Rainfall-runoff Relationship Research Articles

Hydrological Investigations in Red Soils of a Micro Catchment Area for Dugout Farm Pond at UAS Raichur Campus

Aims: To measure and characterize storm wise runoff for the catchment area of the farm pond and to correlate rainfall intensity and runoff relationship for the catchment area will help to design the appropriate size of the farm pond and waste weirs of the bunding system.
 Place and Duration of Study: The study was conducted in a micro catchment (field sized area) of a dugout farm pond, having an area of 6 ha located in the new area of UAS campus Raichur, which comes under Zone II in Region-I of Karnataka state. Geographically it is located at 16° 12′ N latitude and 77° 20′ E longitude and at an elevation of 389 m above the mean sea level (MSL). The study was conducted for a period of one year during 2019.
 Methodology: The existing farm pond constructed was used for conducting hydrological studies. The detailed soil and rainfall characterization of the study area has been made through appropriate methods. The rainfall intensity for each storm has been measured using self-recording rain gauge. The runoff has been measured at the out let of the field sized micro catchment area of farm pond using hydraulic structures coupled with automatic runoff recorder. The event wise rainfall, rainfall intensity and runoff have been measured and analysed to see the relationship between rainfall intensity and runoff with prevailing soil and topographical characteristics of the study area.
 Results: The percent runoff varied from 6.79 to 50.42 and highest was 50.42 per cent occurred on 25-10-2019 followed by 44.03, 39.36 and 37.46 per cent. The data shows that the individual storm wise percent runoff was quite high as compared to annual percent runoff of 15.99 per cent. The storm wise high runoff percent was due to the fact that high intensity of rainfall followed by high AMC in the soil. Further the minimum runoff yield of 142.66 m3 was observed on 18-07-2019 against the rainfall of 35.00 mm and maximum of 2985.48 m3 was yielded on 25-09-2109 against rainfall of 113.00 mm and followed by 1086.64 m3, 944.24 m3, 665.61 m3 and 431.25 m3 against rainfall of 46.00 mm, 42.00 mm, 22.00 mm and 48.00 mm respectively. The total annual runoff yield was found to be 6255.90 m3 against the rainfall of 651.50 mm. Therefore, there is a scope for harvesting excess quantity of runoff which is going as a waste. The existing pond capacity of 547.77 m3 is insufficient to store prevailing runoff generated in the catchment area and hence, pond capacity may be enhanced. The maximum intensity of rainfall and runoff during six events were showed statistically insignificant relationship with R2 value of 0.370. There is no correlation between intensity of rainfall and runoff.

Determination of Runoff Curve Numbers for the Growing Season Based on the Rainfall–Runoff Relationship from Small Watersheds in the Middle Mountainous Area of Romania

The NRCS-CN (Natural Resources Conservation Service curve number) method, developed by the USDA (U.S. Department of Agriculture) is among the most widely used for the estimation of surface runoff from watersheds. Ever since its introduction in the 1950s, although it has been used to a great extent by engineers and hydrologists, the applicability of the original procedure used to determine its main parameter, the curve number (CN), to various regions with diverse environmental conditions, is still subject to many uncertainties and debates. This study presents a comparative analysis of different methods applied to determine curve numbers from local data in four watersheds located in the central part of Romania, within the mountain region surrounding the Brașov Depression. The CN values were not only computed using rainfall–runoff records from 1991 to 2020, but also determined from the standard NRCS tables documented in the National Engineering Handbook part 630 (NEH-630), for comparison purposes. Thus, a total of 187 rainfall–runoff data records from the study watersheds and five different methods were used to assess the accuracy of various procedures for determining the CN values, namely: tabulated CN (CN values selected from NRCS tables, TAB), asymptotic fitting (AF) of both natural and ordered data, median CN (MD), geometric mean CN (GM) and the arithmetic mean CN (AM) methods. The applicability of the aforementioned methods was investigated both for the original fixed initial abstraction ratio λ = 0.2 and its adjustment to λ = 0.05. Relatively similar results were found for the curve number-based runoff estimates related to the field data analysis methods, yet slightly better when the λ was reduced to 0.05. A high overall performance in estimating surface runoff was achieved by most CN-based methods, with the exception of the asymptotic fitting of natural data and the tabulated CN method, with the latter yielding the lowest results in the study area.

Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China

Satellite precipitation products (SPPs) have emerged as an important information source of precipitation with high spatio-temporal resolutions, with great potential to improve catchment water resource management and hydrologic modelling, especially in data-sparse regions. As an indirect precipitation measurement, satellite-derived precipitation accuracy is of major concern. There have been numerous evaluation/validation studies worldwide. However, a convincing systematic evaluation/validation of satellite precipitation remains unrealized. In particular, there are still only a limited number of hydrologic evaluations/validations with a long temporal period. Here we present a systematic evaluation of eight popular SPPs (CHIRPS, CMORPH, GPCP, GPM, GSMaP, MSWEP, PERSIANN, and SM2RAIN). The evaluation area used, using daily data from 2007 to 2020, is the Xiangjiang River basin, a mountainous catchment with a humid sub-tropical monsoon climate situated in south China. The evaluation was conducted at various spatial scales (both grid-gauge scale and watershed scale) and temporal scales (annual and seasonal scales). The evaluation paid particular attention to precipitation intensity and especially its impact on hydrologic modelling. In the evaluation of the results, the overall statistical metrics show that GSMaP and MSWEP rank as the two best-performing SPPs, with KGEGrid ≥ 0.48 and KGEWatershed ≥ 0.67, while CHIRPS and SM2RAIN were the two worst-performing SPPs with KGEGrid ≤ 0.25 and KGEWatershed ≤ 0.42. GSMaP gave the closest agreement with the observations. The GSMaP-driven model also was superior in depicting the rainfall-runoff relationship compared to the hydrologic models driven by other SPPs. This study further demonstrated that satellite remote sensing still has difficulty accurately estimating precipitation over a mountainous region. This study provides helpful information to optimize the generation of algorithms for satellite precipitation products, and valuable guidance for local communities to select suitable alternative precipitation datasets.

Hydrologic performance assessment of nature-based solutions: a case study in North-eastern Italy

The consequences of climate change are exacerbated by land-use changes, which influence the rainfall-runoff relations and consequently the flood risk. Effectively, urbanization is steadily contributing to the increase of impervious areas and reducing the time-to-peak. The effect of Nature Based Solutions (NBSs) on the mitigation of these phenomena is recognized. Nevertheless, these kinds of sustainable infrastructures are still barely known and scarcely adopted in many parts of the European Countries. The LIFE BEWARE project aims to enhance hydraulic safety and spread good practices in rainwater management by promoting and facilitating the adoption of NBSs in the Altovicentino area (Northern Vicenza Province, Veneto Region, Italy). To support the dissemination activities, some full-scale NBSs have been created within the municipality areas involved in the project. The hydrological impact of the structures is continuously monitored thanks to the installation of devices measuring inlet and outlet runoff, and rainfall pattern. This study aims to analyse the monitoring data of the first two years of the built NBSs. Results show that the structures managed almost all the water runoff through processes of infiltration and retention, providing additional insights into understanding the real behaviour of NBSs exposed to the specific environmental conditions of a very rainy foothills area. In particular, mean rain intensity and rainfall duration are the variables that mostly affected the structure performance, especially for events prolonged over time (2-3 days) with mean rainfall intensity in the range of 2-3 mm/h. Therefore, the overall outcomes from this analysis resulted as being useful to improve the design of NBSs and further promote their installation in urban areas.

An Improved Curve-Number Method

The standard curve-number method has shown limited accuracy in estimating observed rainfall-runoff relations, particularly for less extreme rainfall events. Consequently, the Natural Resources Conservation Service is considering revising the standard initial abstraction ratio and associated published curve numbers to improve the performance of the method. A form of the curve-number equation is presented here that improves the prediction accuracy of the curve-number method without the need to adjust the initial abstraction ratio or the existing guideline curve numbers, and the improved runoff estimation is at least as good as could be achieved by adjusting the initial abstraction ratio. The improved curve-number equation can also be used to analytically estimate curve numbers corresponding to any given initial abstraction ratio, and it is demonstrated that these estimates are supported by field measurements.

