Remapping annual precipitation in mountainous areas based on vegetation patterns: a case study in the Nu River basin

Abstract. Accurate high-resolution estimates of precipitation are vital to improving the understanding of basin-scale hydrology in mountainous areas. The traditional interpolation methods or satellite-based remote sensing products are known to have limitations in capturing the spatial variability of precipitation in mountainous areas. In this study, we develop a fusion framework to improve the annual precipitation estimation in mountainous areas by jointly utilizing the satellite-based precipitation, gauge measured precipitation, and vegetation index. The development consists of vegetation data merging, vegetation response establishment, and precipitation remapping. The framework is then applied to the mountainous areas of the Nu River basin for precipitation estimation. The results demonstrate the reliability of the framework in reproducing the high-resolution precipitation regime and capturing its high spatial variability in the Nu River basin. In addition, the framework can significantly reduce the errors in precipitation estimates as compared with the inverse distance weighted (IDW) method and the TRMM (Tropical Rainfall Measuring Mission) precipitation product.

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Remapping precipitation in mountainous area based on vegetation pattern

10.5194/hess-2016-611 ◽

2016 ◽

Author(s):

Xing Zhou ◽

Guang-Heng Ni ◽

Chen Shen ◽

Ting Sun

Keyword(s):

High Resolution ◽

Spatial Variability ◽

River Basin ◽

Vegetation Index ◽

Mountainous Area ◽

Mountainous Areas ◽

Precipitation Regime ◽

Basin Scale ◽

Precipitation Estimation ◽

Nu River

Abstract. Accurate high-resolution estimates of precipitation are vital to improve the understanding on basin-scale hydrology in mountainous areas. The traditional interpolation methods or satellite-based remote sensing products are known to have limitations in capturing spatial variability of precipitation in mountainous areas. In this study, we develop a fusion framework to improve the precipitation estimation in mountainous areas by jointly utilizing the satellite-based precipitation, gauge measured precipitation and vegetation index. The development consists of vegetation data merging, vegetation response establishment, and precipitation remapping. The framework is then applied to the mountainous area of Nu River basin for precipitation estimation. The results demonstrate the reliability of the framework in reproducing the high-resolution precipitation regime and capturing its high spatial variability in Nu River basin. In addition, the framework can significantly reduce the errors in precipitation estimates as compared with the inverse distance weighted (IDW) method and TRMM (Tropical Rainfall Measuring Mission) precipitation product.

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Reliability of Gridded Precipitation Products in the Yellow River Basin, China

Remote Sensing ◽

10.3390/rs12030374 ◽

2020 ◽

Vol 12 (3) ◽

pp. 374 ◽

Cited By ~ 3

Author(s):

Yanfen Yang ◽

Jing Wu ◽

Lei Bai ◽

Bing Wang

Keyword(s):

River Basin ◽

Yellow River ◽

Tropical Rainfall Measuring Mission ◽

Yellow River Basin ◽

Superior Performance ◽

The Yellow River ◽

Basin Scale ◽

Precipitation Estimation ◽

Precipitation Analysis ◽

The Yellow River Basin

Gridded precipitation products are the potential alternatives in hydrological studies, and the evaluation of their accuracy and potential use is very important for reliable simulations. The objective of this study was to investigate the applicability of gridded precipitation products in the Yellow River Basin of China. Five gridded precipitation products, i.e., Multi-Source Weighted-Ensemble Precipitation (MSWEP), CPC Morphing Technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), were evaluated against observations made during 2001−2014 at daily, monthly, and annual scales. The results showed that MSWEP had a higher correlation and lower percent bias and root mean square error, while CMORPH and GSMaP made overestimations compared to the observations. All the datasets underestimated the frequency of dry days, and overestimated the frequency and the intensity of wet days (0–5 mm/day). MSWEP and TRMM showed consistent interannual variations and spatial patterns while CMORPH and GSMaP had larger discrepancies with the observations. At the sub-basin scale, all the datasets performed poorly in the Beiluo River and Qingjian River, whereas they were applicable in other sub-basins. Based on its superior performance, MSWEP was identified as more suitable for hydrological applications.

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Greening dynamics and shrubland extent from remote sensing data using NDVI and NARI Indices: case study of the George River basin (Nunavik, Canada).

