Evaluating Negarim Microcatchment efficiency to Conserve Soil Moisture based on Soil Depth

Evaluating Negarim Microcatchment efficiency to Conserve Soil Moisture based on Soil Depth | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating Negarim Microcatchment efficiency to Conserve Soil Moisture based on Soil Depth Banafshe Kouhzad, Mohammad Reza Yazdani, Mohammad Taghi Dastorani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4704859/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract One of the most important factors in determining the Ecohydrological balance is soil moisture content. Any variation in soil moisture, albeit insignificant, can have a chain effect on the quality of soil structure, soil particles, erosion rate, microbiological activity in soil crust, and infiltration rate. which in turn can affect ecosystem dynamics. Therefore, it is important to use more eco-friendly and less invasive techniques, such as rainwater harvesting structures (RHS), to enrich the current soil moisture content in any ecosystem. The study used an RWH structure, namely the Negarim Microcatchment, to observe its real-time effects on soil moisture variations. The continuous effect of certain treatments was also evaluated. To do this, a small area of approximately 3375 m 2 in the Dehbar watershed (36 ° 18’ N, 59° 24’ E) located in Khorasan Razavi Province of Iran was selected based on the map of water harvesting potential in the watershed that was generated by GIS to construct the structures. This study aimed to evaluate the efficiency of a number of variations of RHS in retaining soil moisture its different soil depths. For this purpose, FAO’s standard measurement was used to calculate and construct three different sizes of structures for this research. FAO’s standard measurement included the 1 x 1 area for the infiltration pit which was used as the medium-sized (standard) structure. The other two structures were one time larger and one time smaller, respectively. Each size group consists of 38 Microcatchment with two treatments of natural (N) and plastic covered (P) for the catchment area and a combination of natural cover (N), seedling (S), rock cover (R), no rock cover (F), summer irrigation (W), and no summer irrigation (D) for the infiltration pits. After each significant rainfall, soil moisture measurement was measured by TDR sensors with a repetition of three times at five-day intervals throughout two crop years. The data analysis results showed that the main control factor of the structure performance was soil depth and the catchment area cover type. Comparing the three depths, the depth of 30 cm showed more significance by a margin of 10–20% over the depths of 20 cm and 10 cm. The same could be said about the difference between plastic-covered catchments and natural-covered ones at 5–10%. The infiltration pit treatments showed a moderate 2–3% effect. It can also be concluded that while each treatment showed a significant interrelationship between different inter-factors, no significance was found between individual factors. The results of this study indicated that overall variation in RHS can be significant in RHS's ability to conserve soil moisture. The provided data can be used for long-term usage and data monitoring of such structures. Soil Moisture Negarim Microcatchment Soil depth Microcatchment size Catchment cover treatments Infiltration pit treatments Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Soil moisture is one of the key factors in the hydrological process, especially in rainfall-runoff dynamics, as it can play a controlling role. The amount of Soil moisture can determine the quality of soil structure, soil particles, erosion rate, microbiological activity in soil crust, and infiltration rate, which in turn can affect ecosystem dynamics. Soil moisture variation is an important aspect of research studies on groundwater recharges, remote sensing and climate studies, weather forecasting, locating potential sites, and optimizing land use. Therefore, understanding soil moisture dynamics and variations is essential to quantify and better understand the relationship among hydrology, ecology, and physiography in a given region (Dai et al., 2022). Recently, many studies have focused on soil moisture properties and predictions. Conserving soil water has an ancient history, with early farmers building small structures to preserve water in their land plots. A comprehensive study was conducted by Boers and Ben Asher (1982) on the revival of indigenous techniques and structures for water harvesting and agricultural soil moisture management for crop survival in arid and semi-arid lands. Printz (1996) and Printz and Malik (2002) conducted extensive studies on runoff farming. The results indicated that runoff farming has proved to be a valuable tool, especially in dry marginal areas, to increase crop yields and reduce cropping risk, improve pasture growth, boost re-afforestation, allow a higher degree of food production, fight soil erosion, make the best use of available water resources, suppress soil salinity, and, in a few cases, recharge the local groundwater. Martinez et al. (2004) coined the term "Oasification" as opposed to "desertification." It entailed the building of small earth structures to collect and infiltrate as much precipitation and runoff as possible by modifying a slope's physiography in a convenient and non-aggressive manner. The results showed that better soil moisture conditions prevailed and the chances of the establishment and growth of woody vegetation markedly improved, thus redressing the dangerous process of desertification. Owais et al. (2004) conducted a study using remote sensing data and GIS to determine potential sites for water harvesting in central Syria. Mouazen and Ramon, (2006) researched the development of an online bulk density measurement system based on online measured draught, depth, and soil moisture content. The results indicated that the developed maps showed no clear correlation between the different parameters measured and the subsoiler used. Tromp-Van Meerveld et al. (2006) studied the interrelations between topography, soil depth, soil moisture, transpiration rates, and species distribution at the hillside scale. The results concluded that the spatial differences in soil depth, total water available at the end of the wet season, and soil moisture content during the summer appear to be responsible for the observed spatial differences in basal area and species distribution between the upslope and mid-slope sections of the hillside. Gebretsadic (2009) conducted a study to evaluate the adaptability and response of potential indigenous trees to water harvesting. The results indicate that different tree species have significantly varying potentials to adapt and rehabilitate degraded hillsides. In addition, water harvesting structures significantly augment the seedling establishment of some tree species on hillsides, whereas other species could establish without them. Tramblay et al. (2010) assessed the initial soil moisture conditions for event-based rainfall-runoff modeling. The results indicated that monitoring soil moisture could help set the initial conditions for simplified event-based models in small basins. Zehe et al. (2010) conducted a study in the Ore Mountains of Germany to assess Plot and field scale soil moisture dynamics and subsurface wetness control on runoff generation in a headwater. The results concluded that the proposed sampling strategy of clustering TDR probes is suitable for assessing unbiased average soil moisture dynamics in critical functional units, in this case, the forested site. this is a much better predictor for event scale runoff formation than pre-event discharge. Razaghi (2011) conducted a review of traditional and modern soil water harvesting methods to classify them according to the size of the catchment area and water storing method, whether in soil, in-ground reservoirs, or behind dams. However, tangible results were not achieved in the first year of the experiments carried out in the crop year of 98–99 due to scanty rain, as quantity and density, but by the end of the 99- 2000 crop year, a good growth of the shrubs used in the experiment have been noticed compared with the ones planted outside the techniques. Ali (2012) studied the water balance model for Microcatchment water harvesting systems for soil water conservation. The results showed that with limited but reliable hydrological data, good agreement between predicted and observed values could be obtained. Yang et al. (2012), in a study on the semi-arid Loess Plateau, China, assessed the response of deep soil moisture to land use and afforestation. The results indicated that the deep soil moisture content decreased by more than 35% after afforestation, and a soil moisture deficit appeared in all types of land with introduced vegetation. Yao et al. (2012) studied the multi-scale spatial variance of soil moisture in the semi-arid Loess Plateau of China. The study determined that land use type was the dominant factor of soil moisture spatial heterogeneity, rather than slope position and precipitation change. Afforestation was the major driver of soil desiccation in the semi-arid Loess Plateau of China. Studer and Liniger (2013) created a manual to provide an overview of proven good practices in water harvesting from all over the world. The manual's objective was to facilitate, share, and upscale good practices in water harvesting given the state of current knowledge based on previous studies and research. Zhou et al. (2013) conducted a study to model the Ecohydrological role of aspect-controlled radiation on tree-grass-shrub coexistence in a semiarid climate. The study results concluded that changes in storm characteristics could lead to a dramatic reorganization of plant composition on topography. The model results underscore the importance of solar irradiance in determining vegetation composition over a complex terrain in a water-limited ecosystem. Hubner et al. (2015) used the framework of surface electrical resistivity tomography (ERT) to enhance the spatial significance of hydrometric point measurements to monitor hillside moisture dynamics. The results showed that the water content calculated from the ERT profile shows similar variations as that of the water content from soil moisture sensors. Consequently, soil moisture dynamics on the hillside scale may be determined not only by expensive invasive punctual hydrometric measurements but also by minimally invasive time-lapse ERT, provided that geophysical relationships are known. Massari et al. (2015) reviewed the data assimilation of satellite soil moisture into rainfall-runoff modeling. The results indicated that data assimilation of soil moisture may not be a simple task, and one should carefully test the optimality of the assimilation experiment before drawing any general conclusions. Gevaert et al. (2016) performed a spatiotemporal evaluation of resolution enhancement for passive microwave soil moisture and vegetation optical depth. This study indicated that resolution enhancement accurately sharpens the boundaries of different vegetation types, lakes, and wetlands. Yumang et al. (2016) conducted research using soil infiltration rate as a parameter for soil moisture and temperature-based Irrigation Systems. The study concluded that soil infiltration rate is determined to control the flow rate of the irrigator depending on the current infiltration rate of the soil. Fernandez-Moran et al. (2017) studied soil moisture and ocean salinity and proposed an alternative framework with a focus on soil moisture and vegetation optical depth product. The framework was found to be better correlated with MODIS NDVI in most regions of the globe, except for the Amazonian basin and the northern mid-latitudes. Dick et al. (2018) used repeat electrical resistivity surveys to assess heterogeneity in soil moisture dynamics under contrasting vegetation types. The study showed that spatial soil moisture patterns were more heterogeneous in the forest site, as were patterns of wetting and drying, which can be linked to vegetation distribution and canopy structure. Nyagumboa et al. (2019) conducted research in a semi-arid region of Zimbabwe to study the effects of three in-field water harvesting technologies on soil water content and maize yields. The results imply that improved water harvesting structures compared with standard contour ridges can increase maize yields in areas with water shortages; hence, they can be a useful strategy for climate change adaptation. Zhang et al. (2019) studied the typical slopes of the Loess Plateau of China during a drought year to assess the relationship between soil water content and soil particle size. Their results provide a case study of the relationships among soil distributions and hydrologic and geomorphic processes for vegetation restoration in drylands with a thick vadose zone. Dai et al (2022) used a soil moisture sensor to conduct a study on continuous volumetric soil moisture measurements during 2015–2016 crop year in Qinghai-Tibet Plateau, with the aim of exploring variations in soil moisture and its response to precipitation infiltration across two vegetation types (alpine meadow and alpine shrub). The results showed that a series of small precipitation events may not have the same effect on soil moisture as a single large precipitation event that produces the equivalent total rainfall. Gou et al (2022) investigated the dynamic changes in soil moisture content and analyzed the fundamental reasons supporting the water diversion plan in three selected typical landscape gradients: a mountain water conservation forest belt, an artificial sand-fixing forest belt at the edge of a desert oasis, and