نشریه مدل سازی و مدیریت آب و خاک
سال یکم شماره 1 (بهار 1400)

The purpose of this study is to generate the relationships to directly calculate waterfall height at the downstream of the spillway to form a jump at the toe of the spillway and to prevent erosion and destruction of the downstream river bed or body of the spillway in a submerged and free hydraulic jump.

The purpose of this study is to provide a relationship to directly calculate the waterfall height (h) without the need for a trial and error procedure. For this purpose, the application of the momentum relationship between two sections (1and 2) yields the waterfall height (h) according to Eq. (1).   h=y1 .....   (1) Since in Eq. (1), Fr1 and y1 are themselves a function of the waterfall height (h), so this equation must be solved by trial and error or by using design charts provided by other researchers. To calculate the waterfall height in oscillating jump conditions (2.5<Fr1<4.5) and also for steady and strong jump conditions (4.5<Fr1<15.5), by assuming different values ​​for p, y0, yt and C (where C is the discharge coefficient), 300 and 1146 series of numbers have been generated by trial and error, respectively. Then, the parameters in Eq. 1 (y1, Fr1) were also calculated. The final waterfall height (h) was obtained for each series of numbers. Using the existing variables, dimensionless parameters ( ) and Fr1 were extracted. Then, multiple linear and nonlinear multiple regression relationships were tested for direct calculation of . Finally, the best relationships with the least error were selected. The results of multiple regression (MR) relationships are also compared with the results of ANN and SVM methods.

The proposed non-linear multiple regression relationship (MR-3) shows higher accuracy in estimating the waterfall height, compared to MR-1 and MR-2 regression models based on three statistical indices (RE%, RMSE, R2) for 2.5<Fr1<4.5 and 4.5<Fr1<15.5. Therefore with having the Froude number related to the initial hydraulic jump depth (Fr1), tail water depth (yt), weir height (P) and initial jump depth (y1), the calculation of waterfall height (h) will be possible using MR-3 model without any trial and error procedures. Moreover, the results show that among the intelligent models, the ANN model has very close results to the proposed nonlinear regression relation (MR-3) based on the statistical indices.

A new method for calculating the height of a waterfall at the toe of the ogee spillway was presented to control hydraulic jump. To calculate the height of the waterfall directly, multiple nonlinear regression (MR) relationships were presented for two ranges of different Froude numbers. The MR, ANN, and SVM models showed good performance in predicting the height of the waterfall downstream of spillways, but the ability of the two MR and ANN models were better than the SVM.

Millions of hectares of the world's irrigated lands need subsurface drainage, but lack of funding is hindering the timely development of these systems, so a solution is required to minimize construction costs (Ritzema and Braun, 2006; Sharifipour et al., 2015). The purpose of this paper is to provide a way to find the "lowest cost depth" for installing subsurface drains.
The costs can be divided into three groups. The first group is the costs that do not depend on the digging time and the depth of the drainage installation; if the drains are installed at any depth, these costs do not change per unit length of subsurface drains. These include the cost of purchasing the filtered pipe, the cost of constructing lateral outlets to collectors, etc. The second group is the costs that depend on the depth of drainage installation and the digging time, such as the cost of capital depreciation to purchase a trencher. By increasing the installation depth of the drain, the digging speed will be reduced, thus reducing the total length of the digging. The third group is the costs that are a function of the drainage installation depth but are not a function of the digging speed, including the cost of constructing of collector drain, which increases with the installation depth of the drain. This paper developed a method for converting these costs into cost per unit area :CA = C′I + C′V + C′C                                                                                                                                                                                   (1)
CA is the total cost of constructing a subsurface drainage system; C′I, C′V, and C′C represent first, second and third groups of construction costs, all in Rial per unit area (ha).
According to the drainage equations, the distance between the drains will increase with increasing depth, so increasing drainage depth decreases the first group of costs per unit area. As the drainage depth increases, the volume of soil displacement increases. The trench digging speed decreases, so the second group of costs, including the depreciation costs of the trencher, fuel, and wages increase per unit length of lateralization. C′C cost, which includes the cost of collector drainage, increases by the depth of drainage installation.
Algebraic operations are possible by converting costs to costs per unit area. Since the drainage density in deep drains is lower and by determining the density of drainage per unit area at each installation depth, the depth that has the lowest cost can be selected as the optimal depth. The raw data for each project may be different; so this data must be collected and used with acceptable accuracy. The cost model is general, but special conditions in some drainage projects may lead to extraordinary costs. In that case, those costs should be converted into costs per unit area similarly and considered as well.

