Daily Rainfall Data Research Articles

Analysis of Extreme Daily Rainfall in Cotonou, Benin (West Africa)

Global warming has a global impact on the water cycle and in particular on precipitation. This work is devoted to the analysis of the variability of extreme rainfall in the economic capital of Benin. For this purpose, the 58-year daily rainfall data collected at the Meteo Benin synoptic station in Cotonou between 1960 and 2018 are used. The annual daily maxima was established for the analysis. The generalized distribution of extreme values ​​(GEV) is used to estimate the quantiles according to the defined return periods. The daily heavy rainfall had a normal character with return periods of less than six years in Cotonou, between 1960 and 2018. There was a marginal increase in frequency of extreme rainfall since the beginning of 21st century. Extreme rainfall events may lead to floods as seen in 2010. Therefore, pro-active short- and long-term measures are the need of the hour to cope up against rainfall variability under the global warming scenario.

ANALISIS INTENSITAS CURAH HUJAN DAN KURVA IDF (INTENSITY-DURATION-FREQUENCY) METODE MONONOBE DI KOTA SALATIGA

Extreme rainfall in Indonesia has led to natural disasters in many areas including Salatiga City. Many human activities depend on the amount of rainfall that falls on the earth's surface. In the field of civil engineering, research on rainfall is very common because many activities related to civil engineering use rainfall data such as for water resource management, drainage development, dam construction and other building construction. This study aims to determine the amount of rainfall intensity and IDF curves in Salatiga City for return periods of 2 years, 5 years, 10 years, 25 years, 50 years and 100 years. The data used in this study is the maximum daily rainfall data collected from Salatiga Central Bureau of Statistics (BPS). The type of distribution selected in this study is the Log Pearson Type III probability distribution. The results of the analysis show that the highest rainfall intensity occurs at a short duration (5 minutes) in the 100-year return period of 106.66 mm/hour and the results of the IDF curve show that the shorter the rainfall time, the higher the rainfall intensity while the longer the rainfall time, the smaller the rainfall intensity at each return period T.

Rainfall And Drought Characteristics For Crop Planning In Cuttack District Of Odishastate, India

The increasing frequency of agricultural droughts and variability in rainfall patterns over the past three decades prompted this study, which analyzed 34 years of block-wise daily rainfall data (1988-2021) from Cuttack district, Odisha. The aim was to determine the long-term average and temporal variability of rainfall on weekly, monthly, seasonal, and annual scales, with a focus on identifying strategies to mitigate production risks in the region. The average annual rainfall in Cuttack was 1597.6 mm, with August receiving the highest amount of rainfall (476 mm), followed by July (407 mm) during the monsoon season. A trend analysis on rainfall over the past 34 years showed an increasing pattern of an average 6.03 mm and 3.6 mm per year in annual and kharif season rainfall, respectively. Agricultural droughts were frequently observed during the early (22-28 Standard Meteorological Week) and late (36-42 SMW) stages of kharif crops. The study also revealed that the station is prone to mild-moderate meteorological droughts of different intensities, including mild,moderate, and severe. To minimize production risks, short duration, low water-requiring, high-value crops such as maize, pulses, oilseeds, and some vegetables are recommended for this region.

Deriving rainfall IDF curves using modified Bartlett-Lewis rectangular pulses (BLRP) model for Babylon City, Iraq

In this study, daily rainfall data spanning 32 years, from 1991 to 2022, were collected from the Babylon City Meteorological Station (BCMS). The modified Bartlett-Lewis Rectangular Pulses (BLRP) model with seven parameters was used to generate the intensity-duration-frequency (IDF) curve for durations of 2, 5, 10, 25, 50, and 100 years. The estimated extreme values obtained from BCMS were fitted with EasyFit software using three statistical continuous distributions: the Gumbel (EV-1), Generalized Extreme Value (GEV), and Log-Pearson type III (LP-3) to determine the optimal distribution model for the IDF curve. The AIC and BIC criteria were employed to rank the generated IDF curves for each duration to select the optimal distribution model. The results of the AIC criteria ranked the PDFs as LP-3 first model, EV-1 second model, and GEV third model with sums of weighted rank values as 16, 17, and 27, respectively. In contrast, the BIC criteria ranked the PDFs as EV-1 first model, LP-3 second model, and GEV third model with sums of weighted rank values as 16, 17, and 27, respectively. The EV-1 and LP-3 models were selected as the most suitable results for simulating the maximum rainfall in Babylon City for various return periods. This decision was based on the total score of the special rankings of the two tests, with EV-1 and LP-3 having the lowest values of AIC and BIC.

