Observation-based Approach Research Articles

CPS-based power tracking control for distributed energy storage aggregator in demand-side management

The deployment of distributed energy storage on the demand side has significantly enhanced the flexibility of power systems. However, effectively controlling these large-scale and geographically dispersed energy storage devices remains a major challenge in demand-side management. In this paper, we propose a CPS-based framework for controlling a distributed energy storage aggregator (DESA) in demand-side management. Within this framework, a distributed power tracking control algorithm is designed to ensure both power tracking and state-of-charge (SoC) balancing among the energy storage units (ESUs) within the DESA. The proposed algorithm utilizes a distributed observation-based approach that relies solely on local communication. It is demonstrated that the algorithm achieves power tracking convergence within a fixed time, while asymptotically achieving SoC balancing when assuming a connected communication network among the storage units. To validate the theoretical analysis and demonstrate the effectiveness of the proposed control strategy, an example scenario comprising six ESUs is presented.

Co-occurrence and abundance of pollinators and pests in horticultural systems in Africa using an integrated Earth observation-based approach

ABSTRACT Flower-visiting insects that are pollinators play a critical role in promoting biodiversity in agroecosystems and agricultural food production through their pollination ecosystem service. However, several factors affect the survival of these pollinators and flower visitors, including the heavy and indiscriminate application of agrochemicals to control crop insect pests, which is impacted by various cropping patterns in a landscape and by shifting environmental conditions. Thus, this study focused on investigating the influence of cropping patterns on the spatial distribution of pollinators (Apis mellifera, Hymenoptera other than A. mellifera, and Syrphidae), flower visitors (Calliphoridae), and pests, i.e. fruit fly (Bactrocera dorsalis) and false codling moth (Thaumatotibia leucotreta) of the avocado, a pollinator-dependent crop. Cropping patterns, earth observation data and relevant environmental variables were used as the predictor variables for modeling the potential distribution and abundance of avocado pollinators, flower visitors and pests in one of the leading regions in avocado production in Kandara, Maragua, and Gatanga sub-Counties in Murang’a County, Kenya. In specific, species distribution modeling (SDM) and species abundance modeling (SAM) techniques, i.e. the maximum entropy (MaxEnt) model (presence-only data) and negative binomial (NB) distribution in a generalized linear model (GLM) (abundance data) were used, respectively. Additionally, the spatial distribution probability of the co-occurrence of the pollinators, flower visitors and pests was also analyzed. This study revealed that cropping patterns was the most consistent influential predictor variables for the distribution of avocado pollinators, flower visitors and pests. A large area of Kandara and some parts of Maragua and Gatanga sub-Counties showed a high spatial distribution probability of the studied avocado pollinators, flower visitors and pests. However, only the majority of Kandara sub-County had a high spatial distribution probability score of the potential co-occurrence of the avocado pollinators, flower visitors and pests. Further, A. mellifera was the most abundant flower-visiting pollinator compared with the other studied pollinators, while B. dorsalis was the most abundant avocado pest compared with T. leucotreta. In addition, GLM analysis indicated that no environmental variable was significant in explaining the abundance of the studied avocado pollinators, whereas precipitation and elevation derivatives of aspect and hillshade were statistically significant (p ≤ 0.05) in explaining the abundance of B. dorsalis. Solar radiation was significant in explaining only the abundance of T. leucotreta. Our study revealed that SDM and SAM modeling outputs can be used to inform decision-making for the implementation of sustainable management efforts regarding pollinators, flower visitors, and insect pests.

