Rainfall Values Research Articles

GIS-based slope-adjusted curve number methods for runoff estimation

Accurate estimation of surface runoff and determination of susceptible lands to runoff generation in ungauged watersheds were the problems for hydrologic engineering which could be predicted through a simple model such as Soil Conservation Service Curve Number (SCS-CN). Due to the slope effects on this method, slope adjustment for curve number was developed to improve its precision. So, the main objectives of this study were to apply GIS-based slope SCS-CN approaches for surface runoff estimation and compare the accuracy of three slope-adjusted models including: (a) model with three empirical parameters, (b) model with two parameters slope function, and (c) model with one parameter in the region located in the central part of Iran. For this purpose, soil texture, hydrologic soil group, land use, slope, and daily rainfall volume maps were utilized. In order to provide the curve number map of the study area, land use and hydrologic soil group layers built in Arc-GIS were intersected and the curve number was determined. Then, three slope adjustment equations were used to modify curve numbers of AMC-II by employing slope map. Finally, recorded runoff data of the hydrometric station were applied to assess the performance of the models through four statistical indicators of the root mean square error (RMSE), the Nash-Sutcliffe efficiency (E), the coefficient of determination [Formula: see text], and percent bias (PB). Land use map analysis showed that rangeland was the dominant land use, whereas the soil texture map specified the greatest and smallest area belonging to loam and sandy loam textures, respectively. Although the runoff results showed the overestimation of large rainfall values and underestimation for rainfall with less than 40mm volume in both models, the values of E (0.78), RMSE (2), PB (16), and [Formula: see text] (0.88) revealed that eq. (a) with three empirical parameters was the most accurate equation. The maximum percent of runoff generated by rainfall for eqs. (a), (b), and (c) were 68.43, 67.28, and 51.57% which showed that bareland located in south part with the slope of more than 5% was susceptible to runoff generation and should be paid attention to watershed management.

Daily Precipitation and Temperature Extremes in Southern Italy (Calabria Region)

We apply extreme value theory (EVT) to study the daily precipitation and temperature extremes in the Calabria region (southern Italy) mainly considering a long-term observational dataset (1990–2020) and also investigating the possible use of the ERA5 (ECMWF Reanalysis v5) fields. The efficiency of the EVT applied on the available observational dataset is first assessed—both through a punctual statistical analysis and return-level maps. Two different EVT methods are adopted, namely the peak-over-threshold (POT) approach for the precipitation and the block-maxima (BM) approach for the temperature. The proposed methodologies appear to be suitable for describing daily extremes both in quantitative terms, considering the punctual analysis in specific points, and in terms of the most affected areas by extreme values, considering the return-level maps. Conversely, the analysis conducted using the reanalysis fields for the same time period highlights the limitations of using these fields for a correct quantitative reconstruction of the extremes while showing a certain consistency regarding the areas most affected by extreme events. By applying the methodology on the observed dataset but focusing on return periods of 50 and 100 years, an increasing trend of daily extreme rainfall and temperature over the whole region emerges, with specific areas more affected by these events; in particular, rainfall values up to 500 mm/day are predicted in the southeastern part of Calabria for the 50-year-return period, and maximum daily temperatures up to 40 °C are expected in the next 100 years, mainly in the western and southern parts of the region. These results offer a useful perspective for evaluating the exacerbation of future extreme weather events possibly linked to climate change effects.

Is the Ollerenshaw fasciolosis forecasting model fit for the 21st century?

The Ollerenshaw forecasting model is based on rainfall and evapotranspiration and has been in use to predict losses from fasciolosis since 1959. We evaluated the performance of the model against observed data. Weather data were used to calculate, map and plot fasciolosis risk values for each year from 1950 to 2019. We then compared the model's predictions with recorded acute fasciolosis losses in sheep from 2010 to 2019 and calculated the sensitivity and specificity of the model. The forecast risk has varied over time but has not markedly increased over the past 70 years. The model correctly forecasted the highest and lowest incidence years at both the regional and national (Great Britain) levels. However, the sensitivity of the model for predicting fasciolosis losses was poor. Modification to include the full May and October rainfall and evapotranspiration values made only a small improvement. Reported acute fasciolosis losses are subject to bias and error due to unreported cases and variations in region size and livestock numbers. The Ollerenshaw forecasting model, in either its original or modified forms, is insufficiently sensitive to be relied upon as a standalone early warning system for farmers.