Characterization of extreme rainfall changes and response to temperature changes in Guizhou Province, China

Different geographical zones have regional heterogeneity in underlying earth surface structure and microclimate which result in different evolution trends and their response to climate change varies in extreme rainfalls in these zones. In the Guizhou province of China, there are complex landforms, which lead to spatial redistribution of rainfall, frequent extreme rainfall, and disasters high risk of geologic disasters. Research on extreme climate in Guizhou mostly paid attention to its spatio-temporal characteristics and modeling, but lack of analysis on its characteristics of extreme rainfall variability and response to temperature changes under different subsurface conditions. This study investigated the characteristics of the extreme rainfall spatiotemporal and recurrence periods in Guizhou province and discussed the relationship between the response of extreme rainfall to temperature change. Daily rainfall data from 1990 to 2020 and 2021–2100 at 31 meteorological observation stations throughout the province were collected to calculate extreme precipitation. This research had the following results. (1) Both historical and future periods show an upward trend in extreme rainfall in Guizhou province, with a spatial distribution pattern of “high in the south and low in the north, high in the east and low in the west” and “high in the southeast and low in the northwest”, respectively; the spatial distribution of extreme rainfall under each recurrence period is consistent with the non-recurrence period. (2) Both historical and future periods show an upward trend in temperature in Guizhou province, with a spatial distribution consistent with that of the extreme rainfall in the corresponding period. (3) The change in extreme rainfall intensity with increasing temperature is almost always greater than the C–C rate for different periods and underlying earth surface structure; Extreme rainfall has a Hook response structure to temperature change, and the climate response structure shifts to the right with climate warming. The results of the study can provide a basis for decision-making on regional disaster prevention and mitigation in the context of temperature change.

Trends in extreme rainfall and their relationship to flooding episodes in Vhembe district, South Africa

Extreme weather events, such as heavy rainfall, are being increased by climate change in various regions, and such events often cause floods. This study examined the trends and variability of extreme rainfall indices using daily rainfall data (1981–2023) from three study sites at different socio-economic development spectra in Vhembe District, Limpopo Province, South Africa. The analyses focus on indices such as the annual total rainfall from wet days (PRCPTOT), the maximum number of consecutive dry days (CDD) and wet days (CWD), annual maximum 1-day and 5-day rainfall (RX1 day and RX5 day), the Simple Daily Intensity Index (SDII), the number of days exceeding varying amounts of precipitation (R10, R20, R40) and the annual number of wet days with rainfall greater than the 95th and 99th percentile (R95p and R99p) of the 1981–2023 daily rainfall. We discuss the observed trends in extreme rainfall indices in light of the actual flood occurrences to establish linkages. Several statistically significant and marginal changes in extreme rainfall trends were identified and provided key insights into reported flooding events in the district—flooding episodes were mainly attributed to the significant increases in total precipitation (PRCPTOT) and rainfall exceeding the 99th percentile of daily rainfall (R99p). Other significant contributors were declining CDD and increasing RX1day at Duthuni, increasing R40 at Musina as well as increasing R1 and declining CDD at Sane. However, the low altitude, urbanization, poor waste management and inadequate drainage systems were among the key non-climatic drivers of flood risk across the study sites, but these warrant further investigation. The complex interplay between climatic and non-climatic drivers of flood risk underscores the importance of localized climate studies and the need for adaptive strategies to minimize loss and damage. Overall, this research provides valuable insights into localized extreme rainfall trends, which are essential for developing site-specific flood mitigation and adaptation strategies. However, such initiatives require placing vulnerable communities at the centre in order to develop solutions that are locally led and relevant.

