought characterization and drought hazard index (DHI) calculation. Although SEDI’s applicability is theoretically proven, it has been rarely applied. Drought risk is generally derived from the interactions between drought hazard (DHI) and vulnerability (DVI) indices but neglects resilience’s inherent impact. Accordingly, we propose incorporating DHI, DVI, and drought resilience index (DREI) to calculate drought risk index (DRI). Since system factors are not equally vulnerable, i.e., they are heterogeneous, our methodology applies the Analytic Hierarchy Process (AHP) to find the weights of the selected factors for the DVI computation. Finally, we propose a geometric mean method for DRI calculation. Results show a rise in DHI during 2006–2010 that affected DRI. We depict the applicability of SEDI via its relationship with El Nino-La Nina and El Salvador’s cereal production. This research provides a systematic drought risk assessment approach that is useful for decision-makers to allocate resources more smartly or intervene in Drought Risk Reduction (DRR). This research is also useful for those interested in socioeconomic drought.
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Drought characterization and risk assessment are of great significance due to drought’s negative impact on human health, economy, and ecosystem. This paper investigates drought characterization and risk assessment in the Lempa River basin in Central America. We applied the Standardized Evapotranspiration Deficit Index (SEDI) for drought characterization and drought hazard index (DHI) calculation. Although SEDI’s applicability is theoretically proven, it has been rarely applied. Drought risk is generally derived from the interactions between drought hazard (DHI) and vulnerability (DVI) indices but neglects resilience’s inherent impact. Accordingly, we propose incorporating DHI, DVI, and drought resilience index (DREI) to calculate drought risk index (DRI). Since system factors are not equally vulnerable, i.e., they are heterogeneous, our methodology applies the Analytic Hierarchy Process (AHP) to find the weights of the selected factors for the DVI computation. Finally, we propose a geometric mean method for DRI calculation. Results show a rise in DHI during 2006–2010 that affected DRI. We depict the applicability of SEDI via its relationship with El Nino-La Nina and El Salvador’s cereal production. This research provides a systematic drought risk assessment approach that is useful for decision-makers to allocate resources more smartly or intervene in Drought Risk Reduction (DRR). This research is also useful for those interested in socioeconomic drought.
Journal article
(2020)
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Vitali Diaz, Gerald A. Corzo Perez, Henny A.J. Van Lanen, Dimitri Solomatine, Emmanouil A. Varouchakis
Drought is a complex natural phenomenon. The description of the way in which drought changes (moves) in space may help to acquire knowledge on its drivers and processes to improve its monitoring and prediction. This research presents the application of an approach to characterise the dynamics of drought. Tracks, severity, duration, as well as localisation (onset and end position), and rotation of droughts were calculated. Results of calculated droughts were compared with documented information. Data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor was used to identify droughts in India as an example for the period 1901–2013. Results show regions where droughts with considerable coverage tend to occur. Paths, i.e. consecutive spatial tracks, of six of the most severe reported droughts were analysed. In all of them, areas overlap considerably over time, which suggest that drought remains in the same region for a period of time. Results of this research are being used to build a model to predict the spatial drought tracks, incl. India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought).
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Drought is a complex natural phenomenon. The description of the way in which drought changes (moves) in space may help to acquire knowledge on its drivers and processes to improve its monitoring and prediction. This research presents the application of an approach to characterise the dynamics of drought. Tracks, severity, duration, as well as localisation (onset and end position), and rotation of droughts were calculated. Results of calculated droughts were compared with documented information. Data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor was used to identify droughts in India as an example for the period 1901–2013. Results show regions where droughts with considerable coverage tend to occur. Paths, i.e. consecutive spatial tracks, of six of the most severe reported droughts were analysed. In all of them, areas overlap considerably over time, which suggest that drought remains in the same region for a period of time. Results of this research are being used to build a model to predict the spatial drought tracks, incl. India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought).
