Rainfall Data Collection Research Articles

Integrating Maize Yield and Agricultural Drought Analysis for Sustainable Food Security: A Provincial Study in South Africa (1993–2022)

ABSTRACTExtreme climatic events, such as droughts, hinder progress toward achieving the sustainable development goal of food security. South Africa is vulnerable to drought‐related agricultural losses, which have led to food insecurity. However, few studies have focused on the long‐term impacts of drought on crop production at a regional scale. Therefore, we aimed to examine the intensity, magnitude, and trend of rainfall‐based short‐term agricultural drought at the provincial scale in South Africa based on the Standardized Precipitation Index (SPI). Additionally, we analyzed the impact of agricultural drought on maize yield by calculating the Standardized Yield Residual Series (SYRS) and Crop Drought‐Resilience Factor (CDRF). To this end, we collected rainfall data from 29 stations across nine provinces along with maize yield data for the period of 1993–2022. Agricultural drought analyses based on the three‐month (SPI‐3) and six‐month (SPI‐6) SPIs demonstrated dynamic variations in occurrence, with Sen's slope indicating that 10 stations exhibited a significant increase in drought events across South Africa. Notably, SPI‐6 analysis showed that Gauteng, Free State, and North West provinces experienced the highest percentages of severe to extreme drought events during the study period, at 4.17%, 3.89%, and 3.61%, respectively. Furthermore, the majority of provinces in South Africa experienced an extreme SPI‐6 magnitude ranging from −46.03 in Western Cape Province to −61.6 in Free State Province. The dynamic effects of agricultural drought on maize yield revealed that the maximum yield loss of 13% occurred in 1993 in Eastern Cape Province, while some provinces experienced no yield loss during certain years. However, CDRF analyses identified Western Cape (CDRF [SPI‐3] = 0.52, CDRF [SPI‐6] = 0.62) and Mpumalanga (CDRF [SPI‐6] = 0.7) provinces as the most vulnerable to food insecurity due to the severe non‐resilience of maize to drought in these regions. This study reveals the complex interplay between climatic extremes and maize yield variability, providing valuable insights for managing regional food production systems and ensuring future food security in South Africa.

Rainwater runoff regulation effect in sponge reconstruction area based on rainstorm flood management model

The study adopts the rainwater regulation strategy based on the rainstorm water management model, and designs a set of rainwater runoff regulation system by monitoring the amount of water, collecting rainfall data, and evaluating the quality of rainwater and natural rainwater, aiming at conserving groundwater, restoring hydrological cycle, reducing urban waterlogging, and optimizing rainwater resources. In the implementation method, the effectiveness of this strategy is evaluated by simulating various scenarios such as without LID facilities, with LID facilities, and secondary rainwater pipe network regulation. The results showed that the Nash efficiency coefficient of the overall model was 0.89, and the changes in chemical oxygen demand, total nitrogen, and total phosphorus water quality indicators were very close to the actual monitoring results. The comparative data before and after the renovation showed that the three water quality indicators were much higher before the renovation. The peak flow rate and outflow time significantly decreased, for example, the peak flow rate decreased from 0.41 m³ /s to 0.39 m³ /s. The outflow time was reduced from 9 min to 6 min. It can be seen that the strategies proposed in the study have effectively improved the quality of urban environment and residents' lives, providing important reference basis for urban rainwater management and water resource protection.

Study of the Spatiotemporal Distribution Characteristics of Rainfall Using Hybrid Dimensionality Reduction-Clustering Model: A Case Study of Kunming City, China

In recent years, the frequency and intensity of global extreme weather events have gradually increased, leading to significant changes in urban rainfall patterns. The uneven distribution of rainfall has caused varying degrees of water security issues in different regions. Accurately grasping the spatiotemporal distribution patterns of rainfall is crucial for understanding the hydrological cycle and predicting the availability of water resources. This study collected rainfall data every five minutes from 62 rain gauge stations in the main urban area of Kunming City from 2019 to 2021, constructing an unsupervised hybrid dimensionality reduction-clustering (HDRC) model. The model employs the Locally Linear Embedding (LLE) algorithm from manifold learning for dimensionality reduction of the data samples and uses the dynamic clustering K-Means algorithm for cluster analysis. The results show that the model categorizes the rainfall in the Kunming area into three types: The first type has its rainfall center distributed on the north shore of Dian Lake and the southern part of Kunming’s main urban area, with spatial dynamics showing the rainfall distribution gradually developing from the Dian Lake water body towards the land. The second type’s rainfall center is located in the northern mountainous area of Kunming, with a smaller spatial dynamic change trend. The water vapor has a relatively fixed and concentrated rainfall center due to the orographic uplift effect of the mountains. The third type’s rainfall center is located in the main urban area of Kunming, with this type of rainfall showing smaller variations in all indicators, mainly occurring in May and September when the temperature is lower, related to the urban heat island effect. This research provides a general workflow for spatial rainfall classification, capable of mining the spatiotemporal distribution patterns of regional rainfall based on extensive data and generating typical samples of rainfall types.

