Relevant bibliographies by topics / Standardized Precipitation and Evapotranspiration index (SPEI)

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  2. Dissertations / Theses
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Academic literature on the topic 'Standardized Precipitation and Evapotranspiration index (SPEI)'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Standardized Precipitation and Evapotranspiration index (SPEI)"

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Ariyanto, Dwi Priyo, Abdul Aziz, Komariah Komariah, Sumani Sumani, and Magarsa Abara. "Comparing the accuracy of estimating soil moisture using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI)." SAINS TANAH - Journal of Soil Science and Agroclimatology 17, no. 1 (June 29, 2020): 23. http://dx.doi.org/10.20961/stjssa.v17i1.41396.

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<span>The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are used to monitor and identify different types of drought, including meteorological, hydrological, and agricultural droughts. This study evaluates the accuracy of estimating soil moisture levels using the two indexes. The analysis correlated the SPI and the SPEI over three years (November 2016–October 2019) using <em>Rstudio</em>, with average monthly soil moisture taken using a Soil Moisture Sensor; 3-, 6- and 12-months SPI and SPEI showed a positive correlation for soil moisture (Sig &lt;0.05), whereas 1-month SPI and SPEI results did not. A regression test was used to get an equation model for estimating soil moisture content. The correlation for soil moisture between the 1-month SPI and SPEI results was insignificant (p-value &gt;0.05). In contrast, the 3-, 6-, and 12-months indexes were significant (p-value &lt;0.05). Estimating soil moisture content using the SPEI (50–59.09%) had a higher accuracy value than the SPI (36.36%), which indicates the SPEI can more reliably predict soil moisture.</span>
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Vicente-Serrano, Sergio M., Santiago Beguería, and Juan I. López-Moreno. "A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index." Journal of Climate 23, no. 7 (April 1, 2010): 1696–718. http://dx.doi.org/10.1175/2009jcli2909.1.

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Abstract The authors propose a new climatic drought index: the standardized precipitation evapotranspiration index (SPEI). The SPEI is based on precipitation and temperature data, and it has the advantage of combining multiscalar character with the capacity to include the effects of temperature variability on drought assessment. The procedure to calculate the index is detailed and involves a climatic water balance, the accumulation of deficit/surplus at different time scales, and adjustment to a log-logistic probability distribution. Mathematically, the SPEI is similar to the standardized precipitation index (SPI), but it includes the role of temperature. Because the SPEI is based on a water balance, it can be compared to the self-calibrated Palmer drought severity index (sc-PDSI). Time series of the three indices were compared for a set of observatories with different climate characteristics, located in different parts of the world. Under global warming conditions, only the sc-PDSI and SPEI identified an increase in drought severity associated with higher water demand as a result of evapotranspiration. Relative to the sc-PDSI, the SPEI has the advantage of being multiscalar, which is crucial for drought analysis and monitoring.
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Zhao, Qingzhi, Xiongwei Ma, Wanqiang Yao, Yang Liu, and Yibin Yao. "A Drought Monitoring Method Based on Precipitable Water Vapor and Precipitation." Journal of Climate 33, no. 24 (December 15, 2020): 10727–41. http://dx.doi.org/10.1175/jcli-d-19-0971.1.

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AbstractPrecipitable water vapor (PWV) with high precision and high temporal resolution can be obtained based on the global navigation and satellite positioning system (GNSS) technique, which is important for GNSS in disaster prevention and mitigation. However, related studies on drought monitoring using PWV have rarely been performed before, which becomes the focus of this paper. This paper proposes a novel drought monitoring method using GNSS-derived PWV and precipitation, and a multi-time-scale standardized precipitation conversion index (SPCI) is established. This index is different from the traditional index in terms of expression, standardization, and time scale. The proposed SPCI is then compared with the standardized precipitation index/standardized precipitation evapotranspiration index/self-calibrating Palmer drought severity index (SPI/SPEI/scPDSI) and applied to local and global drought monitoring. Validated results show that multi-time-scale SPCI has good consistency with the corresponding SPI/SPEI/scPDSI. The correlation between SPCI and SPEI is the strongest (more than 0.96) on a 12-month scale, which indicates the application potential of SPCI in drought monitoring. In addition, applications for regional (Queensland, Australia) and global drought/wet monitoring further verify the capability of the proposed SPCI. The average percentage deviations of drought/wet monitoring between SPCI and SPEI are 2.77% and 3.75%, respectively on a global scale. The above results show that the SPCI developed in this study is efficiently applied to global flood/wet studies.
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Saengrattanayon, Chanattha, Nuttapong Panthong, Parwapath Phunthirawuthi, and Sukrit Kirtsaeng. "Analysis of Standardized Precipitation Evapotranspiration Index over Chiangrai and Phayao." Applied Mechanics and Materials 891 (May 2019): 117–26. http://dx.doi.org/10.4028/www.scientific.net/amm.891.117.

