Irrigation
K. Raispour; B. Salahe; B. Abad
Abstract
Introduction Precipitation is the most important element of water level that recognizing its temporal-spatial characteristics at different scales is an important step towards better understanding and modeling of the hydrological cycle and related phenomena such as floods. Drought, landslides, snow ...
Read More
Introduction Precipitation is the most important element of water level that recognizing its temporal-spatial characteristics at different scales is an important step towards better understanding and modeling of the hydrological cycle and related phenomena such as floods. Drought, landslides, snow and climate change are on a regional and global scale. Despite the large number of studies conducted in this field, there is still a lot of research need in many parts of the world for reasons such as lack of weather stations to access ground observation data and the non-uniform spatial distribution of these stations. Nowadays, with significant technological advances, including the advent of various satellites, access to a variety of precipitation data has been greatly facilitated. Among the latest precipitation products of various satellites, we can refer to the Global Precipitation Measurement (GPM) satellite data. Related to the subject of the present study, it is stated that most of the studies on rainfall in the Jazmourian catchment area have been based on station data, which due to the poor distribution of meteorological stations; it is not possible to estimate the temporal-spatial distribution of precipitation in the study basin. In this study, the temporal-spatial analysis of precipitation using GPM satellite precipitation products as one of the most important climatic parameters in the basin Due to the undeniable importance of rainfall in this basin, it seems that the analysis of variable rainfall can provide valuable climatic information to researchers and planners. To pave the way for new study platforms.Materials and Methods In this study, satellite data (GPM) with a spatial resolution of 0.1 × 0.1 degrees from January 2001 to December 2019 have been used for spatiotemporal analysis of precipitation in the Jazmourian catchment. The GPM satellite provides more accurate and realistic estimates than other TRMM satellites. In this study, a calibrated precipitation product of level 3 of 6 GPM satellite versions was used. Relevant data are in NCDF format and have UTM image system with WGS84 datum, which after quality control and preprocessing, by specialized software (ENVI, ArcGIS and EXCELL) is converted into network data and data tables and the necessary outputs based on the geographical boundary of the catchment was extracted. The average monthly rainfall was extracted from the average daily rainfall belonging to each month and the seasonal average was extracted from the average of three months related to each season. Spatially, the values of each pixel are the conditions of the average amount of precipitation related to each time series (monthly, seasonal and annual) during the statistical period.Results and Discussion Based on the results, the average rainfall in the Jazmourian catchment was estimated as 144 mm, the spatial distribution of which ranged from 83 to 232 mm. The maximum rainfall occurred in the northern and western parts and the minimum occurred in the central and eastern parts of the basin. Furthermore; based on the annual distribution of rainfall during the statistical period under study, the highest rainfall was in 2019 with 239 mm and the lowest with 53 mm in 2001. In terms of seasonal distribution, winter and spring with values of 118 and 88 mm, respectively, showed the highest and autumn and summer with values of 22 and 45 mm, showed the lowest values of precipitation. Also, during the statistical period under study, winter 2005 with 193 mm had the highest and autumn 2003 with 1 mm had the lowest seasonal rainfall in the basin. In addition, an interesting point is the spatial displacement of high-pressure nuclei in different seasons of the year; so that these nuclei are observed in the cold seasons of the year in the northern and western parts and in the warm seasons of the year in the southwestern and southeastern parts of the basin. The spatial distribution of monthly precipitation indicates the occurrence of the highest monthly precipitation in February and March and the lowest in May and September. Also, the monthly rainfall time series indicates the maximum incidence of precipitation in February 2001 (94 mm) and it’s minimum in January 2001 (no precipitation).Conclusion Precipitation as a source of fresh water on Earth is one of the most important hydrological parameters, the importance of which is undeniable in the survival of human communities and natural ecosystems. Due to the large temporal-spatial variations of precipitation, its study seems necessary. But one of the main challenges for studying this phenomenon is the lack of ground stations as well as their improper distribution. Today, with advancement of technology and remote sensing, a diverse range of satellite data has become available to environmental scientists. In this regard, in the present study, using GPM satellite data and in the statistical period 2001-2019, the temporal-spatial distribution of precipitation in the Jazmourian catchment area in southeastern Iran has been investigated. In general, the high variability of rainfall in Jazmourian catchment in different months and seasons of the year, shows the dominance of arid and low climate in this basin. Therefore, due to the rainfall situation and its high fluctuations under climate change conditions, in the near future, this basin will face serious challenges and crises in water resources management and the sustainability of natural ecosystems. The GPM satellite data used in this study showed appropriate and expected results from the spatial-temporal distribution of precipitation in the Jazmourian catchment and showed a good correlation with meteorological stations. In general, the use of GPM satellite data in the present study is appropriate, which due to its appropriate spatio-temporal separation, gives reliable and satisfactory results. On the other hand, inadequate spatial coverage of meteorological stations and their large statistical vacuum in such a relatively large basin justify the use of this valuable and useful satellite data.
