Rainfall Data Research Articles

Analysis of Canopy Interception Characteristics and Influencing Factors in Typical Artificial Forest in the Loess Plateau Semi-Arid Region

Interception loss (IL) is an important process in the hydrological cycle within semi-arid forest ecosystems, directly affecting the amount of effective rainfall. However, the factors influencing IL during individual rainfall events remain to be quantified. This study collected rainfall, vegetation, and interception data during the 2022 and 2023 growing seasons in a typical black locust forest within the Zhifanggou watershed. It employed the Random Forest Regression (RFR) and back-propagation neural network (BPNN) methods to quantitatively evaluate the contribution rates of various factors to the IL and interception loss percentage (ILP). The IL among the 48 effective rainfall events was 172.05 mm, accounting for 19.54% of the rainfall amount. IL and ILP increased as the distance from the trunk decreased. During all rainfall events, both IL and ILP were significantly negatively correlated with the leaf area index (LAI) and canopy cover (CC); IL is significantly positively correlated with total rainfall (TR) and rainfall intensity (RI), while ILP is significantly negatively correlated with TR, RI, and rainfall duration (RD). The BPNN and RFR results indicated that rainfall, canopy, and tree characteristics contributed 43.06%, 44.79%, and 12.15% to IL, respectively, and 57.27%, 34.09%, and 8.63% to ILP, respectively. TR, CC, and LAI represented the primary influencing factors. Rainfall and canopy characteristics were the main factors affecting IL (ILP). As rainfall event magnitude increases, canopy contributions to IL and ILP decrease. In semi-arid areas, managing forest canopies to control IL helps address water imbalances in ecosystems.

Development and Applicability Assessment of an Hourly Water Level Prediction Model for Agricultural Reservoirs Using Automated Machine Learning

Objectives:This study aims to develop and evaluate hourly water level prediction models for agricultural reservoirs using an automated machine learning (AutoML) approach, specifically, employing TPOT.Methods:The study focuses on the Baekrok and Baekryeon reservoirs using rainfall forecast data from the Korea Meteorological Administration, along with observed rainfall and reservoir water level data. TPOT, which utilizes genetic algorithms to automate the generation of optimal machine learning pipelines, was used to build models. Additionally, Random Forest (RF) was implemented as a benchmark to evaluate TPOT’s applicability. Predictions were generated with lead times of 1, 3, 6, and 12 hours, and model accuracy was evaluated using the Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R2), and root-mean-square error (RMSE).Results and Discussion:The predictions from both TPOT and RF showed high accuracy for shorter lead times, with NSE values exceeding 0.99 for the 1-hour predictions. However, predictive accuracy decreased as lead time increased, likely due to greater uncertainty in rainfall forecast data, particularly for the 12-hour predictions. Despite this trend, TPOT-derived models maintained more stable and accurate performance compared to RF models across all lead times.Conclusion:This study demonstrates the applicability of TPOT-based AutoML techniques in predicting hourly water levels in agricultural reservoirs. Future work should explore strategies to mitigate the decline in accuracy for longer lead times.

Technical note: A guide to using three open-source quality control algorithms for rainfall data from personal weather stations

Abstract. The number of rainfall observations from personal weather stations (PWSs) has increased significantly over the past years; however, there are persistent questions about data quality. In this paper, we reflect on three quality control algorithms (PWSQC, PWS-pyQC, and GSDR-QC) designed for the quality control (QC) of rainfall data. Technical and operational guidelines are provided to help interested users in finding the most appropriate QC to apply for their use case. All three algorithms can be accessed within the OpenSense sandbox where users can run the code. The results show that all three algorithms improve PWS data quality when cross-referenced against a rain radar data product. The considered algorithms have different strengths and weaknesses depending on the PWS and official data availability, making it inadvisable to recommend one over another without carefully considering the specific setting. The authors highlight a need for further objective quantitative benchmarking of QC algorithms. This requires freely available test datasets representing a range of environments, gauge densities, and weather patterns.