Hydrogeological characteristics of waterlogged areas in Natore District, Bangladesh

Waterlogging (WL) refers to the process by which water flow is resisted in vertical and horizontal directions and thus water stagnates for a short or long span of time; it is induced by a combination of human and natural factors. In the southwestern region of Bangladesh, including Natore District, WL is a significant issue that needs to be addressed if agricultural activity is to be successful. This study aimed to identify surface WL in Natore District and to characterise the WL scenario in the study area in terms of hydrogeology. Waterlogged areas were identified with a geographic information system using satellite images corresponding to the premonsoon and postmonsoon periods. Using groundwater level data (1990–2017), the pre- and postmonsoon scenarios of the waterlogged areas were indicated by seasonal and perennial types of WL. Groundwater recharge scenarios were classified as long and short lag times. Most of the study area was characterised by thick clay or silty clay surficial layers with low water penetration rates, resulting from low porosity and low hydraulic conductivity. The cross-correlation between rainfall and groundwater level revealed the response of groundwater to rainfall, with a lag time of 1–5 months. Long lag time areas exhibited slow groundwater recharge and significant groundwater level fluctuation, with lower hydraulic conductivity values of 49.37–76.24 m/day. In contrast, short lag time areas displayed rapid groundwater recharge and small groundwater fluctuation due to a good proportional relationship with rainfall and higher hydraulic conductivity values of 74.74–117.79 m/day.

On the relation between rain, clouds, and cosmic rays

ABSTRACT We investigate the correlation between precipitation, clouds and cosmic rays based on field observations to develop a new short-term forecasting tool for their extremely high values. The results show that the cosmic ray intensity appears to be highly correlated with the one-year time-lagged rainfall amount with the cosmic rays leading. A plausible explanation is given for this, based on the modulation of cosmic rays by the solar cycle and an earlier finding of a strong correlation between the stratospheric - lower mesospheric temperature fluctuations and the sunspot number cycle with a time lag of one year (sun leading). This suggests that cosmic rays are not the trigger, but rather signals sunspot activity that with a delay of one year may affect cloud formation and therefore rainfall. In addition, high rates of rainfall exhibit a power-law behavior, such as the Gutenberg-Richter law characterizing the large area and perimeter size of rainfall and clouds, which have been proposed recently. Finally, the new tool for the nowcasting the extremely high cosmic ray intensity values can be used for nowcasting the extremely high rainfall values. Their combination can be used to study the modulation of cosmic ray properties by climate change parameters.

Effect of foliar treatment with chitosan on phenolic composition of ῾Fetească neagră’ grapes and wines

The objective of this study was to determine the influence of chitosan treatments, applied in vineyard for plan protection, on the phenolic quality of grapes. The study was conducted for two consecutive years (2020-2021) in the wine center of Murfatlar, Romania, on ‘Fetească neagră’, an ancient but well-known indigenous Romanian grape variety for red wines. Chitosan is meant to offer an alternative for the classic treatment with Bordeaux mixture (BM), which represented the control variant. A combination treatment with half dose of chitosan and half dose of BM was also applied and evaluated. Chemical composition at harvest time was evaluated by measuring the content of sugar, total acidity, pH, total anthocyanin potential (ApH1), anthocyanins extractable at wine pH (ApH3.2), total polyphenol index, skin tannins, seed tannins and seeds maturity. In wines the colour parameters were determined by spectrophotometry and individual phenolic compounds by UHPLC-HRMS. In grapes, accumulation of anthocyanins increased with the total dose of chitosan applied in the vineyard, with higher values in 2020 when temperatures were higher and the rainfall values were typical for the region. Accumulation of tannins in grape skins followed a similar trend. In wine, chitosan determined a significant 72% increase in colour intensity in 2020. Among the individual phenols gallic acid was predominant, with higher values in the rainier year (2021) and significant increases determined by chitosan treatment (especially in 2021 when it increased by 97% as compared to BM treatment). Catechin and epicatechin recorded important increases in the less favourable year (2021), with confirmed increases in both years elicited by the chitosan. Quercetin and myricetin were not influenced by the chitosan treatment, but their increase was correlated with higher temperatures and inversely correlated to the amount of rainfall. Trans-resveratrol ranged between 4.3-8.0 mg L-1 in 2020 and 5.0-6.5 mg L-1 in 2021, with an important increase determined by the chitosan treatment in 2020 (89% increase compared to BM treatment).