Advanced System for Enhancing Location Identification through Human Pose and Object Detection

Location identification is a fundamental aspect of advanced mobile robot navigation systems, as it enables establishing meaningful connections between objects, spaces, and actions. Understanding human actions and accurately recognizing their corresponding poses play pivotal roles in this context. In this paper, we present an observation-based approach that seamlessly integrates object detection algorithms, human pose detection, and machine learning techniques to effectively learn and recognize human actions in household settings. Our method entails training machine learning models to identify the common actions, utilizing a dataset derived from the interaction between human pose and object detection. To validate our approach, we assess its effectiveness using a diverse dataset encompassing typical household actions. The results demonstrate a significant improvement over existing techniques, with our method achieving an accuracy of over 95% in classifying eight different actions within household environments.. Furthermore, we ascertain the robustness of our approach through rigorous testing in real-world environments, demonstrating its ability to perform well despite the various challenges of data collection in such settings. The implications of our method for robotic applications are significant, as a comprehensive understanding of human actions is essential for tasks such as semantic navigation. Moreover, our findings unveil promising opportunities for future research, as our approach can be extended to learn and recognize a wide range of other human actions. This perspective, which highlights the potential leverage of these techniques, provides an encouraging path for future investigations in this field.

Abstract 129: Determine the genetic similarity of synchronous endometrial and ovarian cancers with variants in polyguanine sequences

Abstract Endometrial and ovarian cancers are both fearsome gynecological diseases with high mortality rates worldwide. Due to their histological similarity, it is still a clinical challenge to distinguish between double primary cancer (DPC) and metastasis cancer (MC) in synchronous endometrial and ovarian cancers in which the latter represents a poor prognostic outcome. Clinically, the standard procedure for examining DPC and MC relies on their morphology and histopathology; however, showing poor accuracy even under explicating criteria. Here, we attempt to utilize indel variants within polyguanine (polyG) sequences to generate genetic distances of synchronous endometrial and ovarian tumors to normal tissue in a patient and then calculate the correlation coefficient for them. Polyguanine sequences are hypermutable guanine repeats that acquire insertion or deletion at a high incident rate (mutation rate: ~10-6 - 10-4; ~1000 folds higher than unique sequence) and, therefore, could serve as a very rich resource for comparing the genetic similarity between samples. The correlation between selected clinical variables, e.g., disease-free survival and the described correlation coefficient is then calculated to test whether this protocol can serve as an optimal tool for distinguishing between DPC and MC. We have assembled a cohort of 39 patients with synchronous endometrial and ovarian cancers in which pathologists cannot determine the relationship between endometrial and ovarian cancer in 23 of them. Preliminarily, we have collected DNA from formalin-fixed paraffin-embedded (FFPE) specimens and completed polyG genotyping for four of them. Indel variants, both insertions and deletions, are rich in all cases indicating the approach is technically rigid for this purpose even with FFPE specimens. Subsequently, we evaluate the similarity between endometrial and ovarian cancer by calculating Spearman's correlation with the genetic distances obtained from polyG genotyping. As the result of preliminary cases, we observe the correlation coefficient spreading between -0.14 to 0.95, implying this methodology can determine the genetic similarity between endometrial and ovarian tumor in a case and, therefore, could be a potential tool to supplement the current observation-based approach in the clinic. Our next step is to correlate the correlation coefficients with meaningful clinical variables once we complete genotyping the entire cohort. We anticipate this approach could provide additional information to distinguish DPC and MC and therefore benefit the patients. Citation Format: Yu-Yi Huang, Pei-Chi Cheng, Ching Hsu, Yung-Ming Jeng, Lin-Hung Wei, Wei-Ting Hung. Determine the genetic similarity of synchronous endometrial and ovarian cancers with variants in polyguanine sequences [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 129.

Future global streamflow declines are probably more severe than previously estimated

Climate change and increasing water use associated with socio-economic growth have exacerbated the water crisis in many parts of the world. Many regional studies rely on Earth System Models that, however, do not fully exploit streamflow observations. Here we offer an observation-based approach to predicting streamflow change on the basis of the elasticity of streamflow to their climate drivers observed at 9,505 catchments across the globe. We show that near-future (2021–2050) global streamflow may be lower than predicted by Earth System Models, particularly in Africa, Australia and North America. The lower streamflow predicted here is due to smaller contributions from precipitation and stronger sensitivity of streamflow to changes in evapotranspiration, which is related to increased radiation energy and vapour transfer, and enhanced vegetation greening. Our estimate points towards the possibility that a future water crisis could be more severe than anticipated. Earth System Models are widely used for projecting future streamflow. The authors show that the models underestimate declines in global streamflow with climate change, indicating that a future water crisis could be more severe than previously estimated.

