Figure 1 - available via license: CC BY-NC
Content may be subject to copyright.
Source publication
In the past three decades, breakthroughs in satellites and remote sensing have highly demonstrated their potential to characterize and model the various components of the hydrological cycle. A wealth of satellite missions are launched and some of the missions are specifically designed for hydrological research. Given the massive big data for hydrol...
Similar publications
This work is devoted to the development of a scientific and educational complex of competence and training of world-class specialists in end-toend technologies of space remote sensing of the Earth. The complex is aimed at developing the competencies of students at all stages of the process of creating space-based means of remote sensing of the Eart...
Citations
... pitation data, remote sensing techniques mix imagery from infrared, passive microwave, and space-borne radar sensors. (Xu et al. 2013;Vernimmen et al. 2012). (Adler et al. 2003). Nowadays, remote sensing data is readily available for various spatial and temporal scales. (Zhang et al. 2023;Abbate et al. 2021;Lengfeld et al. 2020;Ashouri et al. 2016;L. Chen and Wang 2018). ...
... The target of this research is to investigate some products of the newest technologies including NOAA's Climate Prediction Center (CPC) Morphing technique-bias-corrected product (CMORPH-CRT) (L. Chen and Wang 2018), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN) (Ashouri et al. 2016), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product (3B42RT V7) (Ma et al. 2021). ...
The evolving capabilities of satellite remote sensing have significantly improved the understanding and prediction of hydrological processes. This study investigates the potential of integrating satellite products into the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) for accurate runoff estimation. This study compares the products of three satellites, CMORPH-CRT, PERSIANN, and TRMM-3B42RT, with gauge-based datasets. In addition, the HEC-HMS model was used to evaluate their potential for modeling in the Shapoor River Basin (SRB CASE STUDY) of the Fars province, Iran. Iran is located in the Middle East where the rate of precipitation is poor and it plays a vital role in people's lives. This analogy was done for a period of 24-h with a spatial resolution of 0.25 × 0.25° latitude/longitude. The output of a quantitative statistical analogy declares that 3B42RT performs better than PERSIANN and CMORPH-CRT. The 3B42RT is showing an overestimation of the average and downpour rates while there is a different behavior in PERSIANN and CMORPH-CRT. The amount of Probability of Detection (POD) was estimated at 70% so that 3B42RT shows the best performance for streamflow simulations and CMORPH-CRT shows the worst performance over SRB CASE STUDY. The performance of all satellite products for streamflow simulations was poor. This simulation has been improved after recalibrating with input-specific precipitation data, but the results are still unreliable and poor, thus making it impossible to substitute sparse data with these products for SRB CASE STUDY.
... Earth observation datasets based on remote sensing techniques offer an effective solution to this problem (Jeyalakshmi et al., 2021;Nourani et al., 2021;Sun et al., 2019). Remote sensing permits a kind of regular sampling (in time and space) of essential hydrological parameters, such as precipitation, snow cover area, soil moisture, water storage, and evapotranspiration (Chen and Wang, 2018;Shawky et al., 2023;Ustin and Middleton, 2021). By collecting data on relatively large areas at frequent time intervals, remote sensing can help to minimize uncertainties of model parameters that are crucial for an accurate simulation of the water balance (Kunnath-Poovakka et al., 2021;Wambura et al., 2018). ...
The mitigation of uncertainties in the identification of natural systems is a fundamental aspect in the development of hydrological models, and represents a major challenge for the improvement of modelling techniques. In particular, the calibration of hydrological models based on streamflow measurements at the outlet of a catchment is exposed to significant sources of uncertainty, such as the impact of landscape features on runoff generation. Remote sensing-based actual evapotranspiration (AET) data can be incorporated with streamflow to improve model accuracy and reduce the uncertainty in hydrological modelling, resulting in a significant enhancement of the model performance. The selection of the right AET dataset for hydrological modelling is a crucial task, in front of the availability of multi-source datasets that differ in methods, parameters, and spatiotemporal resolution. Despite the existence of a few studies proposing the usage of remote sensing-based AET data, there is a lack of systematic comparisons between different products, in terms of performance for hydrological modelling. This paper aims to compare the efficacy of different remote sensing-based AET products in improving the simulation of hydrological responses, both in single and in multi-variable scenarios. In this investigation, the Soil and Water Assessment Tool (SWAT) hydrological model was calibrated with observed streamflow data by experimenting with eight different AET datasets. The findings of our study suggest that the incorporation of remote sensing-based AET data in the calibration process of a hydrological model can significantly enhance the accuracy and reliability of model predictions. Thus, the proposed approach can contribute to improving the effectiveness of hydrological modelling as a quantitative tool for the management of water resources. Another finding of this study is that the calibration of the model based solely on AET yields reasonable simulation results of the streamflow, which is an advantageous and promising feature for ungauged basins.
