scholarly journals Areal Precipitation Estimation Using Satellite Derived Rainfall Data over an Irrigation Area

2021 ◽  
Vol 9 (2) ◽  
pp. 386-394
Author(s):  
Mehmet Ali Akgül ◽  
Hakan Aksu
Keyword(s):  
Spatial Interpolation ◽  
Spatial Scales ◽  
Precipitation Amount ◽  
Monthly Precipitation ◽  
Left Bank ◽  
Precipitation Data ◽  
Irrigation Area ◽  
Thiessen Polygon ◽  
Precipitation Estimation ◽  
Areal Precipitation

The average precipitation on the irrigation field can be estimated from the Meteorology Observation Stations by using spatial interpolation methods such as Thiessen polygon and isohyetal curves. However, the fact that precipitation doesn't occur homogenous in spatial scales, spatial interpolation methodologies need a large number of meteorology stations for more accurate results. In recent years, remote sensing methods have diversified to estimate precipitation. In this study, performance of the satellite-based precipitation data was assessed to determine areal precipitation over an irrigation area. This study was conducted over left bank irrigation area located in the Çukurova Plain of Turkey. Relationship between CHIRPS satellite based on monthly precipitation data and 4 meteorology stations’ data were analyzed. Determination coefficients (R2) of the stations were found between 0.64 and 0.77, for point based comparison, R2 was calculated as 0.84 with Thiessen polygon method. It is concluded that the precipitation amount in the irrigated area can be estimated as accurately as classical methods such as Thiessen polygon with satellite-based precipitation data.

scholarly journals The use of personal weather station observations to improve precipitation estimation and interpolation

2021 ◽  
Vol 25 (2) ◽  
pp. 583-601
Author(s):  
András Bárdossy ◽  
Jochen Seidel ◽  
Abbas El Hachem
Keyword(s):  
Spatial Interpolation ◽  
Empirical Distribution ◽  
Precipitation Amount ◽  
Distribution Functions ◽  
Quantile Mapping ◽  
Novel Approach ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
High Precipitation ◽  
Empirical Distribution Functions

Abstract. The number of personal weather stations (PWSs) with data available through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation using a novel approach based on indicator correlations and rank statistics. Due to unknown errors and biases of the observations, rainfall amounts from the PWS network are not considered directly. Instead, it is assumed that the temporal order of the ranking of these data is correct. The crucial step is to find the stations which fulfil this condition. This is done in two steps – first, by selecting the locations using the time series of indicators of high precipitation amounts. Then, the remaining stations are then checked for whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the quantiles of the empirical distribution functions are accurate. These quantiles are then transformed to precipitation amounts by a quantile mapping using the distribution functions which were interpolated from the information from the German National Weather Service (Deutscher Wetterdienst – DWD) data only. The suggested procedure was tested for the state of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amount of 1, 3, 6, 12 and 24 h. For each of these temporal aggregations, nearly 200 intense events were evaluated, and the improvement of the interpolation was quantified. The results show that the filtering of observations from PWSs is necessary as the interpolation error after the filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest temporal aggregations.

scholarly journals Evaluation of Biases in JRA-25/JCDAS Precipitation and Their Impact on the Global Terrestrial Carbon Balance

2011 ◽  
Vol 24 (15) ◽  
pp. 4109-4125 ◽  
Author(s):  
Makoto Saito ◽  
Akihiko Ito ◽  
Shamil Maksyutov
Keyword(s):  
Carbon Cycle ◽  
Carbon Balance ◽  
Regional Scale ◽  
Precipitation Amount ◽  
Temporal Variations ◽  
Japan Meteorological Agency ◽  
Total Biomass ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Terrestrial Carbon

Abstract This study evaluates a modeled precipitation field and examines how its bias affects the modeling of the regional and global terrestrial carbon cycle. Spatial and temporal variations in precipitation produced by the Japanese 25-yr reanalysis (JRA-25)/Japan Meteorological Agency (JMA) Climate Data Assimilation System (JCDAS) were compared with two large-scale observation datasets. JRA-25/JCDAS captures the major distribution patterns of annual precipitation and the features of the seasonal cycle. Notable problems include over- and undersimulated areas of precipitation amount in South America, Africa, and Southeast Asia in the 30°N–30°S domain and a large discrepancy in the number of rainfall days. The latter problem was corrected by using a stochastic model based on the probability of the occurrence of dry and wet day series; the monthly precipitation amount was then scaled by the comparison data. Overall, the corrected precipitation performed well in reproducing the spatial distribution of and temporal variations in total precipitation. Both the corrected and original precipitation data were used to simulate regional and global terrestrial carbon cycles using the prognostic biosphere model Vegetation Integrative Simulator for Trace Gases (VISIT). Following bias correction, the model results showed differences in zonal mean photosynthesis uptake and respiration release ranging from −2.0 to +3.3 Pg C yr−1, compared with the original data. The difference in the global terrestrial net carbon exchange rate was 0.3 Pg C yr−1, reflecting the compensation of coincident increases or decreases in carbon sequestration and respiration loss. At the regional scale, the ecosystem carbon cycle and canopy structure, including seasonal variations in autotrophic and heterotrophic respiration and total biomass, were strongly influenced by the input precipitation data. The results highlight the need for precise precipitation data when estimating the global terrestrial carbon balance.

