Improvement of uncertainty estimation in global hydrological models by using high resolution satellite data as an interpolator

2020 ◽  
Author(s):  
Rogier Westerhoff ◽  
Frederika Mourot ◽  
Conny Tschritter
Keyword(s):  
High Resolution ◽  
Water Balance ◽  
Groundwater Recharge ◽  
Satellite Data ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Google Earth ◽  
Data Driven ◽  
Hydrological Models ◽  
Water Balance Components

<p>Global hydrological models often ingest remotely-sensed observations supported by ground-truthed data in attempts to better quantify water balance components, e.g. soil water content, evapotranspiration, runoff/discharge, groundwater recharge. However, the scaling up process from local observations to that global, coarse, scale contains large uncertainty, often undermining the relevance of water balance calculations.</p><p>With recent more advanced high-resolution satellite data, freely available at 10m spatial resolution and (sub-) weekly temporal resolution, there is a possibility to reduce uncertainty in that upscaling. However, there are two challenges in doing so when working with global models: exponential increase of computational effort, and the need for quantifying the yet unknown uncertainty of assumptions that coarse global model cells and their underlying equations bring.</p><p>This study hypothesises that a bottom-up approach with high-resolution satellite data and in situ observations will better constrain and quantify uncertainty. By using these more spatially-explicit data, we make the case that the estimation of water balance components should become more data-driven. We propose a more data-driven model that improves uncertainty of estimation and scalability by using more sophisticated, remotely-sensed interpolation layers.</p><p>Our case study shows New Zealand-wide estimates of evapotranspiration and groundwater recharge at two resolutions: 1km x 1km, using an earlier developed model and MODIS satellite data; and a novel approach at 10m x 10m using Sentinel-1 and Sentinel-2 data to better incorporate impervious areas (e.g., anthropogenic urbanised land covers) and small land patches (e.g., small forestry areas). We then study the implications of using different spatial scales and quantify the differences between 10m x 10m versus 1km x 1km model pixel estimates. Our method is applied in the Google Earth Engine, a free-for-research high performance cloud computing facility, hence providing powerful computational resources and making our approach easily scalable to global, regional and catchment scales. </p><p>Finally, we discuss what underlying model assumptions in traditional models could be changed to facilitate a method that works consistently at these different scales.</p>

Establishing a water balance of an Alpine catchment using satellite data

2021 ◽  
Author(s):  
Roberta Perico ◽  
Paolo Frattini ◽  
Giovanni Battista Crosta ◽  
Philip Brunner
Keyword(s):  
Water Balance ◽  
Groundwater Recharge ◽  
Satellite Data ◽  
Classification System ◽  
Snow Depth ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Cloud Classification ◽  
Lower Accuracy ◽  
Sentinel 2

