Improving a continental hydrological model by enhancing its hydrological representation and implementing at 1km spatial resolution

2020 ◽  
Author(s):  
Cherry May Mateo ◽  
Jai Vaze ◽  
Biao Wang
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Spatial Data ◽  
Hydrological Model ◽  
Water Bodies ◽  
Large Water ◽  
Improved Model ◽  
Agricultural Areas ◽  
Spatial Grids ◽  
Impervious Areas

<p>The Australian Water Resources Assessment Landscape (AWRA-L) model is a continental hydrological model developed by the Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BoM) of Australia which is essential in providing consistent and reliable water resources assessments and accounts across continental Australia. The operational version of the AWRA-L model provides estimates of landscape runoff, evapotranspiration, soil moisture, and groundwater recharge/storage at a spatial resolution of 5km grids. Each 5km grid is assumed to have two hydrological response units (HRUs) – shallow-rooted vegetation and deep-rooted vegetation. To improve the landscape dynamics within the model, CSIRO and BoM increased the number of HRUs from two to five by representing the hydrological processes of the following: irrigated agricultural areas, perennial large water bodies, and impervious areas. The spatial resolution of the model was also increased to 1km grids to improve its applicability for management purposes in local areas.</p><p>In this presentation, a summary of the results of the improved model using the Murrumbidgee River as a test basin will be discussed. Overall, the results suggest that the incorporation of the extra HRUs enabled the explicit representation of hydrological processes in irrigated areas, large water bodies, and impervious areas. Particularly, significant improvement was seen in the comparison of the simulated soil moisture with the observed. With the implementation of the model at a finer 1km spatial resolution, the improved model can now provide more realistic estimates of the water balance which are more suitable for use in catchment and local scale applications.</p><p>To implement the improved model in other catchments within Australia as well as for the entire continent, numerous spatial data inputs to the model must be prepared. To ensure the reliability and consistency of the spatial data layers, the most recent and best available data were used to derive and regenerate the AWRA-L spatial input layers for the Australian continent. The 48 input spatial layers to the improved 5 HRU AWRA-L model have been updated and made available both at 5km and 1km spatial grids. The climatological inputs from 1970-2012 have also been prepared to match with the spatial grids of the AWRA-L model. The updated spatial layers will be shown in this presentation.  The updated input spatial layers are essential for implementing the improved AWRA-L model at any catchment within continental Australia. Local catchments with a high fraction of irrigated agricultural areas, impervious areas, or large water bodies will benefit the most from these updates. While the spatial layers were prepared for use in the AWRA-L model, they may also be useful for the development of large-scale hydrological models as well as to the hydrological community, in general.</p>

scholarly journals Sentinel-2 Application to the Surface Characterization of Small Water Bodies in Wetlands

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1487 ◽  
Author(s):  
Jesús Pena-Regueiro ◽  
Maria-Teresa Sebastiá-Frasquet ◽  
Javier Estornell ◽  
Jesús Antonio Aguilar-Maldonado
Keyword(s):  
Spatial Resolution ◽  
Spatial Data ◽  
Surface Characterization ◽  
Management Strategies ◽  
Water Bodies ◽  
Kappa Index ◽  
Small Water ◽  
Management Actions ◽  
Sentinel 2A ◽  
Small Water Bodies

Developing indicators to monitor environmental change in wetlands with the aid of Earth Observation Systems can help to obtain spatial data that is not feasible with in situ measures (e.g., flooding patterns). In this study, we aim to test Sentinel-2A/B images suitability for detecting small water bodies in wetlands characterized by high diversity of temporal and spatial flooding patterns using previously published indices. For this purpose, we used medium spatial resolution Sentinel-2A/B images of four representative coastal wetlands in the Valencia Region (East Spain, Mediterranean Sea), and on three different dates. To validate the results, 60 points (30 in water areas and 30 in land areas) were distributed randomly within a 20 m buffer around the border of each digitized water polygon for each date and wetland (600 in total). These polygons were mapped using as a base map orthophotos of high spatial resolution. In our study, the best performing index was the NDWI. Overall accuracy and Kappa index results were optimal for −0.30 threshold in all the studied wetlands and dates. The consistency in the results is key to provide a methodology to characterize water bodies in wetlands as generalizable as possible. Most studies developed in wetlands have focused on calculating global gain or loss of wetland area. However, inside of wetlands which hold protection figures, the main threat is not necessarily land use change, but rather water management strategies. Applying Sentinel-2A/B images to calculate the NDWI index and monitor flooded area changes will be key to analyse the consequence of these management actions.

scholarly journals An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

2017 ◽  
Vol 44 ◽  
pp. 89-100 ◽  
Author(s):  
Luca Cenci ◽  
Luca Pulvirenti ◽  
Giorgio Boni ◽  
Marco Chini ◽  
Patrick Matgen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Hydrological Model ◽  
High Spatial Resolution ◽  
Flash Flood ◽  
Soil Moisture Data ◽  
Time Period ◽  
The Impact

Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

scholarly journals Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations

2016 ◽  
Vol 20 (7) ◽  
pp. 3059-3076 ◽  
Author(s):  
Patricia López López ◽  
Niko Wanders ◽  
Jaap Schellekens ◽  
Luigi J. Renzullo ◽  
Edwin H. Sutanudjaja ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Observation System ◽  
Data Set ◽  
Coarse Resolution ◽  
The Impact

Abstract. The coarse spatial resolution of global hydrological models (typically  >  0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (downscaled from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). Downscaled satellite-derived soil moisture (downscaled from  ∼  0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse-resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently made to move to global hyper-resolution modelling and can help to advance this research.

