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.

scholarly journals Evaluation of the ERS Scatterometer-Derived Soil Water Index to Monitor Water Availability and Precipitation Distribution at Three Different Scales in China

2008 ◽  
Vol 9 (3) ◽  
pp. 549-562 ◽  
Author(s):  
Deming Zhao ◽  
Claudia Kuenzer ◽  
Congbin Fu ◽  
Wolfgang Wagner
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Availability ◽  
Local Area ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Precipitation Distribution ◽  
Water Index ◽  
Global Precipitation Climatology Centre

Abstract In this paper, the capability of the European Remote Sensing Satellite (ERS) scatterometer-derived soil water index (SWI) data to disclose water availability and precipitation distribution in China is investigated. Monthly averaged SWI data for the years 1992–2000 are analyzed to evaluate the use of the SWI as an index to monitor water availability and water stress at three different scales in China and to investigate if it reflects general precipitation distribution characteristics in China. Monthly averaged in situ relative soil moisture from Chinese meteorological gauge stations, as well as monthly precipitation data from the Global Precipitation Climatology Centre (GPCC), are employed to perform comparisons with SWI on local, regional, and countrywide scales. First, since soil moisture is highly affected by the precipitation, area-averaged SWI is compared with in situ relative soil moisture and GPCC precipitation data in one local area. Second, area-averaged SWI and GPCC precipitation data are used to perform comparisons in three regions of China. Finally, the relationship between SWI and GPCC precipitation data in China is investigated on a countrywide scale. Such multiscale analyses with SWI data have not been performed before, and SWI has never been investigated in detail for China. ERS-derived SWI data in China for the years 1992–2000 are evaluated to be a good indicator for water availability on local, regional, and countrywide scales. SWI and SWI anomaly data correlate well with precipitation and in situ soil moisture data. SWI has furthermore been demonstrated to reflect extreme events such as droughts and floods in China, occurring during the investigated period between 1992 and 2000. Additionally, the SWI allows one to monitor increasing soil moisture resulting from snowmelt, which cannot be deduced from precipitation data. The freely available 15-yr (1992–2007) time series SWI data are thus a valuable tool to overcome the scarcity of in situ soil moisture observations, which are usually not available on regional and countrywide scales.

scholarly journals Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1006 ◽  
Author(s):  
Xiuna Wang ◽  
Yongjian Ding ◽  
Chuancheng Zhao ◽  
Jian Wang
Keyword(s):  
Bias Correction ◽  
Arid Region ◽  
Rain Gauge ◽  
Climatic Conditions ◽  
Monthly Precipitation ◽  
Observation Data ◽  
Precipitation Data ◽  
Rain Gauges ◽  
Hexi Region ◽  
Precipitation Events

Continuous and accurate spatiotemporal precipitation data plays an important role in regional climate and hydrology research, particularly in the arid inland regions where rain gauges are sparse and unevenly distributed. The main objective of this study is to evaluate and bias-correct the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 rainfall product under complex topographic and climatic conditions over the Hexi region in the northwest arid region of China with the reference of rain gauge observation data during 2009–2015. A series of statistical indicators were adopted to quantitatively evaluate the error of 3B42V7 and its ability in detecting precipitation events. Overall, the 3B42V7 overestimates the precipitation with Bias of 11.16%, and its performance generally becomes better with the increasing of time scale. The agreements between the rain gauge data and 3B42V7 are very low in cold season, and moderate in warm season. The 3B42V7 shows better correlation with rain gauges located in the southern mountainous and central oasis areas than in the northern extreme arid regions, and is more likely to underestimate the precipitation in high-altitude mountainous areas and overestimate the precipitation in low-elevation regions. The distribution of the error on the daily scale is more related to the elevation and rainfall than in monthly and annual scale. The 3B42V7 significantly overestimates the precipitation events, and the overestimation mainly focuses on tiny amounts of rainfall (0–1 mm/d), which is also the range of false alarm concentration. Bias correction for 3B42V7 was carried out based on the deviation of the average monthly precipitation data during 2009–2015. The bias-corrected 3B42V7 was significantly improved compared with the original product. Results suggest that regional assessment and bias correction of 3B42V7 rainfall product are of vital importance and will provide substantive reference for regional hydrological studies.