Comparative Analysis of Rainfall Data Sources in Agro-Ecological Zones of Sri Lanka: Expensive Meteorological Measurements vs Freely Available NASA Data

Rainfall information contributes significantly to weather-related research as it can be directly applied to many theories and models such as climate change, disaster management, environmental assessments. Therefore, the availability of long-term meteorological data and their accuracy are crucial. At the same time the access to these data from different sources have their own drawbacks. For example, meteorological department of Sri Lanka is responsible of collecting rainfall data. However, their data are very expensive, and their data are not available for all desired locations. On the other hand, the freely available satellite data sources have a question of reliability of the data although no missing data are found. Thus, a comparative assessment of rainfall data was carried out from two distinct sources: meteorological department of Sri Lanka and freely available National Aeronautics and Space Administration (NASA) satellite-based data. The objective of this study was to compare and discern the distinctions in rainfall data obtained from these sources for the benefit of future usage of the data in various studies. The study was carried out in different agro-ecological Zones (wet, dry, and intermediate) of Sri Lanka for the period of 1981-2021. The stations were selected randomly to represent the zones. Identified missing values in the meteorological dataset were subsequently addressed using the mean imputation method. Statistical procedures were conducted on the non-normal rainfall data to identify similarities between the sources within individual agro-ecological zones. Results revealed that there is a statistically significant difference at 5% between the sources (NASA and meteorological measurements) in wet and intermediate zones. However, the comparison of the dry zone resulted in inconclusive decisions both at 5% and 1% significance levels. In conclusion, it is statistically advisable to use meteorological rainfall measurements over NASA data for the wet and intermediate zones.
 
 Keywords: Rainfall, Agro-ecological zones, Meteorological data, NASA data, Non-normal data

A Novel Hybrid Spatiotemporal Missing Value Imputation Approach for Rainfall Data: An Application to the Ratnapura Area, Sri Lanka

Meteorological time series, such as rainfall data, show spatiotemporal characteristics and are often faced with the problem of containing missing values. Discarding missing values or modeling data with missing values causes negative impacts on the accuracy of the final predictions. Hence, accurately estimating missing values by considering the spatiotemporal variations in data has become a crucial step in eco-hydrological modeling. The multi-layer perceptron (MLP) is a promising tool for modeling temporal variation, while spatial kriging (SK) is a promising tool for capturing spatial variations. Therefore, in this study, we propose a novel hybrid approach combining the multi-layer perceptron method and spatial kriging to impute missing values in rainfall data. The proposed approach was tested using spatiotemporal data collected from a set of nearby rainfall gauging stations in the Ratnapura area, Sri Lanka. Missing values are present in collected rainfall data consecutively for a considerably longer period. This pattern has scattered among stations discontinuously over five years. The proposed hybrid model captures the temporal variability and spatial variability of the rainfall data through MLP and SK, respectively. It integrates predictions obtained through both MLP and SK with a novel optimal weight allocation method. The performance of the model was compared with individual approaches, MLP, SK, and spatiotemporal kriging. The results indicate that the novel hybrid approach outperforms spatiotemporal kriging and the other two pure approaches.

Flood Study From the Gr2m Model in the Mono Basin at the Outlet of Athieme (South-benin)

Around the world, in particular in Benin and more precisely in the town of Athiémé, floods, caused by high floods or by the overflow of the Mono River as was the case in September 2016, cause great social and economic damage. The main objective of this work is to study the floods in the Mono basin at the Athiémé outlet thanks to the GR2M model. The methodological approach used consisted in the collection and processing of rainfall and hydrological data over the period from 1965 to 2011. These different data are processed by descriptive statistical methods and applied in the GR2M model. The Nash values obtained during the application of GR2M on the Mono to Athiémé, vary from 73% to 88.6% in calibration, and from 67.9% to 93.7% in validation. This study therefore made it possible to note that hydrological fluctuations are sensitive to rainfall forcing, and that rainfall variability has a great impact both on the flow and on recharge and evaporation. In addition, the GR2M model is effective in simulating floods on the Mono in Athiémé.

