Innovatory rainfall simulator design – A concept of moving storm automation

We developed an advanced design programmable rainfall simulator (RS) to simulate a moving storm rainfall condition. The RS consists of an automated nozzle control system coupled with a pressure regulator mechanism for an operating range of 50 kPa to 180 kPa at a drop height of 2000 mm above the soil flume surface. Additionally, a programmable mobile application was developed to regulate all RS valves. Near natural rainfall conditions were simulated at varying spatial and temporal resolutions in a controlled environment. A soil flume of 2500 mm × 1400 mm × 500 mm was fabricated to conduct different hydrological experiments. The flume was designed to record overland, subsurface, and base flows simultaneously. This study focused on a detailed analysis of moving storms and their impact on hydrograph characteristics. Experimental results showed a considerable difference in terms of time to peak (tp), peak discharge (Qp), and hydrograph recession for two different storm movement directions (upstream and downstream). Two multiple regression models indicate a statistically significant relationship between the dependent variable (tp or Qp) and the independent variables (i.e. storm movement direction, storm velocity, and bed slope gradient) at a 5 % level of significance. Further, the impact of these moving storm phenomena reduces with the increase in the storm movement velocity.

Evaluation of CCTV Data For Estimating Rainfall Condition

Rainfall characteristics of Indonesia's tropical climate have high variability according to space and time, so to determine the rainfall pattern of a location, an in situ rainfall measuring instrument (AWS = automatic weather station) is needed with high density. The existence of AWS also requires relatively high maintenance costs and a standard placement location (according to the rules of WMO = World Meteorological Organization) which is relatively broad and is not obstructed by other objects that can make the result of rainfall data is not representative. With the concept of computer vision, research will be carried out to estimate the rainfall condition from the CCTV cameras. The CCTV camera data which have qualitative characteristic into rainfall data which have quantitative characteristics. This research is also motivated by the large number of CCTVs that are placed in a lot of locations by local governments along with the Smart City program in districts and cities throughout Indonesia. The preliminary research was conducted in Center for Atmospheric Science and Technology office in Bandung. Rainfall data from AWS was used to validate CCTV data which placed in same location. The process of converting CCTV data into rainfall data goes through 6 stages. The first is reading the image mapping data and AWS (in rainfall accumulation data form). Second, read the image data in grayscale. Third, extract the features. Fourth, split the reference and sample data. Fifth, conducts the K-NN Mapping Reference Image and rainfall accumulation data. Sixth is to praise K-NN Testing. The accuracy is calculate with comparing the estimated number of CCTV cameras that are correct with the total sample size. The evaluation result states that the highest accuracy is obtained with K = 1. When K=1, the accuracy percentage reaching 94.8%. Accuracy decreases with increasing value of K and drastically decreases with K> 2. In the 1-10 days reference data, the highest accuracy is obtained by the number of reference data for 10 days, which is around 97%, stable until the value of K = 8. While the lowest accuracy is obtained when the reference data is 1 day with an accuracy value of about 43%. Based on the results of this study, it can be concluded that rain data from CCTV can be used to estimate the rainfall data. The best result happened when K-value is equal to 1.

Stability Reinforcement of Slopes Using Vegetation Considering the Existence of Soft Rock

This study investigates the effectiveness of vegetation reinforcement on the stability of a slope with red-bed soft rock in a slope along the Xining-Chengdu railway, China. Four kinds of vegetation were considered to reinforce the soil and the slope. The rooted soil parameters were determined based on the laboratory tests. A numerical model was developed based on the actual geometry and soil layer distributions. The soils were modeled as elastic perfectly plastic materials and the vegetation reinforcement was represented as addition cohesion of a series of subsoil layers within a given depth. The effectiveness of vegetation on slope reinforcement under both dry and rainfall conditions was investigated regarding this case. The potential failure surface and corresponding factor of safety of the red-bed soft rock slope for those different conditions were analyzed and compared. It has been found that the addition of vegetation increased the safety of slope stability whether the slope is under a dry condition or a rainfall condition, while the increasing proportion of factor of safety due to vegetation reinforcement for this case is very limited. The results and findings in this study are still significant for the practitioner to evaluate the reasonability of vegetation reinforcement.

Rainfall Anomalies Pattern in Northwestern Nigeria (NWN)

Constant investigation into rainfall anomaly pattern is very crucial as it enables the detection of any departure from normal rainfall condition. When such departure is persistent and statistically significant, it could indicate climate change. This study seeks to investigate anomaly pattern of rainfall in north western Nigeria with the view to determine any extreme departure from established normal rainfall behavior (mean). The study used thirty years (30) rainfall data from 1987 to 2016. The data was obtained from the archives of Nigerian Meteorological Agency (NIMET) for six selected synoptic stations from the region. Purposive sampling technique was adopted in selecting the six synoptic stations given consideration to stations with longer consistent rainfall records. The data was subjected to Standardized Anomalies also known as Standardized Precipitation Index (SPI) to obtain anomaly values. The values were used to plot time series for each station. They were also used to determine the dry or years with drought i.e. negative values and wet or moisture years i.e. positive values. The findings showed that throughout the thirty years period, normal conditions dominated the study area with few pockets of dry conditions. The study concludes that rainfall anomalies pattern in north western Nigeria over the thirty years period under investigation was not too far from normal rainfall conditions.

Effect of Ridge Height, Row Grade, and Field Slope on Nutrient Losses in Runoff in Contour Ridge Systems under Seepage with Rainfall Condition

Seepage plays a key role in nutrient loss and easily occurs in widely-used contour ridge systems due to the ponding process. However, the characteristics of nutrient loss and its influential factors under seepage with rainfall condition in contour ridge systems are still unclear. In this study, 23 seepage and rainfall simulation experiments are arranged in an orthogonal rotatable central composite design to investigate the role of ridge height, row grade, and field slope on Nitrate (NO3−–N) and Orthophosphate (PO4+3–P) losses resulting from seepage in contour ridge systems. In total, three types of NO3−–N and PO4+3–P loss were observed according to erosion processes of inter-rill–headward, inter-rill–headward–contour failure, and inter-rill–headward–contour failure–rill. Our results demonstrated that second-order polynomial regression models were obtained to predict NO3−–N and PO4+3–P loss with the independent variables of ridge height, row grade, and field slope. Ridge height was the most important factor for nutrient loss, with a significantly positive effect and the greatest contribution (52.35–53.47%). The secondary factor of row grade exerted a significant and negative effect, and was with a contribution of 19.86–24.11% to nutrient loss. The interaction between ridge height and row grade revealed a significantly negative effect on NO3−–N loss, whereas interactions among the three factors did not significantly affect PO4+3–P loss. Field slope only significantly affected NO3−–N loss. The optimal design of a contour ridge system to control nutrient loss was obtained at ridge height of 8 cm, row grade of 2°, and field slope of 6.5°. This study provides a method to assess and model nutrient loss, and improves guidance to implement contour ridge systems in terms of nutrient loss control.