HS3.3

Societies seek to ensure sustainable development in the face of climate change, population increase, and increased demands for natural resources. Understanding, modeling, and forecasting the spatiotemporal patterns of precipitation are central to this effort [1-3]. Spatiotemporal models of precipitation with global validity are not available. This is due to the non-Gaussian distribution of precipitation as well as its intermittent nature and strong dependence on the geographic location and the space-time scales analyzed.  Herein we investigate the spatiotemporal patterns of precipitation on a Mediterranean island using geostatistical methods. 

We use ERA5 reanalysis precipitation products from the Copernicus Climate Change Service [4].  The dataset includes 31980 values of monthly precipitation height (mm) for a period of 492 consecutive months (January 1979 to December 2019) at the nodes of a 5 × 13 spatial grid that covers the island of Crete (Greece). This results in an average spatial resolution of approximately 0.28 degrees (corresponding to an approximate grid cell size of 31 km).  

We construct a spatial model of monthly precipitation using Gaussian anamorphosis (GA). GA employs nonlinear transformations to normalize the probability distribution of the data. It is extensively used in various environmental applications [5-6].  The methodology that we follow involves (i) normalizing the precipitation data per month using GA with Hermite polynomials, (ii) estimating spatial correlations and fitting them to the Spartan variogram family [6], (iii) ordinary kriging (OK) of the normalized data in order to generate precipitation estimates on a denser map grid, and (iv) application of the inverse GA transform to generate monthly precipitation maps. We also use cross-validation analysis to determine the kriging interpolation performance, first using the untransformed precipitation data and then the Hermite-polynomial GA approach outlined above. We find that Hermite-polynomial GA significantly improves the cross-validation measures.

Keywords: Gaussian anamorphosis, Hermite polynomials, Mediterranean island, non-Gaussian, ordinary kriging, Spartan variogram

References

1. D. Allard, and M. Bourotte, 2015. Disaggregating daily precipitations into hourly values with a transformed censored latent Gaussian process. Stochastic Environ. Res. Risk Assess, 29(2), pp. 453– 462. https://doi.org/10.1007/s00477-014-0913-4.

2. A. Baxevani, and J. Lennartsson, 2015. A spatiotemporal precipitation generator based on a censored latent Gaussian field, Water Resources Research, 51(6), 4338–4358. https://doi.org/10.1002/2014WR016455.

3. C. Lussana, T. N. Nipen, I. A. Seierstad, and C. A. Elo, 2020. Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation. Nonlinear Processes in Geophysics, 1–43. https://doi.org/10.5194/npg-2020-20.

4. C3S, C. C. C. S., 2018. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Data retrieved from: https://cds.climate.copernicus.eu/cdsapp#!/home.

5. N. Cressie, 1993. Spatial Statistics. John Wiley and Sons, New York.

6. D. T. Hristopulos, 2020. Random Fields for Spatial Data Modeling. Springer Netherlands, http://dx.doi.org/10.1007/978-94-024-1918-4.

How to cite: Agou, V. D., Pavlides, A., and Hristopulos, D. T.: Space-Time Analysis of Precipitation Reanalysis Data for the Island of Crete using Gaussian Anamorphosis with Hermite Polynomials, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3088, https://doi.org/10.5194/egusphere-egu21-3088, 2021.

Abstract

Most of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.

In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.

Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought

How to cite: Tang, C.-H. and Tsai, C. W.: Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10609, https://doi.org/10.5194/egusphere-egu21-10609, 2021.

