Rainfall-runoff Model Research Articles

Water Flow Analysis of the Kayanga-Anambé-Lake Waïma Hydrological Complex by Flow-flow Correlation and Rainfall-flow Modeling Approaches

This article presents an assessment of the surface water resources of the Kayanga-Anambé hydrological complex, located in West Africa. This hydrological complex, one of the main characteristics of which is exclusively agricultural and pastoral, is subject to a changing climate and strong anthropogenic pressures leading to resource use conflicts. Therefore, a better knowledge of water availability is essential to guide policies for the management and adaptation of this geosystem. Two approaches are used to estimate the contributions from the different sub-watersheds of the Kayanga: (i) a correlation between the flow rates of Wassadou and those of the other hydrometric stations of the Management and Planning Unit (UGP) of Casamance and eastern Senegal – in particular for filling the gaps observed in the data series from the Wassadou, Niandouba and Vélingara Pakane stations – and (ii) the implementation of a rainfall-runoff modeling approach at monthly time intervals, with the GR2M model, to reconstruct seasonal flows and contributions from the Kayanga-Anambé-Lake Waïma complex. The results show correlation coefficients which vary from 0.24 to 0.96 depending on the stations, showing a strong relationship between the flow rates of the Kayanga at Wassadou and those of the other stations. The maximum correlation coefficient is noted at the Mako station (0.96), on the Gambia river. Furthermore, a Nash of 0.86 was obtained with the GR2M model. Thus, the parameters X1 and X2 of the model were used to reconstruct the seasonal flows and contributions from the Kayanga-Anambé-Lac Waïma complex. This made it possible to have long enough time series of flow rates for a better estimation of water resources and their temporal fluctuation.

Estimation of possible extreme droughts for a dam catchment in Korea using a regional-scale weather model and long short-term memory network

Abstract To prepare measures to respond to climate-induced extreme droughts, consideration of various weather conditions is necessary. This study tried to generate extreme drought weather data using the Weather Research and Forecasting (WRF) model and apply it to the Long Short-Term Memory (LSTM), a deep learning artificial intelligence model, to produce the runoff instead of using conventional rainfall–runoff models. Finally, the standardized streamflow index (SSFI), the hydrological drought index, was calculated using the generated runoff to predict extreme droughts. As a result, the sensitivity test of meteorological data to runoff showed that using similar types of meteorological data could not improve runoff simulations with a maximum difference of 0.02 in Nash–Sutcliffe efficiency. During the drought year of 2015, the runoff generated by WRF and LSTM exhibited reduced monthly runoffs and more severe SSFI values below −2 compared to the observed data. This shows the significance of WRF-generated meteorological data in simulating potential extreme droughts based on possible physical atmospheric conditions using numerical representations. Furthermore, LSTM can simulate runoff without requiring specific physical data of the target catchment; therefore, it can simulate runoff in any catchment, including those in developing countries with limited data.

Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China

Satellite precipitation estimate (SPE) dedicated to reservoir inflow forecasting is very attractive as it can provide near-real-time information for reservoir monitoring. However, the potential of SPE retrievals with fine temporal resolution in supporting the high-quality pluvial flood inflow forecast and robust short-term operation of a reservoir remains unclear. In this study, the hydrological applicability of half-hourly Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM IMERG) heavy rainfall data was explored using a synthetic experiment of flood inflow forecast at sub-daily to daily lead times and resultant reservoir short-term operation. The event-based flood forecast was implemented via the rainfall–runoff model GR4H driven by the forecasted IMERG. Then, inflow forecast-informed reservoir multi-objective optimal operation was conducted via a numerical reservoir system and assessed by the risk-based robustness indices encompassing reliability, resilience, vulnerability for water supply, and flood risk ratio for flood prevention. Selecting the Wan’an reservoir located in eastern China as the test case, the results show that the flood forecast forced with IMERG exhibits slightly lower accuracy than that driven by the gauge rainfall across varying lead times. For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The pattern that the forecast errors in IMERG increase with the lead time is changed in the resultant inflow forecast series and dynamics in the robustness indices for the optimal operation decision. This indicates that the flood forecast model coupled with reservoir operation system could partly compensate the original SPE errors. Our study highlights the acceptable hydrological applicability of IMERG rainfall towards reservoir inflow forecast for robust operation, despite the intrinsic error in SPE.