Spectral mixture analysis of AVIRIS-NG data for grouping plant functional types

In a recent scenario, the prediction of environmental changes through plant functional types can aid in simplification of ecosystem processes. The present study attempts to identify and map plant functional types (PFTs) in the AVIRIS-NG campaign site, namely Shoolpaneshwar Wildlife Sanctuary (site id 67), using AVIRIS-NG data combined with spectral mixture analysis that accounts for endmember variability. Due to the occurrence of heterogeneous vegetation in the selected AVIRIS-NG site, the measured spectral signal for every pixel of surface reflectance data will be the outcome of fractions in which various plant functional types and the soil background exist. The interest of the present research lies in these fractions; hence spectral mixture analysis was applied. Ground truthing was carried out simultaneous to AVIRIS-NG flight pass. Six plant functional traits, namely Diameter at breast height (DBH), Height, biomass, leaf chlorophyll content (CC), Fraction of Photosynthetic Active Radiation (FPAR), and Leaf Area Index (LAI), were measured for twenty-three tree species found in the study site which were further used for plant functional grouping by applying k-means clustering algorithm. Trait-based cluster analysis classified the tree species into four plant functional types. Endmember selection from AVIRIS-NG image for these plant functional types was done using a manual field observation-based approach, which was used as input in spectral mixture analysis. The final product of the analysis is a set of fractional abundance images for each plant functional type. Considerable accuracy was obtained on validating the fractional abundance image by in situ data. The study highlighted the potential of a spectral mixture analysis classifier in identifying and mapping different plant functional types using AVIRIS-NG data when performed using an appropriate number of end members.

Kekerabatan merpati hias (Columba livia) dilihat dari profil protein darah

Objective: The purpose of this study was to predict the kinship of ornamental pigeons based on blood proteins. Methods: The research method used is exploratory descriptive with a laboratory observation-based approach. Research procedures include: Blood Sampling, Blood Protein Isolation, Purification, Measurement of concentration with a nanodrop spectrophotometer and Electrophoresis. Data analysis includes: Protein Band Profile and Determination of Protein Molecular Weight with RF formula and Relationship with MVSP version 3.22 program. Results : The protein profile in the plasma of ornamental pigeons is different from one type of pigeon to another. some of the protein appears in the ornamental pigeon species but does not appear in other breeds. Likewise with the target protein to be analyzed, some are expressed and some are not expressed in ornamental pigeons. Results based on the analysis of protein profiles and dendograms of the four specific proteins (hemoglobin, transferrin, posttransferrin, and immunoglobulin) contained in erythrocytes and blood plasma, it can be seen that there are two clusters. Cluster 1 is occupied by varieties of ornamental pigeons Moven and cluster 2 is occupied by varieties of ornamental pigeons Lahore, American fantail, Vienna, Indian Fantail, King, Capucine, Gondok and Modena. Conclusions: The results of the ornamental pigeon kinship prediction based on blood protein showed that each variety had different kinship relationships. King, Capucine, Gondok and Modena pigeons have the closest relationship, and Moven pigeons have the most distant relationship from the researched ornamental pigeons.

