Agricultural Meteorology
S.F. Ziaei Asl; A.A. Sabziparvar
Abstract
Introduction: It is possible to guide the agricultural experts to achieve a suitable genotype and adapt to climatic conditions in proportion to the length of the modified growing season by identifying the impact of climate change in recent years on the cumulative rate of degree-days of plant growth. ...
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Introduction: It is possible to guide the agricultural experts to achieve a suitable genotype and adapt to climatic conditions in proportion to the length of the modified growing season by identifying the impact of climate change in recent years on the cumulative rate of degree-days of plant growth. This will prevent the waste of capital and agricultural inputs and ultimately prevent the reduction of the final crop due to the mismatch of genotype-crop with the current climate. In the present study, an attempt has been made to study and compare the trend in the start and end of the growing season, the growing season length (GSL), and growing degree-days(GDD) during 1959-2018 in the elevated and coastal areas of Iran.Materials and Methods: For this study, the daily temperature of 27 synoptic stations were used including 19 stations in elevated areas and 8 stations in coastal areas during 1959-2018. The first day with a minimum daily temperature equal to or greater than 0, 5, and 10 °C was considered as the start of the growing season (SGS). Moreover, the first day after the start of the growing season which has a minimum daily temperature of less than 0, 5, and 10 °C was considered as the end of the growing season (EGS). Trend analysis was performed in time series of GSL and GDD based on thresholds of 0, 5, and 10 °C using the Mann-Kendall test. To compare the results, the statistical period of 60 years was divided into two periods of 30 years (1959-1988 and 1989-2018). In both periods, the statistical characteristics of the GSL and GDD based on the three thresholds mentioned in coastal and elevated areas were surveyed and compared. In this study, deviation from the mean was used to complete the study of changes in the GSL. This index shows the scatter of data around the mean.Results and Discussion: The GSL extension came from both the advance in SGS and the delay in EGS. Comparison results of the two 30-year periods (1959-1988 and 1989-2018) showed that during 1989-2018, in most stations the GSL has increased. During this period, based on 0 °C, the earliest and latest SGS were on February 24 and April 30 in Yazd and Shahrekord, respectively. Accordingly, the earliest and latest EGS were on October 15 and December 11 in Shahrekord and Gorgan, respectively. Based on 5 °C, the earliest and latest SGS were on February 10 and June 2 in Abadan and Gorgan, respectively. Accordingly, the earliest and latest EGS on September 17 and December 6 were at Shahrekord, Bam, and Abadan, respectively. Based on 10 °C, the earliest and latest SGS was on February 11 and June 20 at stations, respectively. Accordingly, the earliest and latest EGS were on August 27 and December 8 in Shahrekord and Bushehr, respectively. The shortest and longest GSLs based on all three thresholds of 0, 5, and 10 °C were Shahrekord and Bandar Abbas, respectively. The highest and lowest coefficient of variation of GSL were 20.8% in Zanjan and 4.9% in Bandar Abbas, respectively. Based on 0, 5, and 10 °C, the lowest GDDs in GSL are 3233, 1767, and 880 °C.d, respectively, and all of them were obtained at Shahrekord. On the other hand, the highest GDD0, GDD5, and GDD10 in GSL were 6783, 7372, and 5761 °C.d, respectively, in Yazd, Abadan, and Bandar Abbas. The most significant trend in GSL was in Zanjan, Zahedan, and Khorramabad.Conclusion: Examination of changes in the GSL indicates the existence of a significant trend in a limited number of stations. Also, with increasing the threshold from 0 to 5 and from 5 to 10 °C, there is a significant decreasing trend in more stations. At the threshold of 10 °C a significant and decreasing trend of GSL was observed in Urmia, Sanandaj, Khorramabad, Birjand, and Bandar Abbas stations, In following, a significant increasing trend was observed in Tabriz, Tehran, Kermanshah, Isfahan, Yazd, and Bushehr stations. The results of the studies showed fewer changes in the time series of the GSL based on thresholds of 0 and 5 °C in the statistical period of 1989-2018. On the other hand, the results showed that the GSL trend is significant in more stations in the recent period based on the threshold of 10 °C. Deviation from the average GSL in coastal areas was greater than the elevated areas so that the GSL based on 10 °C in both areas increased with greater slope and continuity. This increasing trend of deviation from the average in the coastal areas from the early '70s and the elevated areas from the early '90s and continues until now. In this regard, Bandar Abbas station and then Bushehr station had the longest GSL, and Shahrekord station had the shortest GSL among other stations which has been studied. Comparison of GDDs of the GSL during 1989-2018 showed the decrease of GDDs from south to north and from west to east of the country. Accordingly, in the southern stations of the country, the conditions for tropical plants (threshold of 10 °C) have become more suitable than the cold stations of the west and northwest, Time series analysis of the average annual GDDs based on the three thresholds during 1989-2018 showed a significant increasing (positive) trend in 93% of the stations. During the second 30-years period, Shahrekord and Shiraz stations did not show a significant trend in all three mentioned thresholds. However, the analysis of the annual average of GDDs during 1959-1988 showed the trend in 41% of the stations. According to the results of this study, it can be concluded that in cold regions, due to the increase in GDDs, the supply of cooling units for plants with certain cooling needs is more difficult. In the south of the country, as the total required GDD is achieved earlier, the GSL gets shorter, and therefore less dry biomass will accumulate in the product. This likely leads to a reduction in crop yields in this part of the country.