Pre-event base flow as a useful indicator for flood forecasting

The hydrological cycle comprises several components, including precipitation, infiltration, evaporation, transpiration, and runoff, with the latter being a vital part of the cycle that connects precipitation to bodies of water. modelling is necessary due to limitations in standard hydrological measurement techniques, providing a better understanding of hydrological systems that were previously inaccessible, and making it essential for water resource management. Event-based rainfall-runoff modelling is a prevalent method due to its simplicity, but conventional models are limited in accounting for antecedent conditions. In order to tackle this problem, pre-event baseflow is proposed as an indicator for assessing catchment conditions for flood forecasting. This study employs a novel event-based rainfall-runoff equation that incorporates rainfall characteristics, improving hydrological modelling accuracy, and outperforms conventional models in predicting direct flow in most case studies. Overall, the study demonstrates the potential for the new equation to improve hydrological modelling accuracy and aid in a wide range of applications such as flood prediction, water resources management, and planning.

A General Method to Improve Runoff Prediction in Ungauged Basins Based on Remotely Sensed Actual Evapotranspiration Data

The availability of remotely sensed (RS) actual evapotranspiration (ET) provides a possibility for improving runoff prediction in ungauged basins. To develop a general practical method to improve runoff prediction by directly incorporating RS-ET into rainfall-runoff (RR) models, two modeling schemes are proposed: (i) using RS-ET as direct input; and (ii) using RS-ET as partial direct input. The principle is to use RS-ET in cases where the runoff prediction can be improved. The two schemes are compared in over 200 basins using three RR models (Xinanjiang model, SIMHYD, and GR4J) and RS-ET inverted from AVHRR, and the modeling results in ungauged basins are assessed using the spatial proximity method. Results show that: (i) it is beneficial to incorporate RS-ET into the Xinanjiang model for over 85% of the basins, but this is not the case for SIMHYD and GR4J models; (ii) further model improvements can be obtained by using RS-ET as partial direct input, and are achieved in 91.1%, 59.0%, and 53.2% of the basins for Xinanjiang, SIMHYD, and GR4J, respectively; and (iii) incorporation of RS-ET is more applicable for Xinanjiang while less so for GR4J, and the efficacy is superior for basins that are relatively arid and were originally poorly simulated. Overall, using RS-ET as partial direct input is recommended.

How sensitive are catchment runoff estimates to on-farm storages under current and future climates?

Storage of water in farm dams is important to support irrigation, stock requirements and domestic uses when reticulated water is unavailable. Farm dams that fill by intercepting landscape runoff change the total volume and seasonality of catchment streamflow, potentially impacting water policy outcomes. While numerous studies have quantified how climate change and farm dams independently change streamflow characteristics, few studies have investigated their interactions. This study investigates the interactions between farm dams and climate change in the Murray-Darling Basin of southern and eastern Australia. We use hydrological modelling that explicitly represent farm dams and remotely sensed data describing historical farm dam development to assess the impacts of farm dams for 112 catchments on catchment runoff under current and future climates. Our analysis compares current and future runoff estimates to those derived without explicitly incorporating farm dam impacts to better understand the importance of farm dams in water resources assessments.We find that modelling farm dams in these catchments improves the performance of the hydrological model relative to a traditional rainfall-runoff model ignoring the effects of farm dams. The current level of farm dam development in the Murray-Darling Basin is estimated to reduce annual catchment runoff by 13% (in the range of 8%–19%) under the historical climate across the 112 catchments investigated here. We find that explicitly modelling farm dams increases the sensitivity of simulated streamflow to climate by approximately 10%. This is primarily because farm dams introduce additional catchment storage and water extractions that do not directly follow processes represented in traditional rainfall-runoff models.Our analysis offers important information for water policymakers in Australia and globally. Firstly, explicit modelling of farm dams is critical to understanding the future availability of water resources. Secondly, explicit modelling of farm dams allows their impacts on catchment runoff to be explored under current and future climates, and for policies to manage the impacts of dams to be investigated.