Multi Dimensional Relative Vulnerability Assessment of Santhal Pargana, Jharkhand, India

The concept of vulnerability is multifaceted, encompassing a range of socio-economic, environmental, and climatic elements that necessitate comprehensive analysis to fully grasp its implications for affected populations. The study employs a Fuzzy-AHP methodology to conduct a multi-dimensional relative vulnerability (Meteorological, Agricultural and Socio-economic) assessment in Santhal Pargana, located in Jharkhand. Notably, rainfall data from 1900 to 2020 revealed a downward trend. The findings of study indicate that Sahibganj exhibits the lowest vulnerability, while the Godda faces the highest risk. To validate the model's accuracy, we calculated the Area Under Curve (AUC), which yielded a value of 84.3, demonstrating the robustness of our assessment approach.

MODELING FLOOD SUSCEPTIBILITY IN HYDROLOGICAL DATA-SCARCE STUDY AREAS

Hydrological information is essential for estimating the magnitude of floods, thus helping reduce losses and damage. However, when this data is scarce, it challenges flood modeling and risk assessment. In Brazil's west of Rio Grande do Sul, we used the Santa Maria hydrographic basin as a case study to estimate the river flow for different RP based on the statistical distribution of rainfall and river flow data and to simulate flood scenarios using a 2D IBER hydrological model.River flow estimates for 5, 10, 20, 30, and 50-year RP were obtained using the Giandotti equation. The results showed a high correlation between the river flows calculated from rainfall and the fluviometric data, using GEV and GumbelMax distribution. The areas susceptible to flooding are consistent with past flood events, validated with fieldwork and Sentinel imagery. River flows estimated from rainfall data can promote hydrological studies where fluviometric data is absent. Keywords: River Flow Estimation; Generalized Extreme Values; Flood Susceptibility; Rosário Do Sul; Brazil.

Analisis Cara Penanganan Penurunan Kualitas Batubara di Stock ROM Ulak Pandan Sampai Jetty SDJ di PT Bumi Merapi Energy

Coal quality analysis is increasingly important considering the increasing global energy demand. Coal, as the main source of energy, has varying qualities, affecting combustion efficiency and environmental impact. Two commonly used analysis methods are proximate analysis and ultimate analysis, which provide information about the composition of coal. Previous research has shown that chemical analysis affects energy efficiency and environmental impact. This study aims to analyze the quality of coal using both methods and evaluate its characteristics for industrial applications. The benefits of this research include guidance for coal producers and users, as well as contributions to reducing the environmental impact and sustainability of the energy sector. This study uses a descriptive approach with qualitative and quantitative methods, focusing on Seam E coal at PT Bukit Asam Tbk. Primary data includes coal handling, temperature, and quality, while secondary data includes location maps and rainfall data as a reference. This study resulted in the conclusion that 1) Factors that affect the decline in coal quality at mining sites and stockpiles include the mining process, working front conditions, loading, processing in the stockpile, sampling, and handling in the ROM to Jetty SDJ. 2) Efforts made include contamination avoidance, fine coal handling, stacking management, and blending process.

Early warning system for floods at estuarine areas: combining artificial intelligence with process-based models

AbstractFloods are among the most common natural disasters, causing countless losses every year worldwide and demanding urgent measures to mitigate their impacts. This study proposes a novel combination of artificial intelligence and process-based models to construct a flood early warning system (FEWS) for estuarine regions. Using streamflow and rainfall data, a deep learning model with long short-term memory layers was used to forecast the river discharge at the fluvial boundary of an estuary. Afterwards, a hydrodynamic process-based model was used to simulate water levels in the estuary. The river discharge predictors were trained using different forecasting windows varying from 3 h to 36 h to assess the relationship between the time window and accuracy. The insertion of attention layers into the network architecture was evaluated to enhance forecasting capacity. The FEWS was implemented in the Douro River Estuary, a densely urbanised flood-prone area in northern Portugal. The results demonstrated that the Douro Estuary FEWS is reliable for discharges up to 5000 m3/s, with predictions made 36 h in advance. For values higher than this, the uncertainties in the model predictions increased; however, they were still capable of detecting flood occurrences.

