SWAT use of gridded observations for simulating runoff – a Vietnam river basin study

Vu, Minh Tue; Raghavan, Srivatsan V.; Liong, Shie‐Yui

doi:10.5194/hess-16-2801-2012

Cited by 90 publications

(49 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To further reduce residual cloud contamination, atmospheric, and bidirectional effects, we produced a monthly NDVI dataset using the maximum value composite (MVC) method to obtained monthly NDVI (Holben, 1986;Piao, 2003;Fang, 2004;Peng et al, 2011), which were averaged with the growing season NDVI for further analyses. The bilinear interpolation method was used to extract the NDVI values corresponding to the station data based on grid vegetation data with a spatial resolution of 0.083° (Vu et al, 2012). Finally, monthly NDVI sequence data, corresponding to select climate stations in China and spanning the period from 1982 to 2011, were generated.…”

Section: Ndvi Datamentioning

confidence: 99%

Notable shifting in the responses of vegetation activity to climate change in China

Chen

Wang

et al. 2015

Physics and Chemistry of the Earth, Parts A/B/C

View full text Add to dashboard Cite

a b s t r a c tThe weakening relationship between inter-annual temperature variability and vegetation activity in the Northern Hemisphere over the last three decades has been reported by a recent study. However, how and to what extent vegetation activity responds to climate change in China is still unclear. We applied the Pearson correlation and partial correlation methods with a moving 15-y window to the GIMMS NDVI dataset from NOAA/AVHRR and observed climate data to examine the variation in the relationships between vegetation activity and climate variables. Results showed that there was an expanding negative response of vegetation growth to climate warming and a positive role of precipitation. The change patterns between NDVI and climate variables over vegetation types during the past three decades pointed an expending negative correlation between NDVI and temperature and a positive role of precipitation over most of the vegetation types (meadow, grassland, shrub, desert, cropland, and forest). Specifically, correlation between NDVI and temperature (P NDVI-T ) have shifted from positive to negative in most of the station of temperature-limited areas with evergreen broadleaf forests, whereas precipitationlimited temperate grassland and desert were characterized by a positive P NDVI-P . This study contributes to ongoing investigations of the effects of climate change on vegetation activity. It is also of great importance for designing forest management strategies to cope with climate change.

show abstract

Section: Ndvi Datamentioning

confidence: 99%

Notable shifting in the responses of vegetation activity to climate change in China

Chen

Wang

et al. 2015

Physics and Chemistry of the Earth, Parts A/B/C

View full text Add to dashboard Cite

show abstract

“…The following indicator criteria were applied to evaluate the grid-based data sets based on WSD: the linear correlation coefficient (CF), root mean square error (RMSE), mean absolute error (MAE), multiplicative bias (MBias) and Nash-Sutcliffe Coefficient (NSE) [32,45,47]. The mathematical expressions of these criteria are as follows:…”

Section: Accuracy Assessments Of the Grid-based Data Setsmentioning

confidence: 99%

“…Twenty sensitive parameters were selected according to previous studies [5,47,66,67] and tested in the SWAT-CUP to perform a sensitivity analysis. Fourteen sensitive parameters were selected according to their performances in the sensitivity analysis, and manual calibration and auto-calibration were performed using a Sequential Uncertainty Fitting (SUFI-2) algorithm to achieve acceptable performance [68][69][70][71][72][73].…”

Section: Model Calibration and Validationmentioning

confidence: 99%

Investigating Alternative Climate Data Sources for Hydrological Simulations in the Upstream of the Amu Darya River

