Rainfall-runoff Model Research Articles

Assessing climate change impact on river flow extreme events in different climates of Iran using hybrid application of LARS-WG6 and rainfall-runoff modeling of deep learning

Climate change and its side effects on weather variables and water resources is one of the recent international concerns. This research aims to project future changes in weather variables due to climate change for different emission scenarios of RCP2.6, RCP4.5 and RCP8.5 to assess and compare climate change impact on river flow variation based on hybrid application of deep learning and LARS-WG6 in different climates of Iran (CSA: cold semi-arid; HT: humid temperate; CA: cold arid). For this purpose, best combination of daily rainfall and discharges data with different lags were selected as inputs in rainfall-runoff modeling using Convolutional Neural Networks (CNN) model. Predicted meteorological data by the LARS-WG6 in the future (2021-2040) were then used as inputs for the selected CNN model and consequently runoff was predicted. Obtained findings showed that projected rainfall changes in 2021-2040 compared to the base period in scenarios of RCP2.6, 4.5 and 8.5 will be +14, +11, +6% in CA, +8, +1, +2% in CSA and +3.7, -1, -2.6% in HT regions, respectively. Whereas, maximum discharge in CA climate in scenario RCP2.6 is projected to raise about 18%, in the HT climate shows a reduce of about 5% in RCP8.5 and in CSA will decrease by about 5% in RCP2.6. Considering the effect of climate as well as seasonal changes in each region on the amount and direction of the climate change impacts on meteorological and hydrologic variables changes, assessing climate change impact in each region is necessary to extract adaptation strategies in water resources management.

Comparing the Runoff Decompositions of Small Experimental Catchments: End-Member Mixing Analysis (EMMA) vs. Hydrological Modelling

This study is focused on the comparison of streamflow composition simulated with three well-known rainfall–runoff (RR) models (ECOMAG, HBV, SWAT) against hydrograph decomposition evaluated with End-Member Mixing Analysis (EMMA). In situ observations at two small mountain testbed catchments located in the south of Pacific Russia are used. All applied RR models and EMMA analysis demonstrate that two neighboring catchments disagree significantly on the mutual dynamics of the runoff sources. The RR models' benchmark test is based on proximity to EMMA hydrograph composition. Different aggregation intervals (season, month, and pentad) were applied to find a reasonable generalization period ensuring the clarity of results. ECOMAG is most conformable to EMMA outcome; HBV reflects flood events well enough; SWAT exhibits distinctive behavior compared to the other models. It is shown that, along with standard efficiency criteria of simulated and observed runoff proximity, EMMA analysis might provide useful auxiliary information for the validation of modelling results.

Direct runoff hydrograph model’s collation for a Pakistan’s region

Rainfall-Runoff modeling is among the classical applications of hydrology. This paper examines the results of 3 hydrologic approaches, particularly Clark Model, Nash Model, and Geomorphic Instantaneous Unit Hydrograph Model. Assumptions are forwarded for the long run use of the Rawalpindi Division, Pakistan’s Small Dam Organization. The catchment of Shahpur Dam was an area under consideration for the study. The Digital elevation model (DEM) was implicated to measure the Nash and Clark model’s geomorphic parameters. Using ArcGIS, catchment satellite imagery was processed to estimate geomorphological parameters. The models have been applied to multiple storm cases. Geographic Instantaneous Unit Hydrograph (GIUH) model gave direct surface runoff hydrograph, whereas, on measured precipitation excess rainfall hyetograph was obtained. Four types of statistical parameters, namely efficiency of the model (EFF), percentage defect in time to attain peak (PETP), percent defect in attained peak Q (PEP), percentage defect in runoff rate (PEV) are used to check model’s efficiency. The comparison is done between the findings of Clark and Nash GIUH models and the original Clark and Nash models. It was observed that GIUH models are equally good even when optimization is done for Clark and Nash model’s parameters. Since the results obtained from these models are more credible, so, these models can be used in ungauged catchments to estimate the hydrographs.

A novel ensemble algorithm based on hydrological event diversity for urban rainfall–runoff model calibration and validation

Hydrological models such as the stormwater management model (SWMM) are essential tools for managing water resources. Model performance typically relies on calibration to historic rainfall–runoff events. However, traditional calibration techniques suffer many issues, such as ad-hoc event selection, ignoring model parameter uncertainty, and poor validation techniques. We introduce a novel ensemble framework that relies on data-driven event selection of calibration and validation events to achieve robust model generalisation.The proposed framework is applied to SWMM models for two urbanised catchments in Southern Ontario, Canada. The framework uses unsupervised machine learning to cluster rainfall–runoff events. Events in different clusters have diverse characteristics; sampling events from each cluster ensures a diverse set of events for calibration. Validation events can be sampled in the same manner, which ensures similarity between calibration and validation data. We study the effects that the calibration event cluster has on model sensitivity and generalisation. Only minor differences in model parameter sensitivity were observed between different clusters. However, the cluster(s) used for calibration were found to be very important for model generalisation. Next, we generate ensemble predictions, which use multiple SWMM models, each calibrated to different events. Ensemble performance is compared to the single-model baseline and across increasing numbers of ensemble members. Results demonstrate improvements in generalisation and decrease in prediction variance across increasing ensemble size. Finally, we compare several different techniques for ensemble combination, which increase in complexity from simple averaging to ANN-based stacking augmented with exogenous input features. The ANN-based stacking performs the best according to two out of three evaluation criteria. Simple averaging, while inferior in performance to stacking, consistently outperforms the single model baseline. The novel framework proposed in this work provides a data-driven, adaptable alternative to conventional calibration frameworks that have been proven to improve model performance.