Implementation and validation of the FRi3D flux rope model in EUHFORIA

The “Flux Rope in 3D” (FRi3D, Isavnin, 2016), a coronal mass ejection (CME) model with global three-dimensional (3D) geometry, has been implemented in the space weather forecasting tool EUHFORIA (Pomoell and Poedts, 2018). By incorporating this advanced flux rope model in EUHFORIA, we aim to improve the modelling of CME flank encounters and, most importantly, the magnetic field predictions at Earth. After using synthetic events to showcase FRi3D’s capabilities of modelling CME flanks, we optimize the model to run robust simulations of real events and test its predictive capabilities. We perform observation-based modelling of the halo CME event that erupted on 12 July 2012. The geometrical input parameters are constrained using the forward modelling tool included in FRi3D with additional flux rope geometry flexibilities as compared to the pre-existing models. The magnetic field input parameters are derived using the differential evolution algorithm to fit FRi3D parameters to the in situ data at 1 AU. An observation-based approach to constrain the density of CMEs is adopted, in order to achieve a better estimation of mass corresponding to the FRi3D geometry. The CME is evolved in EUHFORIA’s heliospheric domain and a comparison of FRi3D’s predictive performance with the previously implemented spheromak CME in EUHFORIA is presented. For this event, FRi3D improves the modelling of the total magnetic field magnitude and Bz at Earth by ∼30% and ∼70%, respectively. Moreover, we compute the expected geoeffectiveness of the storm at Earth using an empirical Dst model and find that the FRi3D model improves the predictions of minimum Dst by ∼20% as compared to the spheromak CME model. Finally, we discuss the limitations of the current implementation of FRi3D in EUHFORIA and propose possible improvements.

An observation-based approach to calculating ice-shelf calving mass flux

In order to determine whether the calving flux of an ice shelf is changing, the long-term calving flux needs to be established. Methods used to estimate the calving flux either take into account non-steady-state behaviour by capturing movement of the calving-front location (e.g., using satellite observations), or they assume the calving front is stationary and that the ice is in steady state (e.g., flux-gate methods). Non-steady-state methods are hampered by the issue of temporal aliasing, i.e., when the satellite observation frequency is insufficient to capture the cyclic nature of the calving-front position. Methods that assume a steady state to estimate the calving flux accrue uncertainties if the ice shelf changes its physical state. In order to overcome these limitations we propose and implement a new observation-based approach that combines a time series of calving-front locations with a flux-gate method. The approach involves the creation of a unique semi-temporal domain as a mechanism to overcome the issue of temporal aliasing, and only requires easily accessible ice thickness and surface velocity estimates of the ice shelf. This approach allows for complex calving-front geometries and captures calving events of all sizes that are visible within the satellite imagery. Application of the approach allows the long-term average calving flux to be estimated (provided sufficient temporal coverage by satellite imagery), as well as identification of the minimum temporal baseline needed to produce a representative estimate of the long-term average calving flux, for any ice shelf. Implementation of the approach to multiple ice shelves would enable comparisons to be made regarding the spatial variability in the long-term calving flux of Antarctica's ice shelves, thereby highlighting calving regime change around the continent.

Deep Reinforcement Learning-Based Robotic Grasping in Clutter and Occlusion

In robotic manipulation, object grasping is a basic yet challenging task. Dexterous grasping necessitates intelligent visual observation of the target objects by emphasizing the importance of spatial equivariance to learn the grasping policy. In this paper, two significant challenges associated with robotic grasping in both clutter and occlusion scenarios are addressed. The first challenge is the coordination of push and grasp actions, in which the robot may occasionally fail to disrupt the arrangement of the objects in a well-ordered object scenario. On the other hand, when employed in a randomly cluttered object scenario, the pushing behavior may be less efficient, as many objects are more likely to be pushed out of the workspace. The second challenge is the avoidance of occlusion that occurs when the camera itself is entirely or partially occluded during a grasping action. This paper proposes a multi-view change observation-based approach (MV-COBA) to overcome these two problems. The proposed approach is divided into two parts: 1) using multiple cameras to set up multiple views to address the occlusion issue; and 2) using visual change observation on the basis of the pixel depth difference to address the challenge of coordinating push and grasp actions. According to experimental simulation findings, the proposed approach achieved an average grasp success rate of 83.6%, 86.3%, and 97.8% in the cluttered, well-ordered object, and occlusion scenarios, respectively.