Characteristics of Overburden Damage and Rainfall-Induced Disaster Mechanisms in Shallowly Buried Coal Seam Mining: A Case Study in a Gully Region

Shallow coal mining in gully regions has resulted in significant subsidence hazards and increased the risk of surface water inflow into mining panels, compromising the sustainability of surface water management and underground resource exploitation. In this study, the chain disaster process caused by shallow coal seam mining and heavy rainfall is quantitatively analyzed. The findings reveal that shallow coal seam mining leads to the formation of caved and fractured zones in the vertical direction of the overlying rock. The fractured zone can be further classified into a compression subsidence zone and a shear subsidence zone in the horizontal direction. The shear subsidence zone is responsible for generating compression and shear deformations, intercepting rainfall runoff, and potentially triggering landslides, necessitating crack landfill treatments, which are critical for promoting sustainable mining practices. The HEC-RAS program was utilized to integrate annual maximum daily rainfall data across different frequencies, enabling the establishment of a dynamic risk assessment model for barrier lakes. Numerical simulations based on unsaturated seepage theory provide insights into the infiltration and seepage behavior of rainfall in the study area, indicating a significant increase in saturation within lower gully terrain. Precipitation infiltration was found to enhance the saturation of the shallow rock mass, reducing matric suction in unsaturated areas. Finally, the disaster chain is discussed, and recommendations for managing different stages of risk are proposed. This study offers a valuable reference for the prevention and control of surface water damage under coal mining conditions in gully regions.

Distribution of Rain Intensity: Daily Maximum Rainfall Data in The Province of South Sumatera and West Sumatera

The high intensity of rain in May 2024 caused flooding in the Sumatra region, especially Padang City and Palembang City. The region of West Sumatra is also known as the ethalae of disasters with frequent earthquakes, floods, and landslides. In Indonesia, many frequency analyses are conducted using the Gumbel distribution without testing data and clear hydrological reasons. It is feared that this method is considered as a routine way, thus causing the risk of unwanted deviations, resulting in many disasters and errors in the planning of water buildings. This research aims to determine and know the type of distribution that is representative for the maximum daily rainfall frequency in West Sumatra and South Sumatra Provinces as a reference for development and disaster mitigation in the region. The research location is in West Sumatra and South Sumatra. The data used for this study were taken from daily maximum rainfall data from rain stations for 35 to 40 years from 24 rain stations in West Sumatra Province, and 16 rain stations for South Sumatra region. This type of research is development research with the 4D development model (Define, Design, Develop, Disseminate). The development method consists of four different phases: Initial Investigation, Design, Realisation and Development. The data of each station was then organised into two types of data series, namely the annual maximum data series and the annual minimum data series. The results of this data series test are expected to follow one or more types of distributions commonly used in Hydrology and Drainage. The distribution results obtained for the South Sumatra region are Gama-III and LP-III, while for the West Sumatra region the distribution types are Gama-III, LP-III Normal Log and Gembel. The conclusion obtained is that West Sumatra does not have only one type of distribution. For the type of test using the SPSS.22 application, the results obtained are goodness of fit test, parametric test, Chi-Square test, Kolmogorov Smirnov test and Anderson-Darling test and Histrogram (visual) method.