scholarly journals Spatial Interpolation of Daily Precipitation in a High Mountainous Watershed Based on Gauge Observations and a Regional Climate Model Simulation

2017 ◽  
Vol 18 (3) ◽  
pp. 845-862 ◽  
Author(s):  
Yuhan Wang ◽  
Hanbo Yang ◽  
Dawen Yang ◽  
Yue Qin ◽  
Bing Gao ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Regional Climate Model ◽  
Spatial Interpolation ◽  
Climate Model ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Heihe River ◽  
Topographic Effects ◽  
Precipitation Data ◽  
Meteorological Observations

Abstract Precipitation is a primary climate forcing factor in catchment hydrology, and its spatial distribution is essential for understanding the spatial variability of ecohydrological processes in a catchment. In mountainous areas, meteorological stations are generally too sparse to represent the spatial distribution of precipitation. This study develops a spatial interpolation method that combines meteorological observations and regional climate model (RCM) outputs. The method considers the precipitation–elevation relationship in the mountain region and the topographic effects, especially the mountain blocking effect. Furthermore, using this method, this study produced a 3-km-resolution precipitation dataset from 1960 to 2014 in the middle and upper reaches of the Heihe River basin located on the northern slope of the Qilian Mountains in the northeastern Tibetan Plateau. Cross validation based on the station observations showed that this method is reasonable. The rationality of the interpolated precipitation data was also evaluated by the catchment water balances using the observed river discharge and the actual evapotranspiration based on remote sensing. The interpolated precipitation data were compared with the China Gauge-Based Daily Precipitation Analysis product and the RCM output and was shown to be optimal. The results showed that the proposed method effectively used the information from the meteorological observations and the RCM simulations and provided the spatial distributions of daily precipitations with reasonable accuracy and high resolution, which is important for a distributed hydrological simulation at the catchment scale.

scholarly journals Comparison of spatial interpolation methods for estimating the precipitation distribution in Portugal

2021 ◽  
Author(s):  
Alexandru Antal ◽  
Pedro M. P. Guerreiro ◽  
Sorin Cheval
Keyword(s):  
Spatial Distribution ◽  
Spatial Interpolation ◽  
Polynomial Interpolation ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Precipitation Distribution ◽  
Interpolation Methods ◽  
Mainland Portugal ◽  
The Mean ◽  
Spatial Interpolation Methods

Abstract Precipitation has a strong and constant impact on different economic sectors, environment, and social activities all over the world. An increasing interest for monitoring and estimating the precipitation characteristics can be claimed in the last decades. However, in some areas the ground-based network is still sparse and the spatial data coverage insufficiently addresses the needs. In the last decades, different interpolation methods provide an efficient response for describing the spatial distribution of precipitation. In this study, we compare the performance of seven interpolation methods used for retrieving the mean annual precipitation over the mainland Portugal, as follows: local polynomial interpolation (LPI), global polynomial interpolation (GPI), radial basis function (RBF), inverse distance weighted (IDW), ordinary cokriging (OCK), universal cokriging (UCK) and empirical Bayesian kriging regression (EBKR). We generate the mean annual precipitation distribution using data from 128 rain gauge stations covering the period 1991 to 2000. The interpolation results were evaluated using cross-validation techniques and the performance of each method was evaluated using mean error (ME), mean absolute error (MAE), root mean square error (RMSE), Pearson’s correlation coefficient (R) and Taylor diagram. The results indicate that EBKR performs the best spatial distribution. In order to determine the accuracy of spatial distribution generated by the spatial interpolation methods, we calculate the prediction standard error (PSE). The PSE result of EBKR prediction over mainland Portugal increases form south to north.

scholarly journals Evaluation of Satellite-Based and Reanalysis Precipitation Data in the Tropical Pacific

2013 ◽  
Vol 52 (3) ◽  
pp. 634-644 ◽  
Author(s):  
Uwe Pfeifroth ◽  
Richard Mueller ◽  
Bodo Ahrens
Keyword(s):  
Atmospheric Model ◽  
Rain Gauge ◽  
Tropical Pacific ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Model Based ◽  
Station Data ◽  
Global Precipitation ◽  
Temporal Coverage ◽  
The Tropical Pacific