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Drought indices analysis plays a vital role in flood and drought monitoring and early warning, which is a main responsibility of Thai Meteorological Department (TMD), especially the basins that are limited in use o¬¬f water resources such as Kok and Ing river basins. This study aims to analyze drought situations utilized Standardized Precipitation Evapotranspiration Index (SPEI) at Chiangrai and Phayao provinces (located Kok and Ing basins). Both observed data, precipitation and temperature, are used for calculation (data in between 1951-2018 for Chiangrai and 1981-2018 for Phayao). The result shows that SPEI can determine drought probability and its potential impact in the observed area. This study could be applied to drought monitoring over other basins.
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S L Cheng F Y Zhang Y Q et al, Liu. "Standardized Precipitation Evapotranspiration Index (SPEI) Dataset of Yunnan Province, China." Journal of Global Change Data & Discovery 1, no. 4 (2017): 447–51. http://dx.doi.org/10.3974/geodp.2017.04.11.

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Jamro, Shoaib, Ghulam Hussain Dars, Kamran Ansari, and Nir Y. Krakauer. "Spatio-Temporal Variability of Drought in Pakistan Using Standardized Precipitation Evapotranspiration Index." Applied Sciences 9, no. 21 (October 29, 2019): 4588. http://dx.doi.org/10.3390/app9214588.

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Pakistan is among the top ten countries adversely affected by climate change. More specifically, there is concern that climate change may cause longer and severer spells of droughts. To quantify the change in the characteristics of droughts in Pakistan over the years, we have evaluated spatio-temporal trends of droughts in Pakistan over the period 1902–2015 using Standardized Precipitation Evapotranspiration Index (SPEI). Additionally, the Spatial “K” luster Analysis using Tree Edge Removal (SKATER) method was employed to regionalize droughts into five contiguous zones. The run theory was then applied to each zone to identify drought events and characterize them in terms of duration, severity, intensity, and peak. Moreover, the Modified Mann–Kendall trend test was applied to identify statistically significant trends in SPEI and drought characteristics in each zone. It was found that the southern areas of Pakistan, encompassing Sindh and most of Baluchistan, have experienced a decrease in SPEI, indicating a drying trend. Central Pakistan has witnessed a wetting trend as demonstrated by an increase in SPEI over time, whereas no statistically significant trend was observed for the northern areas of Pakistan. On a zonal basis, the longest duration drought to occur in Pakistan lasted 22 months in zone 5 (Sindh) from 1968 to 1970. In addition, the drought of 1920 and 2000 can be said to be the worst drought in the history of the region as it affected all the zones and lasted for more than 10-months in three zones.
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Ahmadebrahimpour, Edris, Babak Aminnejad, and Keivan Khalili. "Assessing future drought conditions under a changing climate: a case study of the Lake Urmia basin in Iran." Water Supply 19, no. 6 (April 12, 2019): 1851–61. http://dx.doi.org/10.2166/ws.2019.062.

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Abstract This study was conducted to assess the impacts of climate change on drought over the Lake Urmia basin, Iran. Drought events for 2011–2040, 2041–2070, and 2071–2100 were analyzed based on the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) and were compared with the adopted baseline period (1976–2005). The SPI and SPEI were calculated using the precipitation and temperatures obtained from the second-generation Canadian Earth System Model (CanESM2) under Representative Concentration Pathway (RCP) 2.6 and RCP 8.5 as optimistic and pessimistic scenarios respectively. The results of SPI analyses revealed that under RCP 2.6 the frequency of droughts is almost constant while under RCP 8.5 drought frequency increased especially in the period 2071–2100. The calculated SEPI under both scenarios and during all future periods predict that the frequency and duration of droughts will increase. Generally, the difference between the SPI and SPEI is related to the input to each index. SPI is solely based on precipitation while the SPEI accounts for both precipitation and potential evapotranspiration (PET). Under global warming and changing climate, the significant role of PET was highlighted. It was concluded that the SPEI outperformed the SPI for drought studies under a changing climate.
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Katipoğlu, Okan Mert, Reşat Acar, and Selim Şengül. "Comparison of meteorological indices for drought monitoring and evaluating: a case study from Euphrates basin, Turkey." Journal of Water and Climate Change 11, S1 (November 11, 2020): 29–43. http://dx.doi.org/10.2166/wcc.2020.171.

