scholarly journals Impact of Trend Estimates on Predictive Performance in Model Evaluation for Spatial Downscaling of Satellite-based Precipitation Data

2017 ◽  
Vol 33 (1) ◽  
pp. 25-35 ◽  
Author(s):  
Yeseul Kim ◽  
No-Wook Park
Keyword(s):  
Model Evaluation ◽  
Predictive Performance ◽  
Precipitation Data ◽  
Spatial Downscaling

Evaluation and spatial downscaling of CRU TS precipitation data in the Philippines

2018 ◽  
Vol 4 (3) ◽  
pp. 891-898 ◽  
Author(s):  
Arnold R. Salvacion ◽  
Damasa B. Magcale-Macandog ◽  
Pompe C. Sta. Cruz ◽  
Ronaldo B. Saludes ◽  
Ireneo B. Pangga ◽  
...  
Keyword(s):  
The Philippines ◽  
Precipitation Data ◽  
Spatial Downscaling

scholarly journals The role and value of distributed precipitation data in hydrological models

2021 ◽  
Vol 25 (1) ◽  
pp. 147-167
Author(s):  
Ralf Loritz ◽  
Markus Hrachowitz ◽  
Malte Neuper ◽  
Erwin Zehe
Keyword(s):  
Hydrological Model ◽  
Predictive Performance ◽  
Distributed Model ◽  
Precipitation Data ◽  
Hydrological Models ◽  
Adaptive Model ◽  
Runoff Generation ◽  
Spatially Adaptive ◽  
Model Execution ◽  
Model Setup

Abstract. This study investigates the role and value of distributed rainfall for the runoff generation of a mesoscale catchment (20 km2). We compare four hydrological model setups and show that a distributed model setup driven by distributed rainfall only improves the model performances during certain periods. These periods are dominated by convective summer storms that are typically characterized by higher spatiotemporal variabilities compared to stratiform precipitation events that dominate rainfall generation in winter. Motivated by these findings, we develop a spatially adaptive model that is capable of dynamically adjusting its spatial structure during model execution. This spatially adaptive model allows the varying relevance of distributed rainfall to be represented within a hydrological model without losing predictive performance compared to a fully distributed model. Our results highlight that spatially adaptive modeling has the potential to reduce computational times as well as improve our understanding of the varying role and value of distributed precipitation data for hydrological models.

scholarly journals Spatial Downscaling of Remote Sensing Precipitation Data in the Beijing-Tianjin-Hebei Region

2021 ◽  
Vol 09 (06) ◽  
pp. 191-202
Author(s):  
Nuan Wang ◽  
Jie Yu ◽  
Lin Zhu ◽  
Yanbing Wang ◽  
Zhengyang He
Keyword(s):  
Remote Sensing ◽  
Precipitation Data ◽  
Spatial Downscaling

scholarly journals Evaluation of a temporal causal model for predicting the mood of clients in an online therapy

2020 ◽  
Vol 23 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Dennis Becker ◽  
Vincent Bremer ◽  
Burkhardt Funk ◽  
Mark Hoogendoorn ◽  
Artur Rocha ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Causal Model ◽  
Predictive Accuracy ◽  
Simulation Analysis ◽  
Model Performance ◽  
Predictive Performance ◽  
Study Data ◽  
Current Form ◽  
Development Evaluation ◽  
Noisy Measurements

Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity.Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients’ future mood levels.Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes.Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis.

scholarly journals Improvement of Spatial Interpolation of Precipitation Distribution Using Cokriging Incorporating Rain-Gauge and Satellite (SMOS) Soil Moisture Data

2021 ◽  
Vol 13 (5) ◽  
pp. 1039
Author(s):  
Bogusław Usowicz ◽  
Jerzy Lipiec ◽  
Mateusz Łukowski ◽  
Jan Słomiński
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Spatial Interpolation ◽  
Predictive Performance ◽  
Rain Gauge ◽  
Coefficient Of Determination ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Continuous Precipitation ◽  
Precipitation Distribution

Precipitation data provide a crucial input for examining hydrological issues, including watershed management and mitigation of the effects of floods, drought, and landslides. However, they are collected frequently from the scarce and often insufficient network of ground-based rain-gauge stations to generate continuous precipitation maps. Recently, precipitation maps derived from satellite data have not been sufficiently linked to ground-based rain gauges and satellite-derived soil moisture to improve the assessment of precipitation distribution using spatial statistics. Kriging methods are used to enhance the estimation of the spatial distribution of precipitations. The aim of this study was to assess two geostatistical methods, ordinary kriging (OK) and ordinary cokriging (OCK), and one deterministic method (i.e., inverse distance weighting (IDW)) for improved spatial interpolation of quarterly and monthly precipitations in Poland and near-border areas of the neighbouring countries (~325,000 or 800,000 km2). Quarterly precipitation data collected during a 5-year period (2010–2014) from 113–116 rain-gauge stations located in the study area were used. Additionally, monthly precipitations in the years 2014–2017 from over 400 rain-gauge stations located in Poland were used. The spatiotemporal data on soil moisture (SM) from the Soil Moisture and Ocean Salinity (SMOS) global satellite (launched in 2009) were used as an auxiliary variable in addition to precipitation for the OCK method. The predictive performance of the spatial distribution of precipitations was the best for OCK for all quarters, as indicated by the coefficient of determination (R2 = 0.944–0.992), and was less efficient (R2 = 0.039–0.634) for the OK and IDW methods. As for monthly precipitation, the performance of OCK was considerably higher than that of IDW and OK, similarly as with quarterly precipitation. The performance of all interpolation methods was better for monthly than for quarterly precipitations. The study indicates that SMOS data can be a valuable source of auxiliary data in the cokriging and/or other multivariate methods for better estimation of the spatial distribution of precipitations in various regions of the world.

Spatial downscaling of TRMM-based precipitation data using vegetative response in Xinjiang, China

2016 ◽  
Vol 37 (10) ◽  
pp. 3895-3909 ◽  
Author(s):  
Qiang Zhang ◽  
Peijun Shi ◽  
Vijay P. Singh ◽  
Keke Fan ◽  
Jiajun Huang
Keyword(s):  
Precipitation Data ◽  
Spatial Downscaling
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