a desert riparian forest belt in the upper, middle, and lower reaches of the Heihe River Basin in northwest China. These results imply that the lower reaches of the Heihe River may require additional water transfers during the growing season. Rasheed et al. (2022) reviewed methods for estimating surface soil moisture and variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions in Chengdu, China. The results showed that although each method offers a unique set of potential advantages and disadvantages, the most accurate way of identifying the best soil moisture technique is the combination of a value selection method (VSM) and a field method such as a TDR sensor or Neutron probe. Liu et al. (2023) investigated the evolutionary pattern of soil moisture and conducted an attribution analysis from climate and human perspectives. The results reveal an unbalanced surface and rootzone variation trend during 1980–2020. Additionally, the study showed that both climatic and human factors had significant impacts on soil moisture. Specifically, air temperature and evaporation are considered to be the primary climatic factors affecting the seasonal and long-term variability of soil moisture, respectively. The goal of this research was to study soil moisture dynamics under the treatment of the Negarim Microcatchment structure in a semi-arid region in Iran. The selected area for this study has been experiencing unpredictable precipitation pattern, long dry spells and general noticeable change in microclimate. improper urban development in rangelands, mismanaged land use and over exploiting of water resources has escalated this change which resulted in the slow migration of border plant species duo to lack of resources. This study aimed to evaluate the effect of a variation of innovative rain harvesting structures with a selection of treatments on soil moisture at three different soil depths of 10, 20, and 30 cm. based on these variations 108 microstructures with different cover treatments were constructed. The study was carried out over a one-and-a-half-year period, and soil moisture was regularly measured after each significant precipitation during this period. The goal of such data collecting is to determine the effect of specific changes in constructing structures and the efficiency of each treatment in the overall ability of structures to retain and conserve soil moisture for longer periods of time. 2. Materials and Methods 2.1. Study Area This study was conducted in a relatively small area of approximately 3375 m2 in the Dehbar watershed (36 ° 18’ N, 59° 24’ E) located in the Khorasan Razavi Province of Iran, as shown in Fig. 1 , and in a semi-arid region, where the average annual precipitation is approximately 255 mm. The watershed has a moderate drainage density across its area. The watershed consists mostly of rangelands, with a small percentage of agricultural land use and scattered cover of small Cercis graffiti trees. Most of the soil. The chosen site has a clay loam/loam soil texture, which is generally common in this particular watershed, and is classified as hydrological group C. The dominant soil type is the Lithic Xerorthents soil group, which are shallow soils with heavy texture and low organic carbon. As such, moderate to low density vegetation cover is common in the area. 2.2. Plot selection, structure building, and sampling The study area was selected by collecting field data and generating a water harvesting potential map of the watershed by GIS, which concluded that the majority of the watershed had a moderate potential for water harvesting techniques and soil moisture optimization. Using this, a small, homogenous land plot was selected considering accessibility, soil type, land slope, and vegetation cover. The designated structure was the Negarim Microcatchment, which is a diamond-shaped soil structure. This structure was selected because of its suitability for semi-arid conditions, accessibility, and the less invasive nature of the structure. A total of 108 structures of this type with different sizes were built on a sidehill of the relatively same slope. half of the structures were given plastic cover treatment, meaning 54 of them had their catchment area covered in UV plastic (P), while the other half had natural cover (N). In addition, all infiltration pits had a mixed treatment of natural cover (N), seedling (S), rock cover (R), no rock cover (F), summer limited irrigation (W), and no summer limited irrigation (D), with each structure having a specific set of treatments repeated three times. 2.3. soil moisture measurement process The goal of this study was to monitor and measure soil moisture variation during two crop years at three different soil depths of 10 cm, 20cm, and 30 cm and to evaluate whether the treatments had any effect on the dynamics of soil moisture at different soil depths. Sampling was scheduled after each effective precipitation, and measuring was regularly repeated three times after a rainfall occurrence with 5-day intervals. Measurements were carried out by TDR sensors, which were calibrated before each sampling field trip, and additional field sampling was performed to assure the accuracy of the sensor (Fig. 2). An overall view of study process and structural plan is shown in Fig. 3 . Figure2. A view of the constructed Negarim Microcatchment and the sampling process 2.4. analysis of Data All the collected data was analyzed by IBM SPSS. since field data with large quantity are not normally distributed Levene homogeneity test of variance was used. Levene's test is often employed to test the assumption of equality of variances between two or more sample populations when the population samples are generally not normally distributed. The null and alternative hypotheses of Levene's test can be generally stated as follows: H 0 : All of the k sample populations have equal variances. H A : At least one of the k samples population variances are not equal. The test statistic (W) used in Levene's test is defined as: $$\:\varvec{W}=\frac{(\varvec{N}-\varvec{k})}{(\varvec{k}-1)}\:\frac{{\sum\:}_{\varvec{i}=1}^{\varvec{k}}{{\varvec{n}}_{\varvec{i}}\left({\varvec{Z}}_{\varvec{i}}-\varvec{Z}\right)}^{2}}{\sum\:_{\varvec{i}=1}^{\varvec{k}}\sum\:_{\varvec{j}=1}^{\varvec{n}\varvec{i}}\:{\left({\varvec{Z}}_{\varvec{i}\varvec{j}}-\varvec{Z}\varvec{i}\right)}^{2}}\:\:\left(1\right)\:$$ were, k: is the number of groups n i : is the number of samples belonging to the i-th group. N: is the total number of samples. Y ij : is the j-th observation from the i-th group. Welch and Brown-Forsyth tests of equality of means was used in order to use a one-factor analysis of variance (ANOVA). The Welch and Brown-Forsythe versions of one-way ANOVA do not assume that all the groups were sampled from populations with equal variances. The Welch test adjusts the denominator of the F ratio so it has the same expectation as the numerator when the null hypothesis is true, despite the heterogeneity of within-group variance. The p-value can be interpreted in the same manner as in the analysis of variance table. While The Brown-Forsythe test uses a different denominator for the formula of F in the ANOVA. Instead of dividing by the mean square of the error, the mean square is adjusted using the observed variances of each group. The p-value can be interpreted in the same manner as in the analysis of variance table. Games- Howell post hoc test was used to assess the data. The Games-Howell test is a nonparametric post hoc analysis approach for performing multiple comparisons for two or more sample populations. The Games-Howell test is somewhat similar to Tukey's post hoc test. Still, unlike Tukey's test, it does not assume homogeneity of variances or equal sample sizes. Thus, the Games-Howell test can be applied in settings when the assumptions of Tukey's test do not hold. Games-Howell test employs the Welch-Satterthwaite equation for degrees of freedom, which is also known as the pooled degrees of freedom and is based on Tukey's studentized range distribution, denoted q. As the Games-Howell test is nonparametric, it uses the ranks of the observations rather than the raw sample observation values. The Games-Howell test is defined as: $$\:{\varvec{x}}_{\varvec{i}}^{-}-\:{\varvec{x}}_{\varvec{j}}^{-}>\:{\varvec{q}}_{\varvec{\sigma\:}\:,\varvec{k},\varvec{d}\varvec{f}}\:\:\left(2\right)$$ Where σ is equal to standard error: $$\:\varvec{\sigma\:}=\sqrt{\frac{1}{2}\:(\frac{{\varvec{s}}_{\varvec{i}}^{2}}{{\varvec{n}}_{\varvec{i}}}+\:\frac{{\varvec{s}}_{\varvec{j}}^{2}}{{\varvec{n}}_{\varvec{j}}})}\:\:\:$$ 3 $$\:\frac{\:{\left(\frac{{\varvec{s}}_{\varvec{i}}^{2}}{{\varvec{n}}_{\varvec{i}}}+\:\frac{{\varvec{s}}_{\varvec{j}}^{2}}{{\varvec{n}}_{\varvec{j}}}\right)}^{2}}{(\:\frac{{\left(\frac{{\varvec{a}}_{\varvec{i}}^{2}}{{\varvec{n}}_{\varvec{i}}}\right)}^{2}}{{\varvec{n}}_{\varvec{i}}-1}+\:\frac{{\left(\:\frac{{\varvec{a}}_{\varvec{j}}^{2}}{{\varvec{n}}_{\varvec{j}}}\right)}^{2}}{{\varvec{n}}_{\varvec{j}}-1})}\:\:\left(4\right)$$ Degrees of freedom is calculated using Welch's correction: Thus, confidence intervals can be formed with: $$\:{\varvec{x}}_{\varvec{i}}^{-}-\:{\varvec{x}}_{\varvec{j}}^{-}\:\pm\:\varvec{t}\sqrt{\frac{1}{2}\:\left(\frac{{\varvec{s}}_{\varvec{i}}^{2}}{{\varvec{n}}_{\varvec{i}}}+\:\frac{{\varvec{s}}_{\varvec{j}}^{2}}{{\varvec{n}}_{\varvec{j}}}\right)}\:\:$$ 5 p-values are calculated using Tukey's studentized range: $$\:{\varvec{q}}_{\varvec{t}\:}\:\times\:\sqrt{2,\varvec{k},\varvec{d},\varvec{f}}\:$$ 6 3. Results 3.1. soil moisture variation analysis Data from all three sizes of structures were analyzed at three soil depths. Catchment Treatments included: natural (N) and plastic cover (P). Infiltration pit treatments included: natural infiltration pit (N), seedling(S), rock cover(R), no rock cover (F), no irrigation (D), and irrigation (W). each treatment was compared and analyzed in accordance with other treatments’ possible influences. Due to the abundance of the collective data, only a sample of the three graphs is shown in Figs. 4, 5 and 6. An overall soil depth of 30cm showed the greatest capability of conserving soil moisture in all sizes of structures. during both fall seasons, with intervals between each rainfall occurrence, all sizes of structures with natural covered catchment (N) and natural treatment for infiltration pit (N), depth of 30 cm showed the greatest capability in conserving soil moisture with a mean of 5% more than soil depth of 20 and 10 cm, the other two had retained nearly the same amount of soil moisture. Figure 4. Selective soil moisture variation analysis of all three sizes of structures at three soil depths under natural and plastic covered catchment and natural infiltration pit treatments In the case of moisture loss, depths of 20 and 10 cm lost moisture at the same rate of 5–10% more across all sizes of structures in comparison to the depth of 30. During both rainfall seasons, due to over-saturation of the soil, a steady amount of moisture was observed at all depths; however, the depth of 30 cm retained 3–5% more soil moisture across all sizes. As expected, over both dry seasons, a large drop in soil moisture occurred. However, the soil moisture content of all three depths became steady after the first drop. the depth of 30 cm showed a higher ability to retain soil moisture than the other two depths. The recorded soil moisture content was: 23–25% for the largest sized, 19–20% for the medium-sized and 17–18% for the smallest sized structures. while soil moisture content for the depth of 20 cm was at around 13–15% for the biggest sized structures, 13% for the medium-sized structures, and 11% for the smallest sized structures. The soil moisture content for the depth of 10 cm was at 10–11% for all three sizes of structures. In comparison, the same-sized structures with UV plastic-covered catchments (P) and natural infiltration pits (N), while nearly identical in performance, showed that the general soil moisture conserving rate was significantly higher by a 10% margin in the largest structures and 5% for both medium-sized and small-sized structures. This is more significant during both dry seasons, as the stability rate for the depth of 30 cm for the largest structure is approximately 29–30% while the other two depths had significantly less soil moisture content with the depth of 20 cm at 20% and the depth of 10 cm at around 15%. As for the medium-sized structure, a depth of 30 cm is the most significant with a mean of 26%, whereas the depths of 20 and 10 cm are 20% and 15%, respectively. The same can be said about the smallest-sized structures (SS), with a depth of 30 being the most efficient with a mean of 20%, and depths of 20 and 10 cm with 14% and 13%, respectively. Figure 5. Selective soil moisture variation analysis of all three sizes of structures at three soil depths under natural and plastic covered catchment and non-irrigated infiltration pit treatments In all three sizes of structures, the performances of structures with plastic-covered catchments (P) were better compared to those with natural catchments (N). All plastic-covered structures performed better by a margin of 10% for the biggest-sized structures, 7% for medium-sized structures, and 5% for smallest-sized structures. in all structures, a soil depth of 30 cm had the most moisture retention. during the fall season, on both catchment treatments, a depth of 30 cm conserved soil moisture by a margin of 5% more than both 10 and 20 cm in the biggest structures and 3% in medium-sized and smallest-sized structures. in both catchment treatments for the biggest-sized structures during the intervals between rainfall occurrences, for a depth of 30 cm, the moisture loss rate was 10% less than depths 20 and 10 cm. while for the