Pioneer Use of Pseudo Sub-Daily Timestep Model for Rainwater Harvesting Analysis: Acceptance over Hourly Model and Exploring Accuracy of Different Operating Algorithms

This study investigates the effectiveness of a pseudo sub-daily timestep model, which uses 6-hourly variable demands and daily rainfall values split into four equal 6-hourly portions. To assess the achievements through such sub-daily model, simulations were conducted using 6-hourly YBS (yield before storage), 6-hourly YAS (yield after storage), daily YBS and daily YAS models using rainfall data from a station near Melbourne (Australia) city under different input conditions. Results from the developed models were compared with the results of an earlier developed hourly timestep model, which considered hourly rainfall data and hourly variable rainwater demands. From the results, it is found that the results of YAS models are more accurate compared to the results of YBS models. Considering only potential water savings, daily YAS model results are very similar to the 6-hourly YAS model results.

Updating the intensity-duration-frequency curves in major Canadian cities under changing climate using CMIP5 and CMIP6 model projections

Characteristics of extreme rainfall events are often presented using the Intensity-Duration-Frequency (IDF) curves. As Canada gets warmer, the credibility of using historical IDF curves for flood risk analysis and design of various urban infrastructure becomes questionable. This study aims at updating and intercomparing the IDF curves in 30 Canadian cities using the simulations of Climate Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathways 2–4.5 and 5–8.5 and their predecessor CMIP5 under Representative Concentration Pathways 4.5 and 8.5. Quantile-Quantile Downscaling is utilized to estimate extreme rainfall values at fine spatiotemporal scales. Overall, more frequent storms with an average increase of around 30%-40% in their intensity are projected in most cities based on both CMIP5 and CMIP6 models during 2071–2100. For instance, the fast-growing cities of Vancouver, Toronto, and Montreal could expect around 4–11 times more frequent 5-min storms with a 100-year return period during 2071–2100. Moreover, although IDF curves based on CMIP5 and CMIP6 individual models diverge, the expected IDF curves based on their multi-model ensemble mean are somewhat similar if models with a high climate sensitivity based on CMIP6 are excluded from the analysis. This study underlines the need to update IDF curves in Canadian cities.

Rainfall data adjustment to Volta Redonda macro-region

Most Brazilian cities do not have a proper water and soil management system due to lack of planning in this sector and of an easy-consultation local-hydrological bibliography capable of reinforcing the future creation of a hydrological modeling. Volta Redonda macro-region's case can be added to this statistics, since it accounts for several natural hazards linked to floods and inundations in some specific locations. The aims of the present study are to descriptively analyze monthly rainfall data generated by CEMADEN's rainfall gauges and to assess whether monthly rainfall in the region adapts well to probability Gamma Distribution plots. Rainfall data from four points in Volta Redonda, Barra Mansa and Pinheiral cities, recorded for 48 months, and provided by CEMADEN, were used in the study. Gamma Distribution was adjusted to monthly rainfall data. In conclusion, the probability of having a given monthly rainfall value lower than, or equal to, that recorded for the Pinheiral – Volta Redonda – Barra Mansa conurbation area can be estimated through Gamma Distribution. Values recorded for shape (γ) and scale (β) parameters of Volta Redonda macro-region's municipalities were 1.59 and 79.30, on average, respectively.