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Abstract Drought incidents occur due to the fact that precipitation values are below average for many years. Drought causes serious effects in many sectors, such as agriculture, economy, health, and energy. Therefore, the determination of drought and water scarcity, monitoring, management, and planning of drought and taking early measures are important issues. In order to solve these issues, the advantages and disadvantages of five different meteorological drought indices were compared, and the most effective drought index was determined for monitoring drought. Accordingly, in the monthly, 3-month, and 12-month time period, covering the years between 1966 and 2017 (52 years), Standardized Precipitation Index (SPI), Statistical Z-Score Index (ZSI), Rainfall Anomaly Index (RAI), Standardized Precipitation Evapotranspiration Index (SPEI), and Reconnaissance Drought Index (RDI) were used. It was concluded that precipitation-based SPI and ZSI are similar patterns and precipitation, and temperature-based SPEI and RDI are similar patterns. Also, it has been determined that RAI is more effective than other indices in determining the periods of extreme drought or wet. Furthermore, SPEI and RDI have been found to be superior to other indices as they take into account the water consumption and climate effects caused by evapotranspiration.
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Gocic, Milan, Danilo Misic, Slavisa Trajkovic, and Mladen Milanovic. "Using GIS tool for presenting spatial distribution of drought." Facta universitatis - series: Architecture and Civil Engineering 18, no. 1 (2020): 77–84. http://dx.doi.org/10.2298/fuace200409006g.

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By using GIS tools, it is possible to improve the preview of hydrological processes such as evapotranspiration, precipitation, flood and drought. In order to quantify drought, different type of drought indicators have been developed such as Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Standardized Precipitation Evapotranspiration Index (SPEI) or Water Surplus Variability Index (WSVI). In this paper the precipitation-based SPI indicator was applied to the monthly precipitation data from Serbia during the period 1948-2012. The data were processed in the QuantumGIS software package. For the purpose of application in the monitoring of drought at the national level, a spatial presentation of meteorological drought was obtained.
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Serrano-Barrios, L., S. M. Vicente-Serrano, H. Flores-Magdaleno, L. Tijerina-Chávez, and D. Vázquez-Soto. "Variabilidad espacio-temporal de las sequías en la cuenca Pacífico Norte de México (1961-2010)." Cuadernos de Investigación Geográfica 42, no. 1 (June 27, 2016): 185. http://dx.doi.org/10.18172/cig.2857.

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This article analyses the spatio-temporal variability of droughts in the North Pacific Basin of México between 1961 and 2010, using two drought indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). We used data from 48 weather stations with available data of precipitation and monthly minimum and maximum temperature. In 22 of the weather stations, time series of Piché evaporation were also available. The reference evapotranspiration, necessary to obtain the SPEI, was calculated by means of the Hargreaves equation. Results show that major droughts occurred in the 1980s and 2000s, although there is a noticeable spatial variability across the region. Finally, the potential impact of the atmospheric evaporative demand on drought severity observed by the different drought indices is discussed, and the possible implications for an appropriate risk assessment.
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Dissertations / Theses on the topic "Standardized Precipitation and Evapotranspiration index (SPEI)"

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Edossa, D. C., Y. E. Woyessa, and W. A. Welderufael. "Comparison between two meteorological drought indices in the central region of South Africa." Interim : Interdisciplinary Journal, Vol 13, Issue 3: Central University of Technology Free State Bloemfontein, 2013. http://hdl.handle.net/11462/309.