The evolution of spatial and temporal distribution of rainfall erosivity in Henan Province, central China

With the advancement of urbanization, there has been a significant reduction in cultivated land, accompanied by soil erosion. Concurrently, the regularity of rainfall in recent years has been erratic, adversely impacting the grain economy and agricultural development in certain regions. Henan Province, spanning the basins of the Yangtze River, the Yellow River, the Huaihe River, and the Haihe River, possesses complex hydrological conditions and serves as a pivotal agricultural zone in China. Therefore, this paper utilizes daily rainfall data collected over 54 years (1969–2022) from 112 rain measuring stations in Henan Province to calculate the rainfall erosivity using the Zhang model and the erosivity model from the first national water survey. Meanwhile, spatial analysis was performed using the Kriging interpolation method in the ArcGIS Geostatistical Wizard, resulting in detailed spatial distribution maps of rainfall and rainfall erosivity. The study also employed Wavelet and Mann–Kendall tests to analyze the abrupt changes, trends and periodicity of rainfall and rainfall erosivity within the target region. The findings indicate that the average rainfall (1969–2022) in Henan province was 718.26 mm, while the average rainfall erosivity (R) was 3213.46 MJ mm/(hm2 h). R values are positively correlated with rainfall intensity and volume, displaying an annual upward trend. Spatially, R values increase gradually from northwest to southeast, closely aligning with topographical variations. Additionally, the analysis revealed a predominant periodic cycle of 54 years in the precipitation patterns. These results offer valuable insights for environmental and agricultural management in other regions of central China.

Spatial and Temporal Patterns of Rainfall Erosivity in Southern Africa in Extreme Wet and Dry Years

Soil erosivity is a key indicator of the effectiveness of precipitation acting on the land’s surface and is mainly controlled by event-scale and seasonal weather and climatic factors but is also influenced by the nature of the land’s surface, including relief and vegetation cover. The aim of this study is to examine spatial and temporal variations in soil erosivity across southern Africa using rainfall data for the period 2000–2023 and a gridded raster spatial modelling approach. The two wettest and driest years in the record (±>1.5 standard deviation of rainfall values) were identified, which were 2000 and 2006, and 2003 and 2019, respectively. Monthly rainfall values in these extreme wet/dry years were then analyzed for four rainfall regions (arid, semiarid, subhumid, humid), identified according to their annual rainfall totals. These data were then used to calculate Precipitation Concentration Index (PCI) values as an expression of rainfall seasonality, and the modified Fournier index (MFI) was used to quantify rainfall erosivity. The results show that there are significant differences in erosivity between the different climate regions based on rainfall seasonality and also their distinctive environmental settings. In turn, these reflect the synoptic climatic conditions in these regions, their different precipitation sources, and rainfall totals. The results of this study show that calculated MFI values at the national scale, which is the approach taken in most previous studies, cannot effectively describe or account for erosivity values that characterize different climatic regions at the sub-national scale. Furthermore, the mismatch between PCI and MFI spatial patterns across the region highlights that, under semiarid, and highly seasonal rainfall regimes, episodic rainfall events interspersed with periods of dryness result in significant variability in erosivity values that are unaccounted for by rainfall totals or seasonality alone. In these environments, flash floods and wind erosion result in regional-scale soil erosion and land degradation, but these processes and outcomes are not clear when considering MFI values alone. Fully evaluating spatial and temporal patterns of erosivity in their climatic and environmental contexts, as developed in this study, has implications for sediment and carbon exports, as well as identifying the major regions in which land degradation is an environmental and agricultural issue.

Estimating the Confidence Intervals for the Coefficients of Variation Delta-Gamma Distributions

A common application of the coefficient of variation (CV), which is the ratio of the population standard deviation to the population mean, is frequently used to assess quality control and economic processes, among others. The fiducial quantity approach, Bayesian confidence intervals (CIs) using the Jeffreys, uniform, or normal-gamma-beta (NGB) priors, and highest posterior density (HPD) intervals using the Jeffreys, uniform, or NGB priors were used to provide estimators for the CI for the ratio of CV of two delta-gamma distributions. An evaluation of their performance in terms of average length and coverage probability was carried out using Monte Carlo simulations. The results of this study indicate that the HPD using the Jeffreys prior and fiducial quantity methods are the best for estimating the CI for the ratio of the CV of two delta-gamma distributions. Rainfall data from Mae Hong Son province in Thailand was used to illustrate their practicability when analyzing real-life processes.