Integrated tire wear buildup and rainfall-runoff model to simulate tire wear particles in stormwater

This paper presents an approach to integrate tire wear buildup and rainfall-runoff models to simulate tire wear buildup on road surfaces and its subsequent transport in stormwater runoff events. To do so, a buildup model is presented based on vehicle kilometers traveled, vehicle type, vehicle speed, and road roughness within a watershed. This buildup model was integrated into an EPA SWMM model that simulated the runoff of tire wear particles in twelve watersheds in the San Francisco, CA bay area. Results demonstrate that tire wear particle buildup within the watersheds ranged between 0.4 and 0.51 (kg/km2) per hour. Applied to the SWMM model, total event mean tire wear concentrations ranged between 0.5 and 67 μg/L. These concentrations were linearly correlated to depth-integrated samples collected at the outlet of each of the watersheds (R2 = 0.66). The proposed modeling approach can ultimately be applied to create solutions to an emerging stormwater contaminant.

AirGRteaching: an open-source tool for teaching hydrological modeling with R

Abstract. Hydrological modeling is at the core of most studies related to water, especially for anticipating disasters, managing water resources, and planning adaptation strategies. Consequently, teaching hydrological modeling is an important, but difficult, matter. Teaching hydrological modeling requires appropriate software and teaching material (exercises, projects); however, although many hydrological modeling tools exist today, only a few are adapted to teaching purposes. In this article, we present the airGRteaching package, which is an open-source R package. The hydrological models that can be used in airGRteaching are the GR rainfall-runoff models, i.e., lumped processed-based models, allowing streamflows to be simulated, including the GR4J model. In this package, thanks to a graphical user interface and a limited number of functions, numerous hydrological modeling exercises representing a wide range of hydrological applications are proposed. To ease its use by students and teachers, the package contains several vignettes describing complete projects that can be proposed to investigate various topics such as streamflow reconstruction, hydrological forecasting, and assessment of climate change impact.

Impact and analysis of urban water system connectivity project on regional water environment based on Storm Water Management Model (SWMM)

SWMM stands as a dynamic rainfall-runoff urban hydrological model, crafted by the U.S. Environmental Protection Agency. It adeptly replicates urban rainfall production and ground runoff, and the Pressure-State-Response (PSR) theory artfully reflects human-environment interactions. Urban Water System Connectivity (UWSC) fortifies connections among regional rivers, transforming them into an intricate, dense network. To study the improvement of the regional water environment by the UWSC project, this study takes the UWSC project in Runan County, Zhumadian, China as the research object, constructs a hydrodynamic and water quality model based on SWMM, and simulates it under different rainfall situations. At the same time, the ecological service value theory and PSR theory are used to establish an ecological effect index system to analyze the ecological effect before and after the UWSC project. This paper evaluates the urban water system connectivity project from three aspects: hydrodynamic, water quality, and ecological effects. Results underscore that the post-UWSC era witnesses decreased river flood flows in varied design rainfalls, accompanied by delayed flood peaks. Under P = 10a and P = 20a rainfalls, peak flow reductions of 6.6% and 4.96% respectively, manifest with peak time lags of 0.17h. Flow rate adjustments align with flow behavior, exhibiting decreased peak flow rates accompanied by temporal lags. Initial river flow increases by about 30%, amplifying river water body exchange capacity. Following the UWSC implementation, the study area's five primary rivers display TSS reductions ranging from 46% to 86.94%, COD from 36.98% to 92.43%, TN from 45.9% to 92.96%, TP from 35.71% to 84.78%, and NH3–N from 38.52% to 92.19%. Simulated point source pollution, under comparable initial conditions, indicates pollutant decay rates escalating from 3.2%–20%–35.71%–92.43%. Ecological impact ascends from 6.33 × 108 RMB to 9.65 × 108 RMB, reflecting a 52.5% escalation. The UWSC initiative enriches urban river flood resilience and water quality, emblematic of urban water ecological civilization progress.

Effect of Surface Characteristics and Rainfall in Infiltration Rate: A Case Study of Kaligandaki River Basin