PRINCIPLES OF CALCULATIONS AND ARRANGEMENT OF LOCAL DRAINAGE SYSTEMS IN PRIVATE BUILDING TERRITORIES

Abnormally heavy rains in the first two spring months of 2023 revealed the unpreparedness and lack of protection of many settlements in the Kyiv region from excessive moisture and inundation. Among them, is Novi Petrivtsi village, where the natural conditions for surface runoff and precipitation infiltration (lack of visible surface slopes and poorly permeable cover sediments) are unfavourable and significantly complicated by buildings, and a network of highways. The long-term retention of water on the surface, the rise of groundwater levels, and the layered structure of the upper part of the geological section provide grounds for the use of combined local drainage systems with compliance with drainage standards of at least 3,0 m. Since the high density of buildings often does not allow for contour drainage around residential buildings, it is necessary to lay single-line horizontal drainage to a greater depth than for a conventional contour drainage of 3,5 meters or more. However, the lack of roadside ditches and other water intakes and means of orderly drainage do not allow homestead drainage systems to work as efficiently as possible. This requires the creation of an orderly system of water intakes (trenches and closed collectors) on the scale of the village. Foreign experience convinces that the rational planning of such systems is possible under the conditions of establishing the character of rainfall distribution with a resolution of 1–5 minutes in time and a step of 500 m across the area. Meteorological radar is used to record radar images of rain and study its intensity. An effective solution to the water drainage problem is impossible without detailed engineering and geological investigations. Due to them, litho-facies inhomogeneities in the aeration zone and water-saturated stratum, which lead to the retention and support of groundwater, were discovered in the local area. Taking into account the spatial boundaries of these engineering and geological elements allow drainage more efficiently. Drainage capacity is substantiated by forecasts of changes in the maximum amount of precipitation per day and two days in a row. Due calculating the drainage capacity, it should be taken into account that the maximum amount of precipitation in the future period will have a guarantee of 0,5-2,0% less than the actual maximum values. In the calculation part, the main attention is paid to the selection of equations for determining the width of influence of a single horizontal drain. Five formulas have been selected that can be used to solve similar problems. The time of onset of the established mode of operation of a single drain was calculated. Future research should focus on the collection of high-resolution rainfall and local urban runoff data, as well as the implementation of urban drainage models.

Traditional and Novel Methods of Rainfall Observation and Measurement: A Review

Abstract Because of its high spatial and temporal variability, rainfall remains one of the most challenging meteorological variables to measure accurately. Obtaining high-quality rainfall products is essential for flood monitoring, disaster warning, and weather forecasting systems, but this is not always possible on the basis of current rainfall observation networks. Innovative alternatives draw inspiration from “citizen science” and “crowdsourcing,” allowing for opportunistic sensing of rainfall from existing measurements at a low cost, which has become a popular topic and is beginning to play an important role in developing rainfall observation systems. This paper reviews the current state of new rainfall observation approaches and explores their opportunities to complement more traditional ways of rainfall data collection in a hydrological context. Furthermore, the challenges of each new approach are discussed. Although these new options show great potential in enhancing the current rainfall network, they still face problems in terms of their accuracy, real-time accessibility, and limited applicability when individually employed. In contrast, the fusion of new measurements with traditional observation networks is feasible and will be effective for regional rainfall monitoring. This study also serves as an important reference in developing monitoring techniques for other environmental factors. Significance Statement New rainfall observation techniques provide a meaningful supplement to current rainfall networks in terms of spatiotemporal resolution and accuracy. In this paper, we present a comprehensive overview of the innovations in rainfall observation and their popularity in different regions around the world. Then, the application value and future opportunities that new techniques bring to hydrological research are analyzed. It is anticipated that this paper will be of value to researchers with an interest in improving the quality of rainfall data, thus paving the way to accelerate these studies, as well as the application and implementation of their findings, to the next stage. Furthermore, we expect to prompt a rethink on utilizing and exploiting these new rainfall products to enhance our understanding and optimization of current rainfall sensing systems.