Sidike

Chen

et al. 2016

Water

View full text Add to dashboard Cite

Abstract:The main objective of this study is to investigate alternative climate data sources for long-term hydrological modeling. To accomplish this goal, one weather station data set (WSD) and three grid-based data sets including three types of precipitation data and two types of temperature data were selected according to their spatial and temporal details. An accuracy assessment of the grid-based data sets was performed using WSD. Then, the performances of corrected data combination and non-corrected grid-based precipitation and temperature data combinations from multiple sources on simulating river flow in the upstream portion of the Amu Darya River Basin (ADRB) were analyzed using a Soil and Water Assessment Tool (SWAT) model. The results of the accuracy assessments indicated that all the grid-based data sets underestimated precipitation. The Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) precipitation data provided the highest accuracy (correlation coefficients (CF) > 0.89, root mean square error (RMSE) < 41.6 mm), followed by the CRUNCEP reanalysis data (a combination of the CRU TS.3.2 data and the National Centers for Environmental Prediction (NCEP) reanalysis data) (CF > 0.5, RMSE < 58.1 mm) and Princeton's Global Meteorological Forcing Dataset (PGMFD) precipitation data (CF > 0.46, RMSE < 62.8 mm). The PGMFD temperature data exhibited a higher accuracy (CF > 0.98, RMSE < 7.1 • C) than the CRUNCEP temperature data (CF > 0.97, RMSE < 4.9 • C). In terms of the simulation performance, the corrected APHRODITE precipitation and PGMFD temperature data provided the best performance. The CF and Nash-Sutcliffe (NSE) coefficients in the calibration and validation periods were 0.96 and 0.92 and 0.93 and 0.83, respectively. In addition, the combinations of PGMFD temperature data and APHRODITE, PGMFD and CRUNCEP precipitation data produced good results, with NSE ≥ 0.70 and CF ≥ 0.89. The combination of CRUNCEP temperature data and APHRODITE precipitation produced a satisfactory result, with NSE = 0.58 and CF = 0.82. The combinations of CRUNCEP temperature data and PGMFD and CRUNCEP precipitation data produced poor results.

show abstract

“…Looking at the spatial distribution of seasonal precipitation and temperature ( Figures S1 and S3), one could see that there is no significant difference among different climate datasets, however the temporal variability (yearly precipitation) is noticeable ( Figure S2). Many studies have assessed the impacts of gridded data for simulating runoff [14,42,43]. The results showed that their quality vary significantly from one region to the other.…”

Section: Model Performance and Parametersmentioning

confidence: 99%

“…The results showed that their quality vary significantly from one region to the other. For example, Vu et al [42] showed poor performance of the PERSIANN (precipitation estimation from remotely sensed information using artificial neural networks) and TRMM (tropical rainfall measuring mission) rainfall data compared to the station data.…”

Section: Model Performance and Parametersmentioning

confidence: 99%

Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components

Kamali

Abbaspour

Yang

2017

Water

View full text Add to dashboard Cite

A large number of local and global databases for soil, land use, crops, and climate are now available from different sources, which often differ, even when addressing the same spatial and temporal resolutions. As the correct database is unknown, their impact on estimating water resource components (WRC) has mostly been ignored. Here, we study the uncertainty stemming from the use of multiple databases and their impacts on WRC estimates such as blue water and soil water for the Karkheh River Basin (KRB) in Iran. Four climate databases and two land use maps were used to build multiple configurations of the KRB model using the soil and water assessment tool (SWAT), which were similarly calibrated against monthly river discharges. We classified the configurations based on their calibration performances and estimated WRC for each one. The results showed significant differences in WRC estimates, even in models of the same class i.e., with similar performance after calibration. We concluded that a non-negligible level of uncertainty stems from the availability of different sources of input data. As the use of any one database among several produces questionable outputs, it is prudent for modelers to pay more attention to the selection of input data.

show abstract

SWAT use of gridded observations for simulating runoff – a Vietnam river basin study

Cited by 90 publications

References 30 publications

Notable shifting in the responses of vegetation activity to climate change in China

Notable shifting in the responses of vegetation activity to climate change in China

Investigating Alternative Climate Data Sources for Hydrological Simulations in the Upstream of the Amu Darya River

Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components

Contact Info

Product

Resources

About