B. Salahi; R. Maleki Meresht
Abstract
Introduction: Rainfall has the highest variability at time and place scale. Rainfall fluctuation in different geographical areas reveals the necessity of investigating this climate element and suitable models to forecast the rate of precipitation for regional planning. Ardabil province has always faced ...
Read More
Introduction: Rainfall has the highest variability at time and place scale. Rainfall fluctuation in different geographical areas reveals the necessity of investigating this climate element and suitable models to forecast the rate of precipitation for regional planning. Ardabil province has always faced rainfall fluctuations and shortage of water supply. Precipitation is one of the most important features of the environment. The amount of precipitation over time and in different places is subject to large fluctuations which may be periodical. Studies show that, due to the certain complexities of rainfall, the models which used to predict future values will also need greater accuracy and less error. Among the forecasting models, Arima has more applications and it has replaced with other models.
Materials and Methods: In this research, through order 2 Autoregrressive, Winters, and Arima models, monthly rainfalls of Ardabil synoptic station (representing Ardabil province) for a 31-year period (1977-2007) were investigated. To assess the presence or absence of significant changes in mean precipitation of Ardabil synoptic station, rainfall of this station was divided into two periods: 1977-1993 and 1994-2010. T-test was used to statistically examine the difference between the two periods. After adjusting the data, descriptive statistics were applied. In order to model the total monthly precipitation of Ardabil synoptic station, Winters, Autoregressive, and Arima models were used. Among different models, the best options were chosen to predict the time series including the mean absolute deviation (MAD), the mean squared errors (MSE), root mean square errors (RMSE) and mean absolute percentage errors (MAPE). In order to select the best model among the available options under investigation, the predicted value of the deviation of the actual value was utilized for the months of 2006-2010.
Results and Discussion: Statistical characteristics of the total monthly precipitation in Ardabil synoptic station indicates that in May, the highest and in August, the lowest monthly total rainfall accounted in this station. Standard deviation of rainfall reached to the lowest level in August and its peak in November. Coefficients of skewness and kurtosis of total rainfall in all seasons, indicates a lack of compliance with normal distribution. From the view of the range of total monthly rainfall, October and August have highest and the lowest tolerance in these parameters, respectively. The results showed that the percentage of the mean absolute error for Arima, Winters and Autoregressive models was 61.82, 148.39 and 81.54 respectively and its R square came to be 88.28, 61.07 and 85.12 respectively. The comparison of the parameters is an indication of the fact that Arima has the highest R square and the lowest mean absolute error of 88.28 and 61.82 respectively than Winters and Autoregressive models. The presence or absence of significant changes in mean precipitation during 1977-1993 and 2010-1994 in Ardabil synoptic station shows that the difference of rainfall is not significant at the 5% error level from statistical point of view. The comparison between the monthly mean rainfall of Ardabil synoptic station in 1994-2010 and 1977-1993 indicates that rainfall has somewhat decreased in the former in recent years. Considering the low average monthly rainfall of Ardabil synoptic station in 1994-2010 compared to 1977-1993 (21.98 versus 26.11 mm), although no statistically significant difference was found in the average rainfall, low rainfall in this station would not be unexpected in the coming years. The comparison of predicted and actual values from 2011 to 2013 in Ardabil synoptic station showed that fitting real data with expected data was relatively acceptable. The observed differences between the actual and predicted values can be related to the influence of rainfalls and many local and dynamical factors of this area. Therefore, it is necessary for climatologists to better explain and predict phenomena besides statistical models and pay more attention to general circulation models (GCM) under different climate conditions.
Conclusion: Results of rainfall investigation by order 2 Autoregrressive, Winters, and Arima models showed a descending trend in monthly rainfalls in the coming years across the study location. The results of modeling and analysis of monthly rainfalls in Ardabil synoptic station showed that among these models, Arima was better than the other two because it enjoyed the lowest MAPE and the highest R2. AIC, RMSE and MAD scales of different patterns were calculated and finally, SARIMA(1,1,1)(2,0,1)12 pattern having the lowest AIC, RMSE and MAD was selected as the most appropriate pattern for monthly rainfall forecasting in Ardabil synoptic station.