... Big data applications use BDA techniques to efficiently analyze large amounts of data (Mohamed et al., 2020). BD has instigated research on data mining in multiple fields (e.g., Abedini et al., 2018;Chen and Wang, 2018;Tewari and Dwivedi, 2019;Sun and Scanlon, 2019). ...
Due to the complexity of the gold element, the mineralogical and geochemical investigations are significant to trace the gold behavior in sedimentary, metamorphic, magmatic, and post-magmatic processes and to identify the physicochemical conditions (i.e., pH, Eh, temperature, pressure) of concentration in the ore bodies. The trace elements may be concentrated as the primary or secondary haloes in endogenic and exogenic deposits with multiformational types. Identification and separation of multi-formational geochemical halos associated with ore bodies have been the most significant challenge of gold deposits. Mining Geochemistry and Big Data analytics have become essential to identify the multi-formational geochemical anomalies for gold exploration in the Commonwealth of Independent States (CIS) countries. During the last three decades, pattern recognition techniques such as Bayesian and Geochemical spectra have been used to determine mineralogical and geochemical types of multi-formational anomalies. Big Data analytics has been used for integrating multi-type geoscience data and pattern recognition techniques to assess geochemical anomalies. In this study, Big Data analytics were used to generate the mineralogical and geochemical type (MGT) quantitative models based on the Bayesian, geochemical spectrum and hybrid methods for investigating the distribution of trace element contents of pyrite, galena, and sphalerite in the Gandy Gold Deposit, the Toroud-Chah Shirin (TCS) belt, north Iran. The multiformational geochemical anomalies associated with gold mineralization in the Gandy deposit indicated a multi-MGT anomaly superposition, which is a combination of two MGTs: Au + Polymetals and Au + sulfide. The results of this study demonstrated a new perspective on the distribution of geochemical anomalies and the multi- MGT geochemical modeling for gold exploration in the TCS belt. Consequently, the instigated approach is greatly conceivable and applicable to reveal contrasting near-ore multi-formational geochemical haloes for gold exploration in many other metallogenic provinces.
... Numerous satellite missions acquire a huge amount of data of different kinds (altimetry, optical, etc.) every day, which can be use- ful to set up river flow models (see e.g. [9] and references therein). One of the ultimate goals of river flow models is to estimate the space-time varying discharge Q(x, t). ...
Estimating discharges Q(x, t) from altimetric measurements only, for ungauged rivers (in particular, those with unknown bathymetry b(x)), is an ill-posed inverse problem. We develop here an algorithm to estimate Q(x, t) without prior flow information other than global open datasets. Additionally, the ill-posedness feature of this inverse problem is re-investigated. Inversions based on a Variational Data Assimilation (VDA) approach enable accurate estimation of spatio-temporal variations of the discharge, but with a bias scaling the overall estimate. This key issue, which was already highlighted in our previous studies, is partly solved by considering additional hydrological information (the drainage area, A(km2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A\ (km^2)$$\end{document}) combined with a Machine Learning (ML) technique. Purely data-driven estimations obtained from an Artificial Neural Network (ANN) provide a reasonably good estimation at a large scale (≈103\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx 10^3$$\end{document} m). This first estimation is then employed to define the first guess of an iterative VDA algorithm. The latter relies on the Saint-Venant flow model and aims to compute the complete unknowns (discharge Q(x, t), bathymetry b(x), friction coefficient K(x, t)) at a fine scale (approximately 102\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^2$$\end{document} m). The resulting complete inversion algorithm is called the H2iVDI algorithm for "Hybrid Hierarchical Variational Discharge Inference". Numerical experiments have been analyzed for 29 heterogeneous worldwide river portions. The obtained estimations present an overall bias (less than 30% for rivers with similar characteristics than those used for calibration) smaller than previous results, with accurate spatio-temporal variations of the flow. After a learning period of the observed rivers (e.g. one year), the algorithm provides two complementary estimators: a dynamic flow model enabling estimations at a fine scale and spatio-temporal extrapolations, and a low complexity estimator (based on a dedicated algebraic low Froude flow model). This last estimator provides reasonably accurate estimations (less than 30% for considered rivers) at a large scale from newly acquired WS measurements in real-time, therefore making it a potentially operational algorithm.