Could Nearby Star-forming Galaxies Light Up the Pointlike Neutrino Sky?

Abstract Star-forming and starburst galaxies, which are well-known cosmic-ray reservoirs, are expected to emit gamma-rays and neutrinos predominantly via hadronic collisions. In this Letter, we analyze the 10 yr Fermi-LAT spectral energy distributions of 13 nearby galaxies by means of a physical model which accounts for high-energy proton transport in starburst nuclei and includes the contribution of primary and secondary electrons. In particular, we test the hypothesis that the observed gamma-ray fluxes are mostly due to star-forming activity, in agreement with the available star formation rates coming from IR and UV observations. Through this observation-based approach, we determine the most likely neutrino counterpart from star-forming and starburst galaxies and quantitatively assess the ability of current and upcoming neutrino telescopes to detect them as pointlike sources. Remarkably, we find that the cores of the Small Magellanic Cloud and the Circinus galaxy are potentially observable by KM3NeT/ARCA with 6 yr of observation. Moreover, most of the nearby galaxies are likely to be just a factor of a few below the KM3NeT and IceCube-Gen2 pointlike sensitivities. After investigating the prospects for detection of gamma-rays above TeV energies from these sources, we conclude that the joint observations of high-energy neutrinos and gamma-rays with upcoming telescopes will be an objective test for our emission model and may provide compelling evidence of star-forming activity as a tracer of neutrino production.

Evaluating the Impacts of Cloud Microphysical and Overlap Parameters on Simulated Clouds in Global Climate Models

The improvement of the accuracy of simulated cloud-related variables, such as the cloud fraction, in global climate models (GCMs) is still a challenging problem in climate modeling. In this study, the influence of cloud microphysics schemes (one-moment versus two-moment schemes) and cloud overlap methods (observation-based versus a fixed vertical decorrelation length) on the simulated cloud fraction was assessed in the BCC_AGCM2.0_CUACE/Aero GCM. Compared with the fixed decorrelation length method, the observation-based approach produced a significantly improved cloud fraction both globally and for four representative regions. The utilization of a two-moment cloud microphysics scheme, on the other hand, notably improved the simulated cloud fraction compared with the one-moment scheme; specifically, the relative bias in the global mean total cloud fraction decreased by 42.9%–84.8%. Furthermore, the total cloud fraction bias decreased by 6.6% in the boreal winter (DJF) and 1.64% in the boreal summer (JJA). Cloud radiative forcing globally and in the four regions improved by 0.3%−1.2% and 0.2%−2.0%, respectively. Thus, our results showed that the interaction between clouds and climate through microphysical and radiation processes is a key contributor to simulation uncertainty.

Satellite-Based Estimation of the Influence of Land Use and Cover Change on the Surface Shortwave Radiation Budget in a Humid Basin

The surface shortwave radiation budget (Rsn) is one of the main drivers of Earth’s ecosystems and varies with atmospheric and surface conditions. Land use and cover change (LUCC) alters radiation through biogeophysical effects. However, due to the complex interactions between atmospheric and surface factors, it is very challenging to quantify the sole impacts of LUCC. Based on satellite data from the Global Land Surface Satellite (GLASS) Product and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, this study introduces an observation-based approach for detecting LUCC influences on the Rsn by examining a humid basin over the Dongting Lake Basin, China from 2001 to 2015. Our results showed that the Rsn of the study area presented a decreasing trend due to the combined effects of LUCC and climate change. Generally, LUCC contributed −0.45 W/m2 to Rsn at the basin scale, which accounted for 2.53% of the total Rsn change. Furthermore, the LUCC contributions reached −0.69 W/m2, 0.21 W/m2, and −0.41 W/m2 in regions with land transitions of forest→grass, grass→forest, and grass→farmland, which accounted for 5.38%, −4.68%, and 2.40% of the total Rsn change, respectively. Physically, LUCC affected surface radiation by altering the surface properties. Specifically, LUCC induced albedo changes of +0.0039 at the basin scale and +0.0061, −0.0020, and +0.0036 in regions with land transitions of forest→grass, grass→forest, and grass→farmland, respectively. Our findings revealed the impact and process of LUCC on the surface radiation budget, which could support the understanding of the physical mechanisms of LUCC’s impact on ecosystems.