Study of infiltration characteristics is crucial for effective watershed management, as it helps determine the water availability for crops, the need of irrigation, groundwater recharge, and surface runoff. This study is aimed at identifying the relation between infiltration rate and surface characteristics as well as rainfall rate in the Kaligandaki River basin of Nepal using Hydrologic Modelling System (HEC-HMS). HEC-HMS is a physically based, semi-distributed model that uses the weather and spatial information of soil and landuse of the basin for deriving the inputs for the model. A hydrological rainfall-runoff model is developed for the Kaligandaki River basin, with outlet at hydrological station 420, Kotgau, of Department of Hydrology and Meteorology (DHM). The model was then validated at DHM station 420, Angsing, of Kaligandaki River. Model generated subbasin wise infiltration rates and existing landuse were analysed and it was found that sub-basins with dominant landuse as forest had high infiltration rate among prevailing landuse types. Conversely, low infiltration rate was observed for barren area, snow/glacier and grassland landcover type dominated subbasins. A positive correlation among rainfall rate and infiltration rate was seen with linear increase in infiltration rate for an exponential increase of average rainfall intensity. Infiltration rate was found to have positive correlation with the Hydrologic Soil Group types as suggested by NRCS. Moreover, the study signified a greater impact of landcover over soil types on infiltration rates. Whereas, no significant impact of basin slope on infiltration rate could be identified, which may be due to preclusion of multiple topographic features. Understanding the impacts of rainfall intensity and surface characteristics on infiltration enables proactive measures to mitigate the issues like landslide and reduce flooding and soil erosion by improving infiltration rates through surface condition assessment.

Developing a stochastic hydrological model for informing lake water level drawdown management

Winter drawdown (WD) is a common lake management tool for multiple purposes such as flood control, aquatic vegetation reduction, and lake infrastructure maintenance. To minimize adverse impacts to a lake’s ecosystem, regulatory agencies may provide managers with general guidelines for drawdown and refill timing, drawdown magnitude, and outflow limitations. However, there is significant uncertainty associated with the potential to meet management targets due to variability in lake characteristics and hydrometeorology of each lake’s basin, making the use of modeling tools a necessity. In this context, we developed a hydrological modeling framework for lake water level drawdown management (HMF-Lake) and evaluated it at 15 Massachusetts lakes where WDs have been applied over multiple years for vegetation control. HMF-Lake is based on the daily lake water balance, with inflows simulated by a lumped rainfall-runoff model (Cemaneige-GR4J) and outflow rate calculated by a modified Target Storage and Release Based Method (TSRB). The model showed a satisfactory performance of simulating historical water levels (0.53 ≤ NSE ≤ 0.86), however, uncertainties from meteorological inputs and TSRB determined lake outflow rate affected the result accuracy. To account for these uncertainties, the model was executed stochastically to assess the ability of study lakes to follow the Massachusetts’ general WD guidelines: drawdown by Dec 1 and fully refilled by Apr 1. By using the stochastic HMF-Lake, the probabilities of each lake to reach the drawdown level by Dec 1 were calculated for different drawdown magnitudes (1–6 ft). The probability results suggest it was generally less possible for most of study lakes to achieve a drawdown of 3 ft or more by Dec 1. Moreover, we employed the stochastic model to derive the annual latest refill starting dates that ensure a 95 % probability of reaching the normal water level by Apr 1. We found starting a refill in March for drawdowns up to 6 ft was feasible for most of study lakes. These results provide lake managers with a quantitative understanding of the lake’s ability to follow the state guidelines. The model may be used to systematically evaluate current WD management strategies at state or regional scales and support adaptive WD management under changing climates.

SUITABLE BOUNDARY LOCATION IDENTIFCATION FOR RAINFALL-RUNOFF AND SURGE MODEL COUPLING TO EVALUATE COMPOUND FLOOD HAZARDS IN COASTAL REGIONS

Flooding in many coastal regions is exacerbated due to complex interactions of multiple flood drivers such as rainfall-runoff and coastal surge which can occur simultaneously or sequentially during storm events, leading to compound flooding hazards. The Texas Gulf Coasts (USA) are especially vulnerable from compound flooding due to frequent occurrence of tropical storms which bring strong winds to drive storm surge and heavy rainfall to generate inland flooding. Large scale studies {Coastal Texas Restoration and Protection Study (CTX, 2021), Texas General Land Office’s Texas River Basin Study, Base Level Engineering Study} are being conducted by state and federal agencies for flood hazard assessment and for development of mitigation and abatement strategies for reducing this risk and increasing community resilience. Traditionally, large scale storm surge models do not account for runoff contributions to water levels in the interior of their domain. Riverine models for their part use either normal depth or mean high water levels to assign downstream boundary conditions within tidally influenced area, thus ignoring effects of storm surge. In this Planning Assistance to States (PAS) study, located in the Lower Clear Creek and Dickinson Bayou watersheds (Texas, USA), we have evaluated rainfall-runoff and storm surge interactions to determine suitable locations to use as model boundary conditions for exchanging data between rainfall-runoff and surge models under compound flooding conditions.