Application of AI-Based Models for Flood Water Level Forecasting and Flood Risk Classification

Owing to global climate change, the frequency of disasters has increased twelve-fold, with a corresponding approximately seventeen-fold increase in economic damages over the past six decades. Notably, severe flood damage has been occurring in Asia along the Pacific coast due to extreme weather events, including torrential rains and typhoons, which have been becoming increasingly frequent and prolonging the rainy season. In the Eastern Visayas region, the management and monitoring facilities for flood observation data, as well as the forecasting and warning systems suitable for the local area, are insufficient. The warning system introduced through overseas grants is limited in operation in some areas of the city. Furthermore, although an organization has jurisdiction over flood forecasting and warning, the system's operation is not systematic and is limited. Additionally, there is a shortage of technical manpower. In this study, we utilized deep learning models to forecast flood water levels in the CarayCaray Basin on Biliran Island, located in Eastern Visayas, the Philippines. Additionally, a flood risk classification was applied to evaluate the degree of risk associated with the predicted water levels. The predicted water levels for each model were compared with the observed water level data. The evaluation of the predictive performance of each model resulted in an NRMSE value of 9.48. Moreover, the accuracy of the DNN model was found to be the best among the flood water level prediction models. To implement the flood risk classification, we utilized extreme gradient boosting, random forest, and decision tree models. The application of these models resulted in an F1-score of 0.92 for the extreme gradient boost model, which exhibited the highest accuracy. With an increasing need for disaster (flood) management, AI-based predictive models are anticipated to reduce the damage caused by natural disasters and enhance disaster mitigation systems. Real-time collection of rainfall and water level data enables continuous learning. Furthermore, if a clear flood warning based on learned flood level patterns is issued, preemptive measures can be taken before intense flood damage occurs.

Effects of Organic Amendments on Soil Pore Structure under Waterlogging Stress

Organic amendments are a proven method of reducing soil erosion. However, the effect of organic additives on the pore properties of soils waterlogged by extreme rainfall has been minimally investigated. In this study, we collected rainfall data, established a field experiment consisting of randomized groups, and imaged the pore structure of waterlogged soil treated with different organic amendments (9 t ha−1 of maize straw [MS], 2.37 t ha−1 of cattle manure [OF], a mixture of 9 t ha−1 of MS and 1.89 t ha−1 of cattle manure [SOF], 7.4 t ha−1 of biochar [BC], 8.1 t ha−1 of woody peat [WP], and 3 L ha−1 of polyacrylamide [PAM]) in three-dimensions by X-ray microtomography and digital image analysis, which further quantified the effects. The results showed that, compared with the control, BC increased the total porosity by 54.28% and the connected porosity by 119.75%, but did not affect the pore shape and size distribution. BC and SOF improved the soil connectivity indexes; e.g., their C/I ratios increased by 177.44% and 149.62%, and the coordination numbers increased by 6.75% and 15.76%, respectively. MS had fewer, but longer and larger, channels and throats. Extreme precipitation events were significantly negatively correlated with all connectivity indicators. This study shows that organic materials can optimize the pore structure of waterlogged soil, with BC being the most resistant to erosion. However, extreme precipitation events can counteract the benefits organic additives have on soil pore structure.

Intercomparison of global reanalysis precipitation for flood risk modelling

Abstract. Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis and in areas of data scarcity. However, the consequence of this for risk estimation has not been fully explored. We investigate the implications of four reanalysis products (ERA-5, CFSR, MERRA-2 and JRA-55) on simulations of historic flood events in five basins in England. These results are compared to a benchmark national gauge-based product (CEH-GEAR1hr). The benchmark demonstrated better accuracy than reanalysis products when compared with observations of water depth and flood extent. All reanalysis products predicted fewer buildings would be inundated by the events than the national dataset. JRA-55 was the worst by a significant margin, underestimating by 40 % compared with 14 %–18 % for the other reanalysis products. CFSR estimated building inundation the most accurately, while ERA-5 demonstrated the lowest error in terms of river stage (29.4 %) and floodplain depth (28.6 %). Accuracy varied geographically, and no product performed best across all basins. Global reanalysis products provide a useful resource for flood modelling where no other data are available, but they should be used with caution due to the underestimation of impacts shown here. Until a more systematic international strategy for the collection of rainfall and flood impact data ensures more complete global coverage for validation, multiple reanalysis products should be used concurrently to capture the range of uncertainties.