Near-surface soil hydrothermal response feedbacks landslide activity and mechanism

Surface moisture has recently been reported to be used in regional-scale landslide early warning. Nevertheless, near-surface multi-depth hydrothermal measurements as a hillslope scale are often less concerned and rarely linked to landslide kinematics. In this paper, we selected two neighboring landslides with different deformation mechanisms as case studies. Using in-situ multi-source sensors, we monitored real-time soil temperature and moisture at specific depths within approximately 1.5 m. The measurements span two complete monsoon seasons, representing concurrent dry and wet hydrological extremes. Statistical Pearson correlation analysis was employed to quantify the relationships between landslide activity and environmental variables such as soil temperature and moisture content. The results indicate that the near-surface soil temperatures and moisture contents contribute to a better understanding of the factors controlling landslide activity, in which variations synergistically reflect hydrothermal interaction and potential deformation mechanisms. These soil temperatures and moisture contents at certain depths (specifically at 20, 50, and even 100 cm) show moderate to strong correlations (with Pearson correlation coefficient values ranging from 0.4 to 0.8) with landslide deformation. In cases where discrete daily rainfall data exhibited unsatisfactory correlations due to their data attributes, soil temperature and moisture effectively served as alternative indicators for rainfall inputs, aiding in the analysis. Overall, this work emphasizes the critical influence of soil moisture and temperature on landslide dynamics. This study also highlights the need for comprehensive monitoring and forecasting strategies that consider a wide range of environmental factors to mitigate landslide risks associated with climate change, particularly in the context of intensified extreme weather events.

Evaluation of the productivity of maize (ATP) under different sowing dates in the western highlands of Cameroon

This study aims to assess the effects of three sowing dates on the growth and yield of maize. The trial was conducted at the Teaching and Research Farm of the University of Dschang in Bansoa (Penka-Michel) over two growth cycles: 2020 and 2021. The experiment was arranged in a randomized complete block design. Treatments, replicated three times, included a control (T) and three levels of fertilizer input: mineral fertilizer (E), organic fertilizer (F), and organo-mineral fertilizer (EF). The maize variety used was the Acid Tolerable Population (ATP). Sowing occurred sequentially on March 30, April 10, and April 20. Daily rainfall data were collected using a graduated cylinder rain gauge. Observations focused on growth and yield parameters at different sowing dates. The results obtained indicate that maize responded significantly to different sowing dates (p < 0.05). Fertilizer application also positively influenced growth compared to the control. The tallest plants were observed in the F treatment sown during the first date (300 cm). Similarly, the collar diameter followed the same trend, with the F treatment measuring 3.43 cm compared to 2.14 cm for the control, indicating significant differences. Yield results showed the first sowing date > second date > third date. The highest yield was achieved with the EF treatment during the first period (6985 kg/ha), compared to the control during the third period (2009 kg/ha). Within each period, the yield results followed the order: EF > E > F > T. Organic fertilizers alone also produced favorable results compared to the control. Given the current climatic conditions and the results obtained, maize sowing conducted in the first sowing period (March 30) with organo-mineral fertilizer would likely result in optimal grain yield.

Variação das características pluviométricas na Planície de Reeds sob os impactos das mudanças climáticas

ABSTRACT Globally, there is enough evidence to affirm that climate change is one of the main factors causing the changing trends in rainfall features (CTRFs). The aim of this study, therefore, is to assess the CTRFs across the rice cultivation regions (RCRs) of the Plain of Reeds in Vietnam. Vietnam is a prime example of the impacts of climate change (ICC) on agricultural activities. Daily rainfall data series from 16 observation stations during the period 1984-2015 were appraised for quality and then the CTRFs were detected by applying the Mann_Kendall test and Sen’s slope estimator. The quality of rainfall data at all observation stations is homogeneous with a significance level of 0.05. Over a year, the trends in rainfall features slightly increased at most obsrvation stations, except for Hong Ngu and Moc Hoa. Additionally, the trends in rainfall features slightly decreased in rainny season rainfall (RSR) at 10 out of 16 observation stations, while insignificant to significant increasesing in dry season rainfall (DSR) were detected at all observation stations.