Identification of small-scale hydropower potential sites using geographic information system and hydrologic modeling technique: Awata river, Genale Dawa basin, Ethiopia

Small-scale hydropower is a significant source of renewable and inexpensive energy. This study was carried out to identify suitable potential sites for small-scale hydropower for the generation of electricity along the Awata River in Ethiopia. Primary and secondary data collected from various sources were used in the present study to achieve its objective. After the consistency of rainfall was checked and areal rainfall was estimated using the Theissen polygon method, the rainfall–runoff​ relationship was simulated using HEC-HMS. The head of each identified site was estimated by using GIS to overlay the DEM and stream network starting at the outlet. To estimate the discharge, the flow duration curve was developed. HEC-HMS performance was evaluated using Nash–Sutcliffe efficiency (NSE), Mean Absolute Error (MAE), Percent Bias (PBIAS), and coefficient of determination (R2). During calibration, the values were 0.82, 4.39, −2.93, and 0.83, and during validation, the values were 0.76, 5.35, 6.25, and 0.76. The identified sites were ranked using an analytical hierarchy process based on a ratio of five criteria. There are twenty-eight sites identified, with estimated power output and energy output of 45.26 MW and 198.79 GWh at 50% and 6.74 MW and 53.23 GWh at 90%, respectively. The overall rank indicated that the selected site code 29 is ranked first and can be used for the implementation of a small hydropower plant. As a result, the design of all the components of small hydropower plants will be required to implement and generate energy for rural areas in future development.

Modeling the Rainfall–Runoff Relationship with TOPMODEL in the Wadi El Kebir Watershed

The rainfall–runoff relationship was studied in the Wadi El Kebir watershed, located in Northeastern Algeria, using TOPMODEL (topography based hydrological model). This is a geomorphological and semi-distributed model which is used to predict the hydrological behaviour of watersheds and to calculate the water storage deficit of an aquifer in any location. It uses topographic information of the watershed to predict the extent of contributing areas in the production of runoff. TOPMODEL was applied with event-based rainfall–runoff modeling where 13 hourly rainfall series were used to predict the discharge at the basin outlet. A digital elevation model (DEM) was also used to define the contours of the basin and to map out the drainage directions and the topographic index. TOPMODEL was calibrated and validated using the measured discharges and various objective functions such as Nash (Nash-Sutcliffe) and coefficient of determination (R2). The TOPMODEL results showed a high-performance level. Indeed, after the calibration and validation procedure, the performance of the model oscillates between satisfactory and very good. For the calibration, Nash varied between 0.59 and 0.91, and R2 between 0.66 and 0.91. However, the values of these criteria coefficients were slightly reduced during the validation phase, Nash (0.53 to 0.84) and R2 (0.59 to 0.87). Also, the proposed model shows the weak contribution of groundwater flows in the hydrological response of the study area.

Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFIS-based model outperforms the other algorithms. The correlation coefficient of each algorithm’s predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.

Application of Hydrologic Engineering Centers Hydrologic Modeling System (HEC-HMS) Model for Modelling Flood in Sub Basin of Meenachil River, Kerala, India

Meenachil river basin, located in southern part of Kerala, is frequently liable to flood. It is an area predominant with agricultural land and falls under tropical humid zone. Hence, water resources planning and management and understanding of rainfall-runoff relationship along with its land characteristics, is necessary for irrigation scheduling, flood control and design of various engineering structures. HEC-HMS(Hydrologic Engineering Centers Hydrologic Modeling System) which is a widely used rainfall-runoff model was chosen for the simulation of watershed responses and generation of flood hydrographs. This research was done to establish the peak discharge rate of the river as well as calibrate and validate the HEC-HMS model for the Meenachil subbasin and obtain long-term flow data. The performance parameters NSE (Nash–Sutcliffe model efficiency) coefficient and R² (coefficient of determination) values were obtained above 0.7. The Error in Peak Flow and Volume were calculated below 20%. All these values indicated satisfactory performance of the model simulation both in the calibration (2013-2016) and validation (2017-2018) period. Curve number (61.46-79.93 mm), Initial abstraction(10.29-31.87 mm) and Lag time(208.27 min and 4456.2 min) were found to be the most sensitive parameters of the model. Simulated and observed stream flow values (viz. lowest peak flow of river was found during the year 2013 and it was predicted as 168.9 m³/s and observed as 158.5 m³/s) indicated that the model was able to predict and present credible results for the sub basin.