10.5194/egusphere-egu2020-3861 ◽

2020 ◽

Author(s):

Dr. Jean-Pierre Dedieu ◽

Johann Housset ◽

Arthur Bayle ◽

Esther Lévesque ◽

José Gérin-Lajoie

Keyword(s):

Remote Sensing ◽

River Basin ◽

Vegetation Change ◽

Vegetation Index ◽

Spectral Band ◽

Human Populations ◽

Vegetation Types ◽

Local Study ◽

Basin Scale ◽

George River

Arctic greening trends are well documented at various scales (Fraser et al., 2011; Tremblay et al., 2012; Bjorkman et al., 2018). In this context, Remote Sensing offers a unique tool for estimating the high latitude vegetation evolution in the relatively long-term, i.e. the Landsat archive since the 80&#8217;s. Spectral indices derived from visible and infra-red wavelengths provide relations that can be used to quantify vegetation dynamics, we will combine the well-used Normalized Difference Vegetation Index (NDVI) and the recent Normalized Anthocyanins Reflectance Index (Bayle et al., 2019), using red-edge spectral band (690 to 710 &#181;m) from Sentinel-2, to better quantify vegetation change over 30 years.The application area is located in Nunavik, northern Qu&#233;bec (Canada), and concerns the George River catchment (565 km length, 41&#160;700&#160;km&#178;). This large river basin covers vegetation from boreal forest (South) to arctic tundra (North). Local study sites stem from the Kangiqsualujjuaq village (Ungava Bay) to 300 km south, along the main river and its tributaries.NDVI: surface reflectance Landsat bands were gathered for three years 1985, 2000 and 2015 (respectively Landsat missions 5, 7 and 8). For each period of interest, the best August cloud-free scenes were chosen and merged to create a cloud free mosaic covering the study area. NDVI bands were calculated and compared after cloud and water masking. NDVI trends were compared between the main vegetation types following the newly released &#8220;Ecological mapping of the vegetation of northern Quebec&#8221; (MRNFP, 2018). Centroid of polygons within the main vegetation types of the map were used to classify the NDVI results and assess changes per type. Results of NDVI time evolution revealed a clear greening trend at the river basin scale. Although greening was observed across the whole latitudinal gradient, the relative NDVI increase was stronger on the northern half of the study area, mostly covered with tundra and subarctic vegetation. Both shrublands and sparsely vegetated zones dominated by rocks had the greatest relative NDVI increase. This is likely caused by improved growth of established prostrate vegetation over the past 30 years in response to increasing temperatures trend.NARI: greening trends in the Eastern Canadian Arctic have been partly attributed to increases in shrub cover (Myers-smith et al., 2011) and specifically to Betula glandulosa (e.g. Tremblay et al., 2012). Such land cover changes alter species competition (Shevtosa et al., 1997) and soil thermal regime (Domine et al., 2015; Paradis et al., 2016). Transformations in biotic and abiotic conditions reduce the fruit productivity of low stature shrubs of the Ericaceae family (Lussier 2017), which in turn is expected to impact animal (Prescott and Richard 2013) and human populations (L&#233;vesque et al., 2013; Boulanger-Lapointe et al., 2019). An innovative method has been developed in the French Alps to detect the late-fall reddening of shrub leaves and map shrublands (Bayle et al., 2019). Quantifying NARI dynamics related to NDVI dynamics could allow to gain a better understanding of species composition change related to current landscape transformation.

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Trend Analyses of Extreme Precipitation Events in the Yarlung Zangbo River Basin, China Using a High-Resolution Precipitation Product

Sustainability ◽

10.3390/su10051396 ◽

2018 ◽

Vol 10 (5) ◽

pp. 1396 ◽

Cited By ~ 11

Author(s):

Jiao Fan ◽

Wenchao Sun ◽

Yong Zhao ◽

Baolin Xue ◽

Depeng Zuo ◽

...

Keyword(s):