Influence of precipitation change and topography characteristics on the development of farmland gully in the black soil region of northeast China

Gully erosion in cultivated land has seriously damaged land resources and reduced crop production. The long-term changes of gully erosion have not been well understood because of limited continuous observed data on the gully development process, which further causes difficulty in establishing universal models of gully erosion rates. Hillslope gullies and valley floor gullies were continuously monitored by RTK GPS and Unmanned Aerial Vehicle (UAV) from 2002 to 2021 in the Heshan farm of Songnen black soil region of northern China. UAV-generated DEMs, historical remote sensing images, and daily precipitation data were combined to analyze the influence of precipitation changes and topographic differences on the development process of gully erosion. The results showed that gully erosion had strong variability between years due to the difference in annual precipitation. The volumetric increment in high precipitation years was 6.5 times and 16.5 times that in normal precipitation years and low precipitation years, respectively, and the variability of valley floor gullies was greater than that of hillslope gullies. The annual average headcut retreat of valley floor gullies and hillslope gullies was 13.1 m y-1 and 4.2 m y-1, respectively, but the volumetric soil loss was mainly contributed by gully bottom deepening and gully sidewall collapsing. The ratio of volumetric soil loss from the gully head to total volumetric soil loss was the smallest in high precipitation years, followed by normal precipitation years and low precipitation years because more runoff in high precipitation years enhanced erosion kinetic energy, which accelerated gully bottom deepening and gully sidewall collapsing. Topography and rainfall were the important influencing factors for the continuous gully development. The maximum value of 3-day accumulative rainfall best explained valley floor gully erosion, and the accumulative value of erosive rainfall best accounted for hillslope gully erosion. A statistical model for estimating volumetric soil loss rate (△V) was developed that included as independent variables drainage area (A) and rainfall characteristic index (RI): △V = 1.571*10-7*（A*RI）1.983 (R2 = 0.98).

Emigration and survival correlate with different precipitation metrics throughout a grassland songbird's annual cycle

AbstractMany exogenous factors may influence demographic rates (i.e., births, deaths, immigration, emigration), particularly for migratory birds that must cope with variable weather and habitat throughout their range and annual cycle. In midcontinental grasslands, disturbance (e.g., fire and grazing) and precipitation influence variation in grassland structure and function, but we know little about when and why precipitation is associated with grassland species' vital rates. We related estimates of detection, survival, and emigration to a priori sets of precipitation metrics to test the putative alternative factors influencing movement and mortality in grasshopper sparrows (Ammodramus savannarum). This species is a migratory songbird that exhibits exceptionally high rates of within‐season and between‐season dispersal. Between 2013 and 2020, we captured and resighted grasshopper sparrows in northeastern Kansas, USA, compiling capture histories for 1,332 adult males. We tested predictions of climatic hypotheses explaining variation in survival and emigration throughout a grasshopper sparrow's annual cycle; both survival and emigration were associated with the El Niño‐Southern Oscillation precipitation index (ESPI). Survival was positively related with ESPI during winter, and temporary emigration was curvilinearly related to breeding season ESPI lagged 2 years, with the highest site fidelity associated with intermediate rainfall values. The relationship between rainfall and temporary emigration likely reflects the influence of weather over multiple years on vegetation structure with consequent effects on local demography. This study provides compelling support for the idea that grassland species respond to high interannual variability by adopting dispersal strategies unlike those of many well‐studied migrant birds. Furthermore, the results imply that the consequences of increasing climatic extremes may not be immediately apparent, with demographic consequences lasting for at least a few years.