... The need for acquiring fine-scale information on terrain and permafrost conditions across broad spatial scales is being partially overcome by rapid advances in Earth observation sensors, data acquisition platforms, and processing power (Chen and Wang 2018;Giuliani et al. 2019), which have accelerated remote detection of thaw-driven permafrost landscape change at multiple spatial scales Nitze et al. 2018). Machine learning techniques hold significant promise for automating image analyses to recognize fine-scale spatial patterns of Earth surface processes and landforms, with applications to the vast, rapidly changing, and remote permafrost environments (Siewert 2018;Huang et al. 2020). ...
This paper documents the first comprehensive inventory of thermokarst and thaw-sensitive terrain indicators for a 2 million km² region of northwestern Canada. This is accomplished through the Thermokarst Mapping Collective (TMC), a research collaborative to systematically inventory indicators of permafrost thaw sensitivity by mapping and aerial assessments across the Northwest Territories (NT), Canada. The increase in NT-based permafrost capacity has fostered science leadership and collaboration with government, academic, and community researchers to enable project implementation. Ongoing communications and outreach have informed study design and strengthened Indigenous and stakeholder relationships. Documentation of theme-based methods supported mapper training, and flexible data infrastructure facilitated progress by Canada-wide researchers throughout the COVID-19 pandemic. The TMC inventory of thermokarst and thaw-sensitive landforms agree well with fine-scale empirical mapping (69%–84% accuracy) and aerial inventory (74%–96% accuracy) datasets. National- and circumpolar-scale modelling of sensitive permafrost terrain contrasts significantly with TMC outputs, highlighting their limitations and the value of empirically based mapping approaches. We demonstrate that the multiparameter TMC outputs support a holistic understanding and refined depictions of permafrost terrain sensitivity, provide novel opportunities for regional syntheses, and inform future modelling approaches, which are urgently required to comprehend better what permafrost thaw means for Canada's North.
... The structured, semistructured, and unstructured geospatial data generated by the continuous production of natural resources are collected, and a geospatial data system with an integration layer and related technical components is built [7]. Chen et al. developed and opened the online index and query system SKSO pen for large-scale geospatial data, which integrates with Terra Fly geospatial database to visualize query results and data analysis [8]. Cudennec et al. believed that, as the core of geographic information services, the spatial analysis presented two main development trends under the big data background. ...
The hydrogeological investigation is a work carried out by comprehensive utilization of various exploration methods to identify hydrogeological conditions in the target area, and develop and utilize groundwater resources. There are great differences in hydrogeological conditions in different regions. Hence, it is necessary to take exploration technology according to local conditions to master hydrogeological information as much as possible. Among them, the remote sensing (RS) technology can reflect the ground surveying and mapping results with high efficiency and precision through the analysis of satellite or aerial photographs, which is a commonly used method in the current hydrogeological investigation. According to satellite RS data, this work evaluates the distribution of groundwater levels in the study area and explores the geological and hydrogeological conditions of the groundwater system in the affected area. Firstly, the human-computer interactive interpretation method is used to analyze the topography and geomorphology conditions. Secondly, the spectral characteristic curve analysis method is used to extract the spectral characteristics of regional stratum lithology, and analyze and determine the lithology composition and structure of the aquifer. Thirdly, the single-band and multiband models of soil moisture RS estimation of groundwater level are implemented. Finally, the measured data are employed to verify and analyze the estimated value of the model. The results are in line with the actual value, and good results have been achieved.
... In this vein, recent and rapid technological advancements are providing new instrumentation, impressive computational power and huge data storage opportunities to deal with big volumes of hydrological data (Butler 2014;Tauro et al. 2018). In turn, big data mandate advanced data analysis techniques (Chen and Han 2016;Chen and Wang 2018;Blöschl et al. 2019;Sun and Scanlon 2019;Papacharalampous et al. 2021). ...