Journal article
(2020)
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Vitali Diaz, Gerald A. Corzo Perez, Henny A.J. Van Lanen, Dimitri Solomatine, Emmanouil A. Varouchakis
The spatiotemporal monitoring of droughts is a complex task. In the past decades, drought monitoring has been increasingly developed, while the consideration of its spatio-temporal dynamics is still a challenge. This study proposes a method to build the spatial tracks and paths of drought, which can enhance its monitoring. The steps for the drought tracks calculation are (1) identification of spatial units (areas), (2) centroids localisation, and (3) centroids linkage. The spatio-temporal analysis performed here to extract the areas and centroids builds upon the Contiguous Drought Area (CDA) analysis. The potential of the proposed methodology is illustrated using grid data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor over India (1901-2013), as an example. The method to calculate the drought tracks allows for identification of drought paths delineated by an onset and an end in space and time. Tracks, severity and duration of the drought are identified, as well as localisation (onset and end position), and rotation. The response of the drought tracking method to different combinations of parameters is also analysed. Further research is in progress to set up a model to predict the drought tracks for particular regions across the world, including India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought).
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The spatiotemporal monitoring of droughts is a complex task. In the past decades, drought monitoring has been increasingly developed, while the consideration of its spatio-temporal dynamics is still a challenge. This study proposes a method to build the spatial tracks and paths of drought, which can enhance its monitoring. The steps for the drought tracks calculation are (1) identification of spatial units (areas), (2) centroids localisation, and (3) centroids linkage. The spatio-temporal analysis performed here to extract the areas and centroids builds upon the Contiguous Drought Area (CDA) analysis. The potential of the proposed methodology is illustrated using grid data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor over India (1901-2013), as an example. The method to calculate the drought tracks allows for identification of drought paths delineated by an onset and an end in space and time. Tracks, severity and duration of the drought are identified, as well as localisation (onset and end position), and rotation. The response of the drought tracking method to different combinations of parameters is also analysed. Further research is in progress to set up a model to predict the drought tracks for particular regions across the world, including India (https://www.researchgate.net/project/STAND-Spatio-Temporal-ANalysis-of-Drought).
Book chapter
(2019)
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Hung Manh Le, Gerald A. Corzo, Vicente Medina, Vitali Diaz, Bang Luong Nguyen, D.P. Solomatine
Drought indicators are of critical importance in characterization and forecasting. The use of the Standardized Precipitation Index (SPI) has increasingly become the main tool for drought analysis; however, the index lacks hydrological information useful as a proxy for other types of droughts. This study aims at evaluating the SPI against the Standardized Precipitation Evapotranspiration Index (SPEI) in the South Central Region of Vietnam. The indices were calculated using monthly rainfall and temperature data measurements from 30 rainfall and 13 temperature stations, during the period from 1977 to 2014. The study focuses on the spatial-temporal variations of drought events and therefore an area of 1680 grid cells of 4x4 km was selected. Inverse distance weighting was used to interpolate grid rainfall and temperature prior to drought indices estimating at multiple time scales (3, 6, 9, and 12 months). Drought severity was classified from gridded SPIs and SPEIs using a Non-Contiguous Drought Area (NCDA) approach. The result indicated that drought characteristics using the SPEI and NCDA can capture better historical drought conditions than that using the SPI and NCDA. This suggests an important role of temperature factor in the degree of drought severity. The analysis of spatial-temporal drought on the SPEI showed that the occurrence of moderate droughts in the study area was 1-2 years, and the highest percentage of drought in the area was observed in the summer-autumn season at all SPEI time scales. The results of this study may extend our understanding of natural drought mechanisms.