IoT based real-time monitoring system of rainfall and water level for flood prediction using LSTM Network

Floods in recent years have frequently resulted in environmental, economic, as well as loss of human life. People are less aware of incoming floods if there is no early warning system. This proposal outlines the design of a monitoring system to obtain real-time data on rain gauge and water level. The monitoring system is based on IoT via a GSM network to provide real-time data cloud and dashboard display on Grafana platform. The rainfall forecasting model used Long Short-Term Memory (LSTM) networks to predict future rainfall and water level values which could cause floods. The result was experimented with using historical data since the current data of the monitoring system is insufficient yet to make an accurate prediction. The main findings of the research are the predicted values of streamflow and rainfall for historical data, also water level and rain gauge for new data. The primary result was experimented with using historical data on two rainfall stations and one streamflow. Also, the primary result was experimented with using new data on two water level stations and one rainfall. The forecasting method that applied LSTM showed high accuracy of the result reaching more than 90%. Based on these results, the system can be used as a non-structural solution to alleviate the damage caused by urban floods.

Kajian hidrologi dan hidrogeologi daerah penambangan, studi kasus lipatan sinklin di Sungai Mahakam, Kutai Kartanegara , Provinsi Kalimantan Timur

Changes in land use due to open pit mining operations, indirectly affect the hydrological cycle. Open pit mining operations, especially the open pit method, greatly affect subsurface conditions, such as lithology and hydrogeology. The purpose of this study was to analyze the hydrological and hydrogeological conditions before mining on syncline folds in the Mahakam river. This research is an inductive research with an analytical approach which includes the study of land use, hydrological conditions, geology, and hydrogeology. The land use of the research area is dominated by plantations (84%) with a rain catchment area of ​​995.9 hectares. The results of the hydrological analysis yielded daily rainfall values ​​with a 5-year recurrence of 208.4 mm, annual rainfall of 2.177.32 mm, runoff 238.63 mm/yr, evapotranspiration 1,031.16 mm/yr and groundwater recharge 907.53 mm/th. The lithology of the study area is dominated by sandstone and clayey siltstone. The results of the aquifer test (pumping test) showed that the average K value (hydraulic conductivity) of sandstone was 1.33 × 10-2 m/s and claystone was 6.77 × 10-10 m/s. The results of the hydrogeological analysis show that the top layer is an aquitard, below it is an aquifer, then an aquitard, and the bottom is an aquiclude. The type of aquifer in the study area is classified as semi confined aquifer

Water Harvesting in the Garmian Region (Kurdistan, Iraq) Using GIS and Remote Sensing

Rainwater harvesting (RWH) is one of the essential alternative sources of water that may be used. However, finding potential RWH locations on a broad scale has always been challenging, requiring further research. The effectiveness of RWH structures depends on their design and the selection of suitable sites. This study aimed to select potential sites for rainwater harvesting in the Garmian region of Kurdistan-Iraq, using GIS and Remote Sensing (RS) techniques. Two multicriteria analysis (MCA) techniques: the analytical hierarchal process (AHP) and Fuzzy analytical hierarchal process (FAHP) were used for comparison purposes to identify potential RWH sites. A suitability map of potential RWH systems was then generated following the weighted linear combination (WLC) and the Fuzzy Gamma methods. The outcomes showed that, unlike most recent studies, which relied on the minimum and maximum local rainfall values, a minimum agreed-upon rainfall depth should be used as a benchmark when classifying rainfall to obtain realistic results. The findings also showed that 9.7%, 43.4%, 33.6%, and 13.3% of the sites have very high, high, moderate, and low suitability for rainwater harvesting, respectively, depending on the suitability map produced by the WLC method. However, the outcome of the Fuzzy Gamma technique appeared more realistic and revealed that 10.6%, 15.6%, 26.6%, 21.9%, and 25.3% of the sites have very high, high, moderate, low, and very low appropriateness for rainwater harvesting, respectively. The methods adopted in this study can be helpful for researchers in the field of RWH to achieve reliable results. Also, this study’s findings can assist in well-organized water resource management planning to ensure sustainable water development in the Garmian region.