Kajian Penggerusan (Scouring) pada Dinding Saluran Air Waringin Jaya Kecamatan Bojonggede Kabupaten Bogor

The Waringin Jaya river is a tributary of the Ciliwung river that passes through Waringin Jaya village. Although it is not classified as a river with a large discharge, it is capable of eroding riverbanks and retaining walls, endangering the community. This study aims to analyze the magnitude and depth of local scour that occurs on gabion-type retaining walls, located in the Waringin Jaya River basin, Kec. Bojonggede, Kab. Bogor using the Hec-Ras computer application. The calculation of the discharge plan for the return period of 2 years, 5 years, 10 years, 20 years, 25 years, 50 years, and 100 years using the rational method was carried out based on daily rainfall data at the Citeko Meterological Station and the West Java Climatology Station with a data length of 2007 - 2021. Based on the calculations that have been carried out, the water level on the retaining wall with a flood return period of 2 years, 5 years, 10 years, 20 years, 25 years, 50 years, and 100 years is 0.92 m; 0.99 m; 1.03 m; 1.04 m respectively; 1.06 m; 1.08 m; and 1.10 m, while based on the analysis conducted with Hec-Ras bridge scour, the scour that occurs at each return period flood discharge is 1.24 m; 1.32 m; 1.35 m; 1.39 m; 1.40 m; 1.44 m; and 1.46 m, respectively.

Estimation of return dates and return levels of extreme rainfall in the city of Douala, Cameroon

The problem of extreme phenomena with a more precise estimation of their return periods for early warnings, notably to preserve the safety of populations and properties, arises all over the world. This work develops another aspect in the estimation of Return Levels (RLs) and Return Periods (RPs) of extreme precipitation in particular and natural risk in general. In particular, it gives the Return Dates (RDs) with their Confidence Intervals (CIs). The RPs, the RLs and their CIs for extreme rainfall were also investigated. These estimates were made by approaching the peak over a threshold chosen by the Generalized Pareto Distribution (GPD). The CIs of RPs and RLs were determined by the Delta method. The daily rainfall data used were obtained from the data of the synoptic report for the period 2011 to 2021 for the Douala weather station (more details can be found on http://www.ogimet.com/guia.phtml.en). To validate the methods used, real cases of floods occurred in Douala city were considered: for example, a local press compiled flood dates and mentioned that a flood occurred on the April 16, 2013 in this city. Following the data of synoptic report, the corresponding amount of rainfall was around 150 mm. The results obtained have shown a RD on the August 12, 2014. The confidence intervals of return levels and return dates determined by the Delta method were [131.66, 168.456] and [June 23, 2014, January 02, 2015], respectively. These results are in agreement with the data of synoptic report since the rainfall amounts was 132.2 mm (belonging to the confidence interval of return levels), on the August 11, 2014 (belonging to the confidence interval of return dates). These predictions of RDs and RLs with their CIs, at reasonable time scales, can help for efficient management of floods and thus, improve early warnings for safety of populations and goods.

Data assimilation with machine learning for constructing gridded rainfall time series data to assess long-term rainfall changes in the northeastern regions in India

ABSTRACT Data scarcity and unavailability of observed rainfalls in the northeastern states of India limit prediction of extreme hydro-climatological changes. To fill this gap, a data assimilation approach has been applied to re-construct accurate high-resolution gridded (5 km2) daily rainfall data (2001–2020), which include seasonality assessment, statistical evaluation, and bias correction. Random forest (RF) and support vector regression were used to predict rainfall time series, and a comparison between machine learning and data assimilation-based gridded rainfall data was performed. Five gridded rainfall datasets, namely, Indian Monsoon Data Assimilation and Analysis (IMDAA) (12 km2), APHRODITE (25 km2), India Meteorological Department (25 km2), PRINCETON (25 km2), and CHIRPS (25 and 5 km2), have been utilized. For re-constructed rainfall datasets (5 km2), the comparative seasonality and change assessment have been performed with respect to other rainfall datasets. CHIRPS and APHRODITE datasets have shown better similarities with IMDAA. The RF and assimilated rainfall (AR) have superiority based on bias and extremity, and AR data were recognized as the best accurate data (&amp;gt;0.8). Precipitation change analysis (2021–2100) performed utilizing the bias corrected and downscaled CMIP6 datasets showed that the dry spells will be enhanced. Considering the CMIP6 moderate emission scenario, i.e., SSP245, the wet spell will be enhanced in future; however, when considering SSP585 (representing the extreme worst case), the wet spells will be decreased.