<p>Attaining a comprehensive and reliable water balance of snow-dominated alpine catchments is fundamental for a holistic representation of the hydrological and hydrogeological processes. In fact, their contribution to the water balance is extremely important for the water resources management and for a reliable estimation of groundwater recharge. A major limitation to the elaboration of these balances in alpine terrain are the difficultly of data acquisition as well as the limited presence of meteorological stations. These two factors considerably increase the uncertainty of water balances. Remotely sensed data can provide valuable information for the balance elaboration at a regional scale.  Among the satellite data available, the Sentinel data, collected in the ESA missions in the last 6 years, has provided free and global access of observations including optical, thermal, and microwave sensors with high spatial and temporal resolutions.</p><p>In the present work, we estimated groundwater recharge (R) for the last two hydrologic years (from March 2018 to March 2020), based on satellite data. For this purpose, the most recent methods and databases based on satellite observations were tested:  time series of the precipitation (P), the snow water equivalent (SWE), and the evapotranspiration (ET) were retrieved in an extensive Alpine catchment (26,000 km<sup>2</sup>) located in northern Italy. Daily precipitation was calculated from PERSIANN-Cloud Classification System (PERSIANN-CCS, Hong et al. 2004) database at the resolution of 4.0 km.  ET was estimated with the combined use of Sentinel 2 and 3 satellites (Guzinski et al., 2020) at a resolution of 20 m and with weekly return period. The weekly SWE was calculated starting from Sentienl 1 (C-SNOW database, Lievens et al., 2019) and Sentienl 2, at the spatial resolution of 30 m.</p><p>Based on available measurements of P, ET, and snow depth in the catchment, the uncertainty of the hydrologic estimations was quantified. We further carried out a sensitivity analysis, considering the physiographic parameters (altitude, slope, and aspect) and the seasonal conditions. For SWE estimates, an altitude-dependent effect and a lower accuracy in the snowmelt phase have been observed. The results show that the adopted satellite-based methods allow obtaining consistent and physically realistic values of recharge, with relatively low uncertainty.</p><p>References:</p><ul><li>Guzinski, R., Nieto, H., Sandholt, I., & Karamitilios, G. (2020). Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion. Remote Sensing, 12(9), 1433.</li> <li>Hong, Y., Hsu, K. L., Sorooshian, S., & Gao, X. (2004). Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. Journal of Applied Meteorology, 43(12), 1834-1853.</li> <li>Lievens, H., Demuzere, M., Marshall, H. P., Reichle, R. H., Brucker, L., Brangers, I., ... & Jonas, T. (2019). Snow depth variability in the Northern Hemisphere mountains observed from space. Nature communications, 10(1), 1-12.</li> </ul>

Simulating continental scale hydrology under projected climate change conditions: The search for the optimal parameterization

2020 ◽  
Author(s):  
Wendy Sharples ◽  
Andrew Frost ◽  
Ulrike Bende-Michl ◽  
Ashkan Shokri ◽  
Louise Wilson ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
High Resolution ◽  
Water Balance ◽  
Large Scale ◽  
Model Parameters ◽  
Hydrological Models ◽  
Water Balance Components ◽  
Impacts Of Climate Change

<p>Australia has scarce freshwater resources and is already becoming drier under the impacts of climate change. Climate change impacts and other important hydrological processes occur on multiple temporal and spatial scales, prompting the need for large-scale, high-resolution, multidecadal hydrological models. Large-scale hydrological models rely on accurate process descriptions and inputs to be able to simulate realistic multi-scale processes, however parameterization is required to account for limitations in observational inputs and sub-grid scale processes. For example, defining the soil hydraulic boundary conditions at multiple depths using soil input maps at high-resolution across an entire continent is subject to uncertainty. A common way to reduce uncertainty associated with static inputs and parameterization, thereby improving model accuracy and reliability, is to optimize the model parameters toward a long record of historical data, namely calibration. The Australian Bureau of Meteorology’s operational hydrological model (The Australian Water Resources Assessment model: AWRA-L, www.bom.gov.au/water/landscape), which provides real-time monitoring of the continental water balance, is calibrated to a combined performance metric. This metric optimizes model performance against catchment based streamflow and satellite based evaporation and soil moisture observations for 295 sites across the country, where 21 separate parameters are calibrated continentally. Using this approach, AWRA-L has been shown to reproduce independent, historical in-situ data accurately across the water balance.</p><p>Additionally, the AWRA-L model is being used to project future hydrological fluxes and states using bias corrected meteorological inputs from multiple global climate models. Towards improving AWRA-L’s performance and stability for use in hydrological projections, we aim to generate a set of model parameters that perform well under conditions of climate variability as well as under historical conditions, with a two-stage approach. Firstly, a variance based sensitivity analysis for water balance components (e.g. low/mean/high flow, soil moisture and evapotranspiration) is performed, to rank the most influential parameters affecting the water balance components and to subsequently decrease the number of calibratable parameters, thus decreasing dimensionality and uncertainty in the calibration process. Secondly, the reduced parameter set is put through a multi-objective evolutionary algorithm (Borg MOEA, www.borgmoea.org), to capture the tradeoffs between the water balance component performance objectives. The tradeoffs between the water balance component objective functions and in-situ validation data are examined, including evaluation of performance in: a) Climate zones, b) Seasons, c) Wet and dry periods, and d) Trend reproduction. This comprehensive evaluation was undertaken to choose a model parameterization (or set thereof) which produces reasonable hydrological responses under future climate variability across the water balance. The outcome is a suite of parameter sets with improved performance across varying and non-stationary climate conditions. We propose this approach to improve confidence in hydrological models used to simulate future impacts of climate change.</p>