medium-sized and smallest-sized structures, the rate was 15%. During the rainy seasons. During the dry seasons, half of the infiltration pits were not under irrigation treatment; therefore, a loss of moisture was expected. However, after the initial loss, the soil moisture content was steady across all structures. For the biggest sized structures, on a depth of 30 cm, soil moisture content was 22% for natural catchment (N) and 25% for plastic-covered catchments (P). on the depth of 20 cm 15% for natural catchment (N) and 20% for plastic covered catchments (P) and 11% soil moisture content for natural catchment (N) and 15% for plastic covered catchments (P) on the depth of 10 cm was monitored. for medium sized structures, on a depth of 30 cm, soil moisture content for natural catchment (N) was 20% and 22% for plastic-covered catchments (P). also, 15% for natural catchment (N) and 20% for plastic covered catchments (P) on the depth of 20 cm and on the depth of 10 cm, 10% soil moisture content for natural catchment (N) and 14% for plastic covered catchments (P) was observed. for the smallest sized structures, on a depth of 30 cm, soil moisture content was 18% for natural catchment (N) and 20% for plastic-covered catchments (P). for the depth of 20 cm 11% for natural catchment (N) and 13% for plastic covered catchments (P) and 10% soil moisture content for natural catchment (N) and 13% for plastic covered catchments (P) on the depth of 10 cm. Figure 6. Selective soil moisture variation analysis of all three sizes of structures at three soil depths under natural and plastic covered catchment and irrigated infiltration pit treatments The other half of the infiltration pits were under irrigation treatment; therefore, during dry seasons, after the initial loss of moisture, a steady trend of increase in soil moisture content occurred due to the two-week interval of irrigation. For the biggest sized structures, the performances on a depth of 30 cm were 20–28% for natural catchment (N) and 23–35% for plastic-covered catchments (P). while depth of 20 cm performed 20–25% for natural catchment (N) and 18–27% for plastic covered catchments (P). the depth of 10 cm had the performance of 10–18% for natural catchment (N) and 15–21% for plastic covered catchments (P). for the medium-sized structures the performances on a depth of 30 cm were 20–22% for natural catchment (N) and 21–23% for plastic-covered catchments (P). while depth of 20 cm performed 15–17% for natural catchment (N) and 20–21% for plastic covered catchments (P). the depth of 10 cm had the performance of 10–13% for natural catchment (N) and 15–16% for plastic covered catchments (P). for the smallest sized structures, the performances on a depth of 30 cm were 20–21% for natural catchment (N) and 21–23% for plastic-covered catchments (P). while depth of 20 cm performed 12–15% for natural catchment (N) and 18–21% for plastic covered catchments (P). the depth of 10 cm had the performance of 10–13% for natural catchment (N) and 14–15% for plastic covered catchments (P). No significant difference was observed between the performance of rock cover treatment (R) and non-rock cover treatment (F) for infiltration pits of any size and depth. 4. Discussion Despite representing a fraction of water resources on the planet, soil moisture has a crucial role in the Ecohydrological dynamic, and any variation in it can affect the water-energy dynamic of its immediate local ecosystem. In recent decades, there has been an emergence of optimized versions of ancient water harvesting techniques due to climate change, lack of efficient precipitation, and shortage of reliable water resources. The overall goal of these techniques is mainly to enrich soil moisture as a way to revive the more vulnerable local ecosystems. Many research studies have been conducted in different professional fields regarding these techniques and structures, such as Printz (1996), Printz and Malik (2002), Martinez et al. (2004), Owais et al. (2004), Gebretsadic (2009), and Ali (2010). While most studies in this field are very few and done on a larger scale or are used in combination with another methodology or a different purpose, the goal of this study was to implement an innovative variation of rain water harvesting structures to monitor the effects of these new iterations of such structures and all the experimental treatments in real time to create a realistic outlook of the efficiency of said structures and their actual effect on soil moisture content. To assess the accuracy of the results, a statistical analysis of variance with a post hoc test was conducted to confirm that the observed difference between treatments was true. These comparisons were the main concerns. 4.1. Comparison between the three depths By the overall data analysis and graph visualization, with regard to the three depths of the study, the depth of 30 cm showed the highest soil moisture content throughout the entire duration of the study. In comparison to the two other depths, the depth of 30 cm was more efficient with a margin of 10–20%. while the depth of 20 cm performed better than the depth of 10 cm by a margin of 5%. However, to statistically validate the observed data, Levene’s test was used to perform a variance analysis of the dependent variable. Since the significance level of the calculated value of Levene is smaller than the critical level of 0.05, the data have questioned the assumption of the equality of error of variances. first, the normal distribution of the data was examined. To use a one-factor analysis of variance, Welch and Brown-Forsyth tests of equality of means is used. Because the data of this research are field collected and have a large volume, it does not have a normal distribution. Therefore, for one-way analysis of variance, the method was used assuming unequal variances between groups. As shown in table.1 the results show that the effect of depth change is significant in the performance of all structures. The depth of 30 cm was on average 3% better than the depth of 20 cm and 5% better than the depth of 10 cm. Between the depths of 20 and 10 cm, the difference in moisture retention is about 1%. In addition, the two-by-two comparisons of depth groups show that all groups have significant differences with each other. On average, the efficiency of structures in maintaining soil moisture increased steadily with increasing depth. Therefore, as shown in Fig. 7 . the change in sampling depth has a significant effect on the performance of the structures for the absorption and penetration of runoff. Table.1. Descriptive statistics concerning the effect of depth on structural performance Descriptive statistics for the size comparison Depth (cm) N Mean Std. Deviation Std. Error 10 3420 32.86% 11.02% .18% 20 3420 34.28% 10.85% .18% 30 3420 37.37% 9.61% .16% Total 10260 34.84% 10.68% .10% Test of the Homogeneity of Variances Levene Statistic df1 df2 Sig. 57.336 2 10257 .000 Table.2. ANOVA with Robust Tests of the Equality of Means concerning the effect of depth on structural performance ANOVA Sum of Squares df Mean Square F Sig. 36386.384 2 18193.192 164.522 .000 1134237.415 10257 110.582 1170623.799 10259 Robust Tests of the Equality of Means Statistic a df1 df2 Sig. Welch 175.777 2 6810.515 .000 Brown-Forsythe 164.522 2 10118.456 .000 a. Asymptotically F distributed. Table.3. Games-Howell Post hoc test assuming unequal variances between groups and compare depth Games-Howell Post hoc test assuming unequal variances between groups and compare depth (I) Depth Mean Difference (I-J) Std. Error Sig. 10.0 20.0 -1.41%* .26% .000 30.0 -4.51%* .25% .000 20.0 10.0 1.41%* .26% .000 30.0 -3.09%* .24% .000 30.0 10.0 4.51%* .25% .000 20.0 3.09%* .24% .000 While there is a significant difference between the methodology of this study and most referenced source material, due to the combinatorial nature of this research, the data analysis process and results of this study showed the same pattern regarding the interaction between and the effects of different controlled variables on soil moisture dynamics as the research studies of Ali (2010), Tramblay et al. (2010), Zehe et al. (2010), Razaghi (2011), Glendenning et al. (2012), Studer and Liniger (2013), Zhu et al. (2014), Massari et al. (2015), Nyagumboa et al. (2019), Dai et al (2022), Gou et al (2022), Rasheed et al (2022), and Liu et al (2023). This includes the identification of a suitable site for potential water harvesting techniques as well as the use of the Negarim Microcatchment as suitable microstructures regarding the plot area conditions (Zhou et al; 2013, Hubner et al. 2015, Kaliraj et al; 2015). With regard to the influence of the designated treatment on the efficiency of the structures in conserving soil moisture, the study showed that depth, as well as catchment cover treatments, steadily have considerable influence on the structure’s performance during the entire period of sampling, whereas infiltration pit treatment has considerably less significance in defining an impact. This could be due to the effect of the subsurface water movement and lack of exposure to evapotranspiration at a depth of 30 cm, as well as the efficiency of the plastic cover to almost completely isolate the catchment area and increase the infiltration rate in the pits. As for the effects of infiltration pit treatments, while it can be argued that there was a percentage contribution in regards to optimizing the structure’s performance, the volume of this contribution and its statistical and practical significance were not impactful enough to have a realistic effect. However, it is important to note the role of these structures in sustaining small plant species. While the change of treatment in the infiltration pit may not be significantly effective, different catchment treatments were significant and the overall efficiency of the structures themselves. it is important to consider the significance of global warming and consequently climate change on the watershed microclimate. Different weather patterns result in various changes in climatic parameters such as temperature, precipitation, evapotranspiration and soil moisture fluctuance (Sarabi et al. 2021). considering the extreme changes in precipitation, concentration of yearly runoff flows in shorter time frames and long dry periods unusual for this watershed, some local plants and vegetation in the watershed are slowly migrating. The effects of unsuitable land use management, misplaced industrialization of rangelands, overexploitation of surface and underground water resources, changes in the microclimate, and weak rangeland management were observed in the overall density of vegetation cover, specifically border plant species in the watershed. Introducing Negarim micro catchment structures can be suitable as a semi-noninvasive method to help with the long-term sustaining and improvement of soil moisture available for local border plant species in the face of new environmental changes. The ability to retain soil moisture at deeper depths for the root system can be effective for the survival of vulnerable vegetation cover, especially in warm months with little to no precipitation. Although the short-term effects of such methods may not be as visible, steady usage of such low-cost and simple structures can efficiently improve the overall long-term conditions of soil moisture and plant cover density in the watershed. This is especially significant in environmental tolerance thresholds because by conserving enough soil moisture, it can stabilize the border plant species. The low cost and easy-to-implement nature of these structures can also be a motivator for use in different scales by local population. Since these are primarily small-scale soil structures, there is no significant costly structural upkeep routine or heavy invasion of the environment on which they are implemented. Therefore, the effect they have on the surrounding area can be monitored for a long time. In general, these structures preserve the conditions of the region to a significant extent. 5. Conclusion The main purpose of this study was to evaluate the effects of a variation of Negarim Microcatchment structures based on creating new iterations of FAO’s standard measurement on soil moisture content and their efficiency in retaining and conserving the collected rainwater to recharge the soil moisture. To achieve this purpose, various treatments on both catchments and infiltration pits were conducted to determine which treatments can optimize the efficiency of structures the best. The study showed that the depth of 30 cm was by far the most effective depth among the three depths in regards to retaining soil moisture from the depth of 20 cm and 10 cm. The same result was observed in the comparison of natural and plastic covered catchments. With the plastic covered catchments, the efficiency of the structures in conserving soil moisture was higher comparing of natural covered catchments. A combination of infiltration pit treatments showed a moderate difference. In conclusion, it can be said that the main controlling factors affecting the performance of the structures are the soil depth and the treatments that have been conducted for the catchment area, whereas the infiltration pit area treatments played a less significant role. Overall, the performance of the structures showed that implementing these structures can be effective in conserving soil moisture; however, the concentration of different treatments can affect the performance of catchments more significantly than infiltration pits. While, conducting such studies are crucial in better understanding of soil moisture dynamic and its response to varying methods of conservation, data monitoring may face some restrictions. Duo to the unpredictable nature of precipitation pattern, collected field data may not completely reflect on the expected results. Massive quantities of data may also not conform to the expected analyzing methods. Since the conditions of data collecting is as close to be non-invasive as possible, variations in results are not as striking as expected. Based on the results, long-term usage and data monitoring of structural performances is advised. Such methods function by gradually conserving soil moisture to improve the surrounding environment, therefore long-term monitoring can provide a more conclusive view on the impact of such methods on the micro-ecosystem. Declarations Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Banafshe Kouhzad. The first draft of the manuscript was written by Banafshe Kouhzad. Dr. Mohammad Taghi Dastorani and Dr. Mohammad Reza Yazdani reviewed and commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Data Availability: The data supporting this study’s findings are available on request from the corresponding Author. Ethical Approval: Compliance with Ethical Standards. Consent to Participate: All authors consented to participate. Consent to Publish: All authors consented to publish. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. References Al Ali M (2012) Soil water conservation and water balance model for micro catchments water harvesting system. Ms. Thesis Dissertation. Department of Civil and Building Engineering, Loughborough University Boers TM, BenAsher J (1982) A review of rainwater harvesting. Agric Water Manage 5(1):145158 Dai L, Fu R, Guo X, Du Y, Zhang F, Cao G (2022) Soil Moisture Variations in Response to Precipitation Across Different Vegetation Types on the Northeastern Qinghai-Tibet Plateau. Front Plant Sci 13:854152. 10.3389/fpls.2022.854152 Dick J, Tetzlaff D, Bradford J, Soulsby C (2018) Using repeat electrical resistivity surveys to assess heterogeneity in soil moisture dynamics under contrasting vegetation types. J Hydrology V 559:684–697. https://doi.org/10.1016/j.jhydrol.2018.02.062 Fernandez-Moran R, Al-Yaari A, Mialon A, Mahmoodi A, Bitar AA (2017) Gabrielle De Lannoy, Nemesio Rodriguez-Fernandez, Ernesto Lopez-Baeza, Yann Kerr, and Jean-Pierre Wigneron. SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product Remote Sensing 9, no. 5: 457. https://doi.org/10.3390/rs9050457 Gebretsadic W (2009) Evaluation of The Adaptability and Response of Potential Indegenous Trees to Water Harvesting in the Rehabilitation of Kuriftu Lake catchment. Ms. Thesis. University of Natural Resources and Applied Life Sciences (BOKU), Vienna, Austria,4(3):143–162 Gevaert AI, Parinussa RM, Renzullo LJ, Van Dijk AIJM, De Jeo RAM (2016) Int J Appl Earth Observation Geoinf V 45:235–244. https://doi.org/10.1016/j.jag.2015.08.006 Glendenning CJ, Van Ogtrop FF, Mishra AK, Vervoort RW (2012) Balancing watershed and local scale impacts of rainwater harvesting in India—a review.Agric. Water Manag 107:1–13 Gou Q, Guo W, Li Y, Liu J, Wang G (2022) Dynamic changes in soil moisture in three typical landscapes of the Heihe River Basin. Front Environ Sci 10:1049883. 10.3389/fenvs.2022.1049883 Huang X, Shi ZH, Zhu HD, Zhang HY, Ai L, Yin W (2015) Soil moisture dynamics within soil profiles and associated environmental controls. Catena J 136:189–196. http://dx.doi.org/10.1016/j.catena.2015.01.014 Hubner R, Heller K, Gunther T, Kleber A (2015) Monitoring hillslope moisture dynamics with surface ERT for enhancing the spatial significance of hydrometric point measurements. Hydro Earth Syst Sci 19:225–240. https://doi.org/10.5194/hess-19-225-2015 Kaliraj S, Chandrasekar N, Magesh NS (2015) Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India using GIS-based weighted overlay analysis. Environ Earth Sci 74(5):4355e4380 Liu Y, Yang Y, Song J (2023) Variations in Global Soil Moisture During the Past Decades: Climate or Human Causes. J Water Resour Res 59(7). https://doi.org/10.1029/2023WR034915 Martinez de azagra A, Mongil J, Rojo L (2004) Oasification: a forest solution to many problems of desertification, invest agrar. 13(3):437–442 Massari C, Brocca L, Tarpanelli, AMoramarco T (2015) Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe. Remote Sens 7(9):11403–11433. https://doi.org/10.3390/rs70911403 Mouazen AM, Ramon H (2006) Development of an on-line measurement system of bulk density based on online measured draught, depth, and soil moisture content. Soil Tillage Res J 86(2):218–229. https://doi.org/10.1016/j.still.2005.02.026 Nyagumboa I, Nyamadzawo G, Madembo C (2019) Effects of three in-field water harvesting technologies on soil water content and maize yields in a semi-arid region of Zimbabwe. J Agricultural Water Manage 216(1):206–213. https://doi.org/10.1016/j.agwat.2019.02.023 Oweis TY, Oberle A and D.Prinz (2004) Determination of potential sites and methods for water harvesting in central Syria. Adv GeoEcology 31:83–88 Printz D, Malik A (1996) Runoff farming. Institute of Water Resources Management, Hydraulic and Rural Engineering, Dept. of Rural Engineering, University of Karlsruhe, D76128 Karlsruhe. Germany 7(5):12–33 Prinz D, Malik AH (2002) Runoff Farming. Institute of Water Resources Management, Hydraulic and Rural Engineering: Germany Rasheed MW, Tang J, Sarwar A, Shah S, Saddique N, Khan MU, Imran Khan M, Nawaz S, Shamshiri RR, Aziz M et al (2022) Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review. Sustainability 14(18):11538. https://doi.org/10.3390/su141811538 Razzaghi M (2011) Rain Water Harvesting Systems are a Way to Water Conservation. Int J Water Resour Arid Environ 1(4):72–84 Sarabi M, Dastorani MT, Zarrin A (2021) The Impact of Future Climate Change on Hydrological Response in Torogh Dam Watershed. Mashhad J Meteorol Atmos Sci 3(4):310–330 [ In Persian] Studer R, Liniger H (2013) Water Harvesting, Guidelines to Good Practice. Centre for Development and Environment (CDE) and Institute of Geography. University of Bern; Rai Tramblay Y, Bouvier C, Martin C, Didon-Lescot JF, Todorovic D, Domergue JM (2010) Assessment of initial soil moisture conditions for event-based rainfall-runoff modeling. 387(4):176–187. https://doi.org/10.1016/j.jhydrol.2010.04.006 Van Tromp- HJ, MacDonell JJ (2006) On the interrelations between topography, soil depth, soil moisture, transpiration rates, and species distribution at the hillslope scale. Adv Water Resour 29(2):293–310. https://doi.org/10.1016/j.advwatres.2005.02.016 Yang L, Wei W, Chen L, Mo B (2012) Response of deep soil moisture to land use and afforestation in the semi-arid Loess Plateau, China. J Hydrology V 475:111–122. https://doi.org/10.1016/j.jhydrol.2012.09.041 Yao X, Fu B, Lu Y, Chang R, Wang X, Wang Y, Su C (2012) The multi-scale spatial variance of soil moisture in the semi-arid Loess Plateau of China. J Soil Sediment 12:694–703. https://doi.org/10.1007/s11368-012-0481-5 Yumang AN, Pagnilawan AC, Perez LAA, Fidelino JFF, Santos JBC (2016) Soil infiltration rate as a parameter for soil moisture and temperature-based Irrigation System. 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE). 10.1109/ICCSCE.2016.7893586 Zhang X, Zhao W, Wang L, Liu Y, Liu Y, Feng C (2019) Relationship between soil water content and soil particle size on typical slopes of the Loess Plateau during a drought year. Sci Total Environ 648(15):943–954. https://doi.org/10.1016/j.scitotenv.2018.08.211 Zehe E, Geraeff T, Morgner M, Bauer A, Bronstert A (2010) Plot and field scale soil moisture dynamics and subsurface wetness control on runoff generation in a headwater in the Ore Mountains. Hydro Earth Syst Sci 14:873–889. https://doi.org/10.5194/hess-14-873-2010 Zhou, Istanbullouglu E, Vivoni E (2013) Modeling the Ecohydrological role of aspect-controlled radiation on tree-grass-shrub coexistence in a semiarid climate. Water Resour Res J 49(5):2872–2895. https://doi.org/10.1002/wrcr.20259 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4704859","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329196689,"identity":"5d95d188-f62c-40e8-9a6e-756a05bcd084","order_by":0,"name":"Banafshe Kouhzad","email":"","orcid":"","institution":"Semnan University","correspondingAuthor":false,"prefix":"","firstName":"Banafshe","middleName":"","lastName":"Kouhzad","suffix":""},{"id":329196690,"identity":"c30a56fb-4c38-40d3-a38c-ea0e802958ab","order_by":1,"name":"Mohammad Reza 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10:36:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4704859/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4704859/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62218431,"identity":"7c96ecf3-49b3-483f-beeb-8608f2d33c5f","added_by":"auto","created_at":"2024-08-11 12:02:47","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":179143,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation of the study area, its water potential map, and sampling site\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4704859/v1/c98653c49ca88dfa3be954d3.jpeg"},{"id":62219061,"identity":"d2b7e8e9-0021-4c41-87f0-0f82492d6923","added_by":"auto","created_at":"2024-08-11 12:10:48","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":176289,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA view of the constructed Negarim Microcatchment and the sampling process\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4704859/v1/2627934800f8c5586ab07b10.jpeg"},{"id":62219057,"identity":"8662115a-61c0-4222-b411-b7e01ff39da3","added_by":"auto","created_at":"2024-08-11 12:10:47","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAn overall view of study process and structural plan\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4704859/v1/9ecbddb1565bca3a20c5c66c.jpeg"},{"id":62218430,"identity":"73ab61d4-5f79-4640-946f-3c8b4a2f5822","added_by":"auto","created_at":"2024-08-11 12:02:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":16615,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelective soil moisture variation analysis of all three sizes of structures at three soil depths under natural and plastic covered catchment and natural infiltration pit treatments\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4704859/v1/d34983f880cb069d781e7131.png"},{"id":62219059,"identity":"19ca432a-48b0-4fa4-850a-64f999101ae6","added_by":"auto","created_at":"2024-08-11 12:10:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17013,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelective soil moisture variation analysis of all three sizes of structures at three soil depths under natural and plastic covered catchment and non-irrigated infiltration pit treatments\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4704859/v1/4dd9c246f50b54d2b5c36c37.png"},{"id":62219058,"identity":"d25bb036-135b-48d0-81c4-0bffd8ab4582","added_by":"auto","created_at":"2024-08-11 12:10:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":17349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelective soil moisture variation analysis of all three sizes of structures at three soil depths under natural and plastic covered catchment and irrigated infiltration pit treatments\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4704859/v1/9baa9962b6163675a7292fb6.png"},{"id":62218427,"identity":"99750edb-6ece-46ba-9673-5b8ab01c6d5c","added_by":"auto","created_at":"2024-08-11 12:02:47","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":40745,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage soil moisture at different depths\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4704859/v1/5200a12b4ce9d6678646ed47.jpeg"},{"id":62729306,"identity":"c9fe81a1-2f15-4ecb-a056-36224c2942d9","added_by":"auto","created_at":"2024-08-18 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Introduction","content":"\u003cp\u003eSoil moisture is one of the key factors in the hydrological process, especially in rainfall-runoff dynamics, as it can play a controlling role. The amount of Soil moisture can determine the quality of soil structure, soil particles, erosion rate, microbiological activity in soil crust, and infiltration rate, which in turn can affect ecosystem dynamics. Soil moisture variation is an important aspect of research studies on groundwater recharges, remote sensing and climate studies, weather forecasting, locating potential sites, and optimizing land use. Therefore, understanding soil moisture dynamics and variations is essential to quantify and better understand the relationship among hydrology, ecology, and physiography in a given region (Dai et al., 2022).\u003c/p\u003e \u003cp\u003eRecently, many studies have focused on soil moisture properties and predictions. Conserving soil water has an ancient history, with early farmers building small structures to preserve water in their land plots. A comprehensive study was conducted by Boers and Ben Asher (1982) on the revival of indigenous techniques and structures for water harvesting and agricultural soil moisture management for crop survival in arid and semi-arid lands. Printz (1996) and Printz and Malik (2002) conducted extensive studies on runoff farming. The results indicated that runoff farming has proved to be a valuable tool, especially in dry marginal areas, to increase crop yields and reduce cropping risk, improve pasture growth, boost re-afforestation, allow a higher degree of food production, fight soil erosion, make the best use of available water resources, suppress soil salinity, and, in a few cases, recharge the local groundwater. Martinez et al. (2004) coined the term \"Oasification\" as opposed to \"desertification.