High Resolution ◽

River Basin ◽

Extreme Precipitation ◽

Annual Precipitation ◽

Lower Reach ◽

Precipitation Product ◽

Extreme Precipitation Events ◽

Yarlung Zangbo River ◽

Possible Cause ◽

Precipitation Events

The Yarlung Zangbo River Basin (YZRB) is an important transboundary river basin in Tibet, China with south Asian countries. Changes in precipitation are important driving factors of river flow changes. Extreme Precipitation Events (EPE), in particular, have serious impacts on human life and sustainable development. The objective of this study is to explore the temporal changes and the spatial distribution of EPE over the YZRB in recent decades using a precipitation product with a 5 km spatial resolution and the Mann–Kendall nonparametric statistical test method. A more thorough understanding of the spatial heterogeneity in precipitation was expected from using this high resolution dataset. At both basin and pixel scale, both annual precipitation amounts and number of rain days had significant upward trends, indicating that the increase in the number of rain days is one possible cause of the annual precipitation amounts increases. The annual precipitation and number of rain days increased significantly in 50.8% and 75.8% of the basin area, respectively. The areas showing upward trends for the two indexes mostly overlapped, supporting the hypothesis that the increasing number of rain days is one possible cause of the increases in annual precipitation in these areas. General precipitation intensity and EPE intensity increased in the Lhasa regions and in the southern part of the lower-reach region. However, the intensity of general precipitation and EPE decreased in the Nyangqu River Basin. A total of 43.0% of the area in the YZRB exhibits significant upward trends in EPE frequency. The contributions of EPE to total rainfall increase significantly in the Lhasa and Shannan regions. Overall, it was shown that the risk of disasters from EPE in the YZRB increases in the eastern middle-reach region and southern lower-reach region.

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Assessment of Near-Term Runoff Response at a River Basin Scale in Central Vietnam Using Direct CMIP5 High-Resolution Model Outputs

Water ◽

10.3390/w10040477 ◽

2018 ◽

Vol 10 (4) ◽

pp. 477 ◽

Cited By ~ 3

Author(s):

Do Nam ◽

Phan Duong ◽

Duong Thuan ◽

Dang Mai ◽

Nguyen Dung

Keyword(s):

High Resolution ◽

River Basin ◽

River Basin Scale ◽

Central Vietnam ◽

Basin Scale ◽

Resolution Model ◽

High Resolution Model ◽

Near Term

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Uncertainty in soil physical data at river basin scale

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-3-1281-2006 ◽

2006 ◽

Vol 3 (4) ◽

pp. 1281-1313 ◽

Cited By ~ 1

Author(s):

P. van der Keur ◽

B. V. Iversen

Keyword(s):

Spatial Variability ◽

River Basin ◽

Hydraulic Properties ◽

Data Uncertainty ◽

Model Parameters ◽

Length Scales ◽

River Basin Scale ◽

Physical Data ◽

Basin Scale ◽

Modelling Studies

Abstract. For hydrological modelling studies at the river basin scale, decision makers need guidance in assessing the implications of uncertain data used by modellers as an input to modelling tools. Simulated solute transport through the unsaturated zone is associated with uncertainty due to spatial variability of soil hydraulic properties and derived hydraulic model parameters. In general for modelling studies at the river basin scale spatially available data at various scales must be aggregated to an appropriate scale. Estimating soil properties at unsampled points by means of geostatistical techniques require reliable information on the spatial structure of soil data. In this paper this information is assessed by reviewing current developments in the field of soil physical data uncertainty and adopting a classification system. Then spatial variability and structure is inspected by reviewing experimental work on determining spatial length scales for soil physical (and soil chemical) data. Available literature on spatial length scales for soil physical- and chemical properties is reviewed and their use in facilitating change of spatial support discussed. Uncertainty associated to the derivation of hydraulic properties from soil physical properties in this context is also discussed.

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An Improved Spatial–Temporal Downscaling Method for TRMM Precipitation Datasets in Alpine Regions: A Case Study in Northwestern China’s Qilian Mountains

Remote Sensing ◽

10.3390/rs11070870 ◽

2019 ◽

Vol 11 (7) ◽

pp. 870 ◽

Cited By ~ 6

Author(s):

Lei Wang ◽

Rensheng Chen ◽

Chuntan Han ◽

Yong Yang ◽

Junfeng Liu ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Qilian Mountains ◽

Annual Precipitation ◽

Precipitation Data ◽

Improved Method ◽

Monthly Data ◽

Satellite Precipitation ◽

Basin Scale

Remote sensing techniques provide data on the spatial–temporal distribution of environmental parameters over regions with sparse ground observations. However, the resolution of satellite precipitation data is too coarse to be applied to hydrological and meteorological research at basin scales. Downscaling research using coarse remote sensing data to obtain high-resolution precipitation data is significant for the development of basin-scale research. Here, we propose improvements to a spatial–temporal method for downscaling satellite precipitation. The improved method uses a nonlinear regression model and introduces longitude and latitude based on processed normalized difference vegetation index (NDVI) and a digital elevation model (DEM) to stimulate precipitation in the Qilian Mountains during 2006–2015. The final downscaled annual precipitation (FDAP) results are corrected by observed data to obtain corrected final downscaled annual precipitation (CFDAP) datasets. For temporal downscaling, monthly downscaled data are the corrected monthly ratio multiplied by the corresponding downscaled annual datasets. The results indicated that processed NDVI (PNDVI) reflected spatial precipitation patterns more accurately than the original NDVI. The accuracy was significantly improved when the final downscaled annual precipitation data were corrected by observed data. The average annual root mean square error (RMSE) from 2006 to 2015 of CFDAP was 66.48 and 83.07 mm less than that of FDAP and original Tropical Rainfall Measuring Mission (TRMM) data, respectively. Compared with previous methods, which use NDVI and/or DEM to downscale TRMM, the accuracy of FDAP and CFDAP from the improved method was higher, and the RMSE decreased on average by 13.63 and 80.11 mm. The RMSE of monthly data from corrected monthly ratio (CMR) decreased on average by 4.93 mm over monthly data from previous monthly ratio (PMR). In addition, the accuracy of the original satellite data affected the initial downscaling results but had no significant effects on the corrected downscaling results.