scholarly journals High-resolution Data Based Groundwater Recharge Estimations of Aynalem Well Field

2021 ◽  
Vol 13 (1) ◽  
pp. 1-20
Author(s):  
Abdelwassie Hussien ◽  
Tesfamichael Gebreyohannes ◽  
Miruts Hagos ◽  
Gebremedhin Berhane ◽  
Kassa Amare ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Water Balance ◽  
Groundwater Recharge ◽  
Groundwater Level ◽  
Weighted Average ◽  
Level Fluctuation ◽  
Average Value ◽  
Well Field ◽  
Groundwater Level Fluctuation

Due to the ever-increasing demand for water in Aynalem catchment and its surrounding, there has been an increased pressure on the Aynalem well field putting the sustainability of water supply from the aquifer under continuous threat. Thus, it is vital to understand the water balance of the catchment to ensure sustainable utilization of the groundwater resource. This in turn requires proper quantification of the components of water balance among which recharge estimation is the most important. This paper estimates the groundwater recharge of the Aynalem catchment using high-resolution hydro-meteorological data. Daily precipitation and temperature measurement data for years 2001-2018; groundwater level fluctuation records collected at every 30 minutes; and soil and land use maps were used to make recharge estimations. In the groundwater level fluctuation, three boreholes were monitored, but only two were utilized for the analysis because the third was under operation and does not represent the natural hydrologic condition. Thornthwaite soil moisture balance and groundwater level fluctuation methods were applied to determine the groundwater recharge of the Aynalem catchment. Accordingly, the annual rate of groundwater recharge estimated based on the soil-water balance ranges between 7mm/year and 138.5 mm/year with the weighted average value of 89.04 mm/year. The weighted average value is considered to represent the catchment value because the diverse soil and land use/cover types respond differently to allow the precipitation to recharge the groundwater. On the other hand, the groundwater recharge estimated using the groundwater level fluctuation method showed yearly groundwater recharge of 91 to 93 mm/year. The similarity in the groundwater recharge result obtained from both methods strengthens the acceptability of the estimate. It also points out that the previously reported estimate is much lower (36 to 66 mm/year).

Collecting training data to map forest management at global scale

2021 ◽  
Author(s):  
Myroslava Lesiv ◽  
Dmitry Schepaschenko ◽  
Martina Dürauer ◽  
Marcel Buchhorn ◽  
Ivelina Georgieva ◽  
...  
Keyword(s):  
Forest Management ◽  
High Resolution ◽  
Remotely Sensed ◽  
Global Scale ◽  
Google Earth ◽  
Training Data ◽  
Tree Cover ◽  
Data Set ◽  
The World ◽  
Very High