Monthly to seasonal rainfall erosivity over Italy: Current assessment by empirical model and future projections by EURO-CORDEX ensemble

The estimation of rainfall erosivity in the Revised Universal Soil Loss Equation (RUSLE) requires long series of sub-hourly rainfall observations. Due to the endemic unavailability of this information, at least with the required degree of detail and coverage, empirical models are frequently used for the estimation of rainfall erosivity basing on easily retrievable rainfall values at the monthly or annual scale. In this paper, an empirical model is calibrated for Italy to bridge the annual to monthly scale gap by means of 10-year series of monthly rainfall observations at 171 Italian rain gauges, and then applied using the ERA5-Land gridded rainfall dataset to obtain maps of monthly and seasonal rainfall erosivity covering the reference period 1981–2010. Successively, 29 EURO-CORDEX bias-corrected rainfall projections for two future horizons (2021–2050 and 2051–2080 under RCP 2.6, 4.5 and 8.5) are used to obtain expected anomalies of seasonal rainfall erosivity over Italy with respect to the reference period. Statistical and visual analysis of results shows that positive percentage anomalies are expected in large portions of the country, especially under RCP 2.6 but with significant increases expected also under RCPs 4.5 and 8.5, especially in autumn and, secondarily, in summer. This intra-annual rainfall erosivity anomaly pattern entails the existence of increasing trends in the erosion hazard that cannot be captured by empirical models only relying on annual rainfall. The position of those results in the related literature is deeply discussed, providing significant insights on uncertainty sources and possible future research developments.

Developing a prototype centre using agricultural smart sensors to promote agrarian production with technology

This article presents the development of a model center using agricultural intelligent center technology. The goal of this research is 1. To develop a wireless sensor network. 2. To be a source of learning on the use of sensor technology in agriculture. For local and nearby farmers Using the Sufficiency Economy Learning Center, according to King's Science. The Rajamangala University of Technology Suvarnabhumi is a research area. With the problems faced in farming today. It found that the world's climate change whether it's drought. Rains leave ranges and toxic airborne particulate matter caused by farming to match current problem conditions. The researchers then designed a two-part system: 1. Node Moisture Sensor that measures soil moisture and commands the opening – It also controls on-off with a manual switch. Wind speed and wind direction sensors, light intensity sensors, temperature, and humidity sensors, and Particulate Matters Sensor 1.0, 2.5, 10 with environmental reports within the growing area via Wi-F signals to (Sever) Raspberry Pi record real-time data. Every 30 seconds According to research, node moisture sensors can measure soil moisture and record results, and the station measures the environment within the growing area via a Wi-F signal to (Sever) Raspberry Pi. Rainfall values measured by local rainfall sensors measuring up to 35.3 mm are within the threshold of heavy rain. The maximum wind speed measured is 8.5 km/h, the maximum temperature of 35.8 degrees Celsius, and the maximum humidity of 99.9 percent, the light intensity is up to 58,002 Lux, and the Final Particles, with pm 1.0 up to 40.1 microns, PM 2.5 up to 51.3 microns and PM 10 up to 63.5 microns. Apply agriculture to 50 interested farmers after receiving knowledge transfer of smart sensor technology. The expansion has resulted in 3 farmers and will continue to expand in the future. Promote the use of agricultural technology. Intensifying communities and supporting global climate change

Hydrologic evaluation of the global precipitation measurement mission over the U.S.: Flood peak discharge and duration

