Abstract
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.
Highlights
This study showed thatthat the the spatial prediction of quarterly precipcipitation in Poland near-border areas was moreaccurate accuratewhen whenthe theordinary ordinary itation datadata in Poland andand near-border areas was more cokriging(OCK)
Significant improvement of forecasting the spatial distribution of precipitations using ordinary cokriging (OCK) vs. ordinary kriging (OK) and inverse distance weighting (IDW) observed in our study area with variable topography, including mountains, plateaus, plains, and valleys, may result from the fact that the topsoil-measured Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) is sensitive to both small and large precipitation events in various topographic conditions
The present analysis indicates that satellite-based remote sensing spatiotemporal soil moisture data can be a valuable source of an auxiliary variable for the cokriging and/or other multivariate methods for better estimation of precipitations in various regions of the world
Summary
The spatial distribution of rainfall is frequently assessed using ground-based raingauge stations [15,16] since they provide the most precise and reliable measured data [17]. The network of the stations varies considerably and is often scarcely and not adequately distributed to obtain sufficiently dense point measurements for generating. 2021, 13, 1039 continuous high-quality rainfall maps in target areas [7,18,19]. To gain a suitable spatial distribution of precipitations based on point data, spatial interpolation methods should be used [18,20,21]. The recent literature review [22] may suggest a conclusion that, due to the complex spatiotemporal variability and physical mechanism of rainfall, acquisition of rainfall data of high quality and resolution is still challenging
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