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Published Article
The objective of this study was to characterize meteorological droughts in the Central Region of South Africa, Modder River Basin, C52A quaternary catchment using two popular drought indices: Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI) and to compare the two indices. Drought events were characterized based on their frequency, duration, magnitude and intensity. The indices were computed for the time-scales that are important for planning and management of water resources, i.e. 3-, 6- and 12-month time-scales. The basic meteorological input data used in the computation of these indices were 57 years (1950-2007) of monthly precipitation and monthly temperature data which were recorded at The Cliff weather station in the quaternary catchment. It was found that both SPI and SPEI responded to drought events in similar fashion in all time-scales. During the analysis period, a total of 37, 26 and 17 drought events were identified in the area based on 3-, 6-, and 12-month times-scales, respectively. Considering event magnitude as severity parameter, results from both indices identified the periods 1984-1985, 1992-1993 and 2003-2005 as the severest drought periods in the area. However, when the effects of both drought duration and magnitude are considered (drought intensity), the most severest drought events were identified during the years 1982/83, 1966 and 1973 based on 3-, 6- and 12-month timescales, respectively. It was concluded that although the SPEI generally exhibits veracity over SPI by including, apart from precipitation, additional meteorological parameter, mean temperature, SPI should be adopted as an appropriate drought monitoring tool in an area, like Africa, where meteorological data are scarce.
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Rosa, Ricardo Granés Tavares Duarte. "Índices de seca. Aplicação ao continente português." Master's thesis, ISA/UTL, 2011. http://hdl.handle.net/10400.5/4488.

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Mestrado em Engenharia Agronómica - Instituto Superior de Agronomia
The Palmer Drought Severity Index (PDSI) was tested along with two indices resulting from modifying its original formulation, one concerning the replacement of the Thornthwaite equation to compute potential evapotranspiration (ETP) by the FAO Penman-Monteith method, and the other consisting in replacing the soil water balance model and the ETP computation that passes to correspond to the ETP of an olive orchard, generating a new index: the MedPDSI. A factor for the normalization of the index, the climate characteristic (K), was reviewed and the index was calibrated for each analyzed location. The Standardized Precipitation Index (SPI) for the 9 and 12 month time-scales was also tested and compared with the three variants of the PDSI. The results revealed that the modification of K improved the standardization of the PDSI and that the calibration produced statistically improved results. It was found that the MedPDSI anticipates the initiation of droughts over the original PDSI, either with ETP Thornthwaite or with ETP FAO Penman-Monteith, and tends to classify droughts more severely than the first. The use of 9 month time-scale in the SPI is the one that best relates to the PDSI, since it clearly anticipates the onset of droughts relative to the 12 month scale.
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CHHIN, Rattana. "Future Projection of Drought in the Indochina Region Based on the Optimal Ensemble Subset of CMIP5 Models." Kyoto University, 2019. http://hdl.handle.net/2433/242616.

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Patil, Sandeep 1986. "Analysis of Spatial Performance of Meteorological Drought Indices." Thesis, 2012. http://hdl.handle.net/1969.1/148327.

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Meteorological drought indices are commonly calculated from climatic stations that have long-term historical data and then converted to a regular grid using spatial interpolation methods. The gridded drought indices are mapped to aid decision making by policy makers and the general public. This study analyzes the spatial performance of interpolation methods for meteorological drought indices in the United States based on data from the Co-operative Observer Network (COOP) and United States Historical Climatology Network (USHCN) for different months, climatic regions and years. An error analysis was performed using cross-validation and the results were compared for the 9 climate regions that comprise the United States. Errors are generally higher in regions and months dominated by convective precipitation. Errors are also higher in regions like the western United States that are dominated by mountainous terrain. Higher errors are consistently observed in the southeastern U.S. especially in Florida. Interpolation errors are generally higher in the summer than winter. The accuracy of different drought indices was also compared. The Standardized Precipitation and Evapotranspiration Index (SPEI) tends to have lower errors than Standardized Precipitation Index (SPI) in seasons with significant convective precipitation. This is likely because SPEI uses both precipitation and temperature data in its calculation, whereas SPI is based solely on precipitation. There are also variations in interpolation accuracy based on the network that is used. In general, COOP is more accurate than USHCN because the COOP network has a higher density of stations. USHCN is a subset of the COOP network that is comprised of high quality stations that have a long and complete record. However the difference in accuracy is not as significant as the difference in spatial density between the two networks. For multiscalar SPI, USHCN performs better than COOP because the stations tend to have a longer record. The ordinary kriging method (with optimal function fitting) performed better than Inverse Distance Weighted (IDW) methods (power parameters 2.0 and 2.5) in all cases and therefore it is recommended for interpolating drought indices. However, ordinary kriging only provided a statistically significant improvement in accuracy for the Palmer Drought Severity Index (PDSI) with the COOP network. Therefore it can be concluded that IDW is a reasonable method for interpolating drought indices, but optimal ordinary kriging provides some improvement in accuracy. The most significant factor affecting the spatial accuracy of drought indices is seasonality (precipitation climatology) and this holds true for almost all the regions of U.S. for 1-month SPI and SPEI. The high-quality USHCN network gives better interpolation accuracy with 6-, 9- and 12-month SPI and variation in errors amongst the different SPI time scales is minimal. The difference between networks is also significant for PDSI. Although the absolute magnitude of the differences between interpolation with COOP and USHCN are small, the accuracy of interpolation with COOP is much more spatially variable than with USHCN.
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Book chapters on the topic "Standardized Precipitation and Evapotranspiration index (SPEI)"

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Chelcea, Silvia, Monica Ionita, and Mary-Jeanne Adler. "Identification of Dry Periods in the Dobrogea Region." In Civil and Environmental Engineering, 324–39. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9619-8.ch012.

