Forecasting of an extreme flood in mountainous area of North China based on WRF-Hydro with distributing parameters

2020 ◽  
Author(s):  
Wei Wang ◽  
JIa Liu ◽  
Chuanzhe Li ◽  
Qingtai Qiu ◽  
Yuchen Liu
Keyword(s):  
Extreme Event ◽  
Forecast Accuracy ◽  
Northern China ◽  
Calibration Method ◽  
Mountainous Area ◽  
Coupling System ◽  
Extreme Flood ◽  
Spatio Temporal ◽  
Merging Method ◽  
Better Than

<p>The flood events in the mountainous area of northern China has the characteristics of high intensity and strong sudden occurrence, and atmospheric-hydrological coupling system can improve the forecast accuracy and prolong the lead time. This paper discusses the simulations of the enhanced WRF-Hydro model on a historical flood that occurrs in a mesoscale catchment of Taihang mountain on July 21, 2012. Firstly, the precipitation accuracy of WRF, WRF data assimilation, co-kriging merging method of radar QPE data are as three different input sources for WRF-Hydro. The results show that the rainfall of merging QPE can achieve better simulations in time and space. In addition, the rainfall of WRF assimilation data is obviously better than that of WRF, but still underestimates the rainfall values. The extreme event rainstorm mainly <span>proceeds </span>in 5 hours, and for the assimilation data, the spatio-temporal simulations of the rainfall data in the first 2 hours are slightly poor. Hence we compare the combination of the first few hours to use the merging QPE and following by assimilation precipitation as the model input. In addition, according to the parameters of the WRF-Hydro model, a gridding parameter calibration method based on topographic index is constructed.</p>

scholarly journals AN IMPROVED INTERFEROMETRIC CALIBRATION METHOD BASED ON INDEPENDENT PARAMETER DECOMPOSITION

2018 ◽  
Vol XLII-3 ◽  
pp. 321-327
Author(s):  
J. Fan ◽  
X. Zuo ◽  
T. Li ◽  
Q. Chen ◽  
X. Geng
Keyword(s):  
Three Dimensional ◽  
Calibration Method ◽  
Shanxi Province ◽  
Independent Parameter ◽  
Mountainous Area ◽  
Calibration Model ◽  
Flat Area ◽  
Image Pairs ◽  
Interferometric Sar ◽  
Better Than

Interferometric SAR is sensitive to earth surface undulation. The accuracy of interferometric parameters plays a significant role in precise digital elevation model (DEM). The interferometric calibration is to obtain high-precision global DEM by calculating the interferometric parameters using ground control points (GCPs). However, interferometric parameters are always calculated jointly, making them difficult to decompose precisely. In this paper, we propose an interferometric calibration method based on independent parameter decomposition (IPD). Firstly, the parameters related to the interferometric SAR measurement are determined based on the three-dimensional reconstruction model. Secondly, the sensitivity of interferometric parameters is quantitatively analyzed after the geometric parameters are completely decomposed. Finally, each interferometric parameter is calculated based on IPD and interferometric calibration model is established. We take Weinan of Shanxi province as an example and choose 4 TerraDEM-X image pairs to carry out interferometric calibration experiment. The results show that the elevation accuracy of all SAR images is better than 2.54 m after interferometric calibration. Furthermore, the proposed method can obtain the accuracy of DEM products better than 2.43 m in the flat area and 6.97 m in the mountainous area, which can prove the correctness and effectiveness of the proposed IPD based interferometric calibration method. The results provide a technical basis for topographic mapping of 1 : 50000 and even larger scale in the flat area and mountainous area.

scholarly journals Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

scholarly journals Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

2021 ◽  
Vol 13 (4) ◽  
pp. 622
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Ya-Hui Chang ◽  
Cheng-An Lee
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
Temporal Variations ◽  
Rain Gauge ◽  
Advantages And Disadvantages ◽  
Level 3 ◽  
Spatio Temporal ◽  
Typhoon Season ◽  
Early Late ◽  
Better Than

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.

Spatio-Temporal Gaussianization Flows for Extreme Event Detection

2021 ◽  
Author(s):  
Miguel-Ángel Fernández-Torres ◽  
J. Emmanuel Johnson ◽  
María Piles ◽  
Gustau Camps-Valls
Keyword(s):  
Event Detection ◽  
Land Surface ◽  
Extreme Event ◽  
Learning Approaches ◽  
Proposed Model ◽  
Individual Contributions ◽  
Current Context ◽  
Spatio Temporal ◽  
The Individual ◽  
Anomaly Score

<p>Automatic anticipation and detection of extreme events constitute a major challenge in the current context of climate change. Machine learning approaches have excelled in detection of extremes and anomalies in Earth data cubes recently, but are typically both computationally costly and supervised, which hamper their wide adoption. We alternatively present here an unsupervised, efficient, generative approach for extreme event detection, whose performance is illustrated for drought detection in Europe during the severe Russian heat wave in 2010. The core architecture of the model is generic and could naturally be extended to the detection of other kinds of anomalies. First, it computes hierarchical appearance (spatial) and motion (temporal) representations of several informative Essential Climate Variables (ECVs), including soil moisture, land surface temperature, as well as features describing vegetation health. Then, these representations are combined using Gaussianization Flows that yield a spatio-temporal anomaly score. This allows the proposed model not only to detect droughts areas, but also to explain why they were produced, monitoring the individual contributions of each of the ECVs to the indicator at its output.</p>

