Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping

Sangani, Mahmood Fazeli; Khojasteh, Davood Namdar; Owens, Gary

doi:10.1007/s10661-019-7844-y

Cited by 13 publications

(6 citation statements)

References 28 publications

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“…This product integrates various gauge, satellite and reanalysis precipitation datasets and has been rigorously validated at different temporal scales and spatial scales with gauge precipitation observations, showing much improved accuracies compared to previous products. All the data mentioned above were processed into a resolution of 5 km using the inverse distance weighted (IDW) method (Sangani et al, 2019). We also obtained daily river discharge data from the Ministry of Water Resources for the Nuxia station and from our own observational measurements at the Dexing site (Figure 3d).…”

Section: Methods and Datamentioning

confidence: 99%

Impacts of frozen ground degradation and vegetation greening on upper Brahmaputra runoff during 1981–2019

Wang

Bibi

Zhou

et al. 2023

Intl Journal of Climatology

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The Tibetan Plateau (TP) contains the largest permafrost region in the mid–low latitudes and the largest area of glaciers outside of the polar regions. In recent decades, this region has experienced vegetation greening (e.g., increasing leaf area index) due to climate change. As the largest exorheic river on the TP, the Upper Brahmaputra Basin (UBB) is very sensitive to climate change, experiencing the humidifying and significant warming. In this study, we investigated the spatiotemporal variability of frozen ground and vegetation over the last four decades in the UBB and explored how these changes have impacted river runoff using a water‐ and energy‐budget distributed hydrological model (WEB‐DHM). We found that almost 50% of permafrost transformed into seasonally frozen or unfrozen ground from 1981 to 2019 with the great improvement of vegetation leaf area index (LAI). Based on the variable‐controlling approach (set the air temperature or vegetation unchanged), we revealed that frozen ground degradation caused an average of 9.3 billion m3 of water loss per year, accounting for 5.4% of total UBB river runoff, even if frozen ground degradation can increase water resources at the early stage. However, vegetation greening has caused a runoff decline by 10.9 billion m3 (6.4%) annually due to enhanced evapotranspiration. These findings highlight that it is critical to understand and mitigate the impacts of changing frozen ground and vegetation, when managing water resources availability and ecosystem conservation under rapid climate change.

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Section: Methods and Datamentioning

confidence: 99%

Impacts of frozen ground degradation and vegetation greening on upper Brahmaputra runoff during 1981–2019

Wang

Bibi

Zhou

et al. 2023

Intl Journal of Climatology

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“…To ensure the consistency of different products, we interpolated all the products into the same 5 km spatial resolution grid using the inverse distance weighted (IDW) method (Ma et al, 2019;Qiao et al, 2019;Sangani et al, 2019) and calculated them at 3-hourly resolution. Due to its good performance on the TP, we then used the ITP-Forcing data to derive the multiyear mean 3 h data as background climatological precipitation.…”

Section: Methodsmentioning

confidence: 99%

“…Zhou et al, 2015;Fang et al, 2019), especially in mountainous regions with high elevations and fewer ground measurements, such as the upper Brahmaputra (Xia et al, 2015;Xu et al, 2017;Qi et al, 2018). Additionally, there are several reanalysis datasets that have been widely used by researchers, such as the Global Land Data Assimilation System (GLDAS; Rodell et al, 2004;Zaitchik et al, 2010;Wang et al, 2011) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) dataset (Savtchenko et al, 2015;Gelaro et al, 2017;Reichle et al, 2017a, b). Evaluation of GLDAS data has generally been limited to the United States and other regions with adequate ground observations (Kato et al, 2007;Qi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

An integration of gauge, satellite, and reanalysis precipitation datasets for the largest river basin of the Tibetan Plateau