During periods of climate change, catastrophic floods have occurred, mainly due to extreme rainfalls, leading to widespread damages and heavy economic losses, the spread of epidemics, and the mortality of many people. Psychological research related to current global warming also indicates the appearance or exacerbation of mental disorders after the occurrence of this natural event. In this study, the socio-economic and health consequences of floods have been studied, and also, using paleoclimate, archeological, and historical researches, some severe and extensive flood events from prehistory to the present have been presented. Finding reports of flood events from historical documents and discovering evidence of floods among the cultural layers of ancient sites, along with paleoclimate and paleo-flood studies, can yield more accurate results from past climatic and environmental conditions. In the studies of environmental sedimentology of some ancient sites of Iran, evidence of catastrophic floods belonging to the mid-fourth millennium B.C. has been found and some have been reported in the historical books of the Islamic period. These events coincide with periods of climate change called medieval warming and the Little Ice Age and occurred mostly in Iran due to extreme rainfalls and flooding of rivers and seasonal streams.

In this study, first, the devastating social, economic, and health consequences of floods are explained. Then, archaeological evidence is examined, some of which are the result of field research. Finally, historical documents and reports that mention the occurrence of great and influential floods from the early Islamic period to the present are presented.

Floods kill more than 2,000 people each year and affect 75,000,000 of the world's population. The reason is the geographical distribution of alluvial fans and shorelines that have long been attractive for human habitation. The occurrence of floods, due to the extreme rainfalls related to climate change, mainly overlapped with drought periods. One of the most important archeological evidence of floods dates back to the fourth millennium B.C. According to the high-resolution paleoclimate research of Lake Neor in Ardabil, from about 4200 to 3000 B.C., there was a very dry period with increasing dust. During this period, at least two periods of severe drought occurred, 3600-3700 B.C. and 3150-3250 B.C., which are shown by the paleoclimate research of Soreq Cave in the west of Jerusalem. Archaeological evidence of floods in the middle and late fourth millennium B.C. as a result of environmental sedimentology and archaeological excavations in the sites of Mafin Abad Islamshahr, Meymanat Abad Robat Karim and Qara Tepe of Qomroud in North Central Iran, as well as in the sites of Shuruppak, Kish and Ur in Iraq have been identified. The flood of 628 A.D., which occurred due to the flooding of the Tigris and Euphrates rivers in Mesopotamia, was probably one of the main reasons for the fall of the Sassanid dynasty. Blazeri, the historian of the Islamic period, attributes the occurrence of this great flood to the end of the reign of Khosrow Parviz. This event has led to the death of many people, the destruction of crops, famine, displacement, and the spread of plague.

Therefore, it can be said that if flood prevention and control in Iran are not managed efficiently and effectively, extreme rainfalls related to current climate change (global warming) can cause serious damages and irreparable losses.

Desertification and land degradation have caused major problems worldwide, especially in semi-arid to semi-humid vulnerable areas (Afsharinia, 2020). Natural regeneration is based on management practices that improve the vigor of plants and accelerate the growth of the remaining quality plants. However, the response of vegetation to the breeding program varies from habitat to habitat. Analysis of processes related to the environment is provided only by understanding the ecosystems. Desert management and control schemes with widespread effects on desert ecosystems are the source of major changes in socio-economic and environmental.

This research has investigated the performance of implemented natural resources projects in the field of desert work using socio-economic and environmental criteria in Kashan City. The extent of knowledge of the projects and their effectiveness in the form of survey research method has been evaluated from the perspective of desert ecosystem residents. The effectiveness of the plans in controlling desert conditions, including controlling windfall sediments, agricultural production, and increasing settlement and controlling migration has been investigated. The data collection method was based on random sampling with a sample size of 150 from the statistical population of households that were active in the field of agriculture and livestock. Also, in order to collect information, a questionnaire was used, the validity of which was confirmed by experts and professors.

The reliability of the questionnaire was calculated by Cronbach's alpha coefficient greater than 0.75, which indicates the acceptable reliability of the questionnaire. The results of the sample demographic composition indicate that there is no significant relationship between people's participation rate and their age, people's participation rate, and their literacy level, people's participation rate and their type of job. The results of the Spearman correlation analysis indicate a significant positive relationship between residents' opinions and the effectiveness of desert management plans. There is a significant correlation between the effectiveness of wind sediment control, agricultural production, and settlement. Therefore, due to the obvious effects of desert rehabilitation projects by residents, increasing life expectancy in desert settlements and life expectancy in these ecosystems has increased.

The area of desert lands is large and this limits the implementation of desert management plans. Lands should not be pushed to the point of complete destruction so that they can no longer be returned. Adequate information on the implementation of desert management plans should be accompanied by sufficient information measured by temperature stations as well as the study of climatic parameters and should be given priority. It is desirable to take the necessary measures to connect the government and the indigenous people with the aim of better protection of natural resources as well as the preservation of the implemented projects.