Understanding the response of a catchment is a crucial problem in hydrology, with a variety of practical and theoretical implications. Dissecting the role of sub-basins is helpful both for advancing current knowledge of physical processes and for improving the implementation of simulation or forecast models. In this context, recent advancements in sensitivity analysis tools could be worthwhile for bringing out hidden dynamics otherwise not easy to distinguish in complex data driven investigations. In the present work seven feature importance measures are described and tested in a specific and simplified proof of concept case study. In practice, simulated runoff time series are generated for a watershed and its inner 15 sub-basins. A machine learning tool is calibrated using the sub-basins time series for forecasting the watershed runoff. Importance measures are applied on such synthetic hydrological scenario with the aim to investigate the role of each sub-basin in shaping the overall catchment response. This proof of concept offers a simplified representation of the complex dynamics of catchment response. The interesting result is that the discharge at the catchment outlet depends mainly on 3 sub-basins that are consistently identified by alternative sensitivity measures. The proposed approach can be extended to real applications, providing useful insights on the role of each sub-basin also analyzing more complex scenarios.
... In this vein, recent and rapid technological advancements are providing new instrumentation, impressive computational power and huge data storage opportunities to deal with big volumes of hydrological data (Butler 2014;Tauro et al. 2018). In turn, big data mandate advanced data analysis techniques (Chen and Han 2016;Chen and Wang 2018;Blöschl et al. 2019;Sun and Scanlon 2019;Papacharalampous et al. 2021). ...
Understanding the response of a catchment is a crucial problem in hydrology, with a variety of practical and theoretical implications. Dissecting the role of sub-basins is helpful both for advancing current knowledge of physical processes and for improving the implementation of simulation or forecast models. In this context, recent advancements in sensitivity analysis tools could be worthwhile for bringing out hidden dynamics otherwise not easy to distinguish in complex data driven investigations. In the present work seven feature importance measures are described and tested in a specific and simplified proof of concept case study. In practice, simulated runoff time series are generated for a watershed and its inner 15 sub-basins. A machine learning tool is calibrated using the sub-basins time series for forecasting the watershed runoff. Importance measures are applied on such synthetic hydrological scenario with the aim to investigate the role of each sub-basin in shaping the overall catchment response. This proof of concept offers a simplified representation of the complex dynamics of catchment response. The interesting result is that the discharge at the catchment outlet depends mainly on 3 sub-basins that are consistently identified by alternative sensitivity measures. The proposed approach can be extended to real applications, providing useful insights on the role of each sub-basin also analyzing more complex scenarios.
... Geographic Information Systems (GIS) are increasingly being applied in academic research and teaching in a wide range of fields. A huge amount of geospatial data has become available from the thousands of satellite, airborne, and ground-based remote sensing systems, including data provided by "citizen science" (Butler, 2014;Chen and Wang, 2018). However, our ability to collect data has outpaced our ability to process and analyse it (Reichstein et al., 2019). ...
In this paper, we discuss new challenges and opportunities in teaching geoinformatics on a conceptual level. We pose that spatial is not special anymore from a technological point of view, but at the same time teaching spatial thinking is more challenging than ever. We summarize our key positions and conclude with a call to action to rethink how we can ensure that our teaching of core geoinformation sciences and technologies remains a strong and supportive education pillar in our modern data-driven world.
... In this vein, recent and rapid technological advancements are providing new instrumentation, impressive computational power and huge data storage opportunities to deal with big volumes of hydrological data (Butler, 2014;Tauro et al, 2018). In turn, big data mandate advanced data analysis techniques (Chen and Han, 2016;Chen and Wang, 2018;Blöschl et al, 2019;Sun and Scanlon, 2019;Papacharalampous et al, 2021). Among emerging statistical and data mining methods, machine learning (ML) approaches have had an impressive diffusion in the environmental sciences and specifically in hydrology. ...
Understanding the response of a catchment is a crucial problem in hydrology, with a variety of practical and theoretical implications. Dissecting the role of sub-basins is helpful both for advancing current knowledge on physical processes and for improving the implementation of simulation or forecast models. In this context, recent advancements in sensitivity analysis tools could be worthwhile for bringing out hidden dynamics otherwise not easy to discriminate in complex data driven investigations. In the present work seven feature importance measures are described and tested in a specific and simplified proof of concept case study. In practice, simulated runoff time series are generated for a watershed and its inner 15 sub-basins. A machine learning tool is calibrated using the sub-basins time series for forecasting the watershed runoff. Importance measures are applied on such synthetic hydrological scenario with the aim to investigate the role of each sub-basin in shaping the overall catchment response. This proof of concept offers a simplified representation of the complex dynamics of catchment response. The interesting result is that the discharge at the catchment outlet depends mainly on 3 sub-basins that are consistently identified by alternative sensitivity measures. The proposed approach can be extended to real applications, providing useful insights on the role of each sub-basin also analyzing more complex scenarios.