This study aims to propose the use of spatio-temporal Remote Sensing information and Machine learning techniques (ML) for Active Moving Area Identification and Forecast. Mass Movements are frequent in Central American countries, mainly due to the combined extreme hydro-meteorological events with the seismic activity and the characteristics of the geological formations in the region. Ometepe Island is located in Lake Cocibolca, Nicaragua; it has two volcanoes (one active) and Mass Movements happen quite often in the area, where many of them represent a big risk for the population. The triggering factors for these Mass Movements are mainly volcanic activity in conjunction with high and quick precipitations. The process of identification of a Mass Movement from Remote Sensing images is used first as a way to characterise the data, and then a lagged time step was used to evaluate the forecasting capabilities in a time window of precipitation forecast. For this, Remote Sensing was used to create the Active Moving Area Inventory, using InSAR technique with Sentinel-1 SAR images. SNAP software was used to locate occurrences of displacements in the island. This inventory was used to develop ML models that had Rainfall and Soil Moisture as dynamic variables; and DEM, Land Use, Geomorphology, and others as static variables. These were trained and evaluated using Logistic Regression (LR), Random Forest (RF) and Long Short-Term Memory (LSTM) to detect occurrence of Displacement in a particular area of the island. The results were analysed performance-wise and compared to each other. The results of this methodology are a first step into a larger framework of spatiotemporal analysis for forecasting using Machine Learning.

How to cite: Arcia, V., Corzo, G., and Calderón, H.: Active Moving Area Identification using Machine Learning. Case study: Ometepe Island, Nicaragua, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8462, https://doi.org/10.5194/egusphere-egu21-8462, 2021.

The North Atlantic Oscillation (NAO) is often cited as the primary atmospheric-oceanic circulation or teleconnection influencing regional climate in Great Britain. As our ability to predict the NAO several months in advance improves, it is important that we also continue to develop our spatial and temporal understanding of the rainfall signatures which the circulation produces.

We present a novel application of spatial statistics to explore variability in monthly NAO rainfall signatures using a 5km gridded monthly Standardised Precipitation Index (SPI) dataset. We first use the Getis-Ord Gi* statistic to map spatially significant hot and cold spots (clusters of high/wet and low/dry SPI values) in average monthly rainfall signatures under NAO Positive and Negative conditions over the period 1900-2015. We then look across the record and explore the temporal variability in these signatures, in other words how often a location is in a significant spatial hot/cold spot (high/low SPI) at a monthly scale under NAO Positive/Negative conditions.

The two phases of the NAO are typically more distinctive in the winter months, with stronger and more variable NAO Index values. The average monthly SPI analysis reveals a north-west/south-east ‘spatial divide’ in rainfall response. NAO Positive phases result in a southerly North Atlantic Jet Stream bringing warm and wet conditions from the tropics, increasing rainfall particularly in the north-western regions. However, under NAO Negative phases which result in a northerly Jet Stream, much drier conditions in the north-west prevail. Meanwhile in the south-eastern regions under both NAO phases a weaker and opposite wet/dry signal is observed. This north-west/south-east ‘spatial divide’ is marked by the location of spatially extensive hot/cold spots. The Getis-Ord Gi* result identifies that the spatial pattern we detect in average winter rainfall is statistically significant. Looking across the record, this NW/SE opposing response appears to have a relatively high degree of spatio-temporal consistency. This suggests that there is a high probability that NAO Positive and Negative phases will result in this NW/SE statistically significant spatial pattern.

Even though the phases of the NAO in the summer months are less distinctive they still produce rainfall responses which are evident in the monthly average SPI. However, the spatiality in wet/dry conditions is more homogenous across the country. In other words the ‘spatial divide’ observed in winter is diluted in summer. As a result, the occurrence of significant hot/cold spots is more variable in space and time.

Our analysis demonstrates a novel application of the Getis-Ord Gi* statistic which allows for spatially significant patterns in the monthly SPI data to be mapped for each NAO phase. In winter months particularly, this analysis reveals statistically significant opposing rainfall responses, which appear to have long-term spatio-temporal consistency. This is important because as winter NAO forecasting skill improves, the findings of our research enable a more spatially reliable estimate of the likely impacts of NAO-influenced rainfall distribution.

How to cite: West, H., Quinn, N., and Horswell, M.: Mapping Spatio-Temporal Variability in NAO Rainfall Signatures, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10982, https://doi.org/10.5194/egusphere-egu21-10982, 2021.

Arachthos River is the largest river in Epirus and the 8^th largest in Greece; it is 110 km long and its drainage area is 2209 km2. After emanating from Pindus mountains (near Metsovo), it enters into the Pournari Reservoir in Arta, passes through Arta and discharges into the Ambracian Gulf near Kommeno. Arachthos River prevents flooding of the city of Arta and supplies water to most of Epirus.