Wang

et al. 2020

Earth Syst. Sci. Data

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Abstract. As the largest river basin of the Tibetan Plateau, the upper Brahmaputra River basin (also called “Yarlung Zangbo” in Chinese) has profound impacts on the water security of local and downstream inhabitants. Precipitation in the basin is mainly controlled by the Indian summer monsoon and westerly and is the key to understanding the water resources available in the basin; however, due to sparse observational data constrained by a harsh environment and complex topography, there remains a lack of reliable information on basin-wide precipitation (there are only nine national meteorological stations with continuous observations). To improve the accuracy of basin-wide precipitation data, we integrate various gauge, satellite, and reanalysis precipitation datasets, including GLDAS, ITP-Forcing, MERRA2, TRMM, and CMA datasets, to develop a new precipitation product for the 1981–2016 period over the upper Brahmaputra River basin, at 3 h and 5 km resolution. The new product has been rigorously validated at different temporal scales (e.g., extreme events, daily to monthly variability, and long-term trends) and spatial scales (point and basin scale) with gauge precipitation observations, showing much improved accuracies compared to previous products. An improved hydrological simulation has been achieved (low relative bias: −5.94 %; highest Nash–Sutcliffe coefficient of efficiency (NSE): 0.643) with the new precipitation inputs, showing reliability and potential for multidisciplinary studies. This new precipitation product is openly accessible at https://doi.org/10.5281/zenodo.3711155 (Wang et al., 2020) and additionally at the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn, last access: 10 July 2020, login required).

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“…Deterministic and geostatistical interpolation methods are commonly used for this purpose [5][6][7][8][9]. On the basis of similarity or smoothness within the study area, deterministic interpolation methods create surfaces using known points [10,11]. The deterministic interpolation methods can be divided into two types: global and local.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation of Groundwater Extraction Intensity Based on Geostatistics—Set Pair Analysis in Daxing District of Beijing, China

Men

Yin

2022

Sustainability

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In this paper, the authors studied the impact of human activities on the groundwater environment to reduce the impacts such activities for sustainable groundwater use. The authors took the monthly water table depth data of 32 long-term observation wells in the Daxing District of Beijing from 1986 to 2016 as samples. The authors used seven interpolation methods in the statistics module of ArcGIS by comparing the average error (ME) and root mean square error (RMSE) between the measured and predicted values so that the authors can select the best interpolation method. Using the geostatistical variogram model variation, the authors analyzed the nugget effect through time in the study area. On the basis of the set pair analysis, the main factors causing the increase in groundwater exploitation intensity were quantitatively evaluated and identified. The results were as follows. (1) After comparing the simulation accuracy of the seven interpolation methods for water table depth, ordinary Kriging interpolation was selected as the best interpolation model for the study area. (2) The spatial correlation of the water table depth gradually weakened, and the nugget effect from 2006 to 2016 was 25.92% (>25%). The data indicated that human groundwater exploitation activities from 2006 to 2016 greatly influenced the spatial correlation of the water table depth. (3) The average mining intensity of groundwater from 2006 to 2016 was medium (Level II), and a bleak gradual deterioration trend was observed. The evaluation results of the subtraction set pair potentials in 2010 and 2013, the years of key regulation of groundwater exploitation intensity, are partial negative potential and negative potential, respectively. In 2010, three indicators had partial negative potential: industrial product, tertiary industry product, and irrigated field area. In 2013, five indicators were in negative potential: irrigated area, vegetable area, facility agricultural area, fruit tree area, and the number of wells. Herein, the spatial and temporal variations in the water table depth of the study area are analyzed using a geostatistical method. Moreover, the influence of each water part on the groundwater exploitation intensity is further diagnosed and evaluated based on set pair analysis. The obtained results can provide a theoretical and methodological reference for the sustainable utilization of groundwater in regions where groundwater is the main water supply source, providing a basis for industrial regulation policies in the region.

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Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping

Cited by 13 publications

References 28 publications

Impacts of frozen ground degradation and vegetation greening on upper Brahmaputra runoff during 1981–2019

Impacts of frozen ground degradation and vegetation greening on upper Brahmaputra runoff during 1981–2019

An integration of gauge, satellite, and reanalysis precipitation datasets for the largest river basin of the Tibetan Plateau

Spatiotemporal Variation of Groundwater Extraction Intensity Based on Geostatistics—Set Pair Analysis in Daxing District of Beijing, China

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