Calibration of pneumotachographs using a calibrated syringe

2003 ◽  
Vol 95 (2) ◽  
pp. 571-576 ◽  
Author(s):  
Yongquan Tang ◽  
Martin J. Turner ◽  
Johnny S. Yem ◽  
A. Barry Baker
Keyword(s):  
Calibration Method ◽  
Linear Range ◽  
Data Sets ◽  
Constant Flow ◽  
Calibration Constant ◽  
Third Order ◽  
Polynomial Coefficients ◽  
Calibration Curves ◽  
Order Polynomial ◽  
Better Than

Pneumotachograph require frequent calibration. Constant-flow methods allow polynomial calibration curves to be derived but are time consuming. The iterative syringe stroke technique is moderately efficient but results in discontinuous conductance arrays. This study investigated the derivation of first-, second-, and third-order polynomial calibration curves from 6 to 50 strokes of a calibration syringe. We used multiple linear regression to derive first-, second-, and third-order polynomial coefficients from two sets of 6–50 syringe strokes. In part A, peak flows did not exceed the specified linear range of the pneumotachograph, whereas flows in part B peaked at 160% of the maximum linear range. Conductance arrays were derived from the same data sets by using a published algorithm. Volume errors of the calibration strokes and of separate sets of 70 validation strokes ( part A) and 140 validation strokes ( part B) were calculated by using the polynomials and conductance arrays. Second- and third-order polynomials derived from 10 calibration strokes achieved volume variability equal to or better than conductance arrays derived from 50 strokes. We found that evaluation of conductance arrays using the calibration syringe strokes yields falsely low volume variances. We conclude that accurate polynomial curves can be derived from as few as 10 syringe strokes, and the new polynomial calibration method is substantially more time efficient than previously published conductance methods.

scholarly journals Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production

MATEMATIKA ◽  
2018 ◽  
Vol 34 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Suhartono Suhartono ◽  
Dedy Dwi Prastyo ◽  
Heri Kuswanto ◽  
Muhammad Hisyam Lee
Keyword(s):  
Forecast Accuracy ◽  
Oil Production ◽  
Training Data ◽  
Temporal Data ◽  
Monthly Data ◽  
Vector Autoregressive ◽  
Temporal Models ◽  
Accurate Forecast ◽  
Temporal Model ◽  
Spatio Temporal

Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy.

scholarly journals Spatio-temporal Inversion using the Selection Kalman Model

2020 ◽  
Author(s):  
Maxime Conjard ◽  
Henning Omre
Keyword(s):  
Gaussian Distribution ◽  
Extreme Event ◽  
Recursive Algorithm ◽  
Initial Distribution ◽  
Pollution Source ◽  
Pollution Monitoring ◽  
Initial State ◽  
Linear Dynamic ◽  
Spatio Temporal ◽  
Spatial Histogram

<p>The challenge in data assimilation for models representing spatio-temporal phenomena is made harder when the spatial histogram of the variable of interest appears with multiple modes. Pollution source identification constitutes one example where the pollution release represents an extreme event in a fairly homogeneous background. Consequently, our prior belief is that the spatial histogram is bimodal. The traditional Kalman model is based on a Gaussian initial distribution and Gauss-linear dynamic and observation models. This model is contained in the class of Gaussian distribution and is therefore analytically tractable. These properties that make its strenght also render it unsuitable for representing multimodality. To address the issue, we define the selection Kalman model. It is based on a selection-Gaussian initial distribution and Gauss-linear dynamic and observation models. The selection-Gaussian distribution may represent multimodality, skewness and peakedness. It can be seen as a generalization of the Gaussian distribution. The proposed selection Kalman model is contained in the class of selection-Gaussian distributions and therefore analytically tractable. The recursive algorithm used for assessing the selection Kalman model is specified. We present a synthetic case study of spatio-temporal inversion of an initial state containing an extreme event. The study is inspired by pollution monitoring. The results suggest that the use of the selection Kalman model offers significant improvements compared to the traditional Kalman model when reconstructing discontinuous initial states. </p>

scholarly journals Hydro-meteorological characteristics and occurrence probability of extreme flood events in Moroccan High Atlas

2020 ◽  
Vol 11 (S1) ◽  
pp. 310-321 ◽  
Author(s):  
Mohamed El Mehdi Saidi ◽  
Tarik Saouabe ◽  
Abdelhafid El Alaoui El Fels ◽  
El Mahdi El Khalki ◽  
Abdessamad Hadri
Keyword(s):  
Return Period ◽  
Peak Flow ◽  
Extreme Event ◽  
Rare Event ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
High Atlas ◽  
Flood Water ◽  
Extreme Flood ◽  
Water Volumes

Abstract Flood frequency analysis could be a tool to help decision-makers to size hydraulic structures. To this end, this article aims to compare two analysis methods to see how rare an extreme hydrometeorological event is, and what could be its return period. This event caused many deadly floods in southwestern Morocco. It was the result of unusual atmospheric conditions, characterized by a very low atmospheric pressure off the Moroccan coast and the passage of the jet stream further south. Assessment of frequency and return period of this extreme event is performed in a High Atlas watershed (the Ghdat Wadi) using historical floods. We took into account, on the one hand, flood peak flows and, on the other hand, flood water volumes. Statistically, both parameters are better adjusted respectively to Gamma and Log Normal distributions. However, the peak flow approach underestimates the return period of long-duration hydrographs that do not have a high peak flow, like the 2014 event. The latter is indeed better evaluated, as a rare event, by taking into account the flood water volumes. Therefore, this parameter should not be omitted in the calculation of flood probabilities for watershed management and the sizing of flood protection infrastructure.

Sign in / Sign up

Export Citation Format

Share Document