\" It entailed the building of small earth structures to collect and infiltrate as much precipitation and runoff as possible by modifying a slope's physiography in a convenient and non-aggressive manner. The results showed that better soil moisture conditions prevailed and the chances of the establishment and growth of woody vegetation markedly improved, thus redressing the dangerous process of desertification. Owais et al. (2004) conducted a study using remote sensing data and GIS to determine potential sites for water harvesting in central Syria. Mouazen and Ramon, (2006) researched the development of an online bulk density measurement system based on online measured draught, depth, and soil moisture content. The results indicated that the developed maps showed no clear correlation between the different parameters measured and the subsoiler used. Tromp-Van Meerveld et al. (2006) studied the interrelations between topography, soil depth, soil moisture, transpiration rates, and species distribution at the hillside scale. The results concluded that the spatial differences in soil depth, total water available at the end of the wet season, and soil moisture content during the summer appear to be responsible for the observed spatial differences in basal area and species distribution between the upslope and mid-slope sections of the hillside. Gebretsadic (2009) conducted a study to evaluate the adaptability and response of potential indigenous trees to water harvesting. The results indicate that different tree species have significantly varying potentials to adapt and rehabilitate degraded hillsides. In addition, water harvesting structures significantly augment the seedling establishment of some tree species on hillsides, whereas other species could establish without them. Tramblay et al. (2010) assessed the initial soil moisture conditions for event-based rainfall-runoff modeling. The results indicated that monitoring soil moisture could help set the initial conditions for simplified event-based models in small basins. Zehe et al. (2010) conducted a study in the Ore Mountains of Germany to assess Plot and field scale soil moisture dynamics and subsurface wetness control on runoff generation in a headwater. The results concluded that the proposed sampling strategy of clustering TDR probes is suitable for assessing unbiased average soil moisture dynamics in critical functional units, in this case, the forested site. this is a much better predictor for event scale runoff formation than pre-event discharge. Razaghi (2011) conducted a review of traditional and modern soil water harvesting methods to classify them according to the size of the catchment area and water storing method, whether in soil, in-ground reservoirs, or behind dams. However, tangible results were not achieved in the first year of the experiments carried out in the crop year of 98\u0026ndash;99 due to scanty rain, as quantity and density, but by the end of the 99- 2000 crop year, a good growth of the shrubs used in the experiment have been noticed compared with the ones planted outside the techniques. Ali (2012) studied the water balance model for Microcatchment water harvesting systems for soil water conservation. The results showed that with limited but reliable hydrological data, good agreement between predicted and observed values could be obtained. Yang et al. (2012), in a study on the semi-arid Loess Plateau, China, assessed the response of deep soil moisture to land use and afforestation. The results indicated that the deep soil moisture content decreased by more than 35% after afforestation, and a soil moisture deficit appeared in all types of land with introduced vegetation. Yao et al. (2012) studied the multi-scale spatial variance of soil moisture in the semi-arid Loess Plateau of China. The study determined that land use type was the dominant factor of soil moisture spatial heterogeneity, rather than slope position and precipitation change. Afforestation was the major driver of soil desiccation in the semi-arid Loess Plateau of China. Studer and Liniger (2013) created a manual to provide an overview of proven good practices in water harvesting from all over the world. The manual's objective was to facilitate, share, and upscale good practices in water harvesting given the state of current knowledge based on previous studies and research.\u003c/p\u003e \u003cp\u003eZhou et al. (2013) conducted a study to model the Ecohydrological role of aspect-controlled radiation on tree-grass-shrub coexistence in a semiarid climate. The study results concluded that changes in storm characteristics could lead to a dramatic reorganization of plant composition on topography. The model results underscore the importance of solar irradiance in determining vegetation composition over a complex terrain in a water-limited ecosystem. Hubner et al. (2015) used the framework of surface electrical resistivity tomography (ERT) to enhance the spatial significance of hydrometric point measurements to monitor hillside moisture dynamics. The results showed that the water content calculated from the ERT profile shows similar variations as that of the water content from soil moisture sensors. Consequently, soil moisture dynamics on the hillside scale may be determined not only by expensive invasive punctual hydrometric measurements but also by minimally invasive time-lapse ERT, provided that geophysical relationships are known. Massari et al. (2015) reviewed the data assimilation of satellite soil moisture into rainfall-runoff modeling. The results indicated that data assimilation of soil moisture may not be a simple task, and one should carefully test the optimality of the assimilation experiment before drawing any general conclusions. Gevaert et al. (2016) performed a spatiotemporal evaluation of resolution enhancement for passive microwave soil moisture and vegetation optical depth. This study indicated that resolution enhancement accurately sharpens the boundaries of different vegetation types, lakes, and wetlands. Yumang et al. (2016) conducted research using soil infiltration rate as a parameter for soil moisture and temperature-based Irrigation Systems. The study concluded that soil infiltration rate is determined to control the flow rate of the irrigator depending on the current infiltration rate of the soil. Fernandez-Moran et al. (2017) studied soil moisture and ocean salinity and proposed an alternative framework with a focus on soil moisture and vegetation optical depth product. The framework was found to be better correlated with MODIS NDVI in most regions of the globe, except for the Amazonian basin and the northern mid-latitudes. Dick et al. (2018) used repeat electrical resistivity surveys to assess heterogeneity in soil moisture dynamics under contrasting vegetation types. The study showed that spatial soil moisture patterns were more heterogeneous in the forest site, as were patterns of wetting and drying, which can be linked to vegetation distribution and canopy structure. Nyagumboa et al. (2019) conducted research in a semi-arid region of Zimbabwe to study the effects of three in-field water harvesting technologies on soil water content and maize yields. The results imply that improved water harvesting structures compared with standard contour ridges can increase maize yields in areas with water shortages; hence, they can be a useful strategy for climate change adaptation. Zhang et al. (2019) studied the typical slopes of the Loess Plateau of China during a drought year to assess the relationship between soil water content and soil particle size. Their results provide a case study of the relationships among soil distributions and hydrologic and geomorphic processes for vegetation restoration in drylands with a thick vadose zone. Dai et al (2022) used a soil moisture sensor to conduct a study on continuous volumetric soil moisture measurements during 2015\u0026ndash;2016 crop year in Qinghai-Tibet Plateau, with the aim of exploring variations in soil moisture and its response to precipitation infiltration across two vegetation types (alpine meadow and alpine shrub). The results showed that a series of small precipitation events may not have the same effect on soil moisture as a single large precipitation event that produces the equivalent total rainfall. Gou et al (2022) investigated the dynamic changes in soil moisture content and analyzed the fundamental reasons supporting the water diversion plan in three selected typical landscape gradients: a mountain water conservation forest belt, an artificial sand-fixing forest belt at the edge of a desert oasis, and a desert riparian forest belt in the upper, middle, and lower reaches of the Heihe River Basin in northwest China. These results imply that the lower reaches of the Heihe River may require additional water transfers during the growing season. Rasheed et al. (2022) reviewed methods for estimating surface soil moisture and variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions in Chengdu, China. The results showed that although each method offers a unique set of potential advantages and disadvantages, the most accurate way of identifying the best soil moisture technique is the combination of a value selection method (VSM) and a field method such as a TDR sensor or Neutron probe. Liu et al. (2023) investigated the evolutionary pattern of soil moisture and conducted an attribution analysis from climate and human perspectives. The results reveal an unbalanced surface and rootzone variation trend during 1980\u0026ndash;2020. Additionally, the study showed that both climatic and human factors had significant impacts on soil moisture. Specifically, air temperature and evaporation are considered to be the primary climatic factors affecting the seasonal and long-term variability of soil moisture, respectively.\u003c/p\u003e \u003cp\u003eThe goal of this research was to study soil moisture dynamics under the treatment of the Negarim Microcatchment structure in a semi-arid region in Iran. The selected area for this study has been experiencing unpredictable precipitation pattern, long dry spells and general noticeable change in microclimate. improper urban development in rangelands, mismanaged land use and over exploiting of water resources has escalated this change which resulted in the slow migration of border plant species duo to lack of resources.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the effect of a variation of innovative rain harvesting structures with a selection of treatments on soil moisture at three different soil depths of 10, 20, and 30 cm. based on these variations 108 microstructures with different cover treatments were constructed. The study was carried out over a one-and-a-half-year period, and soil moisture was regularly measured after each significant precipitation during this period. The goal of such data collecting is to determine the effect of specific changes in constructing structures and the efficiency of each treatment in the overall ability of structures to retain and conserve soil moisture for longer periods of time.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eThis study was conducted in a relatively small area of approximately 3375 m2 in the Dehbar watershed (36 \u0026deg; 18\u0026rsquo; N, 59\u0026deg; 24\u0026rsquo; E) located in the Khorasan Razavi Province of Iran, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and in a semi-arid region, where the average annual precipitation is approximately 255 mm. The watershed has a moderate drainage density across its area. The watershed consists mostly of rangelands, with a small percentage of agricultural land use and scattered cover of small Cercis graffiti trees. Most of the soil. The chosen site has a clay loam/loam soil texture, which is generally common in this particular watershed, and is classified as hydrological group C. The dominant soil type is the Lithic Xerorthents soil group, which are shallow soils with heavy texture and low organic carbon. As such, moderate to low density vegetation cover is common in the area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Plot selection, structure building, and sampling\u003c/h2\u003e \u003cp\u003eThe study area was selected by collecting field data and generating a water harvesting potential map of the watershed by GIS, which concluded that the majority of the watershed had a moderate potential for water harvesting techniques and soil moisture optimization. Using this, a small, homogenous land plot was selected considering accessibility, soil type, land slope, and vegetation cover. The designated structure was the Negarim Microcatchment, which is a diamond-shaped soil structure. This structure was selected because of its suitability for semi-arid conditions, accessibility, and the less invasive nature of the structure. A total of 108 structures of this type with different sizes were built on a sidehill of the relatively same slope. half of the structures were given plastic cover treatment, meaning 54 of them had their catchment area covered in UV plastic (P), while the other half had natural cover (N). In addition, all infiltration pits had a mixed treatment of natural cover (N), seedling (S), rock cover (R), no rock cover (F), summer limited irrigation (W), and no summer limited irrigation (D), with each structure having a specific set of treatments repeated three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. soil moisture measurement process\u003c/h2\u003e \u003cp\u003eThe goal of this study was to monitor and measure soil moisture variation during two crop years at three different soil depths of 10 cm, 20cm, and 30 cm and to evaluate whether the treatments had any effect on the dynamics of soil moisture at different soil depths. Sampling was scheduled after each effective precipitation, and measuring was regularly repeated three times after a rainfall occurrence with 5-day intervals. Measurements were carried out by TDR sensors, which were calibrated before each sampling field trip, and additional field sampling was performed to assure the accuracy of the sensor (Fig.