Does non-stationarity induced by multiyear drought invalidate the paired-catchment method?

Abstract. Multiyear drought has been demonstrated to cause non-stationary rainfall–runoff relationship. But whether this change can occur in catchments that have also experienced vegetation change and whether it invalidates the most widely used methods for estimating impacts of vegetation change – i.e., the paired-catchment method (PCM), the time–trend method (TTM), and the sensitivity-based method (SBM) – on runoff is still unknown and rarely discussed. Estimated inconsistent afforestation impacts were 32.8 %, 93.5 %, and 76.1 % of total runoff changes in the Red Hill paired experimental catchments in Australia during the period of 1990–2015 by the PCM, TTM, and SBM, respectively. In addition to afforestation, the Red Hill paired experimental catchments have experienced a 10-year drought (2000–2009) and have been demonstrated to lead to non-stationary rainfall–runoff relationships of paired catchments. Estimated impacts of vegetation change by the PCM (32.8 %) is still reliable and is not invalided by multiyear drought-induced non-stationarity, because the PCM can eliminate all impacts by different factors on paired catchments (multiyear drought and climate variability), except the purposed treatment (afforestation). For the TTM and SBM, traditional application did not further differentiate different drivers of non-stationary rainfall–runoff relationship (i.e., multiyear drought and vegetation change), which led to significant overestimation of afforestation effects. A new framework was further proposed to separate the effects of three factors on runoff changes, including vegetation change, climate variability, and hydroclimatic non-stationarity (i.e., multiyear drought). Based on the new framework, impacts of multiyear drought and climate variability on runoff of the control catchment (Kileys Run) were 87.2 % and 12.8 %, respectively. Impacts of afforestation, multiyear drought, and climate variability on runoff of the treated catchment (Red Hill) were 32.8 %, 54.7 %, and 23.9 %, respectively. Impacts of afforestation on runoff were 38.8 % by the TTM and 21.4 % by the SBM, agreeing well with that by the PCM (32.8 %). This study not only demonstrated that multiyear drought can induce non-stationary rainfall–runoff relationship using field observations, but also proposed a new framework to better separate the impact of vegetation change on runoff under climate-induced non-stationary condition. More importantly, it is shown that non-stationarity induced by multiyear drought does not invalidate the PCM, and PCM is still the most reliable method even though the control catchment experienced climate-induced shift in the rainfall–runoff relationship.

Invasion of natural grasslands by exotic trees increases flood risks in mountainous landscapes in South India

Invasive trees in catchments around the world are a source of concern due to their hydrological and ecological impacts. A large number of studies have focused on their impact on dry-season stream discharge due to enhanced transpiration. The impact of invasive trees on stream discharge and flood risk during extreme rain events, which are becoming more frequent due to climate change, has not been addressed adequately. We examined the influence of land-cover, canopy cover, and other catchment morphological characteristics on stream discharges in hilly catchments during light, heavy, and extreme rain events. Three years of rainfall-runoff observations, between January 2014 and December 2016, were collected in eleven neighbouring mountainous catchments in Nilgiris, South India. Each catchment had a distinct land cover, namely shola forests, montane grasslands, and catchments invaded by wattle (Acacia mearnsii). Hourly rain intensities between the percentiles 25–90, 90–95 and over 95 were categorised as light, heavy and extreme respectively, and were used to study hourly peak stream discharge responses. We compared discharge between native grassland catchments and grassland catchments invaded by wattle. Discharge rates were corrected for the catchment area. We found that land cover and canopy-cover influenced the hydrologic response to extreme rain events. Regression models with flood event as the response variable suggested an increase in rainfall-runoff response with an increase in canopy cover. The maximum hourly discharge observed in the reference grassland catchment (0.00098 mm s−1) was lower than the maximum discharge observed in low density (0.00529 mm s−1) and high density (0.00497 mm s−1) wattle catchments. At higher discharges, the flood magnitude in wattle invaded catchments was much greater than that in the grassland catchment for a given flood frequency – indicating a higher risk of severe flooding in wattle-dominated catchments. We attribute the enhanced flood discharges to the effects of spreading wattle roots on the enhancement of rapid, shallow sub-surface flows in invaded catchments. Catchments dominated by shallow sub-surface flows are known to have reduced water retention times in response to high rain intensities. Antecedent moisture conditions and drainage density were other factors that influenced the local rainfall-runoff relationships. In the Western Ghats and other parts of India grasslands have been planted with exotic woody trees, and some have become invasive. Our study indicates that replacing grasslands with woody plants at landscape scales, could affect hydrological processes and could increase flood risks during the monsoon, especially in mountainous regions. Management of invaded catchments by regulation of wattle and restoration of semi-natural grassland, combined with the installation of early warning systems in these impacted areas will help reduce flood risk during extreme rain under climate change.