The design of flood protection works in Arachthos River is currently in progress; it is performed by a consortium of Greek Consulting Firms for the Ministry of Infrastructure and Transportation. In the present work, we examine the effect of Flood Retention Ponds on the inundation area and the subsequent flood risk for the city of Arta. The Flood Retention Ponds are constructed immediately downstream of the Pournari Reservoir and 5600 m upstream of the historic Bridge of Arta; their exact locations were identified after a preliminary study and field surveys. Firstly, we performed the design of the Flood Retention Ponds, based on international standards and specifications found in the international literature; then, we performed hydrodynamic calculations using the Hydrologic Engineering Center's-River Analysis System (HEC-RAS) 1D/2D with and without the Flood Retention Ponds. Thirdly, we compared the calculations and the corresponding inundation areas and derive conclusions on the effect of Flood Retention Ponds.

How to cite: Rontiris, G., Mitsopoulos, G., Panagiotatou, E., and Stamou, A. I.: Modelling the effect of Flood Retention Ponds in Arachthos River (Arta, Greece)  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15381, https://doi.org/10.5194/egusphere-egu21-15381, 2021.

Floods are one of the worst natural hazards around the globe and around 40% of all losses worldwide due to natural hazard have been caused by floods since 1980s. In India, more than 40 million hectares of area are affected by floods annually which makes it one of the worst affected country in the world. In particular, the Ganga river basin in northern India which hosts nearly half a billion people, is one of the worst floods affected regions in the country. The Ghaghra river is one of the highest discharge-carrying tributaries of the Ganga river, which originates from High Himalaya. Despite severally affected by floods each year, flood frequencies of the Ghaghra river are poorly understood, making it one of the least studied river basins in the Ganga basin. It is important to note that, like several other rivers in India, the Ghaghra also has several hydrological stations where only stage data is available, and therefore traditional flood frequency analysis using discharge data becomes difficult. In this work, we have performed flood frequency analysis using both stage and discharge dataset at three different gauge stations in the Ghaghra river basin to compare the results using statistical methods. The L-moment analysis is applied to assess the probability distribution for the flood frequency analysis. Further, we have used the TanDEM-x 90m digital elevation model (DEM) to map the flood inundation regions. Our results suggest the Weibull is statistically significant distribution for the discharge dataset. However, stage above danger level (SADL) follows General Pareto (GP3) and Generalized Extreme Value (GEV) distributions. The quantile-quantile plot analysis suggests that the SADL probability distributions (GP3 and GEV) are closely following the theoretical probability distributions. However, the discharge distribution (Weibull) is showing a relatively weak corelation with the theoretical probability distribution. We further used the probability distribution to assess the SADL frequencies at 5-, 10-, 20-, 50- and 100-year return periods. The magnitudes of SADL at different return periods were then used to map the water inundation areas around different gauging stations. These inundation maps were cross-validated with the globally available flooding extent maps provided by Dartmouth flood observatory. Overall, this work exhibits a simple and novel technique to generate inundation maps around the gauging locations without using any sophisticated hydraulics models.

How to cite: Singh, S., Swarnkar, S., and Sinha, R.: Exploring stage-based flood frequency analysis for flood inundation mapping, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1924, https://doi.org/10.5194/egusphere-egu21-1924, 2021.

Abstract

The flash flood in Mandra on the 15^th of November 2017 was the third most disastrous “November” flood in Attica; it was characterized by heavy sediment and debris transport that can be easily observed in Figure 1.

We applied the Hydrologic Engineering Center's-River Analysis System (HEC-RAS) to model sediment transport using the Ackers-White sediment transport equation that is engraved in HEC-RAS to analyze sediment transport characteristics. The required input data were based on a limited number of available studies, which mainly include a survey performed by the Hellenic Centre for Marine Research in the coastal area of the Elefsis Bay where sediments were deposited after the catastrophic event. We compared the results of the model with calculations performed within a previous Thesis in 2018 using TELEMAC-2D and SISYPHE.