Strategies for hydrologic ensemble generation and calibration: On the merits of using model-based predictors

This paper investigates the hydrometeorological chain with an ensemble approach. The objective is the generation of Hydrologic Ensemble Forecasts (HEF) on the Odet catchment (France, Brittany), using the Quantile Regression Forest (QRF) method usually applied for the ensemble calibration of meteorological forecats. First, a Global Sensitivity Analysis (GSA) in the distributed MORDOR-TS model is carried out taking into account uncertainty in forecasted rain with AromeEPS-RR1 and in model parameters. GSA highlights the role and importance of the different hydrologic model parameters during rain events and allows to only take into account the most influent parameters for the generation of an Hydrologic Ensemble Forecast (HEF).Three strategies for the generation of HEF are then compared. First (i), a raw ensemble is built with a model-based only approach using the deterministic forecast rainfall Expert-RR3 and perturbed model parameters, without further statistical calibration. Then, the QRF calibration method is used to generate two ensembles of quantiles: (ii) the observation-based approach uses only predictors that are independent from hydrology, whereas (iii) the combined model and observation approach combines these predictors with statistics of the raw hydrologic ensemble (mean, standard deviation). This latter approach was shown to outperfom the previous ones, enhancing the importance of the choice of the predictors in the QRF method. In the prospect of using the hydrologic ensemble as input for hydraulic simulation, the Ensemble Copula Coupling method (ECC) and a trajectory smoothing procedure is then applied on (iii). This step slightly deteriorates the reliability of hourly streamflows, yet Continuous Ranked Probability Score (CRPS) and forecast skills on the cumulated or maximum streamflows are improved.

Comparison Of Observation-Based And Model-Based Identification Of Alert Concentrations From Concentration-Expression Data.

MotivationAn important goal of concentration–response studies in toxicology is to determine an ‘alert’ concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest.ResultsIn a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance.Availability and implementationThe code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations.Supplementary information Supplementary data are available at Bioinformatics online.

Comparison of Approaches for Data Analysis of Multi-Parametric Monitoring Systems: Insights from the Acuto Test-Site (Central Italy)

This paper deals with monitoring systems to manage the risk due to fast slope failures that involve rock masses, in which important elements (such as infrastructures or cultural heritages, among the others) are exposed. Three different approaches for data analysis were here compared to evaluate their suitability for detecting mutual relations among destabilising factors, acting on different time windows, and induced strain effects on rock masses: (i) an observation-based approach (OBA), (ii) a statistics-based approach (SBA) and (iii) a semi-empirical approach (SEA). For these purposes, a test-site has been realised in an abandoned quarry in Central Italy by installing a multi-parametric monitoring sensor network on a rock wall able to record strain effects induced by natural and anthropic forcing actions (like as temperature, rainfall, wind and anthropic vibrations). The comparison points out that the considered approaches allow one to identify forcing actions, responsible for the strain effects on the rock mass over several time windows, regarding a specific size (i.e., rock block dimensional scale). The OBA was more suitable for computing the relations over short- to medium time windows, as well as the role of impulsive actions (i.e., hourly to seasonal and/or instantaneous). The SBA was suitable for computing the relations over medium- to long time windows (i.e., daily to seasonal), also returning the time lag between forcing actions and induced strains using the cross-correlation statistical function. Last, the SEA was highly suitable for detecting irreversible strain effects over long- to very long-time windows (i.e., plurennial).