Explaining changes in rainfall–runoff relationships during and after Australia's Millennium Drought: a community perspective

Abstract. The Millennium Drought lasted more than a decade and is notable for causing persistent shifts in the relationship between rainfall and runoff in many southeastern Australian catchments. Research to date has successfully characterised where and when shifts occurred and explored relationships with potential drivers, but a convincing physical explanation for observed changes in catchment behaviour is still lacking. Originating from a large multi-disciplinary workshop, this paper presents and evaluates a range of hypothesised process explanations of flow response to the Millennium Drought. The hypotheses consider climatic forcing, vegetation, soil moisture dynamics, groundwater, and anthropogenic influence. The hypotheses are assessed against evidence both temporally (e.g. why was the Millennium Drought different to previous droughts?) and spatially (e.g. why did rainfall–runoff relationships shift in some catchments but not in others?). Thus, the strength of this work is a large-scale assessment of hydrologic changes and potential drivers. Of 24 hypotheses, 3 are considered plausible, 10 are considered inconsistent with evidence, and 11 are in a category in between, whereby they are plausible yet with reservations (e.g. applicable in some catchments but not others). The results point to the unprecedented length of the drought as the primary climatic driver, paired with interrelated groundwater processes, including declines in groundwater storage, altered recharge associated with vadose zone expansion, and reduced connection between subsurface and surface water processes. Other causes include increased evaporative demand and harvesting of runoff by small private dams. Finally, we discuss the need for long-term field monitoring, particularly targeting internal catchment processes and subsurface dynamics. We recommend continued investment in the understanding of hydrological shifts, particularly given their relevance to water planning under climate variability and change.

The influence of Low Impact Development (LID) on basin runoff in a half-urbanized catchment: A case study in San Antonio, Texas

Low Impact Development (LID) was promoted as an alternative to conventional urban drainage methods. The effects of LID at the site or urban scales have been widely evaluated. This project aims to investigate the impact of LID implementation on basin runoff at a regional scale in a half-urbanized catchment, particularly the overlap of urban and rural sub-flows at peak times. A SUPERFLEX conceptual model framework is adapted as a semi-distributed model to simulate the rainfall-runoff relationship in the catchment for San Antonio, Texas, as a case study. Scenario analyses of both urban development and LID implementation are conducted. Results show that (1) the infill urban development strategy benefits more from runoff control than the sprawl urban development; (2) in non-flood season, permeable pavements, bioretention cells, and vegetated swales decrease peak runoff significantly, and permeable pavements, bioretention cells, and green roofs are good at runoff volume retention; (3) contrary to the general opinion about the peak reduction effect of LID, for a partly urbanized, partly rural basin, the LID implementation delays urban peaks and may cause larger stacking of rural and urban peak runoffs, leading to larger basin peaks under extremely wet conditions.