AbstractGlobal precipitation monitoring is essential for understanding the earth’s water and energy cycle. Therefore, usage of satellite-based precipitation data is necessary where in situ data are rare. In addition, atmospheric-model-based reanalysis data feature global data coverage and offer a full catalog of atmospheric variables including precipitation. In this study, two model-based reanalysis products, the interim reanalysis by the European Centre for Medium-Range Weather Forecasts (ERA-Interim) and NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA), as well as two satellite-based datasets obtained by the Global Precipitation Climatology Centre (GPCP) and Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) are evaluated. The evaluation is based on monthly precipitation in the tropical Pacific Ocean during the time period 1989–2005. Rain-gauge atoll station data provided by the Pacific Rainfall Database (PACRAIN) are used as ground-based reference. It is shown that the analyzed precipitation datasets offer temporal correlations ranging from 0.7 to 0.8 for absolute amounts and from 0.6 to 0.75 for monthly anomalies. Average monthly deviations are in the range of 20%–30%. GPCP offers the highest correlation and lowest monthly deviations with reference to PACRAIN station data. The HOAPS precipitation data perform in the range of the reanalysis precipitation datasets. In high native spatial resolution, HOAPS reveals deficiencies owing to its relatively sparse temporal coverage. This result emphasizes that temporal coverage is critical for controlling the performance of precipitation monitoring. Both reanalysis products show similar systematic behaviors in overestimating small and medium precipitation amounts and underestimating high amounts.

scholarly journals ANALYSIS OF TREND OF THE PRECIPITATION DATA: A CASE STUDY OF KANGRA DISTRICT, HIMACHAL PRADESH

2015 ◽  
Vol 3 (9) ◽  
pp. 87-95
Author(s):  
Arijit Ganguly ◽  
Ranjana Ray Chaudhuri ◽  
Prateek Sharma
Keyword(s):  
Flood Control ◽  
Rain Gauge ◽  
Himachal Pradesh ◽  
Flood Frequency ◽  
Negative Trend ◽  
Annual Rainfall ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Level Of Significance

The current study is carried out to determine the potential trend of rainfall and assess its significance in Kangra district of Himachal Pradesh. Rainfall is a key characteristic of any watershed which plays a significant role in flood frequency, flood control studies and water planning and management. In this case study,mean monthly rainfall has been analysed to determine the variability in magnitude over the period 1950-2005.  Trend in mean monthly precipitation data and mean seasonal trends are analysed using Mann-Kendall test and Sen’s slope estimation for the data period 1950-2005. Analysis of monthly trend in precipitation shows negative trend for the months of July, August, September and October in all the rain gauge stations. However, the falling trend is significant for the month of August for Dharamshala(0.05 level of significance). Interestingly the month of June shows rising trend of rainfall in all the stations, however, at Dharamshala the trend is significant (0.01 level of significance). The winter rainfall in the month of January and February record decreasing trend, with DeraGobipur and Kangra recording significant decreasing trend for the month of January at 0.01 level of significance and 0.05 level of significance respectively. Trend analysis for annual rainfall data shows significant negative trend for Dharamshala.

scholarly journals Spatial Distribution of Soil Moisture in Mongolia Using SMAP and MODIS Satellite Data: A Time Series Model (2010–2025)

2021 ◽  
Vol 13 (3) ◽  
pp. 347
Author(s):  
Enkhjargal Natsagdorj ◽  
Tsolmon Renchin ◽  
Philippe De Maeyer ◽  
Bayanjargal Darkhijav
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Soil Moisture ◽  
Correlation Coefficient ◽  
Crop Yield ◽  
Arima Model ◽  
Agricultural Drought ◽  
Monthly Precipitation ◽  
Precipitation Temperature ◽  
Moisture Estimation