Precipitation Estimation at the Site of Transmission Tower Using Geographic Information System

2014 ◽  
Vol 955-959 ◽  
pp. 3869-3874
Author(s):  
Leehter Yao ◽  
Teng Shih Tsai ◽  
Rong Chi Chang
Keyword(s):  
Information System ◽  
Spatial Interpolation ◽  
Precipitation Data ◽  
Transmission Tower ◽  
Transmission Towers ◽  
Computational Speed ◽  
Distance Weighting ◽  
Precipitation Estimation ◽  
Central Weather Bureau ◽  
Inverse Distance

A method calculating the precipitation at the site of every transmission tower based on the precipitation estimation data is proposed. An inverse-distance weighting (IDW) method is modified for spatial interpolation of the precipitation data received from Central Weather Bureau. Accuracy and computational speed are both put into consideration in the design of the proposed approach because vast amount of transmission towers is required to process at a time.

scholarly journals Evaluation of Precipitation Estimation Accuracy in Reanalyses, Satellite Products, and an Ensemble Method for Regions in Australia and South and East Asia

2013 ◽  
Vol 14 (4) ◽  
pp. 1323-1333 ◽  
Author(s):  
Jorge L. Peña-Arancibia ◽  
Albert I. J. M. van Dijk ◽  
Luigi J. Renzullo ◽  
Mark Mulligan
Keyword(s):  
East Asia ◽  
Precipitation Amount ◽  
Department Of Energy ◽  
Estimation Accuracy ◽  
Monthly Precipitation ◽  
Precipitation Regime ◽  
Monsoon Period ◽  
Ensemble Mean ◽  
Precipitation Estimation ◽  
Precipitation Estimates

Abstract Precipitation estimates from reanalyses and satellite observations are routinely used in hydrologic applications, but their accuracy is seldom systematically evaluated. This study used high-resolution gauge-only daily precipitation analyses for Australia (SILO) and South and East Asia [Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE)] to calculate the daily detection and accuracy metrics for three reanalyses [ECMWF Re-Analysis Interim (ERA-Interim), Japanese 25-yr Reanalysis (JRA-25), and NCEP–Department of Energy (DOE) Global Reanalysis 2] and three satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) 3B42V6, Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)]. A depth-frequency-adjusted ensemble mean of the reanalyses and satellite products was also evaluated. Reanalyses precipitation from ERA-Interim in southern Australia (SAu) and northern Australasia (NAu) showed higher detection performance. JRA-25 had a better performance in South and East Asia (SEA) except for the monsoon period, in which satellite estimates from TRMM and CMORPH outperformed the reanalyses. In terms of accuracy metrics (correlation coefficient, root-mean-square difference, and a precipitation intensity proxy, which is the ratio of monthly precipitation amount to total days with precipitation) and over the three subdomains, the depth-frequency-adjusted ensemble mean generally outperformed or was nearly as good as any of the single members. The results of the ensemble show that additional information is captured from the different precipitation products. This finding suggests that, depending on precipitation regime and location, combining (re)analysis and satellite products can lead to better precipitation estimates and, thus, more accurate hydrological applications than selecting any single product.

scholarly journals Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling

2013 ◽  
Vol 17 (7) ◽  
pp. 2415-2434 ◽  
Author(s):  
D. Duethmann ◽  
J. Zimmer ◽  
A. Gafurov ◽  
A. Güntner ◽  
D. Kriegel ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Reanalysis Data ◽  
Hydrological Modelling ◽  
Performance Criteria ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Mountainous Catchments ◽  
Precipitation Estimates ◽  
Areal Precipitation

Abstract. In data sparse mountainous regions it is difficult to derive areal precipitation estimates. In addition, their evaluation by cross validation can be misleading if the precipitation gauges are not in representative locations in the catchment. This study aims at the evaluation of precipitation estimates in data sparse mountainous catchments. In particular, it is first tested whether monthly precipitation fields from downscaled reanalysis data can be used for interpolating gauge observations. Secondly, precipitation estimates from this and other methods are evaluated by comparing simulated and observed discharge, which has the advantage that the data are evaluated at the catchment scale. This approach is extended here in order to differentiate between errors in the overall bias and the temporal dynamics, and by taking into account different sources of uncertainties. The study area includes six headwater catchments of the Karadarya Basin in Central Asia. Generally the precipitation estimate based on monthly precipitation fields from downscaled reanalysis data showed an acceptable performance, comparable to another interpolation method using monthly precipitation fields from multi-linear regression against topographical variables. Poor performance was observed in only one catchment, probably due to mountain ridges not resolved in the model orography of the regional climate model. Using two performance criteria for the evaluation by hydrological modelling allowed a more informed differentiation between the precipitation data and showed that the precipitation data sets mostly differed in their overall bias, while the performance with respect to the temporal dynamics was similar. Our precipitation estimates in these catchments are considerably higher than those from continental- or global-scale gridded data sets. The study demonstrates large uncertainties in areal precipitation estimates in these data sparse mountainous catchments. In such regions with only very few precipitation gauges but high spatial variability of precipitation, important information for evaluating precipitation estimates may be gained by hydrological modelling and a comparison to observed discharge.