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Drought indicators are of critical importance in characterization and forecasting. The use of the Standardized Precipitation Index (SPI) has increasingly become the main tool for drought analysis; however, the index lacks hydrological information useful as a proxy for other types of droughts. This study aims at evaluating the SPI against the Standardized Precipitation Evapotranspiration Index (SPEI) in the South Central Region of Vietnam. The indices were calculated using monthly rainfall and temperature data measurements from 30 rainfall and 13 temperature stations, during the period from 1977 to 2014. The study focuses on the spatial-temporal variations of drought events and therefore an area of 1680 grid cells of 4x4 km was selected. Inverse distance weighting was used to interpolate grid rainfall and temperature prior to drought indices estimating at multiple time scales (3, 6, 9, and 12 months). Drought severity was classified from gridded SPIs and SPEIs using a Non-Contiguous Drought Area (NCDA) approach. The result indicated that drought characteristics using the SPEI and NCDA can capture better historical drought conditions than that using the SPI and NCDA. This suggests an important role of temperature factor in the degree of drought severity. The analysis of spatial-temporal drought on the SPEI showed that the occurrence of moderate droughts in the study area was 1-2 years, and the highest percentage of drought in the area was observed in the summer-autumn season at all SPEI time scales. The results of this study may extend our understanding of natural drought mechanisms.
Understanding, characterizing, and predicting drought is vital for the reduction of its consequences. In the last few decades, many studies have moved drought analysis from the conventional lumped approach to a more spatiotemporal analysis. Two main developments have motivated this: one is global data availability and the other is the number of models developed to understand and quantify drought. The first one relates to information available from reanalysis products, and the second regards global and regional, distributed and semidistributed model data. Moreover, nowadays, different organizations provide drought monitoring information in near real time. However, a few spatiotemporal analysis studies have been developed slowly and the availability of comprehensive tools is still limited. This chapter proposes a new toolbox that performs the Spatio-Temporal ANalysis of Drought (STAND) in MATLAB, step by step. The toolbox collects some of the applications of previous studies and innovates new concepts on the characterization of drought. The methodologies here allow estimation of drought duration, severity (magnitude), and area, redefining the drought event in space and time. A key component in the analysis is the visualization of outcomes, as well as the spatial interpolation of pointwise data. The proposed STAND toolbox is explained and its use is illustrated with two large-scale examples (India and Mexico). The results have been compared with local reported information. STAND outcomes have been shown to help follow space–time events in terms of patterns, and provide information related to the characterization of extremes for drought analysis.
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Understanding, characterizing, and predicting drought is vital for the reduction of its consequences. In the last few decades, many studies have moved drought analysis from the conventional lumped approach to a more spatiotemporal analysis. Two main developments have motivated this: one is global data availability and the other is the number of models developed to understand and quantify drought. The first one relates to information available from reanalysis products, and the second regards global and regional, distributed and semidistributed model data. Moreover, nowadays, different organizations provide drought monitoring information in near real time. However, a few spatiotemporal analysis studies have been developed slowly and the availability of comprehensive tools is still limited. This chapter proposes a new toolbox that performs the Spatio-Temporal ANalysis of Drought (STAND) in MATLAB, step by step. The toolbox collects some of the applications of previous studies and innovates new concepts on the characterization of drought. The methodologies here allow estimation of drought duration, severity (magnitude), and area, redefining the drought event in space and time. A key component in the analysis is the visualization of outcomes, as well as the spatial interpolation of pointwise data. The proposed STAND toolbox is explained and its use is illustrated with two large-scale examples (India and Mexico). The results have been compared with local reported information. STAND outcomes have been shown to help follow space–time events in terms of patterns, and provide information related to the characterization of extremes for drought analysis.
Extreme hydrological events (EHEs), such as droughts and floods, vary spatially and temporally in nature. The increase in the number of events in the last few decades has motivated the research of the spatiotemporal variability of the future extreme precipitation and temperature. To study the consequences on the EHEs due to the uncertainty of projected climate changes, the analysis in more detail of precipitation and temperature, in space and time, is vital. In addition, for proper planning and decision-making process to address EHEs, understanding such climate changes requires more information. In this chapter we present a summarized assessment of the spatiotemporal variations of climate projections. A simplified way to aggregate global data is used for the spatiotemporal analysis of precipitation and temperature. To carry out this analysis, the Spatio-TempoRal distribution and Interannual VarIability of projections (STRIVIng) toolbox is proposed for statistical exploratory analysis of climate projections. Three large-scale applications were carried out for illustration: Dominican Republic (48,670 km2), Mexico (1,972,550 km2), and Amazon basin (6,171,148.7 km2). The methodology and toolbox presented here allow regions to be identified where the changes are expected to be more severe on precipitation and temperature, as well as months in which those changes are likely to occur. The STRIVIng toolbox is open source and helps to provide basic information to increase the interpretations and research in the space–time analysis of extremes.