Development of simple distributed hydrological model based on soil moisture simulation

2020 ◽  
Author(s):  
Jiyu Seo ◽  
Jeonghyeon Choi ◽  
Sangdan Kim
Keyword(s):  
Soil Moisture ◽  
Spatial Data ◽  
Hydrological Model ◽  
Hydrologic Model ◽  
Complex Model ◽  
Computer Hardware ◽  
Distributed Hydrological Model ◽  
Model Framework ◽  
Model Based ◽  
Ungauged Watershed

<p>One of challenges to hydrologists is to estimate runoff from ungauged watershed. Hydrologic estimation through modelling is a reasonable, economical and useful approach to quantity and quality management of watershed. The model framework has been comprehensive and complex to reproduce natural phenomena more realistically with the development of computer hardware. However, driving a complex model requires a lot of effort and time, and the use of many parameters reduces the accessibility of end users and the applicability to the ungauged watershed. In this study, we developed a distributed hydrologic model based on soil moisture simulation using simple composition and fewer parameters. Instead of minimizing the number of parameters, GIS data were used to reflect the watershed characteristics into the model. The proposed model was applied to the four dam watersheds in Korea to assess its performance. As a result, it is confirmed that reasonable hydrologic components simulation is possible through the simulation of soil moisture, even though it was a simple model with only three input parameters. If spatial data such as satellite data is additionally applied, the performance of the model is expected to improve further.</p><p><strong>Acknowledgment:</strong> This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Public Technology Program based on Environmental Policy Project, funded by Korea Ministry of Environment(MOE)(2016000200002).</p><p><strong>Keywords</strong>: Distributed hydrological model; Hydrologic components simulation; Soil moisture; Simple hydrological model.</p>

scholarly journals Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations

2015 ◽  
Vol 12 (10) ◽  
pp. 10559-10601 ◽  
Author(s):  
P. Lopez Lopez ◽  
N. Wanders ◽  
J. Schellekens ◽  
L. J. Renzullo ◽  
E. H. Sutanudjaja ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Added Value ◽  
Coarse Resolution ◽  
The Impact

Abstract. The coarse spatial resolution of global hydrological models (typically > 0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tuned river models. A possible solution to the problem may be to drive the coarse resolution models with locally available high spatial resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee river basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (from 0.5° downscaled to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high resolution gauging station based gridded dataset (0.05°). Downscaled satellite derived soil moisture (from approx. 0.5° downscaled to 0.08° resolution) from AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently done to go to global hyper-resolution modelling and can help to advance this research.

Analyzing different ways of assimilating volume change estimates for surface water bodies into a hydrological model

2020 ◽  
Author(s):  
Olga Engels ◽  
Kerstin Schulze ◽  
Jürgen Kusche ◽  
Simon Deggim ◽  
Annette Eicker ◽  
...  
Keyword(s):  
Surface Water ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Horizontal Resolution ◽  
Water Bodies ◽  
Satellite Mission ◽  
Grace Data ◽  
Spatial Noise ◽  
Lakes And Reservoirs ◽  
Surface Water Bodies

<p>To better understand global freshwater resources, we combine the state-of-the-art global hydrological model WGHM with Total Water Storage Anomalies (TWSA) derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission in an ensemble-based calibration and data assimilation (CDA) framework. However, when dealing with GRACE data, their limited horizontal resolution represents a major challenge. Filtering and/or ’destriping’ is the usual approach for suppressing GRACE-specific spatial noise, which causes spatial leakage and in turn attenuation of signal and reduction of spatial resolution. In GlobalCDA project, we derive altimetry-based storage variations along with corresponding uncertainties of surface water bodies, such as lakes and reservoirs, that feature significantly higher spatial resolution compared to GRACE-based TWSA. These can, additionally, be incorporated into the CDA framework.</p><p>In this study, we investigate several possibilities on how to use the additional remote sensing observations within the CDA over the Mississippi basin for the time span 2003 - 2016. For this, we run the CDA (i) using GRACE-based TWSA only, (ii) removing altimetry-based storage variations of surface water bodies from GRACE-TWSA, (iii) removing and restoring altimetry-based storage variations for GRACE-TWSA, and (iv) directly using altimetry-based storage variations. New observation operators are constructed for (ii) and (iv). The results are validated against independent discharge observations.</p>

Risk of infestation of population by opisthorchiasis causative agent in the middle and lower current of the Iset river. influence of loimopotential of the focus of infestation on the risk values

2017 ◽  
pp. 52-56
Author(s):  
A.V. Ushakov ◽  
R.G. Fattakhov ◽  
T.F. Stepanova
Keyword(s):  
Causative Agent ◽  
Water Bodies ◽  
Risk Of Infection ◽  
Natural Focus ◽  
Opisthorchis Felineus ◽  
Large Water

The risk of infestation of the population by the opisthorchiasis causative agent in the middle and lower reaches of the Iset River was estimated. Areas with the highest risk of peoples infection by Opisthorchis felineus’ metacercaria are identified. These territories are confined to the zones of removal of the opisthorchiasis causative agent, which are river beds and large water bodies that constantly connect with rivers. Steady risk of infection of the population is determined by the loimopotential of the opisthorchiasis natural focus. The general infestation of juveniles fishes in the middle and lower current of the Iset River made 9,9 %, annuals – 21,5 %, two-year-olds – 19,5 %.

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