In recent years, a great amount of research has been done towards evaluating precipitation data generated by satellites, but less has focused on how these estimates and their uncertainties manifest further into the water cycle. In this study, ten years of satellite-based and ground-based radar data are used as forcings for a distributed hydrologic model across the Continental United States. They are compared using a methodology designed to assess the flood signals and characteristics generated by the model. By focusing on how well the model reproduces flood characteristics rather than fits traditional bulk statistics, this research provides robust insights into satellite precipitation deficiencies. It is found that satellite data has greater success at resolving lower magnitude flood events while tending to generate floods of longer durations. Additionally, flood managers should note that satellites tend to generate floods that characteristically both begin earlier and end later than the ground radar reference. Subsequent research is recommended into other satellite data products in order to better understand these discrepancies and mitigate or plan for them in the future. Plain Language SummaryIn recent years, satellites have been increasingly used to provide valuable insights on rainfall across the globe, especially in locations where radars are unable to be installed on the ground. By nature, however, the rainfall data provided by satellites has uncertainties. Large amounts of research has gone into the difference in accuracy between satellite radar and ground references, but less has focused on how these differences impact flood simulations, respectively. In this study it was found that the rainfall data from satellites tends to predict floods that are more severe (have higher magnitudes), last longer (have longer durations), and have different timings (start earlier and end later) than the reference. It is also found that satellites struggle with predictions involving extreme rainfall values. Further research is suggested to better understand these issues, as well as compare against other forms of satellite rainfall data.

Estimation of Extreme Daily Rainfall Probabilities: A Case Study in Kyushu Region, Japan

Extreme rainfall causes floods and landslides, and so damages humans and socioeconomics; for instance, floods and landslides have been triggered by repeated torrential precipitation and have caused severe damage in the Kyushu region, Japan. Therefore, evaluating extreme rainfall in Kyushu is necessary to provide basic information for measures of rainfall-induced disasters. In this study, we estimated the probability of daily rainfall in Kyushu. The annual maximum values for daily rainfall at 23 long-record stations were normalized using return values at each station, corresponding to 2 and 10 years, and were combined by the station-year method. Additionally, the return period (RP) was calculated by fitting them to the generalized extreme value distribution. Based on the relationship between the normalized values of annual maximum daily rainfall and the RP, we obtained a regression equation to accurately estimate the RP up to 300 years by using data at given stations, considering outliers. In addition, we verified this equation using data from short-record stations where extreme rainfall events triggering floods and landslides were observed, and thereby elucidated that our method was consistent with previous techniques. Thus, this study develops strategies of measures for floods and landslides.

Rainfall Mapping in Medan Selayang Sub-District using Geographic Information System

Indonesia is a tropical country which has two seasons, namely the rainy season and the dry season. Rainfall that occurs will vary in each area depending on the altitude of the area, climate, season, and other factors. Medan Selayang Subdistrict is the Subdistrict with the closest distance to the rain station. Where the closer the location of the study area to the rain station, the more accurate the rainfall data obtained will be. One way to make it easier to record and process data is to create a rainfall map by utilizing spatial analysis through a Geographic Information System (GIS). The data in this study are rainfall data and geographic coordinate data obtained from the Meteorology, Climatology and Geophysics Agency in the Medan City area. After data collection, data processing was carried out using the QGIS 3.10 application. with the Inverse Distance Weighted (IDW) interpolation method. From the results of processing rainfall maps in the Medan Selayang sub-district using Inverse Distance Weighted (IDW) interpolation, the rainfall values (rainfall) ranged from 3029.74 mm/year – 3164.20 mm/year.

Risk evaluation and mitigation against flood danger in an arid environment. A case study (El Bayadh region, Algeria).

Floods are among the most serious and devastating phenomena of natural disasters. Cities adjacent to flood-prone areas in the last decades have played a major role in increasing the potential adverse effects of flood damage. This research study aims to evaluate and mitigate the risks of flood events in the El Bayadh region, which suffers from poor infrastructure and drained networks. To achieve this, it is necessary to evaluate rainfall intensities and their limits for durations from 0.167 to 24h with return periods from 2 to 1000years. Eight different frequency analysis distributions were fit to the historical rainfall data series over 43years (1970-2012) using hypothesis-based goodness tests and information-based criteria. The most appropriate distributions were used to develop the rainfall intensity-duration-frequency (IDF) and flood risk-duration-frequency (RDF) curves for the study area. The results show that high-intensity rainfall values last for short durations, while high flood risk values last for intermediate durations. The results of the flood RDF curves can provide useful information for policy makers to make the right decisions regarding the effectiveness of the region's protection structures against future flood risks.