<p>Spatially explicit information on forest management at a global scale is critical for understanding the current status of forests for sustainable forest management and restoration. Whereas remotely sensed based datasets, developed by applying ML and AI algorithms, can successfully depict tree cover and other land cover types, it has not yet been used to depict untouched forest and different degrees of forest management. We show for the first time that with sufficient training data derived from very high-resolution imagery a differentiation within the tree cover class of various levels of forest management is possible.</p><p>In this session, we would like to present our approach for labeling forest related training data by using Geo-Wiki application (https://www.geo-wiki.org/). Moreover, we would like to share a new open global training data set on forest management we collected from a series of Geo-Wiki campaigns. In February 2019, we organized an expert workshop to (1) discuss the variety of forest management practices that take place in different parts of the world; (2) generalize the definitions for the application at global scale; (3) finalize the Geo-Wiki interface for the crowdsourcing campaigns; and (4) build a data set of control points (or the expert data set), which we used later to monitor the quality of the crowdsourced contributions by the volunteers. We involved forest experts from different regions around the world to explore what types of forest management information could be collected from visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, in combination with Sentinel time series and Normalized Difference Vegetation Index (NDVI) profiles derived from Google Earth Engine (GEE). Based on the results of this analysis, we expanded these campaigns by involving a broader group of participants, mainly people recruited from remote sensing, geography and forest research institutes and universities.</p><p>In total, we collected forest data for approximately 230 000 locations globally. These data are of sufficient density and quality and therefore could be used in many ML and AI applications for forests at regional and local scale.  We also provide an example of ML application, a remotely sensed based global forest management map at a 100 m resolution (PROBA-V) for the year 2015. It includes such classes as intact forests, forests with signs of human impact, including clear cuts and logging, replanted forest, woody plantations with a rotation period up to 15 years, oil palms and agroforestry. The results of independent statistical validation show that the map’s overall accuracy is 81%.</p>

scholarly journals Detection of Archaeological Looting from Space: Methods, Achievements and Challenges

2019 ◽  
Vol 11 (20) ◽  
pp. 2389 ◽  
Author(s):  
Deodato Tapete ◽  
Francesca Cigna
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
High Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Google Earth ◽  
Optical Data ◽  
Mena Region ◽  
Processing Methods ◽  
Optical Imagery

Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment.

scholarly journals High-Resolution Sea Surface Temperature and Salinity in the Global Ocean from Raw Satellite Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2191 ◽  
Author(s):  
Encarni Medina-Lopez ◽  
Leonardo Ureña-Fuentes
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Data ◽  
Google Earth ◽  
Sea Surface ◽  
Global Ocean ◽  
The Neural Network ◽  
Wide Range ◽  
Sentinel 2

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82 % and 84 % for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 ∘ C and 0 . 4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

scholarly journals A simple method for water balance estimation based on the empirical method and remotely sensed evapotranspiration estimates

2020 ◽  
Vol 22 (2) ◽  
pp. 440-451
Author(s):  
George Falalakis ◽  
Alexandra Gemitzi
Keyword(s):  
Water Balance ◽  
Performance Metrics ◽  
Empirical Relationship ◽  
Remotely Sensed ◽  
Empirical Method ◽  
Data Availability ◽  
Water Balance Components ◽  
Time Step ◽  
Simple Method ◽  
Satisfactory Outcome

Abstract Developing a methodology for water balance estimation is a significant challenge, especially in areas with little or no gauging. This is because direct measurements of all the water balance components are not feasible. To overcome this issue, we propose a simple methodology based on the predefined empirical relationship between remotely sensed evapotranspiration (ET), i.e. Moderate Resolution Imaging Spectroradiometer (MODIS) ET and groundwater recharge (GR), and readily available precipitation data at the monthly time step. The developed methodology was applied in seven catchments in NE Greece using time series of precipitation and remotely sensed ET from 2009 to 2019. The potential of the proposed method to accurately estimate the water balance was assessed by the comparison of the individual water balance components against modeled values. Three performance metrics were examined and indicated that the methodology produces a satisfactory outcome. Our results indicated mean ET accounting for approximately 54% of precipitation, mean GR of 24% and mean surface runoff approximately 22% of precipitation in the study area. The proposed approach was implemented using freely available remotely sensed products and the free R software for statistical computing and graphics, offering thus a convenient and inexpensive alternative for water balance estimation, even for basins with limited data availability.

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