Daily Rainfall Patterns During Storm “Daniel” Based on Different Satellite Data

Extreme rainfall from a long-lived weather system called storm “Daniel” occurred from 4th to 11th September 2023 over the central and eastern Mediterranean, leading to many devastating flood events mainly in central Greece and the western coastal parts of Libya. This study analyzes the daily rainfall amounts over all the affected geographical areas during storm “Daniel” by comparing three different satellite-based rainfall data products. Two of them are strictly related to Meteosat multispectral imagery, while the other one is based on the Global Precipitation Measurement (GPM) satellite mission. The satellite datasets depict extreme daily rainfall (up to 450 mm) for consecutive days in the same areas, with the spatial distribution of such rainfall amounts covering thousands of square kilometers almost during the whole period that the storm lasted. Moreover, the spatial extent of the heavy rainfall patterns was calculated on a daily basis. The convective nature of the rainfall, which was also recorded, characterizes the extremity of this weather system. Finally, the intercomparison of the datasets used highlights the satisfactory efficiency of the examined satellite datasets in capturing similar rainfall amounts in the same areas (daily mean error of 15 mm, mean absolute error of up to 35 mm and correlation coefficient ranging from 0.6 to 0.9 in most of the examined cases). This finding confirms the realistic detection and monitoring of the different satellite-based rainfall products, which should be used for early warning and decision-making regarding potential flood events.

Establishing Climate Change on Rainfall Trend, Variation and Change Point Pattern in Umuahia, Nigeria

This study investigates the prevailing conditions of changing climatic trends and change point dates in Umuahia, Nigeria, using daily annual maximum series (AMS) rainfall data spanning 31 years. The Indian Meteorological Department (IMD) was employed to downscale the time series data into durations ranging from 5 minutes to 24 hours. Mann-Kendall (MK) trend and Sen's Slope Estimator (SSE) tests revealed a statistically significant increasing trend across all durations. The Sen's Slope magnitude ranged from 0.1373 mm/year for 5-minute duration to 0.9045 mm/year for 24-hour duration, corresponding to a variation rate of 9.05 mm/decade for the 24-hour duration. Trend change-point analysis, utilizing Distribution-free CUSUM and Sequential Mann-Kendall tests, identified 2002 as the initial rainfall change-point date. These results provide robust evidence of positive changing climatic conditions for rainfall in Umuahia, indicating that non-stationary Intensity-Duration-Frequency (IDF) modelling may be more appropriate for flood risk management and urban planning in this area.

ANALISIS HIDROLOGI BENDUNG GEBONG KECAMATAN PRINGGARATE KABUPATEN LOMBOK TENGAH

Every weir construction requires appropriate hydrological analysis so that the weir planning can be technically and economically appropriate. Apart from that, in the weir planning process it is necessary to optimize its storage, so that the weir function can run optimally. The aim of this research is to determine the availability of irrigation water needs in the Gebong Weir irrigation area. After calculating and analyzing rainfall data, researchers can draw conclusions regarding the availability and need for water for irrigation at Gebong Dam, that the availability of irrigation water for farmers is still sufficient, the first planting season is February, the available debit is 78,065,054 lt/sec/ha, the season the second planting season in June, the available debit was 3,261,647 lt/sec/ha and the third planting season in October the debit was 7,8343,683 lt/sec/ha, while the water requirement for Gebong irrigation in February was 271.77 lt/sec/ha, The water requirement for the second planting season in June is 272.31 lit/sec/ha and for the third planting season in October it is 272.74 lit/sec/ha

Spatiotemporal changes of landslide susceptibility in response to rainfall and its future prediction — A case study of Sichuan Province, China