Soil moisture is one of the essential variables of the water cycle, and plays a vital role in agriculture, water management, and land (drought) and vegetation cover change as well as climate change studies. The spatial distribution of soil moisture with high-resolution images in Mongolia has long been one of the essential issues in the remote sensing and agricultural community. In this research, we focused on the distribution of soil moisture and compared the monthly precipitation/temperature and crop yield from 2010 to 2020. In the present study, Soil Moisture Active Passive (SMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) data were used, including the MOD13A2 Normalized Difference Vegetation Index (NDVI), MOD11A2 Land Surface Temperature (LST), and precipitation/temperature monthly data from the Climate Research Unit (CRU) from 2010 to 2020 over Mongolia. Multiple linear regression methods have previously been used for soil moisture estimation, and in this study, the Autoregressive Integrated Moving Arima (ARIMA) model was used for soil moisture forecasting. The results show that the correlation was statistically significant between SM-MOD and soil moisture content (SMC) from the meteorological stations at different depths (p < 0.0001 at 0–20 cm and p < 0.005 at 0–50 cm). The correlation between SM-MOD and temperature, as represented by the correlation coefficient (r), was 0.80 and considered statistically significant (p < 0.0001). However, when SM-MOD was compared with the crop yield for each year (2010–2019), the correlation coefficient (r) was 0.84. The ARIMA (12, 1, 12) model was selected for the soil moisture time series analysis when predicting soil moisture from 2020 to 2025. The forecasting results are shown for the 95 percent confidence interval. The soil moisture estimation approach and model in our study can serve as a valuable tool for confident and convenient observations of agricultural drought for decision-makers and farmers in Mongolia.

scholarly journals A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data

2019 ◽  
Vol 11 (24) ◽  
pp. 2962 ◽  
Author(s):  
Dong Fan ◽  
Hua Wu ◽  
Guotao Dong ◽  
Xiaoguang Jiang ◽  
Huazhu Xue
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
Daily Precipitation ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Hysteresis Phenomenon ◽  
Monthly Precipitation ◽  
Soil Moisture Data ◽  
Spatio Temporal ◽  
The Relationship

Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the Normalized Difference Vegetation Index (NDVI) and other land surface variables to improve the spatial resolution of satellite precipitation datasets. However, the strong spatio-temporal heterogeneity of precipitation and the “hysteresis phenomenon” of the relationship between precipitation and vegetation has limited the application of these downscaling methods to high temporal resolutions. To overcome this limitation, a new temporal downscaling method was proposed in this study by introducing daily soil moisture data to explore the relationship between precipitation and the soil moisture increment index. The performance of this proposed temporal downscaling was assessed by downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from a monthly scale to a daily scale over the Hekouzhen to Tongguan of the Yellow River in 2013, and the downscaled daily precipitation datasets were validated with in-situ measurement from 23 rainfall observation stations. The validation results indicate that the downscaled daily precipitation agrees with the rain gauge observations, with a correlation coefficient of 0.59, a mean error range of 1.70 mm, and a root mean square error of 5.93 mm. In general, the monthly precipitation decomposition method proposed in this paper has combined the advantage of both microwave remote sensing products. It has acceptable precision and can generate precipitation on a diurnal scale. It is an important development in the field of using auxiliary data to perform temporal downscaling. Furthermore, this method also provides a reference example for the temporal downscaling of other low temporal resolution datasets.

scholarly journals Estimating the rainfall erosivity factor from monthly precipitation data in the Madrid Region (Spain)

2015 ◽  
Vol 63 (1) ◽  
pp. 55-62 ◽  
Author(s):  
David Hernando ◽  
Manuel G. Romana
Keyword(s):  
Mean Squared Error ◽  
Rain Gauge ◽  
Accurate Estimate ◽  
Coefficient Of Determination ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Monthly Precipitation ◽  
Precipitation Concentration Index ◽  
R Factor ◽  
Record Length

Abstract The need for continuous recording rain gauges makes it difficult to determine the rainfall erosivity factor (Rfactor) of the Universal Soil Loss Equation in regions without good spatial and temporal data coverage. In particular, the R-factor is only known at 16 rain gauge stations in the Madrid Region (Spain). The objectives of this study were to identify a readily available estimate of the R-factor for the Madrid Region and to evaluate the effect of rainfall record length on estimate precision and accuracy. Five estimators based on monthly precipitation were considered: total annual rainfall (P), Fournier index (F), modified Fournier index (MFI), precipitation concentration index (PCI) and a regression equation provided by the Spanish Nature Conservation Institute (RICONA). Regression results from 8 calibration stations showed that MFI was the best estimator in terms of coefficient of determination and root mean squared error, closely followed by P. Analysis of the effect of record length indicated that little improvement was obtained for MFI and P over 5- year intervals. Finally, validation in 8 additional stations supported that the equation R = 1.05·MFI computed for a record length of 5 years provided a simple, precise and accurate estimate of the R-factor in the Madrid Region.

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