Assessing future urban flood hazard: A comprehensive approach to estimating the implications of future rainfall scenarios

AbstractEstimating potential changes in future flood patterns based on anticipated changes in hydrological characteristics within the basin is crucial for mitigating flood damage and managing flood risk. In this study, nonparametric probability models are used to estimate future rainfall patterns in Seoul under the GCM‐based climate change scenarios (CCS), and the estimated future daily rainfall data was temporally downscaled to hourly units using the KNNR‐GA technique. Changes in flood hazard and runoff characteristics of the target area based on the estimated future rainfall data are quantitatively assessed. The results highlight that under CCS, flood runoff may increase further into the future, resulting in more significant changes in flood patterns and accelerating the increase in flood hazard. The delta change factor of flood risk indicators increased relatively significantly in more severe CCS. This study also proposed a process to estimate future flood runoff and mitigation effects according to CCS by reflecting various flood mitigation measures in the urban drainage system model. These findings can offer valuable insights for setting the direction of current and future mitigation measures.

Effects of climate change on dry-wet and its driving factors in the Palu watershed, Indonesia

Abstract Assessing dry-wet climate change is very important in the context of global climate change to ensure regional water resources, stabilize ecosystems, and develop socio-economic development. This study aims to investigate the temporal variations in aridity index (AI), precipitation (P), reference evapotranspiration (ET0), and the underlying driving climatic factors. The methodology used includes the aridity index (AI), trend analysis, sensitivity, and contributors. Daily temperature and rainfall data were collected from 5 meteorological stations in the Palu Watershed (Bora, Kalawara, Kulawi, Palolo, Wuasa) from 2002 - 2022. The results show that 84.58% of the Palu watershed experienced wetting while only 15.42% experienced drought during the last two decades. AI changes are mainly determined by changes in the ET0 trend. During observations (2002-2021), ET0 experienced a significant decrease at a rate of 4.08mm/day. The trend of ET0 is negatively affected by a decrease in wind speed and duration of sunlight and is positively affected by a decrease in relative humidity and an increase in air temperature. Air temperature (T) and relative humidity (RH) contribute to increasing ET0 while wind speed (V) and sunshine duration (S) contribute to decreasing ET0.

The trends and spatiotemporal variability of temperature and rainfall in Hulbarag district, Silte Zone, Ethiopia

Ethiopia gets its agricultural water primarily from rainfall. This study was intended to investigate current climate variability and trends across space and time. Daily gridded temperature and rainfall data from 1993 to 2022 in the Hulbarag district, Silte Zone of Ethiopia was obtained from the Ethiopian National Metrological Institute and the Climate Hazard Group Infrared Precipitation with Station. The trends and variability of temperature and rainfall were analyzed using the Mann-Kendall trend test, Sen's slope, coefficient of variation, precipitation concentration index, and rainfall anomaly index. The results indicated that annual, spring, and summer rainfall revealed statistically significant decreasing trends at Hulbarag and Sankura stations, with the magnitude of −13.4, −11.6, and −10.6 mm per year and −6.8, −3.6 and −10.9 mm per year respectively. Conversely, autumn and winter season rainfall showed statistically significant increasing trends at Hulbarag and Sankura stations, with the magnitude of 5.1 and 5.5mm per year and 3.4 and 1.84mm per year consecutively. Between 43 % and 47 % of the observation periods had negative anomalies. The average yearly temperature, average yearly minimum temperature, and average yearly maximum temperatures at Sankura and Fonko stations all displayed statistically significant increasing trends, with a magnitude of 0.091 °C, 0.009 °C and 0.051 °C per year and 0.03 °C,0.01 °C and 0.0022 °C per year successively. It is advisable to develop a farming system that is climate-resilient by improving the adaptive capacity of wheat and maize-growing farmers by expanding the availability of early maturing seeds, changing crop calendars, and enhancing proactive and credible climate information services.