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 Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales

2021 ◽  
Vol 13 (2) ◽  
pp. 254 ◽  
Author(s):  
Jie Hsu ◽  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Xiuzhen Li
Keyword(s):  
Annual Cycle ◽  
Interannual Variation ◽  
Winter Precipitation ◽  
Monthly Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Precipitation Variation ◽  
Global Precipitation ◽  
Multiple Timescale ◽  
Better Than

The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database.

scholarly journals Error Correction of Multi-Source Weighted-Ensemble Precipitation (MSWEP) over the Lancang-Mekong River Basin

2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Xiongpeng Tang ◽  
Jianyun Zhang ◽  
Guoqing Wang ◽  
Gebdang Biangbalbe Ruben ◽  
Zhenxin Bao ◽  
...  
Keyword(s):  
Error Correction ◽  
River Basin ◽  
Regional Scale ◽  
Mekong River ◽  
Precipitation Data ◽  
Climate Data ◽  
Hydrological Simulation ◽  
Mekong River Basin ◽  
Precipitation Estimation ◽  
Simulation Results

The demand for accurate long-term precipitation data is increasing, especially in the Lancang-Mekong River Basin (LMRB), where ground-based data are mostly unavailable and inaccessible in a timely manner. Remote sensing and reanalysis quantitative precipitation products provide unprecedented observations to support water-related research, but these products are inevitably subject to errors. In this study, we propose a novel error correction framework that combines products from various institutions. The NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), the Multi-Source Weighted-Ensemble Precipitation Version 1.0 (MSWEP), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Records (PERSIANN) were used. Ground-based precipitation data from 1998 to 2007 were used to select precipitation products for correction, and the remaining 1979–1997 and 2008–2014 observe data were used for validation. The resulting precipitation products MSWEP-QM derived from quantile mapping (QM) and MSWEP-LS derived from linear scaling (LS) are evaluated by statistical indicators and hydrological simulation across the LMRB. Results show that the MSWEP-QM and MSWEP-LS can better capture major annual precipitation centers, have excellent simulation results, and reduce the mean BIAS and mean absolute BIAS at most gauges across the LMRB. The two corrected products presented in this study constitute improved climatological precipitation data sources, both time and space, outperforming the five raw gridded precipitation products. Among the two corrected products, in terms of mean BIAS, MSWEP-LS was slightly better than MSWEP-QM at grid-scale, point scale, and regional scale, and it also had better simulation results at all stations except Strung Treng. During the validation period, the average absolute value BIAS of MSWEP-LS and MSWEP-QM decreased by 3.51% and 3.4%, respectively. Therefore, we recommend that MSWEP-LS be used for water-related scientific research in the LMRB.

An Analysis and Comparison of Areal Precipitation Estimation

2015 ◽  
Vol 737 ◽  
pp. 737-740
Author(s):  
Xia Zhao ◽  
Wei Hua Zhang ◽  
Ming Xing Wang
Keyword(s):  
Modified Method ◽  
Precipitation Estimation ◽  
Areal Precipitation ◽  
Polygon Method

This article illustrates different methods of calculating average areal precipitation, especially Thieseen polygon method and its modified method: percentage weighted polygon method.it compares various conditions of application and gives some suggestions for further study about areal precipitation estimation.

scholarly journals Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium

2011 ◽  
Vol 15 (7) ◽  
pp. 2259-2274 ◽  
Author(s):  
S. Ly ◽  
C. Charles ◽  
A. Degré
Keyword(s):  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Gaussian Model ◽  
Daily Rainfall ◽  
Spherical Model ◽  
Daily Basis ◽  
Catchment Scale ◽  
Multivariate Geostatistics ◽  
Variogram Models ◽  
Thiessen Polygon

Abstract. Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably the interpolation with the Thiessen polygon, commonly used in various hydrological models. Integrating elevation into Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) did not improve the interpolation accuracy for daily rainfall. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases. Care should be taken in applying UNK and KED when interpolating daily rainfall with very few neighbourhood sample points. These recommendations complement the results reported in the literature. ORK, UNK and KED using only spherical model offered a slightly better result whereas OCK using seven variogram models achieved better result.

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