Keynote lecture. Landslide Early Warning Systems: Resources or Problems?

Recent estimates suggest that landslides occur in about 17.1% of the landmasses, that about 8.2% of the global population live in landslide prone areas, and that population exposure to landslides is expected to increase. It is threfore not surprising that landslide early warning is gaining attention in the scientific and the technical literature, and among decision makers. Thanks to important scientific and technological advancements, landslide prediction and early warning are now possible, and landslide early warning systems (LEWSs) are becoming valuable resources for risk mitigation. A review of geographical LEWSs examined 26 regional, national and global systems in the 44.5-year period from January 1977 to June 2019. The study relevaled that only five nations, 13 regions, and four metropolitan areas benefited from operational LEWSs, and that large areas where landslide risk to the population is high lack LEWS coverage. The review also revealed that the rate of LEWSs deployment has increased in the recent years, but remains low, and that reniewed efforts are needed to accelerate the deployment of LEWSs. Building on the review, recommendations for the further development and improvement of geographical LEWSs are proposed. The recommendations cover six areas, including design, deployment, and operation of LEWS; collection and analysis of landslide and rainfall data used to design, operate, and validate LEWSs; landslide forecast models and advisories used in LEWSs; LEWSs evaluation and performance assessment; operation and management; and communication and dissemination. LEWSs are complex and multi-faceted systems that require care in their design, implementation and operation. To avoid failures that can lead to loss of credibility and liability consequences, it is critical that the community of scientists and professionals who design, implement and operate LEWSs takes all necessary precautions, guided by rigorous scientific practices.

PEMETAAN DISTRIBUSI CURAH HUJAN RATA-RATA MENGGUNAKAN METODE ISOHYET DI WILAYAH KABUPATEN TANGERANG

The purpose of this study was to obtain information related to the distribution mapping of average rainfall in the Tangerang Regency area. Hydrological analysis is needed in determining the average rainfall in an area, one of which is by using the Isohyet method. The isohyet method is a method of making a connecting line that brings together points of the same rain depth. The research was conducted by analyzing rainfall data in the Tangerang Regency area in the last five years, namely the period 2016 to 2020. The research data were obtained from three observation stations, namely: 1. Budiarto Curug Meteorological Station, 2. Pondok Betung Climatology Station, and 3. Ciputat Region II BMKG Station. This research was conducted in two activities, namely: 1. Rainfall data collection, and 2. Data processing using Minitab and ArcGIS applications. Based on the results of processing rainfall data for the 2016-2020 period at three observation stations, the P-Value was 0.115 and the Kolmogorof Smirnov test value was 0.103 for heading 1, P-Value was 0.115 and the Kolmogorof Smirnov test value was 0.103 for heading 2, and P-Value < 0.010 and the Kolmogorof-Smirnov test value of 0.195 for heading 3. The results of the calculation of rainfall from the three observation posts show that the rainfall in the Tangerang Regency area is normally distributed. The results of the rainfall mapping using the Isohyet method also show that the rainfall in the Tangerang Regency area is evenly distributed.

Climate Change Effects on Noel Puames River Area Rainfall in Kupang District

The Noel Puames River is the main river in the Raknamo reservoir used for various purposes, such as irrigation water needs, raw water, fisheries, and others. In the rainy season, the Noel Puames River has enough large discharges, while, in the dry season, the Noel Puames River has almost no base flow. This study aims to determine the effect of climate change on rainfall characteristics in the Raknamo watershed. The methods used include the collection of rainfall data and watershed maps. The rainfall data were used by the Raknamo and Camplong rainfall stations for 25 years of observation resulting in the characteristics of rainfall decrease from time to time, especially the maximum daily rainfall. Annual rainfall tends to increase at Camplong station, but not at Raknamo station, which has the closest location to the Raknamo watershed. There has been a climate change in the Raknamo watershed as indicated by the Z value, which is not equal to zero (Z ≠ 0) although not significant except in March and September.