\u0026nbsp;2). An overall view of study process and structural plan is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure2. \u003cb\u003eA view of the constructed Negarim Microcatchment and the sampling process\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. analysis of Data\u003c/h2\u003e \u003cp\u003eAll the collected data was analyzed by IBM SPSS. since field data with large quantity are not normally distributed Levene homogeneity test of variance was used. Levene's test is often employed to test the assumption of equality of variances between two or more sample populations when the population samples are generally not normally distributed. The null and alternative hypotheses of Levene's test can be generally stated as follows: H\u003csub\u003e0\u003c/sub\u003e: All of the k sample populations have equal variances. H\u003csub\u003eA\u003c/sub\u003e: At least one of the k samples population variances are not equal. The test statistic (W) used in Levene's test is defined as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{W}=\\frac{(\\varvec{N}-\\varvec{k})}{(\\varvec{k}-1)}\\:\\frac{{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{k}}{{\\varvec{n}}_{\\varvec{i}}\\left({\\varvec{Z}}_{\\varvec{i}}-\\varvec{Z}\\right)}^{2}}{\\sum\\:_{\\varvec{i}=1}^{\\varvec{k}}\\sum\\:_{\\varvec{j}=1}^{\\varvec{n}\\varvec{i}}\\:{\\left({\\varvec{Z}}_{\\varvec{i}\\varvec{j}}-\\varvec{Z}\\varvec{i}\\right)}^{2}}\\:\\:\\left(1\\right)\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewere,\u003c/p\u003e \u003cp\u003ek: is the number of groups\u003c/p\u003e \u003cp\u003en\u003csub\u003ei\u003c/sub\u003e: is the number of samples belonging to the i-th group.\u003c/p\u003e \u003cp\u003eN: is the total number of samples.\u003c/p\u003e \u003cp\u003eY\u003csub\u003eij\u003c/sub\u003e: is the j-th observation from the i-th group.\u003c/p\u003e \u003cp\u003eWelch and Brown-Forsyth tests of equality of means was used in order to use a one-factor analysis of variance (ANOVA). The Welch and Brown-Forsythe versions of one-way ANOVA do not assume that all the groups were sampled from populations with equal variances. The Welch test adjusts the denominator of the F ratio so it has the same expectation as the numerator when the null hypothesis is true, despite the heterogeneity of within-group variance. The p-value can be interpreted in the same manner as in the analysis of variance table. While The Brown-Forsythe test uses a different denominator for the formula of F in the ANOVA. Instead of dividing by the mean square of the error, the mean square is adjusted using the observed variances of each group. The p-value can be interpreted in the same manner as in the analysis of variance table. Games- Howell post hoc test was used to assess the data. The Games-Howell test is a nonparametric post hoc analysis approach for performing multiple comparisons for two or more sample populations. The Games-Howell test is somewhat similar to Tukey's post hoc test. Still, unlike Tukey's test, it does not assume homogeneity of variances or equal sample sizes. Thus, the Games-Howell test can be applied in settings when the assumptions of Tukey's test do not hold. Games-Howell test employs the Welch-Satterthwaite equation for degrees of freedom, which is also known as the pooled degrees of freedom and is based on Tukey's studentized range distribution, denoted q. As the Games-Howell test is nonparametric, it uses the ranks of the observations rather than the raw sample observation values. The Games-Howell test is defined as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{x}}_{\\varvec{i}}^{-}-\\:{\\varvec{x}}_{\\varvec{j}}^{-}\u0026gt;\\:{\\varvec{q}}_{\\varvec{\\sigma\\:}\\:,\\varvec{k},\\varvec{d}\\varvec{f}}\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere σ is equal to standard error:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{\\sigma\\:}=\\sqrt{\\frac{1}{2}\\:(\\frac{{\\varvec{s}}_{\\varvec{i}}^{2}}{{\\varvec{n}}_{\\varvec{i}}}+\\:\\frac{{\\varvec{s}}_{\\varvec{j}}^{2}}{{\\varvec{n}}_{\\varvec{j}}})}\\:\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\frac{\\:{\\left(\\frac{{\\varvec{s}}_{\\varvec{i}}^{2}}{{\\varvec{n}}_{\\varvec{i}}}+\\:\\frac{{\\varvec{s}}_{\\varvec{j}}^{2}}{{\\varvec{n}}_{\\varvec{j}}}\\right)}^{2}}{(\\:\\frac{{\\left(\\frac{{\\varvec{a}}_{\\varvec{i}}^{2}}{{\\varvec{n}}_{\\varvec{i}}}\\right)}^{2}}{{\\varvec{n}}_{\\varvec{i}}-1}+\\:\\frac{{\\left(\\:\\frac{{\\varvec{a}}_{\\varvec{j}}^{2}}{{\\varvec{n}}_{\\varvec{j}}}\\right)}^{2}}{{\\varvec{n}}_{\\varvec{j}}-1})}\\:\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDegrees of freedom is calculated using Welch's correction:\u003c/p\u003e \u003cp\u003eThus, confidence intervals can be formed with:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{x}}_{\\varvec{i}}^{-}-\\:{\\varvec{x}}_{\\varvec{j}}^{-}\\:\\pm\\:\\varvec{t}\\sqrt{\\frac{1}{2}\\:\\left(\\frac{{\\varvec{s}}_{\\varvec{i}}^{2}}{{\\varvec{n}}_{\\varvec{i}}}+\\:\\frac{{\\varvec{s}}_{\\varvec{j}}^{2}}{{\\varvec{n}}_{\\varvec{j}}}\\right)}\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ep-values are calculated using Tukey's studentized range:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{q}}_{\\varvec{t}\\:}\\:\\times\\:\\sqrt{2,\\varvec{k},\\varvec{d},\\varvec{f}}\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. soil moisture variation analysis\u003c/h2\u003e \u003cp\u003eData from all three sizes of structures were analyzed at three soil depths. Catchment Treatments included: natural (N) and plastic cover (P). Infiltration pit treatments included: natural infiltration pit (N), seedling(S), rock cover(R), no rock cover (F), no irrigation (D), and irrigation (W). each treatment was compared and analyzed in accordance with other treatments\u0026rsquo; possible influences. Due to the abundance of the collective data, only a sample of the three graphs is shown in Figs.\u0026nbsp;4, 5 and 6.\u003c/p\u003e \u003cp\u003e An overall soil depth of 30cm showed the greatest capability of conserving soil moisture in all sizes of structures. during both fall seasons, with intervals between each rainfall occurrence, all sizes of structures with natural covered catchment (N) and natural treatment for infiltration pit (N), depth of 30 cm showed the greatest capability in conserving soil moisture with a mean of 5% more than soil depth of 20 and 10 cm, the other two had retained nearly the same amount of soil moisture.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;4. Selective soil moisture variation analysis of all three sizes of structures at three soil depths under natural and plastic covered catchment and natural infiltration pit treatments\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the case of moisture loss, depths of 20 and 10 cm lost moisture at the same rate of 5\u0026ndash;10% more across all sizes of structures in comparison to the depth of 30. During both rainfall seasons, due to over-saturation of the soil, a steady amount of moisture was observed at all depths; however, the depth of 30 cm retained 3\u0026ndash;5% more soil moisture across all sizes. As expected, over both dry seasons, a large drop in soil moisture occurred. However, the soil moisture content of all three depths became steady after the first drop. the depth of 30 cm showed a higher ability to retain soil moisture than the other two depths. The recorded soil moisture content was: 23\u0026ndash;25% for the largest sized, 19\u0026ndash;20% for the medium-sized and 17\u0026ndash;18% for the smallest sized structures. while soil moisture content for the depth of 20 cm was at around 13\u0026ndash;15% for the biggest sized structures, 13% for the medium-sized structures, and 11% for the smallest sized structures. The soil moisture content for the depth of 10 cm was at 10\u0026ndash;11% for all three sizes of structures.\u003c/p\u003e \u003cp\u003e In comparison, the same-sized structures with UV plastic-covered catchments (P) and natural infiltration pits (N), while nearly identical in performance, showed that the general soil moisture conserving rate was significantly higher by a 10% margin in the largest structures and 5% for both medium-sized and small-sized structures. This is more significant during both dry seasons, as the stability rate for the depth of 30 cm for the largest structure is approximately 29\u0026ndash;30% while the other two depths had significantly less soil moisture content with the depth of 20 cm at 20% and the depth of 10 cm at around 15%. As for the medium-sized structure, a depth of 30 cm is the most significant with a mean of 26%, whereas the depths of 20 and 10 cm are 20% and 15%, respectively. The same can be said about the smallest-sized structures (SS), with a depth of 30 being the most efficient with a mean of 20%, and depths of 20 and 10 cm with 14% and 13%, respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;5. Selective soil moisture variation analysis of all three sizes of structures at three soil depths under natural and plastic covered catchment and non-irrigated infiltration pit treatments\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn all three sizes of structures, the performances of structures with plastic-covered catchments (P) were better compared to those with natural catchments (N). All plastic-covered structures performed better by a margin of 10% for the biggest-sized structures, 7% for medium-sized structures, and 5% for smallest-sized structures. in all structures, a soil depth of 30 cm had the most moisture retention. during the fall season, on both catchment treatments, a depth of 30 cm conserved soil moisture by a margin of 5% more than both 10 and 20 cm in the biggest structures and 3% in medium-sized and smallest-sized structures. in both catchment treatments for the biggest-sized structures during the intervals between rainfall occurrences, for a depth of 30 cm, the moisture loss rate was 10% less than depths 20 and 10 cm. while for the medium-sized and smallest-sized structures, the rate was 15%.\u003c/p\u003e \u003cp\u003eDuring the rainy seasons. During the dry seasons, half of the infiltration pits were not under irrigation treatment; therefore, a loss of moisture was expected. However, after the initial loss, the soil moisture content was steady across all structures.\u003c/p\u003e \u003cp\u003e For the biggest sized structures, on a depth of 30 cm, soil moisture content was 22% for natural catchment (N) and 25% for plastic-covered catchments (P). on the depth of 20 cm 15% for natural catchment (N) and 20% for plastic covered catchments (P) and 11% soil moisture content for natural catchment (N) and 15% for plastic covered catchments (P) on the depth of 10 cm was monitored. for medium sized structures, on a depth of 30 cm, soil moisture content for natural catchment (N) was 20% and 22% for plastic-covered catchments (P). also, 15% for natural catchment (N) and 20% for plastic covered catchments (P) on the depth of 20 cm and on the depth of 10 cm, 10% soil moisture content for natural catchment (N) and 14% for plastic covered catchments (P) was observed. for the smallest sized structures, on a depth of 30 cm, soil moisture content was 18% for natural catchment (N) and 20% for plastic-covered catchments (P). for the depth of 20 cm 11% for natural catchment (N) and 13% for plastic covered catchments (P) and 10% soil moisture content for natural catchment (N) and 13% for plastic covered catchments (P) on the depth of 10 cm.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;6. Selective soil moisture variation analysis of all three sizes of structures at three soil depths under natural and plastic covered catchment and irrigated infiltration pit treatments\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe other half of the infiltration pits were under irrigation treatment; therefore, during dry seasons, after the initial loss of moisture, a steady trend of increase in soil moisture content occurred due to the two-week interval of irrigation.\u003c/p\u003e \u003cp\u003eFor the biggest sized structures, the performances on a depth of 30 cm were 20\u0026ndash;28% for natural catchment (N) and 23\u0026ndash;35% for plastic-covered catchments (P). while depth of 20 cm performed 20\u0026ndash;25% for natural catchment (N) and 18\u0026ndash;27% for plastic covered catchments (P). the depth of 10 cm had the performance of 10\u0026ndash;18% for natural catchment (N) and 15\u0026ndash;21% for plastic covered catchments (P). for the medium-sized structures the performances on a depth of 30 cm were 20\u0026ndash;22% for natural catchment (N) and 21\u0026ndash;23% for plastic-covered catchments (P). while depth of 20 cm performed 15\u0026ndash;17% for natural catchment (N) and 20\u0026ndash;21% for plastic covered catchments (P). the depth of 10 cm had the performance of 10\u0026ndash;13% for natural catchment (N) and 15\u0026ndash;16% for plastic covered catchments (P). for the smallest sized structures, the performances on a depth of 30 cm were 20\u0026ndash;21% for natural catchment (N) and 21\u0026ndash;23% for plastic-covered catchments (P). while depth of 20 cm performed 12\u0026ndash;15% for natural catchment (N) and 18\u0026ndash;21% for plastic covered catchments (P). the depth of 10 cm had the performance of 10\u0026ndash;13% for natural catchment (N) and 14\u0026ndash;15% for plastic covered catchments (P).\u003c/p\u003e \u003cp\u003eNo significant difference was observed between the performance of rock cover treatment (R) and non-rock cover treatment (F) for infiltration pits of any size and depth.