Desertification is equivalent to land degradation in arid, semi-arid, and semi-humid arid regions affected by climate change and human activities. Recognition of climatic anomalies that are effective in aggravating desert conditions that cause climatic conditions to distance themselves from normal long-term conditions in a certain spatial and temporal range as a major factor or precondition for the intensification of human activities is essential. Climate change, has multiple effects on various ecosystems. Given the wide-ranging effects of climate change on desertification and economic and social issues, knowing how such changes occur in environmental planning will be very effective.

Investigating temperature and precipitation changes, as two main elements of the climate structure, in the statistical period 1989-2010 and predict these changes in the study periods 2011-2030, 2046-2065, and 2080-2099, in three decades 2020, 2050 and 2090 were performed at Baft Synoptic Station using LARS-WG micro-scale method. Baft City is located in the southwest of Kerman Province. Daily data of rainfall, temperature parameters, and sunshine duration during the period 1989-2010 were collected. A homogeneity test was performed. The LARS-WG model is a multivariate regression model for the production of climatic data by statistical micro-scale techniques according to a specific climate change scenario in the future. The monthly and seasonally results of the HadCM3 model were generated under the emission scenario of A2, A1B, and B1.

The highest increase in average daily temperature is related to scenario A2 in autumn, scenario A2 in spring and scenario A2 in summer. Comparison of seasonal temperature changes in the future also shows an increase in temperature in all seasons, especially spring and summer. The highest rainfall is related to the months of February in scenario A1B, March in scenario A2, January in scenario A1B and February in scenario A2, respectively, and only in July and December the precipitation decreased in all three scenarios. In the period 2046-2065, the highest increase in rainfall is related to January and February in scenario A1B, March in scenario B1, and January in scenario A2, respectively, and the least rainy month compared to the base period is December. In the period 2080-2099, the highest increase in rainfall is related to March in scenario A1B and scenario B1 and January in scenario A2, respectively, and the least rainy month compared to the base period is December.

The temperature will increase in all months as well as decrease rainfall in summer and increase it in autumn and winter. This increase in temperature and decrease in rainfall are among the factors aggravating desertification based on climate criteria. The findings can be used to estimate changes in water resources, agricultural crop yields, droughts and floods in the future.

Landslide is an important geological hazard and one of the natural disasters that are constantly happening around the world. The factors that cause landslides are numerous and complex. Today, due to the importance of landslides and the effects of not paying attention to this issue, risk prevention has become an important tool in land use planning and management. Given the sensitivity and importance of this issue, the preparation of zoning maps of landslide sensitivity is very important and should be considered. These maps show landslide-prone areas and safe areas. Effective use of the landslide map can reduce the potential damage to the event and thus avoid many hazards. Landslides are natural events, but they can become dangerous and cause casualties and damage to man-made and natural structures.

The study area is the Neka Watershed located in the east of Mazandaran Province. To study landslides in this study, from nine maps including land use, slope, geology, slope direction, land curvature, distance from faults, communication routes, sewage from the river, and rainfall were selected and each map was extracted to produce the final landslide map. Finally, the maps were finalized in GIS software and the final map was prepared using the fuzzy logic method. In the fuzzy model, each of the pixels in the map is given a value between zero and one. To perform fuzzy in ArcGIS software the fuzzy Membership tool was applied. In this research, the Shannon entropy method was also used. Finally, the landslide map was extracted and evaluated. According to the final map and its studies, landslide-sensitive areas were identified.

Various factors, in relation to each other and in relation to local characteristics, cause domain instability. Instability factors with different contributions to the occurrence of mass movements, especially in the occurrence of landslides. To study landslides in this study, from 9 maps including land use, slope, geology, slope direction, land curvature, distance from the fault, communication paths (sewage from the road), sewage from the river and rainfall, and the map of each separately The final landslide was extracted to prepare the map. The maps were prepared in the final GIS software and the final map was prepared using the fuzzy logic method. The fuzzy model was performed using Arc GIS software and each of the pixels in the map was given a value between zero and one. The Shannon entropy method was used in this study. Finally, the landslide map was extracted and evaluated. According to the final map and its studies, landslide-sensitive areas were identified.

Based on the knowledge-based approach, the evaluation of several parameters such as geology, slope, land cover, slope direction, land curvature, rainfall, distance to flow, distance to road, and distance to fault were overlayed and the landslide map was extracted. A numerical scale (1-5) from very high to very low impact was used. Areas with high and very high sensitivity have been recorded in areas without vegetation and with high slopes and high rainfall. Sub-watersheds N2 and N1 are ranked 1st and 2nd, respectively, in terms of high landslide potential. The reason for the high intensity of landslides in these two sub-watersheds is low vegetation and a high slope.