The role of matching pursuit algorithm and multi-scale daily rainfall data obtained from decomposition in runoff prediction

Study RegionHuai River Basin, China Study FocusRunoff data preprocessing and strongly correlated influencing factors are vital to predicting runoff by machine learning methods due to the impact of human activities and changing climate. To address the issue of noise interference in the runoff components post-decomposition. This study introduces a novel denoising technique that integrates the matching pursuit algorithm with permutation entropy. Furthermore, to enhance the correlation of the input factors, multi-scale daily rainfall data obtained through decomposition is utilized as input to the model for the first time. The aim of preprocessing the original runoff is to diminish noise interference and reduce the complexity of the subsequences. This method effectively increases the prediction accuracy. New hydrological insights for the regionThe matching pursuit algorithm considerably decreases the noise interference and the complexity of the runoff. The effectiveness of the proposed method is shown through comparison with typical denoising methods. Meanwhile, multi-scale daily rainfall data acquired through rainfall data decomposition have stronger correlation with runoff components. Through data preprocessing methods and strongly correlated factors, despite the significant fluctuations and severe seasonal variations observed at the three selected test hydrological stations in the Huaihe River basin, the proposed model still achieves accurate prediction results. This offers new viewpoints on forecasting runoff in basins characterized by significant flow variations.

Rainfall-runoff modelling – a comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS)

ABSTRACT This article presents comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) model for rainfall-runoff (R-R) process. Aim of the present work is to forecast runoff one day ahead at Shivade station of Upper Krishna Basin, India, using 17 years of daily rainfall and discharge data. The R-R modelling can be exercised using various traditional methods which generally require exogenous data in the form of basin parameters. Unavailability of such data becomes major impediment in applying these models at many basins. In such situations, soft computing techniques like ANNs have been extensively applied to model R-R process. Though ANN is now an established tool in hydrology, compared to HEC-HMS, its results are viewed with suspicion owing to its data-driven nature rather than a model-driven nature. In this study, ANN model performed reasonably well, with a higher correlation coefficient (0.87) and the lowest Root Mean Square Error (136.28 m3/s) when compared with HEC-HMS (0.76, 139.8 m3/s) respectively. Novelty of the present work lies in model development using restricted basin data. Both models showed less accuracy in predicting extreme events. Finally, it is concluded that ANN model can be used as a supplementary technique along with HEC-HMS for this phenomenon.

An underground drip water monitoring network to characterize rainfall recharge of groundwater at different geologies, environments, and climates across Australia

Abstract. Understanding when and why groundwater recharge occurs is of fundamental importance for the sustainable use of this essential freshwater resource for humans and ecosystems. However, accurately capturing this component of the water balance is widely acknowledged to be a major challenge. Direct physical measurements identifying when groundwater recharge is occurring are possible by utilizing a sensor network of hydrological loggers deployed in underground spaces located in the vadose zone. Through measurements of water percolating into these spaces from above, we can record the potential groundwater recharge process in action. By using automated sensors, it is possible to precisely determine when recharge occurs (which event, month, or season and for which climate condition). Combined with daily rainfall data, it is possible to quantify the “rainfall recharge threshold”, the amount of rainfall needed to generate groundwater recharge, and its temporal and spatial variability. Australia's National Groundwater Recharge Observing System (NGROS) provides the first dedicated sensor network for observing groundwater recharge at an event scale across a wide range of geologies, environments, and climate types representing a wide range of Australian hydroclimates. Utilizing tunnels, mines, caves, and other subsurface spaces located in the vadose zone, the sensors effectively record “deep drainage”, water that can move beyond the shallow subsurface and root zone to generate groundwater recharge. The NGROS has the temporal resolution to capture individual recharge events, with multiple sensors deployed at each site to constrain the heterogeneity of recharge between different flow paths, and to quantify (including uncertainty bounds) rainfall recharge thresholds. Established in 2022, the network is described here together with examples of data being generated.