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eDespite representing a fraction of water resources on the planet, soil moisture has a crucial role in the Ecohydrological dynamic, and any variation in it can affect the water-energy dynamic of its immediate local ecosystem. In recent decades, there has been an emergence of optimized versions of ancient water harvesting techniques due to climate change, lack of efficient precipitation, and shortage of reliable water resources. The overall goal of these techniques is mainly to enrich soil moisture as a way to revive the more vulnerable local ecosystems. Many research studies have been conducted in different professional fields regarding these techniques and structures, such as Printz (1996), Printz and Malik (2002), Martinez et al. (2004), Owais et al. (2004), Gebretsadic (2009), and Ali (2010). While most studies in this field are very few and done on a larger scale or are used in combination with another methodology or a different purpose, the goal of this study was to implement an innovative variation of rain water harvesting structures to monitor the effects of these new iterations of such structures and all the experimental treatments in real time to create a realistic outlook of the efficiency of said structures and their actual effect on soil moisture content. To assess the accuracy of the results, a statistical analysis of variance with a post hoc test was conducted to confirm that the observed difference between treatments was true. These comparisons were the main concerns.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Comparison between the three depths\u003c/h2\u003e \u003cp\u003eBy the overall data analysis and graph visualization, with regard to the three depths of the study, the depth of 30 cm showed the highest soil moisture content throughout the entire duration of the study. In comparison to the two other depths, the depth of 30 cm was more efficient with a margin of 10\u0026ndash;20%. while the depth of 20 cm performed better than the depth of 10 cm by a margin of 5%. However, to statistically validate the observed data, Levene\u0026rsquo;s test was used to perform a variance analysis of the dependent variable. Since the significance level of the calculated value of Levene is smaller than the critical level of 0.05, the data have questioned the assumption of the equality of error of variances. first, the normal distribution of the data was examined. To use a one-factor analysis of variance, Welch and Brown-Forsyth tests of equality of means is used. Because the data of this research are field collected and have a large volume, it does not have a normal distribution. Therefore, for one-way analysis of variance, the method was used assuming unequal variances between groups. As shown in table.1 the results show that the effect of depth change is significant in the performance of all structures. The depth of 30 cm was on average 3% better than the depth of 20 cm and 5% better than the depth of 10 cm. Between the depths of 20 and 10 cm, the difference in moisture retention is about 1%. In addition, the two-by-two comparisons of depth groups show that all groups have significant differences with each other. On average, the efficiency of structures in maintaining soil moisture increased steadily with increasing depth. Therefore, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e. the change in sampling depth has a significant effect on the performance of the structures for the absorption and penetration of runoff.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.1. Descriptive statistics concerning the effect of depth on structural performance\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eDescriptive statistics for the size comparison\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e32.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e34.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e37.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e34.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTest of the Homogeneity of Variances\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLevene Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003edf1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edf2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e57.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.2. ANOVA with Robust Tests of the Equality of Means concerning the effect of depth on structural performance\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36386.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e18193.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e164.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1134237.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e110.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1170623.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRobust Tests of the Equality of Means\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStatistic\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003edf1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003edf2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWelch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e175.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e6810.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBrown-Forsythe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e164.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e10118.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003ea. Asymptotically F distributed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.3. Games-Howell Post hoc test assuming unequal variances between groups and compare depth\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eGames-Howell Post hoc test assuming unequal variances between groups and compare depth\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(I) Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Difference (I-J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.41%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.51%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.09%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.51%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.09%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile there is a significant difference between the methodology of this study and most referenced source material, due to the combinatorial nature of this research, the data analysis process and results of this study showed the same pattern regarding the interaction between and the effects of different controlled variables on soil moisture dynamics as the research studies of Ali (2010), Tramblay et al. (2010), Zehe et al. (2010), Razaghi (2011), Glendenning et al. (2012), Studer and Liniger (2013), Zhu et al. (2014), Massari et al. (2015), Nyagumboa et al. (2019), Dai et al (2022), Gou et al (2022), Rasheed et al (2022), and Liu et al (2023). This includes the identification of a suitable site for potential water harvesting techniques as well as the use of the Negarim Microcatchment as suitable microstructures regarding the plot area conditions (Zhou et al; 2013, Hubner et al. 2015, Kaliraj et al; 2015). With regard to the influence of the designated treatment on the efficiency of the structures in conserving soil moisture, the study showed that depth, as well as catchment cover treatments, steadily have considerable influence on the structure\u0026rsquo;s performance during the entire period of sampling, whereas infiltration pit treatment has considerably less significance in defining an impact. This could be due to the effect of the subsurface water movement and lack of exposure to evapotranspiration at a depth of 30 cm, as well as the efficiency of the plastic cover to almost completely isolate the catchment area and increase the infiltration rate in the pits. As for the effects of infiltration pit treatments, while it can be argued that there was a percentage contribution in regards to optimizing the structure\u0026rsquo;s performance, the volume of this contribution and its statistical and practical significance were not impactful enough to have a realistic effect. However, it is important to note the role of these structures in sustaining small plant species. While the change of treatment in the infiltration pit may not be significantly effective, different catchment treatments were significant and the overall efficiency of the structures themselves. it is important to consider the significance of global warming and consequently climate change on the watershed microclimate. Different weather patterns result in various changes in climatic parameters such as temperature, precipitation, evapotranspiration and soil moisture fluctuance (Sarabi et al. 2021). considering the extreme changes in precipitation, concentration of yearly runoff flows in shorter time frames and long dry periods unusual for this watershed, some local plants and vegetation in the watershed are slowly migrating. The effects of unsuitable land use management, misplaced industrialization of rangelands, overexploitation of surface and underground water resources, changes in the microclimate, and weak rangeland management were observed in the overall density of vegetation cover, specifically border plant species in the watershed. Introducing Negarim micro catchment structures can be suitable as a semi-noninvasive method to help with the long-term sustaining and improvement of soil moisture available for local border plant species in the face of new environmental changes. The ability to retain soil moisture at deeper depths for the root system can be effective for the survival of vulnerable vegetation cover, especially in warm months with little to no precipitation. Although the short-term effects of such methods may not be as visible, steady usage of such low-cost and simple structures can efficiently improve the overall long-term conditions of soil moisture and plant cover density in the watershed. This is especially significant in environmental tolerance thresholds because by conserving enough soil moisture, it can stabilize the border plant species. The low cost and easy-to-implement nature of these structures can also be a motivator for use in different scales by local population. Since these are primarily small-scale soil structures, there is no significant costly structural upkeep routine or heavy invasion of the environment on which they are implemented. Therefore, the effect they have on the surrounding area can be monitored for a long time. In general, these structures preserve the conditions of the region to a significant extent.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe main purpose of this study was to evaluate the effects of a variation of Negarim Microcatchment structures based on creating new iterations of FAO\u0026rsquo;s standard measurement on soil moisture content and their efficiency in retaining and conserving the collected rainwater to recharge the soil moisture. To achieve this purpose, various treatments on both catchments and infiltration pits were conducted to determine which treatments can optimize the efficiency of structures the best. The study showed that the depth of 30 cm was by far the most effective depth among the three depths in regards to retaining soil moisture from the depth of 20 cm and 10 cm. The same result was observed in the comparison of natural and plastic covered catchments. With the plastic covered catchments, the efficiency of the structures in conserving soil moisture was higher comparing of natural covered catchments. A combination of infiltration pit treatments showed a moderate difference. In conclusion, it can be said that the main controlling factors affecting the performance of the structures are the soil depth and the treatments that have been conducted for the catchment area, whereas the infiltration pit area treatments played a less significant role. Overall, the performance of the structures showed that implementing these structures can be effective in conserving soil moisture; however, the concentration of different treatments can affect the performance of catchments more significantly than infiltration pits. While, conducting such studies are crucial in better understanding of soil moisture dynamic and its response to varying methods of conservation, data monitoring may face some restrictions. Duo to the unpredictable nature of precipitation pattern, collected field data may not completely reflect on the expected results. Massive quantities of data may also not conform to the expected analyzing methods. Since the conditions of data collecting is as close to be non-invasive as possible, variations in results are not as striking as expected. Based on the results, long-term usage and data monitoring of structural performances is advised. Such methods function by gradually conserving soil moisture to improve the surrounding environment, therefore long-term monitoring can provide a more conclusive view on the impact of such methods on the micro-ecosystem.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Banafshe Kouhzad. The first draft of the manuscript was written by Banafshe Kouhzad. Dr. Mohammad Taghi Dastorani and Dr. Mohammad Reza Yazdani reviewed and commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe data supporting this study\u0026rsquo;s findings are available on request from the corresponding Author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e Compliance with Ethical Standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e All authors consented to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u003c/strong\u003e All authors consented to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting\u003c/strong\u003e \u003cstrong\u003eInterests:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl Ali M (2012) Soil water conservation and water balance model for micro catchments water harvesting system. Ms. Thesis Dissertation. Department of Civil and Building Engineering, Loughborough University\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoers TM, BenAsher J (1982) A review of rainwater harvesting. Agric Water Manage 5(1):145158\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai L, Fu R, Guo X, Du Y, Zhang F, Cao G (2022) Soil Moisture Variations in Response to Precipitation Across Different Vegetation Types on the Northeastern Qinghai-Tibet Plateau. Front Plant Sci 13:854152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpls.2022.854152\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.854152\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDick J, Tetzlaff D, Bradford J, Soulsby C (2018) Using repeat electrical resistivity surveys to assess heterogeneity in soil moisture dynamics under contrasting vegetation types. J Hydrology V 559:684\u0026ndash;697. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2018.02.062\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2018.02.062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandez-Moran R, Al-Yaari A, Mialon A, Mahmoodi A, Bitar AA (2017) Gabrielle De Lannoy, Nemesio Rodriguez-Fernandez, Ernesto Lopez-Baeza, Yann Kerr, and Jean-Pierre Wigneron. SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product Remote Sensing 9, no. 5: 457. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs9050457\u003c/span\u003e\u003cspan address=\"10.3390/rs9050457\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGebretsadic W (2009) Evaluation of The Adaptability and Response of Potential Indegenous Trees to Water Harvesting in the Rehabilitation of Kuriftu Lake catchment. Ms. Thesis. University of Natural Resources and Applied Life Sciences (BOKU), Vienna, Austria,4(3):143\u0026ndash;162\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGevaert AI, Parinussa RM, Renzullo LJ, Van Dijk AIJM, De Jeo RAM (2016) Int J Appl Earth Observation Geoinf V 45:235\u0026ndash;244. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jag.2015.08.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jag.2015.08.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlendenning CJ, Van Ogtrop FF, Mishra AK, Vervoort RW (2012) Balancing watershed and local scale impacts of rainwater harvesting in India\u0026mdash;a review.Agric. Water Manag 107:1\u0026ndash;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGou Q, Guo W, Li Y, Liu J, Wang G (2022) Dynamic changes in soil moisture in three typical landscapes of the Heihe River Basin. Front Environ Sci 10:1049883. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fenvs.2022.1049883\u003c/span\u003e\u003cspan address=\"10.3389/fenvs.2022.1049883\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang X, Shi ZH, Zhu HD, Zhang HY, Ai L, Yin W (2015) Soil moisture dynamics within soil profiles and associated environmental controls. Catena J 136:189\u0026ndash;196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.catena.2015.01.014\u003c/span\u003e\u003cspan address=\"10.1016/j.catena.2015.01.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHubner R, Heller K, Gunther T, Kleber A (2015) Monitoring hillslope moisture dynamics with surface ERT for enhancing the spatial significance of hydrometric point measurements. Hydro Earth Syst Sci 19:225\u0026ndash;240. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/hess-19-225-2015\u003c/span\u003e\u003cspan address=\"10.5194/hess-19-225-2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaliraj S, Chandrasekar N, Magesh NS (2015) Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India using GIS-based weighted overlay analysis. Environ Earth Sci 74(5):4355e4380\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Yang Y, Song J (2023) Variations in Global Soil Moisture During the Past Decades: Climate or Human Causes. J Water Resour Res 59(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2023WR034915\u003c/span\u003e\u003cspan address=\"10.1029/2023WR034915\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez de azagra A, Mongil J, Rojo L (2004) Oasification: a forest solution to many problems of desertification, invest agrar. 13(3):437\u0026ndash;442\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassari C, Brocca L, Tarpanelli, AMoramarco T (2015) Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe. Remote Sens 7(9):11403\u0026ndash;11433. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs70911403\u003c/span\u003e\u003cspan address=\"10.3390/rs70911403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMouazen AM, Ramon H (2006) Development of an on-line measurement system of bulk density based on online measured draught, depth, and soil moisture content. Soil Tillage Res J 86(2):218\u0026ndash;229. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.still.2005.02.026\u003c/span\u003e\u003cspan address=\"10.1016/j.still.2005.02.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyagumboa I, Nyamadzawo G, Madembo C (2019) Effects of three in-field water harvesting technologies on soil water content and maize yields in a semi-arid region of Zimbabwe. J Agricultural Water Manage 216(1):206\u0026ndash;213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agwat.2019.02.023\u003c/span\u003e\u003cspan address=\"10.1016/j.agwat.2019.02.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOweis TY, Oberle A and D.Prinz (2004) Determination of potential sites and methods for water harvesting in central Syria. Adv GeoEcology 31:83\u0026ndash;88\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrintz D, Malik A (1996) Runoff farming. Institute of Water Resources Management, Hydraulic and Rural Engineering, Dept. of Rural Engineering, University of Karlsruhe, D76128 Karlsruhe. Germany 7(5):12\u0026ndash;33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrinz D, Malik AH (2002) Runoff Farming. Institute of Water Resources Management, Hydraulic and Rural Engineering: Germany\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasheed MW, Tang J, Sarwar A, Shah S, Saddique N, Khan MU, Imran Khan M, Nawaz S, Shamshiri RR, Aziz M et al (2022) Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review. Sustainability 14(18):11538. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su141811538\u003c/span\u003e\u003cspan address=\"10.3390/su141811538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRazzaghi M (2011) Rain Water Harvesting Systems are a Way to Water Conservation. Int J Water Resour Arid Environ 1(4):72\u0026ndash;84\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarabi M, Dastorani MT, Zarrin A (2021) The Impact of Future Climate Change on Hydrological Response in Torogh Dam Watershed. Mashhad J Meteorol Atmos Sci 3(4):310\u0026ndash;330 [ In Persian]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStuder R, Liniger H (2013) Water Harvesting, Guidelines to Good Practice. Centre for Development and Environment (CDE) and Institute of Geography. University of Bern; Rai\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTramblay Y, Bouvier C, Martin C, Didon-Lescot JF, Todorovic D, Domergue JM (2010) Assessment of initial soil moisture conditions for event-based rainfall-runoff modeling. 387(4):176\u0026ndash;187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2010.04.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2010.04.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Tromp- HJ, MacDonell JJ (2006) On the interrelations between topography, soil depth, soil moisture, transpiration rates, and species distribution at the hillslope scale. Adv Water Resour 29(2):293\u0026ndash;310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.advwatres.2005.02.016\u003c/span\u003e\u003cspan address=\"10.1016/j.advwatres.2005.02.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Wei W, Chen L, Mo B (2012) Response of deep soil moisture to land use and afforestation in the semi-arid Loess Plateau, China. J Hydrology V 475:111\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2012.09.041\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2012.09.041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao X, Fu B, Lu Y, Chang R, Wang X, Wang Y, Su C (2012) The multi-scale spatial variance of soil moisture in the semi-arid Loess Plateau of China. J Soil Sediment 12:694\u0026ndash;703. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11368-012-0481-5\u003c/span\u003e\u003cspan address=\"10.1007/s11368-012-0481-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYumang AN, Pagnilawan AC, Perez LAA, Fidelino JFF, Santos JBC (2016) Soil infiltration rate as a parameter for soil moisture and temperature-based Irrigation System. 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/ICCSCE.2016.7893586\u003c/span\u003e\u003cspan address=\"10.1109/ICCSCE.2016.7893586\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Zhao W, Wang L, Liu Y, Liu Y, Feng C (2019) Relationship between soil water content and soil particle size on typical slopes of the Loess Plateau during a drought year. Sci Total Environ 648(15):943\u0026ndash;954. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2018.08.211\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2018.08.211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZehe E, Geraeff T, Morgner M, Bauer A, Bronstert A (2010) Plot and field scale soil moisture dynamics and subsurface wetness control on runoff generation in a headwater in the Ore Mountains. Hydro Earth Syst Sci 14:873\u0026ndash;889. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/hess-14-873-2010\u003c/span\u003e\u003cspan address=\"10.5194/hess-14-873-2010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, Istanbullouglu E, Vivoni E (2013) Modeling the Ecohydrological role of aspect-controlled radiation on tree-grass-shrub coexistence in a semiarid climate. Water Resour Res J 49(5):2872\u0026ndash;2895. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wrcr.20259\u003c/span\u003e\u003cspan address=\"10.1002/wrcr.20259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil Moisture, Negarim Microcatchment, Soil depth, Microcatchment size, Catchment cover treatments, Infiltration pit treatments","lastPublishedDoi":"10.21203/rs.3.rs-4704859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4704859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne of the most important factors in determining the Ecohydrological balance is soil moisture content. Any variation in soil moisture, albeit insignificant, can have a chain effect on the quality of soil structure, soil particles, erosion rate, microbiological activity in soil crust, and infiltration rate. which in turn can affect ecosystem dynamics. Therefore, it is important to use more eco-friendly and less invasive techniques, such as rainwater harvesting structures (RHS), to enrich the current soil moisture content in any ecosystem. The study used an RWH structure, namely the Negarim Microcatchment, to observe its real-time effects on soil moisture variations. The continuous effect of certain treatments was also evaluated. To do this, a small area of approximately 3375 m\u003csup\u003e2\u003c/sup\u003e in the Dehbar watershed (36 \u0026deg; 18\u0026rsquo; N, 59\u0026deg; 24\u0026rsquo; E) located in Khorasan Razavi Province of Iran was selected based on the map of water harvesting potential in the watershed that was generated by GIS to construct the structures. This study aimed to evaluate the efficiency of a number of variations of RHS in retaining soil moisture its different soil depths. For this purpose, FAO\u0026rsquo;s standard measurement was used to calculate and construct three different sizes of structures for this research. FAO\u0026rsquo;s standard measurement included the 1 x 1 area for the infiltration pit which was used as the medium-sized (standard) structure. The other two structures were one time larger and one time smaller, respectively. Each size group consists of 38 Microcatchment with two treatments of natural (N) and plastic covered (P) for the catchment area and a combination of natural cover (N), seedling (S), rock cover (R), no rock cover (F), summer irrigation (W), and no summer irrigation (D) for the infiltration pits. After each significant rainfall, soil moisture measurement was measured by TDR sensors with a repetition of three times at five-day intervals throughout two crop years. The data analysis results showed that the main control factor of the structure performance was soil depth and the catchment area cover type. Comparing the three depths, the depth of 30 cm showed more significance by a margin of 10\u0026ndash;20% over the depths of 20 cm and 10 cm. The same could be said about the difference between plastic-covered catchments and natural-covered ones at 5\u0026ndash;10%. The infiltration pit treatments showed a moderate 2\u0026ndash;3% effect. It can also be concluded that while each treatment showed a significant interrelationship between different inter-factors, no significance was found between individual factors. The results of this study indicated that overall variation in RHS can be significant in RHS's ability to conserve soil moisture. The provided data can be used for long-term usage and data monitoring of such structures.\u003c/p\u003e","manuscriptTitle":"Evaluating Negarim Microcatchment efficiency to Conserve Soil Moisture based on Soil Depth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 12:02:42","doi":"10.21203/rs.3.rs-4704859/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c9117d6-10de-49d1-87ae-277562e2d9e2","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-18T22:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-11 12:02:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4704859","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4704859","identity":"rs-4704859","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}