M. Moravej; K. Khalili; J. Behmanesh
Abstract
Introduction: Studying the hydrological cycle, especially in large scales such as water catchments, is difficult and complicated despite the fact that the numbers of hydrological components are limited. This complexity rises from complex interactions between hydrological components and environment. Recognition, ...
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Introduction: Studying the hydrological cycle, especially in large scales such as water catchments, is difficult and complicated despite the fact that the numbers of hydrological components are limited. This complexity rises from complex interactions between hydrological components and environment. Recognition, determination and modeling of all interactive processes are needed to address this issue, but it's not feasible for dealing with practical engineering problems. So, it is more convenient to consider hydrological components as stochastic phenomenon, and use stochastic models for modeling them. Stochastic simulation of time series models related to water resources, particularly hydrologic time series, have been widely used in recent decades in order to solve issues pertaining planning and management of water resource systems. In this study time series models fitted to the precipitation, evaporation and stream flow series separately and the relationships between stream flow and precipitation processes are investigated. In fact, the three mentioned processes should be modeled in parallel to each other in order to acquire a comprehensive vision of hydrological conditions in the region. Moreover, the relationship between the hydrologic processes has been mostly studied with respect to their trends. It is desirable to investigate the relationship between trends of hydrological processes and climate change, while the relationship of the models has not been taken into consideration. The main objective of this study is to investigate the relationship between hydrological processes and their effects on each other and the selected models.
Material and Method: In the current study, the four sub-basins of Lake Urmia Basin namely Zolachay (A), Nazloochay (B), Shahrchay (C) and Barandoozchay (D) were considered. Precipitation, evaporation and stream flow time series were modeled by linear time series. Fundamental assumptions of time series analysis namely normalization and stationarity were considered. Skewness test applied to evaluate normalization of evaporation, precipitation and stream flow time series and logarithmic transformation function executed for in order to improve normalization. Stationarity of studied time series were evaluated by well-known powerful ADF and KPSS stationarity tests. Time series model's order was determined using modified AICC test and the portmanteau goodness of fit test was used to evaluate the adequacy of developed linear time series models. Man-Kendall trend analysis was also conducted for the precipitation amount, the number of rainy days, the maximum precipitation with 24 hours duration, the evaporation and stream flow in monthly and annual time scales.
Results and Discussion: Inferring to the physical base of ARMA models provided by Salas et al (1998), the precipitation has been considered independently and stochastically. If this assumption is not true in a given basin, it is expected that the MA component of stream flow discharge model be eliminated or washed out. This case occurred in basins A, B and C. In these basins, the behavior of precipitation and evaporation was autoregressive. It was observed that the stream flow discharge behavior also follows autoregressive models that had greater lags than precipitation and evaporation lags. This result proved that the precipitation, evaporation, and stream flow processes in the basin were regular processes. In basin D, the behavior of precipitation was stochastic and followed the MA model, which was related to the stochastic processes. In this basin, the stochastic behavior of precipitation affected the stream flow behavior, and it was observed that the stochastic term of MA also appeared in the stream flow. Thus, this leads to decrease the memory of stream flow discharge. The fact that the MA component in the stream flow discharge was greater than the MA component in precipitation indicated that during the process of producing stream flow discharge from precipitation, the stochastic factors performed an important role.
Conclusion: A comprehensive investigation on hydrological time series models of precipitation, evaporation and stream flow were investigated in this study. The framework of the study consists of trend analysis using Mann-Kendall test and time series. Trend analysis results indicate the significant changes of water resources in the studied area. It means that sustainable development in studied area is greatly threatened. The results of parallel modeling of precipitation, evaporation and stream flow time series showed that the behavior of stream flow models are greatly affected by precipitation models. In other words, this study evaluate the physical concept of ARMA models in real-world monthly time scale for three main hydrologic cycle components and suggest that considering parallel hydrological time series modeling could increase the accuracy to select a model for simulation and prediction of stream flow time series. In addition, it suggested that there is a relation between climate pattern and hydrological time series models.
Keywords: ARMA models, Stationarity, Trend analysis, Water cycle components
Mohammad ali Ghorbani
Abstract
Time series analysis methods have been detected as important tools for evaluating the issues related to water resources management. Spatial differences in streamflow trends can occur as a result of spatial differences in the changes in rainfall and temperature, spatial differences in the catchment characteristics ...
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Time series analysis methods have been detected as important tools for evaluating the issues related to water resources management. Spatial differences in streamflow trends can occur as a result of spatial differences in the changes in rainfall and temperature, spatial differences in the catchment characteristics and human activities and, trend analysis of this time series is necessary in causes of these differences. On the other hand, these series have a different structure, so that the successive values of them are interdependent. In this study has attempted to analyze of this time series for Aji Chai sub basin using of Seasonal Kendall method. In addition, Hurst exponent value, as an effective factor in trend and seasonality of time series, is also evaluated using Variance, R/S and DFA methods. The results showed a significant increasing trend for temperature and, both significant decreasing and increasing trend for precipitation at 10% significance level over sub basin. Also, for discharge, downstream stations showed significant decreasing trend. The analysis results of Hurst exponent estimation methods showed that precipitation and discharge time series have a relatively moderate long term persistence (H~0.65). The Hurst phenomenon existence has also been confirmed as a factor affecting on the seasonality and trend of precipitation and discharge time series using α > 0.5 exponent by DFA method.