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Uncertainty in soil physical data at river basin scale – a review

Hydrology and Earth System Sciences ◽

10.5194/hess-10-889-2006 ◽

2006 ◽

Vol 10 (6) ◽

pp. 889-902 ◽

Cited By ~ 16

Author(s):

P. van der Keur ◽

B. V. Iversen

Keyword(s):

Spatial Variability ◽

River Basin ◽

Hydraulic Properties ◽

Data Uncertainty ◽

Model Parameters ◽

Length Scales ◽

River Basin Scale ◽

Physical Data ◽

Basin Scale ◽

Modelling Studies

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A Methodological Framework to Combine Multiple Precipitation Datasets for Improving Streamflow Simulations: A test study in the Saskatchewan River basin, Canada

10.5194/egusphere-egu2020-12547 ◽

2020 ◽

Author(s):

Jefferson Wong ◽

Fuad Yassin ◽

James Famiglietti

Keyword(s):

River Basin ◽

Land Surface ◽

Large River ◽

Combination Method ◽

Methodological Framework ◽

Surface Hydrology ◽

Test Study ◽

Basin Scale ◽

Precipitation Estimation

Obtaining reliable precipitation measurements and accurate spatiotemporal distribution of precipitation remains as a challenging task for driving Hydrologic-Land Surface Models (H-LSMs) and better hydrological simulations and predictions. To further improve the accuracy of precipitation estimation for hydrological applications, the idea of generating a hybrid dataset by combining existing precipitation products has become a more appealing approach in recent years. The reliability of the hybrid dataset is evaluated against in-situ climate stations and error characteristics are calculated to compare to the existing products. However, the robustness of the hybrid dataset in representing spatial details could be problematic when evaluated only using a sparse network of in-situ observations at regional or basin scales. This study aims to develop a methodological framework that combines multiple precipitation products based on evaluation against not only climate stations but also streamflow stations that are spatially representative across large river basin. The framework is illustrated using a Canadian H-LSM named MESH (Mod&#233;lisation Environmentale communautaire - Surface Hydrology) in the Saskatchewan River basin, Canada over the period of 2002 to 2012. Five existing precipitation datasets are considered as the candidates for generating the hybrid dataset. The framework consists of three components. The first component evaluates each precipitation candidate against the local gauge data for benchmarking, runs each candidate through MESH with 10 km spatial resolution and default parameterization, and calculates the overall streamflow performance in each sub-basins with equal weighting of three evaluation metrics. The second component generates the hybrid dataset by combining the best performing candidates (annual or seasonal) at sub-basin scale. The third component assesses the performance of the hybrid dataset at downstream gauge stations along the mainstream as a validation mechanism for comparison with the performance of the candidate datasets. Results shows that the hybrid dataset is able to perform equally well with the existing precipitation products in the headwater while improve the streamflow performance downstream. The successful application of the framework in this river basin could build the foundation and the confidence in applying the combination method to data-limited river basins in northern Canada.

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Snowmelt and Snow Sublimation in the Indus Basin

Water ◽

10.3390/w13192621 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2621

Author(s):

Simon Gascoin

Keyword(s):

Spatial Variability ◽

Indus Basin ◽

Annual Precipitation ◽

High Mountain ◽

Snowmelt Runoff ◽

Basin Scale ◽

The Mean ◽

The One ◽

Average Annual Precipitation

The Indus basin is considered as the one with the highest dependence on snowmelt runoff in High Mountain Asia. The recent High Mountain Asia snow reanalysis enables us to go beyond previous studies by evaluating both snowmelt and snow sublimation at the basin scale. Over 2000–2016, basin-average snowmelt was 101 ± 11 Gt.a−1 (121 ± 13 mm.a−1), which represents about 25–30% of basin-average annual precipitation. Snow sublimation accounts for 11% of the mean annual snow ablation, but with a large spatial variability across the basin.

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