scholarly journals Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data

2016 ◽  
Vol 20 (6) ◽  
pp. 2227-2250 ◽  
Author(s):  
Valentin Heimhuber ◽  
Mirela G. Tulbure ◽  
Mark Broich
Keyword(s):  
Time Series ◽  
Surface Water ◽  
River Basin ◽  
Spatial Modeling ◽  
Earth Observation ◽  
Observation Data ◽  
Grid Cells ◽  
Modeling Framework ◽  
Basin Scale ◽  
Lag Times

Abstract. The usage of time series of Earth observation (EO) data for analyzing and modeling surface water extent (SWE) dynamics across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWE dynamics from a unique, statistically validated Landsat-based time series (1986–2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWE as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET), and rainfall (P). To enable a consistent modeling approach across space, we modeled SWE dynamics separately for hydrologically distinct floodplain, floodplain-lake, and non-floodplain areas within eco-hydrological zones and 10km × 10km grid cells. We applied this spatial modeling framework to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWE dynamics. Based on these automatically quantified flow lag times and variable combinations, SWE dynamics on 233 (64 %) out of 363 floodplain grid cells were modeled with a coefficient of determination (r2) greater than 0.6. The contribution of P, ET, and SM to the predictive performance of models differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The spatial modeling framework presented here is suitable for modeling SWE dynamics on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.

scholarly journals Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of earth observation data

2015 ◽  
Vol 12 (11) ◽  
pp. 11847-11903 ◽  
Author(s):  
V. Heimhuber ◽  
M. G. Tulbure ◽  
M. Broich
Keyword(s):  
Time Series ◽  
Surface Water ◽  
River Basin ◽  
Earth Observation ◽  
Water Dynamics ◽  
Observation Data ◽  
Grid Cells ◽  
Modeling Framework ◽  
Basin Scale ◽  
Lag Times

Abstract. The usage of time series of earth observation (EO) data for analyzing and modeling surface water dynamics (SWD) across broad geographic regions provides important information for sustainable management and restoration of terrestrial surface water resources, which suffered alarming declines and deterioration globally. The main objective of this research was to model SWD from a unique validated Landsat-based time series (1986–2011) continuously through cycles of flooding and drying across a large and heterogeneous river basin, the Murray–Darling Basin (MDB) in Australia. We used dynamic linear regression to model remotely sensed SWD as a function of river flow and spatially explicit time series of soil moisture (SM), evapotranspiration (ET) and rainfall (P). To enable a consistent modeling approach across space, we modeled SWD separately for hydrologically distinct floodplain, floodplain-lake and non-floodplain areas within eco-hydrological zones and 10 km × 10 km grid cells. We applied this spatial modeling framework (SMF) to three sub-regions of the MDB, for which we quantified independently validated lag times between river gauges and each individual grid cell and identified the local combinations of variables that drive SWD. Based on these automatically quantified flow lag times and variable combinations, SWD on 233 (64 %) out of 363 floodplain grid cells were modeled with r2 ≥ 0.6. The contribution of P, ET and SM to the models' predictive performance differed among the three sub-regions, with the highest contributions in the least regulated and most arid sub-region. The SMF presented here is suitable for modeling SWD on finer spatial entities compared to most existing studies and applicable to other large and heterogeneous river basins across the world.

Identification of Synchronicity in Deterministic Chaotic Attractors for the Downscaling Process in the Bogotá River Basin

2021 ◽  
Author(s):  
Santiago Duarte ◽  
Gerald Corzo ◽  
Germán Santos
Keyword(s):  
Time Series ◽  
Time Delay ◽  
River Basin ◽  
Spatial Scales ◽  
Climatic Variables ◽  
Chaotic Attractors ◽  
Multiple Attractors ◽  
Nearest Neighbours ◽  
Basin Scale ◽  
Non Linear