In recent decades, global warming has significantly altered both the spatial and temporal distribution of rainfall patterns. This change has heightened the risk of rainfall-induced landslides, which are the most prevalent natural disasters in the mountainous regions of southwestern China. These events pose unpredictable and severe threats to the region, making it essential to forecast future rainfall trends and assess how landslide susceptibility will respond to these changes. Understanding these dynamics is crucial for developing effective strategies to mitigate and adapt to the changing rainfall patterns that influence landslides. This study focuses on Sichuan Province, China, and uses annual cumulative rainfall (ACR) as a key dynamic variable to create landslide susceptibility maps (LSMs). The goal is to explore the evolving relationship between rainfall and landslide susceptibility and use future rainfall projections to predict these risks. To achieve this, a historical landslide geospatial database was compiled across five temporal categories: 2000, 2001–2005, 2006–2010, 2011–2015, and 2016–2020. The extreme learning machine (ELM) was applied to generate LSMs for the years 2000 to2020, while an elasticity framework was used to assess how sensitive landslide susceptibility is to rainfall variations. To project future scenarios, a long short-term memory (LSTM) model was employed to project the ACR for 2030, using monthly rainfall data from 2000 to 2020. This projected ACR was then used to estimate future landslide susceptibility. Results showed a marked increase in high landslide susceptibility areas: 5.6 % by 2005, 0.3 % by 2010, 0.2 % by 2015, and 12.9 % by 2020, all relative to the year 2000. The elasticity analysis revealed that from 2000 to 2020, a 1 % change in rainfall would cause an average 1.35 % change in landslide susceptibility. Looking forward to 2030, the projected rise in ACR is expected to lead to a 2.44 % increase in areas of high landslide susceptibility. Multiple validation techniques were applied to ensure reliability and robustness of these findings.

Cider Terroir: Influence of Regionality on Australian Apple Cider Quality

Understanding how regionality influences the key characteristics of cider will assist the industry to produce a premium and consistent quality product. Three dessert apple varieties were sourced from three (minimum) orchards per region from nine major growing regions across Australia over two seasons. Ciders produced from these apples were analysed for base quality characteristics, including total phenolic content (TPC), pH, total soluble solids (TSS) and titratable acidity (TA). Across both seasons, region had a significant influence on TPC, with the region model explaining approximately 25% of the variation in TPC. TSS and TA were significantly influenced by an interaction between variety and region, with the model accounting for approximately 60% and 75% of the variation, respectively, over both seasons. An interaction between variety and region influenced pH results in the first season, with only mean effects in the second season. A climate model was developed using average rainfall data and growing degree days (GDDs). Differences in climate accounted for most, but not all, of the regional variation observed in cider quality measurements. These results demonstrate that the apple growing region can significantly impact the resulting cider quality. Such findings are critical for supporting cider producers to make informed decisions when sourcing fruit.

Assessing rainfall threshold for shallow landslides triggering: a case study in the Alpes Maritimes region, France

AbstractIn this work, we use a statistical approach for modeling shallow landslide rainfall thresholds (Caine 1980) with a case study for the Alpes-Maritimes region (France). Cumulated rainfall / duration (ED) thresholds are obtained with the CTRL-T algorithm (Melillo and al. 2018) for different non-exceedance probabilities from a landslide and two climatic datasets. This tool allows to automatically define rainfall events that might trigger landslides, ensuring robustness and objectivity in this process. The first climate dataset stores high resolution gridded rainfall data (1km resolution, hourly), which provides rainfall data with high temporal and spatial accuracy. This dataset, coming from radar data, is calibrated with rainfall gauges, ensuring a higher accuracy of the rainfall measurements. It provides the rainfall records directly used in the threshold construction The second dataset contains lower resolution gridded rainfall, snow, temperature, and evapotranspiration data (8km resolution, daily); it enables to assess the region’s climate through parameters imported in CTRL-T. The thresholds are then validated using a method designed by Gariano and et al. (2015). Several improvements are made to the initial method. First, evapotranspiration values approximated in the process are replaced by values from the second climate dataset, the result accounting best for the regional climate. Then, computing duration values used for isolating events and sub-events for each mesh point allows to consider the heterogeneity of the Alpes-Maritimes climate. Rainfall thresholds are eventually obtained, successively from a set of probable conditions (MRC) and a set of highly probable conditions (MPRC). The validation process strengthens the analysis as well as enables to identify best performing thresholds. This work represents novel scientific progress towards landslide reliable warning systems by (a) making a case study of empirical rainfall thresholds for Alpes-Maritimes, (b) using high-resolution rainfall data and (c) adapting the method to climatically heterogeneous zones.