Development of a Conceptual Snow Sub-Model: Application in Meteorological Stations, Slovakia

Abstract A semi-distributed rainfall-runoff Approximate Redistributive Balance (ARB) model is currently in development as a tool for the assessment and analysis of the water management balance at the level of micro-basins on the territory of Slovakia. For the winter season, it is necessary to supplement the model with a sub-model for calculating the snow water equivalent (SWE) with a comparatively low amount of the input data necessary. Since SWE models generally operate in a daily time step, a new sub-model was developed and tested in monthly and weekly time steps in 30 meteorological stations in the north of Slovakia. When compared in a weekly time step with the snow sub-model of the HBV rainfall-runoff model and when the impact of switching from a monthly to weekly time step on the quality of the runoff simulation was evaluated, the results showed that the snow sub-model does react to sudden snowmelt better when compared to the modified version of the HBV snow sub-model used. Using a weekly time step for the snow sub-model in a monthly ARB model runoff simulation showed an increase of accuracy (NSE change from 0.89 to 0.92) in one case, while maintaining the same level of accuracy in the second one.

Characterising the regional growth of on-farm storages and their implications for water resources under a changing climate

Water demand for agricultural activities has caused a growth in small on-farm storages (i.e., ≥ 0.1 ha to ≤ 10 ha) in many areas worldwide, thereby increasing the capture of landscape runoff which otherwise would enter the catchment drainage system. The cumulative effects of these on-farm storages (colloquially known as farm ponds or farm dams) can result in reduced mean annual flows, particularly during dry years. Climate change, which will very likely exacerbate extreme weather conditions including droughts, may accelerate the growth of farm dams in semi-arid agricultural areas with limited access to surface water. The aim of this study is to evaluate farm dam development and total farm dam water volumes at regional scales using Landsat water indices (WIs). The Murray-Darling Basin (MDB, 1.061 million km2) in south and eastern Australia, which has experienced a sustained growth in farm dams in the 1990s and early 2000s, is used as a case study. Using a pixel validation dataset, four WIs were trialled for their capabilities to detect the presence of water in farm dams. Of the WIs tested here, AWEIshadow showed the best performance. As a result, AWEIshadow was used to establish the year in which water was first observed (i.e., its year of commission) in 727,081 farm dams across the MDB and to calculate the total farm dam volume in the region. Farm dams’ volume in the MDB increased more than twofold between 1990 and 2020, from 1241.0 MCM (million cubic meters) to 2,629.5 MCM. The growth differed across MDB regions, generally accelerating during the ‘Millennium Drought’ (2001–2009). In the entire MDB, there was rapid development in the 1990s with 3.1% growth. Growth was 2.5% from 2000 to 2010, tapering off to 0.6% from 2010 to 2020.In Robertson et al. (this issue), the year-by-year growth in farm dams’ volume were used in a rainfall-runoff model thereby incorporating the time-varying effects of farm dams. Robertson et al. (this issue) show that explicitly modelling farm dams improves the model’s performance relative to a traditional rainfall-runoff model that ignores the effects of farm dams. Hence, this improves the capability of assessing the direct impacts of farm dams and their potential growth in climate change studies.

Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California

Abstract. Environmental streamflow management can improve the ecological health of streams by returning modified flows to more natural conditions. The Ecological Limits of Hydrologic Alteration (ELOHA) framework for developing regional environmental flow criteria has been implemented to reverse hydromodification across the heterogenous region of coastal southern California (So. CA) by focusing on two elements of the flow regime: streamflow permanence and flashiness. Within ELOHA, classification groups streams by hydrologic and geomorphic similarity to stratify flow–ecology relationships. Analogous grouping techniques are used by hydrologic modelers to facilitate streamflow prediction in ungaged basins (PUB) through regionalization. Most watersheds, including those needed for stream classification and environmental flow development, are ungaged. Furthermore, So. CA is a highly heterogeneous region spanning gradients of urbanization and flow permanence, which presents a challenge for regionalizing ungaged basins. In this study, we develop a novel classification technique for PUB modeling that uses an inductive approach to group perennial, intermittent, and ephemeral regional streams by modeled hydrologic similarity followed by deductively determining class membership with hydrologic model errors and watershed metrics. As a new type of classification, this hydrologic-model-based classification (HMC) prioritizes modeling accuracy, which in turn provides a means to improve model predictions in ungaged basins while complementing traditional classifications and improving environmental flow management. HMC is developed by calibrating a regional catalog of process-based rainfall–runoff models, quantifying the hydrologic reciprocity of calibrated parameters that would be unknown in ungaged basins and grouping sites according to hydrologic and physical similarity. HMC was applied to 25 USGS streamflow gages in the “South Coast” region of California and was compared to other hybrid PUB approaches combining inductive and deductive classification. Using an average cluster error metric, results show that HMC provided the most hydrologically similar groups according to calibrated parameter reciprocity. Hydrologic-model-based classification is relatively complex and time-consuming to implement, but it shows potential for simplifying ungaged basin management. This study demonstrates the benefits of thorough stream classification using multiple approaches and suggests that hydrologic-model-based classification has advantages for PUB and building the hydrologic foundation for environmental flow management.

Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India.

The growing concerns surrounding water supply, driven by factors such as population growth and industrialization, have highlighted the need for accurate estimation of streamflow at the river basin level. To achieve this, rainfall-runoff models are widely employed as valuable tools in watershed management. For this specific study, two modelling approaches were employed: the Soil and Water Assessment Tool (SWAT) model and a set of eight artificial intelligence (AI) models. The AI models consisted of seven data-driven approaches, namely k-nearest neighbour regression, support vector regression, linear regression, artificial neural networks, random forest regression, XGBoost, and Histogram-based Gradient Boost regression. Additionally, a deep learning model known as Long Short-Term Memory (LSTM) was also utilized. The study focused on monthly streamflow modelling in the Murredu River basin, with a calibration period from 1999 to 2003 and a validation period from 2004 to 2005, spanning a total of 7years from 1999 to 2005. The results indicated that all nine models were generally suitable for simulating the rainfall-runoff process, with the LSTM model demonstrating exceptional performance in both the calibration (R2 is 0.97 and NSE is 0.96) and validation (R2 is 0.97 and NSE is 0.92) periods. Its high coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) values indicated its superior ability to accurately model the rainfall-runoff relationship. While the other models also produced satisfactory results, the findings suggest that selecting the most efficient model, such as the LSTM model, could significantly contribute to the effective management and planning of sustainable water resources in the Murredu watershed.

Impact of suspicious streamflow data on the efficiency and parameter estimates of rainfall–runoff models

ABSTRACT Many sources of error in hydroclimatic data can affect hydrological modelling, yet the impact of streamflow data quality is poorly quantified. This work aims to investigate whether inconsistencies found in streamflow time series commonly available for hydrological studies (typically in national streamflow archives) have an impact on the efficiency and the parameter estimates of rainfall–runoff models. Hydroclimatic data were gathered at the hourly time step over the period 1998–2018 for a set of 30 catchments in France. Hydrological modelling was carried out with the lumped conceptual GR5H (standing for modèle du Génie Rural à 5 paramètres Horaire, i.e. Hourly 5-parameter rural engineering model) model. A typology of “realistic” suspicious streamflow was established to set up several error models in order to corrupt the data. Our results suggest that common suspicious streamflow data do not have a strong impact on model efficiency and parameter estimates overall, but may be an important source of instability and lack of robustness when working on a single catchment.

Statistical Analysis to Quantify the Impact of Map Type on Estimating Peak Discharge in Non-Instrumented Basins

The calculation of peak discharge in non-instrumented basins requires including morphometric parameters, which in turn depend on the map type used. This study analyses the impact of and variation in peak discharges of the Caño Ricaurte basin, Colombia, based on three types of maps at different resolution scales. The reference map used was the map made for the detailed designs of the channel analysed, which was extracted from the Master Plan of the City. Additionally, maps from a 90 × 90 m digital elevation model and contour lines extracted from Google Earth were used. The time of concentration was determined by different equations (Kirpich, Témez, Bureau, and TR-55) using the mapping methods described above, and the peak discharge was determined using rainfall-runoff models.

Application for the parametrization and automatic running of the HEC-HMS rainfall-runoff model

Application for the parametrization and automatic running of the HEC-HMS rainfall-runoff model

Evaluation of IHACRES, Conceptual Rainfall Runoff Model and Artificial Neural Network Models in Simulation and Stream flow Prediction in Bakhtiary River Basin

Evaluation of IHACRES, Conceptual Rainfall Runoff Model and Artificial Neural Network Models in Simulation and Stream flow Prediction in Bakhtiary River Basin