The present paper is based on the Diploma Thesis of the first author; it was performed within the project “National Network on Climate Change and its Impacts (CLIMPACT)” of the General Secretariat of Research and Technology.

Figure 1. The greater area of Mandra (a) before and (b) after the flood event

How to cite: Sant, V., Mitsopoulos, G., Bloutsos, A., and Stamou, A.: Modelling Sediment Transport in the disastrous Flash Flood of November 2017 in Mandra (Attica, Greece)  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10561, https://doi.org/10.5194/egusphere-egu21-10561, 2021.

The region of Attica is characterized by a relatively large number of floods over a long period of time. The flash flood in Mandra on the 15^th of November 2017 was the third most disastrous “November” flood in Attica; most of the population was affected by the flood (23 deaths and 24 people injured), while basements and ground floors of buildings in the town were seriously impacted.

The two main streams that pass through the town of Mandra are Soures and Agia Aikaterini, whose catchment area is equal to 23.0 and 22.0 km², respectively. These streams are characterized by significant morphological changes due to the intensive construction activities in the greater area that resulted in a dramatic decrease of their available cross-sectional areas and the occurrence of floods even at low flow rates.

We applied the HEC-RAS 1D/2D to model the flash flood using a high resolution Digital Surface Model (DSM) and topographic survey data, to obtain the most accurate representation of the area of Mandra. Moreover, we imported to the model all technical works, such as culverts and bridges that affect the flow. For the model calibration, we employed (a) videos, photographs, information from the local population and satellite images to determine the inundation area and (b) in situ measurements of the flood water depth, in various locations within the town of Mandra. The results of the model were compared with calculations performed within a previous Thesis in 2018 using TELEMAC-2D.

The present paper is based on the Master Thesis of the first author; it was performed within the project “National Network on Climate Change and its Impacts (CLIMPACT)” of the General Secretariat of Research and Technology.

How to cite: Tsokanis, K., Mitsopoulos, G., Bloutsos, A., and Stamou, A. I.: Modelling the disastrous Flash Flood of November 2017 in Mandra (Attica, Greece)  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15041, https://doi.org/10.5194/egusphere-egu21-15041, 2021.

Drought is often conceptualised as an extreme weather event generated by anomalies in water resources availabilities. Understanding the behaviour and spatiotemporal distribution of drought events has become very important due to the possible teleconnections of drought propagation patterns. This understanding and if is possible representation of teleconnections between patterns could lead to better prediction and management of extreme events.
This study develops a methodology to monitor spatiotemporal drought events in the dry corridor of Central America using the drought index SPI and SPEI for the period 1981 to 2020.
This methodology consists of five stages. 1) collection and quality validation of the data sets used. 2) ERA5 and Observation datasets allow calibrating the precipitation and temperature values from historical gauge measurements. 3) Then, by the estimation and trend analysis of the drought index in different time scales (3, 6, 12 months) an initial baseline is defined. 4) Spatiotemporal association algorithms (based on computer vision) are used to characterise and monitoring the most extensive drought events. For this, the extreme and severe events (DI values below -1) threshold is estimated. 5)  Synchronic Integration between temporal patterns and spatial propagation is carried out to evaluate possible interactions or connections of drought events along the dry corridor of Central America. These results provide valuable information to evaluate the impacts on different sectors threatened by drought throughout the territory. This work presents preliminary results of an extended project looking at the dry corridor in Central America. 

How to cite: Sanchez Hernandez, K. A., Corzo Perez, G. A., and Santos Granados, G. R.: Spatiotemporal and Synchronous Monitoring of Drought in the Dry Corridor of Central America, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13870, https://doi.org/10.5194/egusphere-egu21-13870, 2021.