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Extreme hydrological events (EHEs), such as droughts and floods, vary spatially and temporally in nature. The increase in the number of events in the last few decades has motivated the research of the spatiotemporal variability of the future extreme precipitation and temperature. To study the consequences on the EHEs due to the uncertainty of projected climate changes, the analysis in more detail of precipitation and temperature, in space and time, is vital. In addition, for proper planning and decision-making process to address EHEs, understanding such climate changes requires more information. In this chapter we present a summarized assessment of the spatiotemporal variations of climate projections. A simplified way to aggregate global data is used for the spatiotemporal analysis of precipitation and temperature. To carry out this analysis, the Spatio-TempoRal distribution and Interannual VarIability of projections (STRIVIng) toolbox is proposed for statistical exploratory analysis of climate projections. Three large-scale applications were carried out for illustration: Dominican Republic (48,670 km2), Mexico (1,972,550 km2), and Amazon basin (6,171,148.7 km2). The methodology and toolbox presented here allow regions to be identified where the changes are expected to be more severe on precipitation and temperature, as well as months in which those changes are likely to occur. The STRIVIng toolbox is open source and helps to provide basic information to increase the interpretations and research in the space–time analysis of extremes.
Journal article
(2018)
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Khalidou M. Bâ, Luis Balcázar, Vitali Diaz, Febe Ortiz, Miguel A. Gómez-Albores, Carlos Díaz-Delgado
This study highlights the advantage of satellite-derived rainfall products for hydrological modeling in regions of insufficient ground observations such as West African basins. Rainfall is the main input for hydrological models; however, gauge data are scarce or difficult to obtain. Fortunately, several precipitation products are available. In this study, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) was analyzed. Daily discharges of three rivers of the Upper Senegal basin and one of the Upper Niger basin, as well as water levels of Manantali reservoir were simulated using PERSIANN-CDR as input to the CEQUEAU model. First, CEQUEAU was calibrated and validated using raw PERSIANN-CDR, and second, rainfalls were bias-corrected and the model was recalibrated. In both cases, ERA-Interim temperatures were used. Model performance was evaluated using Nash-Sutcliffe efficiency (NSE), mean percent bias (MPBIAS), and coefficient of determination (R
2
). With raw PERSIANN-CDR, most years show good performance with values of NSE > 0.8, R
2
> 0.90, and MPBIAS < 10%. However, bias-corrected PERSIANN-CDR did not improve the simulations. The findings of this study can be used to improve the design of dam projects such as the ongoing dam constructions on the three rivers of the Upper Senegal Basin.
...
This study highlights the advantage of satellite-derived rainfall products for hydrological modeling in regions of insufficient ground observations such as West African basins. Rainfall is the main input for hydrological models; however, gauge data are scarce or difficult to obtain. Fortunately, several precipitation products are available. In this study, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) was analyzed. Daily discharges of three rivers of the Upper Senegal basin and one of the Upper Niger basin, as well as water levels of Manantali reservoir were simulated using PERSIANN-CDR as input to the CEQUEAU model. First, CEQUEAU was calibrated and validated using raw PERSIANN-CDR, and second, rainfalls were bias-corrected and the model was recalibrated. In both cases, ERA-Interim temperatures were used. Model performance was evaluated using Nash-Sutcliffe efficiency (NSE), mean percent bias (MPBIAS), and coefficient of determination (R 2 ). With raw PERSIANN-CDR, most years show good performance with values of NSE > 0.8, R 2 > 0.90, and MPBIAS < 10%. However, bias-corrected PERSIANN-CDR did not improve the simulations. The findings of this study can be used to improve the design of dam projects such as the ongoing dam constructions on the three rivers of the Upper Senegal Basin.