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The main objective of this study is to identify different types of drought (moderate, severe and extreme) in the Dobrogea region based on three indicators: the Standardized Precipitation Index (SPI), the Standardized Precipitation and Evapotranspiration Index (SPEI) and the Standardized Flow Index (SFI). The dry periods, from a meteorological point of view, were identified based on a drought index that takes into account only precipitation (SPI) and another one that takes into account both precipitation as well as mean air temperature (SPEI). To highlight the dry periods from a hydrological point of view we applied the procedure for calculating the SPI to monthly discharge time series, through the Standardized Flow Index (SFI).
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Deo, Ravinesh C., Sancho Salcedo-Sanz, Leopoldo Carro-Calvo, and Beatriz Saavedra-Moreno. "Drought Prediction With Standardized Precipitation and Evapotranspiration Index and Support Vector Regression Models." In Integrating Disaster Science and Management, 151–74. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-812056-9.00010-5.

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Dayal, Kavina S., Ravinesh C. Deo, and Armando A. Apan. "Development of copula-statistical drought prediction model using the Standardized Precipitation-Evapotranspiration Index." In Handbook of Probabilistic Models, 141–78. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-816514-0.00006-0.

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"Evolution characteristics of droughts during winter wheat growth in Shandong province since 1960 based on standardized precipitation evapotranspiration index." In Emerging Economies, Risk and Development, and Intelligent Technology, 429–38. CRC Press, 2015. http://dx.doi.org/10.1201/b18509-58.

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Conference papers on the topic "Standardized Precipitation and Evapotranspiration index (SPEI)"

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Manganhar, Suhail, and Humayoon Sial. "Projection of Geographical Variability and Temporal Trends on Drought Characteristics Using Standardized Precipitation Evapotranspiration Index (SPEI) for Water Resource Management of Critical Zones in Sindh, Pakistan." In 5th International Electronic Conference on Water Sciences. Basel, Switzerland: MDPI, 2020. http://dx.doi.org/10.3390/ecws-5-08449.

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ŻARSKI, Jacek, Stanisław DUDEK, and Renata KUŚMIEREK-TOMASZEWSKA. "DRIP IRRIGATION AS A FACTOR MITIGATING DROUGHT IMPACT IN CORN CULTIVATION IN CENTRAL POLAND." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.167.

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The aim of the paper was to develop a model determining production effects of corn irrigation depending on a drought level in the growing season.Corn is particularly useful for cultivation in the conditions of irrigation since its yield in central Poland depends significantly on rainfall distribution in growing season. Corn was grown under irrigation conditions in the years 2005-2016 in the region of central Poland, meteorological data, were gained from the research station of the UTP University of Science and Technology in Bydgoszcz. Based on the results of twelve-year effects of corn irrigation and meteorological data, the most relevant relationships between irrigation productivity and chosen indices during the period of high water needs of corn (from 1 June to 31 July) were searched. The following indicators were taken into account: absolute precipitation totals, relative precipitation index (RPI), standardized precipitation index (SPI) and the ratio of precipitation to reference evapotranspiration. The results shown that yield of non-irrigated corn were characterized by a very high variability and depended significantly on indicators, based mainly on rainfall conditions. The production effects of drip irrigation correlated significantly with the indicators during the period of high water needs of plants, covering June and July. In wet periods the increases in grain yields due to irrigation were non-significant and about four-fold lower and in the dry seasons – more than half higher than the average increases. There is a high importance of the results presented in the paper, because they can be used as a model for forecasting corn production and its economic effects as well as for planning the development of irrigation systems in a given area.
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Wang, Weidan, Li Sun, Zhiyuan Pei, Yuanyuan Chen, and Xiaomei Zhang. "Analysis of Temporal and Spatial Variation of Growing Season Drought in Jiling Province Based on Standardized Precipitation Evapotranspiration Index." In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2019. http://dx.doi.org/10.1109/agro-geoinformatics.2019.8820436.

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