<p>Bogotá’s River Basin, it’s an important basin in Cundinamarca, Colombia’s central region. Due to the complexity of the dynamical climatic system in tropical regions, can be difficult to predict and use the information of GCMs at the basin scale. This region is especially influenced by ENSO and non-linear climatic oscillation phenomena. Furthermore, considering that climatic processes are essentially non-linear and possibly chaotic, it may reduce the effectiveness of downscaling techniques in this region. </p><p>In this study, we try to apply chaotic downscaling to see if we could identify synchronicity that will allow us to better predict. It was possible to identify clearly the best time aggregation that can capture at the best the maximum relations between the variables at different spatial scales. Aside this research proposes a new combination of multiple attractors. Few analyses have been made to evaluate the existence of synchronicity between two or more attractors. And less analysis has considered the chaotic behaviour in attractors derived from climatic time series at different spatial scales. </p><p>Thus, we evaluate general synchronization between multiple attractors of various climate time series. The Mutual False Nearest Neighbours parameter (MFNN) is used to test the “Synchronicity Level” (existence of any type of synchronization) between two different attractors. Two climatic variables were selected for the analysis: Precipitation and Temperature. Likewise, two information sources are used: At the basin scale, local climatic-gauge stations with daily data and at global scale, the output of the MPI-ESM-MR model with a spatial resolution of 1.875°x1.875° for both climatic variables (1850-2005). In the downscaling process, two RCP (Representative Concentration Pathways)  scenarios are used, RCP 4.5 and RCP 8.5.</p><p>For the attractor’s reconstruction, the time-delay is obtained through the  Autocorrelation and the Mutual Information functions. The False Nearest Neighbors method (FNN) allowed finding the embedding dimension to unfold the attractor. This information was used to identify deterministic chaos at different times (e.g. 1, 2, 3 and 5 days) and spatial scales using the Lyapunov exponents. These results were used to test the synchronicity between the various chaotic attractor’s sets using the MFNN method and time-delay relations. An optimization function was used to find the attractor’s distance relation that increases the synchronicity between the attractors.  These results provided the potential of synchronicity in chaotic attractors to improve rainfall and temperature downscaling results at aggregated daily-time steps. Knowledge of loss information related to multiple reconstructed attractors can provide a better construction of downscaling models. This is new information for the downscaling process. Furthermore, synchronicity can improve the selection of neighbours for nearest-neighbours methods looking at the behaviour of synchronized attractors. This analysis can also allow the classification of unique patterns and relationships between climatic variables at different temporal and spatial scales.</p>

scholarly journals Impact of Climate Variabilities and Human Activities on Surface Water Extents in Reservoirs of Yongding River Basin, China, from 1985 to 2016 Based on Landsat Observations and Time Series Analysis

2019 ◽  
Vol 11 (5) ◽  
pp. 560 ◽  
Author(s):  
Mingli Wang ◽  
Longjiang Du ◽  
Yinghai Ke ◽  
Maoyi Huang ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Water Resources ◽  
River Basin ◽  
Change Point ◽  
Human Activities ◽  
Reservoir Water ◽  
Change Period ◽  
Statistical Time ◽  
Yongding River

Yongding River is the largest river flowing through Beijing, the capital city of China. In recent years, Yongding River Basin (YDRB) has witnessed increasing human impacts on water resources, posing serious challenges in hydrological and ecological health. In this study, remote sensing techniques and statistical time series approaches for hydrological studies were combined to characterize the dynamics and driving factors of reservoir water extents in YDRB during 1985–2016. First, 107 Landsat 4, 5, 7 and 8 images were used to extract surface water extents in YDRB during 1985–2016 using a combination of water indices and Otsu threshold algorithm. Significant positive correlation was found between water extents and the annual inflow for the two biggest reservoirs, the downstream Guanting and upstream Cetian reservoirs, proving their representativeness of surface water availability in this basin. Then, statistical time series approaches including trend-free pre-whitening Mann-Kendall trend test, Pettit change-point test and double mass curve method, which are frequently used in hydrological studies, were adopted to quantify the trend of reservoir water extents dynamics and the relative contributions of climate variability and human activities. Results showed that the water extents in both reservoirs exhibited significant downward trend with change point occurring in 2001 and 2005 for Guanting and Cetian, respectively. About 74%~75% of the shrinkage during the post-change period can be attributed to human activities, among which GDP, population, electricity power production, raw coal production, steel and crude iron production, value of agriculture output, and urban area were the major human drivers. Hydrological connectivity between the upstream Cetian and downstream Guanting reservoirs declined during the post-change period. Since 2012, water extents in both reservoirs recovered as a result of various governmental water management policies including the South-to-North Water Diversion Project. The methodology presented in this study can be used for analyzing the dynamics and driving mechanism of surface water resources, especially for un-gauged or poorly-gauged watersheds.

Integration of earth observation data in distributed hydrological models: the Senegal River basin

2003 ◽  
Vol 29 (6) ◽  
pp. 701-710 ◽  
Author(s):  
Inge Sandholt ◽  
Jens Andersen ◽  
Gorm Dybkjær ◽  
Lotte Nyborg ◽  
Medou Lô ◽  
...  
Keyword(s):  
River Basin ◽  
Earth Observation ◽  
Observation Data ◽  
Hydrological Models ◽  
Senegal River ◽  
Earth Observation Data ◽  
Distributed Hydrological Models ◽  
Senegal River Basin

scholarly journals Studying groundwater and surface water interactions using airborne remote sensing in Heihe River basin, northwest China

2015 ◽  
Vol 368 ◽  
pp. 361-365
Author(s):  
C. Liu ◽  
J. Liu ◽  
Y. Hu ◽  
C. Zheng
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
River Basin ◽  
Cold Water ◽  
Hot Spot ◽  
Heihe River Basin ◽  
Heihe River ◽  
Resource Exploitation ◽  
Surface Water And Groundwater ◽  
Basin Scale