Abstract

Human activity is directly responsible for land use and land cover changes, affecting different ecosystem services. Thus, from the perspective of land use management is critical to project potential future land-use changes. This study aimed: (i) to detect possible changes in land-use structure in response to different four scenarios, namely: business as usual, urbanization, afforestation and land abandonment, and agricultural intensification scenario; and (ii) to measure the landscape habitat quality (an ecosystem services proxy) according to those projected futures. We selected as case study Lithuania due to the potential future increased human pressures on the landscape, and due to the high landscape value of this territory. The projected year was 2050, and we used the Cellular Automata method (applying the Dinamica EGO software) to project future land-use changes, and the InVEST model to assess the habitat quality. The land-use scenarios outcomes were validated using a fuzzy comparison function, and 80% of accuracy was achieved (comparing a simulated land use map of 2018, and the observed map for the same year). The results showed that the agricultural intensification scenario represents the greatest predicted landscape deterioration (from 0.71 in 2018 to 0.64). In the urbanization scenario, the highest landscape degradation prediction is identified around the most important cities (Vilnius, Kaunas, and Klaipėda). In the opposite direction, the afforestation and land abandonment scenario show the highest improvement on the habitat quality, from 0.71 in 2018 to 0.74. 

Acknowledgements

“Lithuanian National Ecosystem Services Assessment and Mapping (LINESAM)” No. 09.3.3-LMT-K-712-01-0104 is funded by the European Social Fund according to the activity “Improvement of researchers’ qualification by implementing world-class R&D projects” of Measure No. 09.3.3-LMT-K-712.

How to cite: Pereira, P., Gomes, E., Inacio, M., Bogdzevič, K., Karnauskaite, D., and Kalinauskas, M.: Mapping and assessment of future land use change impacts on habitat quality in Lithuania, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3267, https://doi.org/10.5194/egusphere-egu21-3267, 2021.

The multi-resolution approximation approach (MRA) [1] provides an efficient representation of Gaussian processes that scales beyond millions of observations. MRA leaves flexibility in the selection of covariance functions and allows to trade off computation time against prediction performance, depending on the selection of parameters. Recent work [2] has shown how MRA can be used for global spatiotemporal processes by integrating nonstationary covariance functions, where parameters vary over space and/or time following a kernel convolution approach. As such, MRA turns out to be a promising approach for geostatistical modelling of global spatiotemporal datasets, such as those coming from Earth observation satellites.

In this work, we show how MRA can be used for spatiotemporal analysis from a practical perspective. In the first part, we will discuss the influence of parameters (spatiotemporal shape of partitioning regions, the number of basis functions, and the number of partitioning levels) by analyzing a real world dataset. In the second part, we will present and discuss our implementation as an R package stmra[3]. We will demonstrate how traditional models as from the gstat package can be implemented efficiently with MRA, and how non-stationary models can be defined by users in a relatively simple way. 

[1] Katzfuss, M. (2017). A multi-resolution approximation for massive spatial datasets. Journal of the American Statistical Association, 112(517), 201-214

[2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.

[3] https://github.com/appelmar/stmra

How to cite: Appel, M. and Pebesma, E.: Implementation of geostatistical models for large spatiotemporal datasets using multi-resolution approximations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6585, https://doi.org/10.5194/egusphere-egu21-6585, 2021.

The gridded interpolated daily precipitation data has a vast application in hydrometeorology. The fine resolution gridded precipitation gained terrestrial measurements is a tool to evaluate satellite, reanalysis, and radar-based gridded products. In this study, the daily time series of 1561 rain gauges over Iran for the period of 2003-20010 is used to compute 1 km * 1 km interpolated maps. The nearest neighborhood, Inverse Distance Weighting (IDW), Ordinary Kriging (OK), External Drift Kriging (EDK), and Quantile Kriging (QK) interpolation methods are applied to compare their performance. Due to the large size of the interpolated region and different climates, six clusters for estimating the variogram function are determined. The distinct interpolation methods lead to different daily precipitation estimates, however in the same spatial resolution, OK is showed slightly better results with the mean RMSE and correlation equal to 2.355 and 0.766, respectively. Also, the spatially aggregated gridded maps illustrated that the interpolation methods only play a significant role in the fine resolutions than the coarser ones.

How to cite: Modiri, E. and Bárdossy, A.: A new insight to daily precipitation estimation using different interpolation methods, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15958, https://doi.org/10.5194/egusphere-egu21-15958, 2021.