Evaluation of factors affecting the quality of citizen science rainfall data in Akaki catchment, Addis Ababa, Ethiopia

Citizen Science can fulfill the quest for high-quality and sufficient environmental data, such as rainfall. However, the factors affecting the quality of rainfall data collected by the citizen scientists are not well understood. In this study, we examined the effect of citizen scientists’ attributes on the quality of rainfall data. For this purpose, Principal Component Analysis (PCA), stepwise regression and Multiple Linear Regressions (MLR) were used. A quality control procedure was developed and applied for daily observed rainfall data collected in the summer rainy season of 2020. Attributes of the citizen scientists' were gathered for those who collected rainfall data in the urban and peri-urban Akaki catchment which is located in the Upper Awash sub-basin, Ethiopia. We found that easy-to-detect errors, which were identified during the initial stage of quality control, formed most of the errors in the rainfall data. The PCA and the stepwise regression results revealed that four dominant attributes (education level, gauge relative location, use of smartphone app, and supervisor’s travel distance) highly affected the rainfall data quality. The MLR model using these four prominent dominant variables performed very well with R2 value of 0.98. The k-fold cross validation result showed that the developed model can be used to predict the relationships between data quality and attributes of citizen scientists with high accuracy. Hence, the PCA technique, stepwise regression and MLR model can provide useful information regarding the influence of citizen scientists’ attributes on rainfall data quality. Therefore, future studies should carefully consider citizen scientists' attributes when engaging and supervising citizen scientists, with a comprehensive data quality control while monitoring rainfall.

Pemodelan Debit dengan Data Curah Hujan dari Rain Gauges dan Data TRMM pada DAS Temef di Pulau Timor - NTT

Rainfall data collection based on the TRMM (Tropical Rainfall Measuring Mission) satellite provides a good alternative in estimating rainfall. TRMM technology can minimize manual rainfall recording errors and increase rainfall accuracy for hydrological analysis. From the results of the statistical relationship analysis, the most accurate results obtained are 9 years of calibration and 1 year of validation, the selected equation is a polynomial with the following equation y = 0.0112 x2 - 0.2564x + 4,1293, so that validation analysis is carried out with the selected equation so that the validation results are obtained against 1 year rainfall data for the RMSE value of 7.022. The NSE value is 0.923, the Correlation Coefficient is 0.94 and the relative error value is 5.48 and the results of the equation for discharge are modeling return periods of discharge 2, 5, 10, 20 and 25 years for discharge observations of 2 years return period; Q = 84.44, m3/s at 5 years return period; Q = 105.95 m3/s, 10 year return period; Q = 120.86 m3/s, 20 years return period; Q = 135.16 m3/s, 25 years return period; Q = 139.69 m3/s. Meanwhile, for the simulation discharge, the return period is 2 years; Q =51,84 m3/s at 5 year return period; Q = 65,35 m3/s, 10 year return period; Q = 74,15 m3/s, 20 years return period; Q = 84,89 m3/s, return period of 25 years; Q = 94,65 m3/s.

Evaluation of Different Interpolation Techniques in GIS for Rainfall Data in Sri Lanka (2013-2018) and Identification of the Validation of the Techniques

Geographic Information Systems (GIS) and modelling are becoming powerful tools in geographical and agricultural research. Most data for environmental variables are collected from point sources. The spatial array of these data may enable a more precise estimation of the value of properties at unsampled sites than simple averaging between sampled points. Therefore, the ArcGIS software for interpolation technique was used to identify those differences in terms of rainfall variations. The main objective of this study is to identify the different interpolation techniques for rainfall data and evaluate the most accurate method for Sri Lanka. This study mainly collected rainfall data for weather stations in Sri Lanka. That data was collected in different categories. In this study, only 23 minor meteorological observatories were used for the accuracy assessment of the interpolation methods. For use, all stations are increasing time and cost, and data collection is very difficult. Therefore, 23 minor meteorological observatories were selected randomly from all over the study area. Five interpolation techniques were used. Researcher mainly used the Cross-Validation Method to identify the most suitable interpolation method for rainfall data in Sri Lanka. The main station’s data helped predict rainfall all over the study area, and deleted locations were used to extract estimated rainfall data. Finally, all estimated rainfall data was compared using their actual rainfall values to determine the accuracy of interpolation methods, and the statistical correlation method was used to determine the appropriate interpolation technique for the rainfall data. All estimated values are extracted from rainfall surfaces using an ArcGIS tool called “Extract Values from Points”. Those values are compared with actual observed rainfall values for these sub-stations for the same period. According to this method, then used the correlation statistical (F test) method to reveal the variation of both data sets. F tests were done using SPSS and F values. According to the correlation analysis, the suitable interpolation surfaces are the Spline and Kriging. Consideration of the other four methods (IDW, Kriging (Ordinary), Spline, and Natural Neighbour) shows some similarities. However, finally, researcher identified that the Spline method shows an R value of 0.99 while others show low values. Therefore, the Spline method was found to have the greatest efficiency and be the best method of all five.
 Keywords: ArcGIS, Inverse distance weighted, Interpolation, Kriging, Spline