Statistical estimation of global surface temperature response to forcing under the assumption of temporal scaling

Abstract. Reliable quantification of the global mean surface temperature (GMST) response to radiative forcing is essential for assessing the risk of dangerous anthropogenic climate change. We present the statistical foundations for an observation-based approach using a stochastic linear response model that is consistent with the long-range temporal dependence observed in global temperature variability. We have incorporated the model in a latent Gaussian modeling framework, which allows for the use of integrated nested Laplace approximations (INLAs) to perform full Bayesian analysis. As examples of applications, we estimate the GMST response to forcing from historical data and compute temperature trajectories under the Representative Concentration Pathways (RCPs) for future greenhouse gas forcing. For historic runs in the Model Intercomparison Project Phase 5 (CMIP5) ensemble, we estimate response functions and demonstrate that one can infer the transient climate response (TCR) from the instrumental temperature record. We illustrate the effect of long-range dependence by comparing the results with those obtained from one-box and two-box energy balance models. The software developed to perform the given analyses is publicly available as the R package INLA.climate.

Characterizing channel-floodplain connectivity using satellite altimetry: Mechanism, hydrogeomorphic control, and sediment budget

In this study, the mechanism of channel-floodplain seasonal connectivity along the Amazon River is analyzed over a full hydrological year through the use of satellite radar altimetry data (Jason-2). This is done via a novel observation-based approach which employs the concurrent measurement of water levels (WLs) over river and floodplain, analyzing seasonal changes in water surface height differences between the two water bodies. Hydrological connectivity thresholds at different stages during the rising phase were identified, and then validated using field data and remote sensing-driven surface suspended sediment maps. Successful decoupling of the two indiscrete flooding processes during the rising phase: channelized and overbank dispersion processes, is one of the major outcomes of this study. Different roles of the connectivity processes on floodplain hydrogeomorphology are highlighted – that channelized flows determine inundation frequency, residence time and development of positive topographic features in the floodplain, while overbank flows contribute a good part of the seasonal water storage and sediment budget in the floodplain, and tends to smooth positive topography built by channelized flows. The zones of overbank flooding, however, are rather localized due to the well-developed natural levee complex and stable channel-dominated floodplain along the river bank. Lastly, the presented approach is straightforward and solely based on publicly available operational data, making it easily adaptable by non-remote sensing experts. Thus, along with the emergence of new radar altimetry platforms, such as ICESat-2 or Jason-3 that could measure WL of smaller lakes, the proposed approach offers the potential to contribute to research on channel-floodplain systems in other rivers at a global scale.

An observation-based method to quantify the human influence on hydrological drought: upstream–downstream comparison

ABSTRACT In this present era of the Anthropocene, human activities affect hydrology and droughts. Quantifying this human influence improves our understanding and builds fundamental knowledge for water resource management. Analysis of observation data is useful in progressing this knowledge as these human activities and feedbacks are intrinsically included. Therefore, here we present an observation-based approach, the upstream–downstream comparison, to quantify changes in hydrological drought downstream of a human activity. We demonstrate this approach in a basin in northern Chile, where a reservoir was introduced. A sensitivity analysis is performed to assess how different choices of drought analysis threshold can affect the results and interpretation. We find that many commonly used choices do not exclude human activities from the threshold and therefore could be underestimating the change detected due to the human influence. The upstream–downstream comparison avoids this through the application of the upstream station threshold rather than the human-influenced downstream station.

An observation-based perspective of winter haze days in four major polluted regions of China.

An observation-based approach is used to examine key characteristics of winter haze days in four major polluted regions in China. Major findings in this study are: first, there was no significant trend in the number of winter haze days in most provinces and districts in eastern China from 1973 to 2012, contrary to the 2.5-fold increase in the emissions of particulate matter and its precursors (PM emissions) in the same period of time. Second, meteorological and climate conditions rather than PM emissions are in control of the interannual variabilities and trends of winter haze days. These interannual variabilities (ranging from 24 to 125%) pose a substantial masking effect that must be overcome by any control of PM emissions before its impact becomes statistically detectable. Finally, we find that global warming may have contributed significantly to the trend of winter haze days in eastern China.