Abstract. Managing surface water and groundwater as a unified system is important for water resource exploitation and aquatic ecosystem conservation. The unified approach to water management needs accurate characterization of surface water and groundwater interactions. Temperature is a natural tracer for identifying surface water and groundwater interactions, and the use of remote sensing techniques facilitates basin-scale temperature measurement. This study focuses on the Heihe River basin, the second largest inland river basin in the arid and semi-arid northwest of China where surface water and groundwater undergoes dynamic exchanges. The spatially continuous river-surface temperature of the midstream section of the Heihe River was obtained by using an airborne pushbroom hyperspectral thermal sensor system. By using the hot spot analysis toolkit in the ArcGIS software, abnormally cold water zones were identified as indicators of the spatial pattern of groundwater discharge to the river.

scholarly journals Fire Monitoring - The use of medium resolution satellites (AVHRR, MODIS, TET) for long time series processing and the implementation in User Driven Applications and Services

2015 ◽  
Vol XL-7/W3 ◽  
pp. 797-804
Author(s):  
E.-M. Fuchs ◽  
E. Stein ◽  
G. Strunz ◽  
C. Strobl ◽  
C. Frey
Keyword(s):  
Time Series ◽  
Hot Spot ◽  
Earth Observation ◽  
Observation Data ◽  
Fire Monitoring ◽  
Open Platform ◽  
Spot Detection ◽  
Medium Resolution ◽  
Long Time ◽  
Earth Observation Data

This paper introduces fire monitoring works of two different projects, namely TIMELINE (TIMe Series Processing of Medium Resolution Earth Observation Data assessing Long –Term Dynamics In our Natural Environment) and PHAROS (Project on a Multi-Hazard Open Platform for Satellite Based Downstream Services). It describes the evolution from algorithm development from in applied research to the implementation in user driven applications and systems. Concerning TIMELINE, the focus of the work lies on hot spot detection. A detailed description of the choice of a suitable algorithm (round robin approach) will be given. Moreover, strengths and weaknesses of the AVHRR sensor for hot spot detection, a literature review, the study areas and the selected approach will be highlighted. The evaluation showed that the contextual algorithm performed best, and will therefore be used for final implementation. Concerning the PHAROS project, the key aspect is on the use of satellite-based information to provide valuable support to all phases of disaster management. The project focuses on developing a pre-operational sustainable service platform that integrates space-based EO (Earth Observation), terrestrial sensors and communication and navigation assets to enhance the availability of services and products following a multi-hazard approach.

Joint multifractal approach to characterize nonlinear relationships of climate and cereal growth in semiarid

2021 ◽  
Author(s):  
David Rivas-Tabares ◽  
Ana María Tarquis Alfonso
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Complex Networks ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Observation Data ◽  
Central Spain ◽  
Nonlinear Relationships ◽  
Fractal Properties

<p>Rainfed crops as cereals in the semiarid are common and extensive land cover in which climate, soils and atmosphere interact trough nonlinear relationships. Earth Observations coupled to ground monitoring network allow to improve the understanding of these relationships during each cropping season. However, novel analysis is required to understand these relationships in larger periods to improve sustainability and suitability of the productive areas in the semiarid.</p><p>The aim of this work is to use a joint multifractal approach using vegetation indices, precipitation, and temperatures to analyze atmosphere-plant nonlinear relationships. For this, time series of 20 cropping seasons were used to characterize these relationships in central Spain. The Generalized Structure Function and the derived Generalized Hurst Exponent analysis were implemented to investigate precipitation, vegetation indices and temperature time series. For this, an exhaustive selection based on land use and a land cover change analysis was performed to detect plots in which cereal crop sequences are dedicated to barley and wheat over the period 2000 to 2020.</p><p>As a result, two agro zones were characterized by different multifractal properties. Precipitation series show antipersistent characteristics and fractal properties between zones while original vegetation indices show trending behavior but shifted between analyzed zones. Nonetheless, soils and rainfall events can vary interannual conditions in which the crop is developing. For vegetation indices long-term series the trend is persistent. Even so, the dynamics of vegetation indices also provide more information when annual patterns are extracted from the series, exhibiting fractal properties mainly from rainfall pattern of each zone. Finally, in this case, the joint multifractal analysis served to characterize agro zones using earth observation and climate data for extensive cereals in Central Spain.</p><p><strong>Reference</strong></p><p>Rivas-Tabares D., Tarquis A.M. (2021) Towards Understanding Complex Interactions of Normalized Difference Vegetation Index Measurements Network and Precipitation Gauges of Cereal Growth System. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_51</p><p><strong>Acknowledgements</strong></p><p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330 and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020.</p>

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