Spatiotemporal deep learning rainfall-runoff forecasting combined with remote sensing precipitation products in large scale basins

Rainfall-runoff modeling is a complex nonlinear spatiotemporal prediction problem. However, few studies have considered the spatial characteristics of rainfall-runoff relationship in runoff forecasts based on machine learning. With the emergence of high-resolution Satellite-based Precipitation Products (SPPs) and the continuous improvement of rainfall estimation accuracy, the shortcoming of sparse spatial information for in-situ rainfall monitoring has been made up. Therefore, this study developed a large scale spatiotemporal deep learning rainfall-runoff (SDLRR) forecasting model for hydrological stations in the upper Yangtze River, and evaluated the positive impact of utilizing spatial information of three SPPs on reducing errors of runoff forecasts. The adopted remote sensing precipitation products are bias-corrected Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Integrated Multi-satellite Retrievals for Global Precipitation Measurement data (IMERG) and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis data (TMPA). For runoff forecasting at the Luoduxi (LDX) hydrological station, compared to regular Long Short Term Memory Network (LSTM) model, the proposed SDLRR model that utilizing IMERG data as precipitation input (IMERG_SDLRR) improved 15% in terms of Coefficient of Determination (R2) and improved 25% in terms of Root Mean Squared Error (RMSE). Compared to the best performance model among models using area-averaged precipitation as input, IMERG_SDLRR improved 5% in terms of R2 and 11% in terms of RMSE. Good performance was also acquired in the other hydrological stations. For extreme flood forecasts, IMERG_SDLRR decreased Mean Relative Error (MRE) by 0.29 and increased Qualified Rate (QR) by 53% compared to LSTM, and decreased MRE by 0.08 and increased QR by 6% compared to the best performance model using area-averaged precipitation as input. The utilization of IMERG or TMPA spatial information improved the accuracy of runoff forecasting. The accuracy evaluation of SPPs based on the results of spatiotemporal rainfall-runoff forecasts method was also demonstrated. The research is of great significance for developing runoff forecasting methods and optimizing water resources management.

Event-based Bayesian causal modelling for flood hydrograph prediction, Upper Andarax intermittent stream, Spain

Study regionThe study region comprises an intermittent stream named Upper Andarax, within a semi-arid watershed in SE Spain (Andarax river basin), characterised by uneven topography and its striking hydraulic heritage. Study focusHigh flow extreme events are becoming more common and less exceptional, leading to devastating losses with very high associated economical costs. This paper presents the development of a predictive Bayesian Causal Model (BCM) for high-flow extreme hydrological events assessment, accounting for: extreme events characterisation and sub-modelling; base-flow consideration; and previous soil moisture influence. It comprises three main phases that are: 1) Data preparation; 2) Extreme events characterisation and analysis; and 3) BCM predictive modelling. This study could be a decision-making tool for flood risk management in the Andarax River basin. New hydrological insights for the regionResults suggest a complex and dynamic non-linear rainfall-runoff relationship in the Upper Andarax watershed, whereas, there is a strong similarity in the temporal distributions of flow discharges, being, in general, symmetric, as a result of symmetric hyetographs. The statistical dependency showed that the basin response depends much more on the rainfall intensity rather than on the soil antecedent moisture conditions. The simulated BCMs demonstrated that all events’ types may lead to river flooding, while the most hazardous are those of quartiles III and IV.

MONTHLY STREAM FLOWS ESTIMATION IN THE KARASU RIVER OF EUPHRATES BASIN WITH ARTIFICIAL NEURAL NETWORKS APPROACH

This study aims to estimate streamflow values with artificial neural networks (ANN) using various meteorological parameters. In developing the ANN model, various combinations of precipitation, air temperatures, and potential evapotranspiration values were used as inputs, and streamflow values were obtained. Meteorological data is divided into 70% train, 15% test, and 15% validation. In the model's design, various training algorithms, network architecture, input combinations, and the number of iterations were tried, and the most suitable model was tested. Correlation coefficient (R), coefficient of determination (R2), absolute error (AE), and absolute relative error (ARE) coefficients were compared, and the most suitable model was selected. According to the analysis results, the optimal model was obtained using 2000 iterations, the architecture of the 4-4-1 model, and the Quasi-Newton algorithm. It was determined that the ANNs successfully modeled the rainfall-runoff relationship and produced reliable estimates. In addition, it was revealed that the inclusion of potential evapotranspiration values obtained by the Thornthwaite method into the model increases the model's success.