Estimation of Intensity Duration Frequency for Ungauged Basin in Lampung Province, Indonesia

Frequency duration intensity (IDF) analysis was conducted to estimate the peak flow rate based on the minimum rainfall data collection station. Rainfall data used is data with high intensity that occurs in a short time from automatic rainfall recording stations. Currently, the availability and distribution of automatic rain recording stations in Lampung Province, Indonesia, are still limited. Therefore, this study aims to use the IDF approach in the ungauged basin area for areas with rainfall data that do not meet the hydrological analysis criteria by interpolating rainfall data from 126 manual rainfall measuring stations in Lampung Province, Indonesia. The research method includes analysis of rainfall intensity using the Mononobe equation at various durations and returns periods. Next, create a rainfall intensity map (isohyet) using ArcGis. Finally, compare the IDF analysis of daily rainfall data at 4 automatic rainfall gauge stations with the estimation results based on the intensity map (isohyet). Based on the results of data analysis, it is known that from the available 126 rainfall climatology stations, there are 113 rainfall climatology stations with complete data for 10 years and 13 rainfall climatology stations with incomplete data for 10 years. In addition, the study results show that 45.24% of the daily rainfall in Lampung province is in the low category, 53.97% is in the medium category, and 0.79% is in the high category. This study indicates that rainfall intensity data from climatological rainfall stations that do not meet the hydrological criteria can be found by interpolating rainfall intensity maps from the nearest rain climatology station that meet the hydrological analysis criteria. The relationship test of the actual rainfall intensity variable at 4 automatic rainfall gauge stations with the rainfall intensity from the map (isohyet) using MAPE showed satisfactory results.

Small Scale Rainwater Harvesting Design for External Usage

Water is a life-sustaining resource for humans and all other creatures. Humans, animals, and plants all need water to survive. A proper rainwater harvesting system (RWH) could produce clean and efficient water resources. This study aims to identify outdoor water consumption in UTHM main campus, to choose the appropriate capacity for RWH tank, and to suggest a rainwater harvesting design for outdoor water consumption in UTHM's main campus. This study involved fieldwork, literature study, collection of rainfall data, simulations using Tangki NAHRIM software, and a semi-structured interview with UTHM's assistant engineer. The results of this study identified a few outdoor water consumption in the UTHM's main campus, namely vehicle cleaning and watering the garden. Based on the rainfall data and the catchment area, the average daily harvested rainwater for a week is 2286.3ℓ. Therefore, a water tank system with capacity of 3000ℓ has been proposed as a solution. The results demonstrated two possible RWH designs, first, a stainless steel tank with a capacity of 3000ℓ, second, two polyethene tanks, each with 1500ℓ in capacity. Simulation results from Tangki NAHRIM showed that the proposed RWH system has high storage efficiency up to 97.86%. This research could serve as a guide for future researchers, the Center of Development and Maintenance UTHM (PPP UTHM), and contractors in designing future small-scale rainwater harvesting systems for outdoor water consumption in the UTHM Main Campus.

Flood resilience through crowdsourced rainfall data collection: Growing engagement faces non-uniform spatial adoption

Crowdsourced Personal Weather Stations (PWSs) adoption has been growing rapidly and provides the potential to fill in hyper-local rainfall observation gaps. However, current adoption patterns exhibit spatial biases that must be understood when using the data for modeling and decision-making. Here, we first examine the PWS rainfall spatial representation at HUC-12 watersheds in twelve metropolitan areas in the U.S. Furthermore, by modeling the PWS adoption using socio-economic and flood-related data at census tract level, the results suggest current adoption patterns exhibit spatial biases toward wealthier neighborhoods and flood-prone regions. The findings provide insights to inform how policies could be made to distribute resources to improve the rainfall data collection efforts in PWS-underrepresented regions. As crowdsourced data are increasingly used for decision-making by policymakers, efforts to close the gap in current non-uniform PWS spatial adoption will allow crowdsourced rainfall data to be better positioned to support decision-makers in their flood resilience efforts.