Evaluating the spatiotemporal patterns of drought characteristics in a semi‐arid region of Limpopo Province, South Africa

Drought is a complex phenomenon resulting from below-average rainfall and is characterized by frequency, duration, and severity, occurring at a regional scale with dire consequences, especially in semiarid environments. This study used the Reconnaissance Drought Index (RDI) to assess drought severity in two district municipalities in Limpopo Province. Rainfall and air temperature data from 12 stations covering 1970–2020 were obtained from the Agricultural Research Council. The calculation of RDI relies on the monthly accumulation ratio of total rainfall to potential evapotranspiration (PET). For this study, PET was estimated using the Hargreaves and Samani temperature-based approach. The RDI results showed a high spatial–temporal variation in drought characteristics over the study area. All stations experienced extreme drought conditions in different years, with the maximum drought severity (-3.40) occurring from 2002–2003 in the western parts of the study area, indicating extreme drought. Furthermore, the results revealed continuous drought conditions over various periods, including severe droughts between 1995 and 1998 and between 2014 and 2016, with the severity varying between mild and moderate drought conditions. The results reveal notable but nonuniform drought patterns as the climate evolves, with potential implications for water availability and livelihoods. The study's findings underscore the significance of adopting multidimensional approaches to drought assessment that encompass meteorological and hydrological factors to inform strategies for adaptive water management and policy formulation in the face of a changing climate.

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Effects of Different Land Uses and Slope on Runoff and Soil Loss on the Loess Plateau of China

ABSTRACTThe Loess Plateau has one of the most serious areas of soil erosion in China; therefore, studying the effect of soil and water conservation measures on the erosion of loess soil slopes is important for land management and agricultural development. This study is based on 5 years of field runoff observations and rainfall data from 2015 to 2019. Correlation and regression analyses were used to study the runoff and sediment production of loess soil and its relationship with rainfall, slope and land use. The results show that > 80% of the erosive rainfall occurs mainly in July to August in the area. Runoff and soil loss from the plots differed substantially, depending on land use, soil conservation measures and slope degree. Bare plots experienced the highest soil loss rate of over 27 t/ha with higher runoff depth, followed by cultivated plots (4.55 t/ha), grass plots (0.43 t/ha), shrub plots (0.35 t/ha) and forest plots (0.13 t/ha). Among the four soil and water conservation measures, the runoff and sediment reduction benefits of forest and shrub plots were the highest. The runoff reduction benefit fluctuated at approximately 0.8, and the benefit of sediment reduction was ≈1. The overall performance was forest > shrubland > grassland > cultivated land > bare land, and the benefits of sediment reduction were greater than those of runoff reduction. Forests had the highest soil retention capacity on slopes from 5° to 25°, and forests on gentle slopes had the best water storage capacity. Shrub plots had the best water storage capacity when they were on steep slopes. Rainfall erosion forces were significantly and positively correlated (p < 0.01) with runoff depth and soil loss, making it the most significant rainfall indicator of sediment production. Under the same rainfall conditions, soil and water conservation measures are still required on bare slopes, land use is the main control factor for slope sediment production, and appropriate land use can greatly reduce the threat of slopes in terms of soil erosion. This study shows that soil and water conservation measures are imperative to prevent runoff and soil loss, especially for bare land without any measures; on steep slopes, a vegetation combination primarily featuring shrubs is expected to achieve greater benefits in reducing runoff and sediment. This study can provide a scientific basis for the management of vegetation measures and the implementation of soil and water conservation measures on loess soil slopes.

Adaptation climate change through application of green infrastructure household scale rainwater harvesting in tropical coastal areas based fuzzy logic

Freshwater problems in coastal areas include the process of salt intrusion which occurs due to decreasing groundwater levels below sea level which can cause an increase in salt levels in groundwater so that the water cannot be used for water purposes, human consumption and agricultural needs. The main objective of this research is to implementation of RWH to fulfill clean water needs in tropical coastal area in Tanah Merah Village, Indragiri Hilir Regency, with the aim of providing clean water to coastal communities. The approach method used based on fuzzy logic (FL). The model input data includes the effective area of the house’s roof, annual rainfall, roof runoff coefficient, and water consumption based on the number of families. The BWS III Sumatera provided the rainfall data for this research, which was collected from the Keritang rainfall monitoring station during 2015 and 2021. The research findings show that FL based on household scale RWH technology is used to supply clean water in tropical coastal areas that the largest rainwater contribution for the 144 m2 house type for the number of residents in a house of four people with a tank capacity of 29 m2 is 99.45%.