A stochastic, daily time-step model for conjunctive water use at local authority level

South Africa has a broadly developed water resource infrastructure, based mainly on surface water, localised groundwater, and limited desalination. The Department of Water and Sanitation projects that increasing requirements will exceed the capacity of the country's bulk water supply systems by 2025. To mitigate water scarcity at a local authority level, more conjunctive water use solutions need to be investigated. A Microsoft Excel 2016 model, estimating supply capacity on a daily basis, was developed for local authorities to better manage their water supply systems. The model utilises synthetically generated stochastic streamflow sequences, based on historical data. Monthly streamflow is disaggregated into daily streamflow and a rainfall-runoff relationship is established to produce synthetic rainfall sequences. Groundwater is modelled using an approach similar to the Aquifer Firm Yield Model. Recharge percentages (rainfall data) are obtained from the Groundwater Resource Assessment Phase 2 project. The Cooper-Jacob model evaluates borehole drawdowns and their effect on surrounding boreholes. Desalination and water reclamation are modelled as one source. The model evaluates and compares the historical, as well as stochastic available yield of a conjunctive use water supply system, for different operational scenarios. The water supply system of Stellenbosch was used as a case study to illustrate the model's functionality.

Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm

Abstract Rainfall–runoff modeling is one of the most well-known applications of hydrology. The goal of rainfall–runoff modeling is to simulate the peak river flow caused by an actual or hypothetical rainfall force. In existing methods, the rainfall–runoff relationships are quantified to predict the daily streamflow of each catchment from its landscape attributes to measure the daily rainfall. However, the structural model error, infiltration rate, and the steep slopes of the hill affect the prediction process. To tackle these issues, this paper proposed a novel rainfall prognostic model-based artificial framework, which predicts day-to-day rainfall to prevent environmental disasters. The day-to-day predictions minimize the risks to life and property and also manage the agricultural farms in a better way because the possibility of rainfall has been estimated earlier. Furthermore, the posterior fire-breathing network is utilized to estimate model errors in the computational runoff by using time-dependent and random noise to the model's internal storage to solve the uncertainty problem. Since the model errors are estimated, there are limits to the infiltration rate and thus a prophetic multilayer network is utilized which relies on the soil runoff levels. Moreover, the network measures the dynamics of soil moisture to regulate the infiltration rate according to the rural or urban section. Moreover, to measure the surface water from the steep slopes, the system offered a well-ordered selective genetic algorithm to calculate the velocity of runoff in different bend areas to overcome the numerical problem. Thus, the model results showed that the work effectively predicts the rainfall from the investigation of model errors, infiltration rates, and velocity to achieve a better prediction range in the rainfall.

Determination of rainfall-runoff relationship using SCS- CN method in Batticaloa lagoon area, Sri Lanka

The official journal of Agricultural Sciences published by the Faculty of Agriculture, Eastern University, Chenkalady, Sri Lanka.AGRIEAST publishes research information of broad practical significance pertaining to both tropical and subtropical Agriculture.

Analysis of Runoff Coefficient Prediction Based on LM-BP Neural Network

Runoff coefficients are affected by many factors, and their complex nonlinear relationships make it difficult to calculate accurate runoff coefficients using experimental physical models. In this paper, we improved the traditional BP neural network model based on the Levenberg-Marquardt method and established an S-type/S-type mathematical model of the relationship between runoff coefficients and influencing factors to predict each surface runoff coefficient under different rainfall conditions and different subsurface conditions, and compared it with other methods. The results calculated after the actual case simulation showed that the error of LM-BP neural network simulation was within the range of 0.03~0.09, the error was smaller, the calculation results were more accurate, and the prediction of runoff coefficient had the advantages of strong generalization ability and high prediction accuracy, which was a great improvement to the traditional rainfall-runoff coefficient best-fit relational fitting relationship method. Besides, in order to reduce the problem of sample overtraining, the more detailed the hydrological information, the better.