Analysis of Weather Radar Datasets through the Implementation of a Gridded Rainfall-Runoff Model

Analysis of Weather Radar Datasets through the Implementation of a Gridded Rainfall-Runoff Model

Increasing extreme flood risk under future climate change scenarios in South Korea

The endeavor to reduce the multiple risks of climate extremes takes the future of civil society. Many global climate models (GCMs) and regional climate models (RCMs) have been developed to project the future changing climate. To quantitatively evaluate future flood risks, the results of the current hydrological model based on simulated data from the climate model should be analyzed. This paper focuses the connection of technological and policy aspects for the future flood management in South Korea. Accordingly, this study assesses the changes in flood quantiles based on the two representative concentration pathways (RCP4.5 and RCP8.5) scenarios. The HadGEM3-RA regional climate model is used to predict future changes in extreme rainfall events, and the regional quantile delta mapping (RQDM) method is adopted to correct for any inherent bias in the climate models. The climate change scenario is divided into the following four periods: S0 (1976–2005), S1 (2011–2040), S2 (2041–2070), and S3 (2071–2100). A regional frequency analysis (RFA) is applied to estimate the rainfall quantiles as input data in rainfall-runoff model. The flood quantiles are simulated using the HEC-1 (rainfall-runoff) model for historical and future periods, and the variations in the flood quantiles are calculated quantitatively for the five major river basins (Hangang, HAN; Nakdonggang, NAK; Geumgang, GUM; Seomjingang, SJG; Yeongsangang, YSG) in South Korea. As a result, it is found that the highest flood risk can occur in the YSG basin, and the YSG basin should be considered the most urgent and important river basin for climate change adaptation plans compared with other major basins in South Korea.

Evaluating regional climate model simulations at Wadi El Abid catchment (northeastern Tunisia) using HBV rainfall-runoff model

Evaluating regional climate model simulations at Wadi El Abid catchment (northeastern Tunisia) using HBV rainfall-runoff model

Hydrological Model Evaluation of Ground, GPM IMERG, and CHIRPS precipitation data for Shabelle Basin in Ethiopia

Consistent and accurate precipitation measurement is a fundamental input component of the hydrological model. However, most developing countries, including Ethiopia, lack consistent and precise precipitation measurements. Nevertheless, satellitebased precipitation data may play a crucial role in bridging the data gap and providing precipitation inputs for rainfall-runoff models in regions with limited ground data. The study compares the ground precipitation data from the Ethiopian meteorological agency and SWALIM to the satellite-based GPM_IMERG and CHIRPS precipitation products. HECHMS software was used to model rainfall-runoff simulation, and the study area’s spatial characteristics and model development were processed with ArcGIS and HEC- GeoHMS. Since the data on hand is enough and fit for continuous event assessment, the deficit and constant loss method, a continuous event-based loss method, was tested, and found good model performance results. The onhand 14 years of precipitation and discharge data were divided into seven years for calibration and seven years for validation. Using Nash–Sutcliffe efficiency (ENS), coefficient of determination (R2), Root Mean Square Error (RMSE), and percentage bias objective functions, the performance of the satellite precipitation was evaluated. During calibration periods (Jan 2013 to Dec 2009), the model performance showed ENS values of 0.65, R2 values of 0.68, RMSE values of 0.6, a percent bias of 0.58% for the metrological stations, ENS values of 0.61, R2 values of 0.64, RMSE values of 0.6, a percent bias of 5.38% for CHIRPS, and ENS values of 0.63, R2 values of 0.66, RMSE values of 0.7, and a percent bias of 18.9% for GMP_IMERG satellite precipitation products. During validation periods (Jan 2010 to Dec 2016), the model performance showed ENS values of 0.75, R2 values of 0.78, RMSE values of 0.6, a percent bias of -16.9% for the metrological stations, ENS values of 0.71, R2 values of 0.74, RMSE values of 0.6, a percent bias of -22.12% for CHIRPS2, and ENS values of 0.76, R2 values of 0.76, RMSE values of 0.5, and a percent bias of -0.03% for GPM_IMERG satellite precipitation products. The study showed that the HEC-HMS model performed well and gave very good results for the hydrological model for the CHIRPS and GPM_IMERG rainfall products. The study also indicates that the model outperformed well during the validation period. Overall, the study found that the simulated GPM_IMERG product gave better results than the simulated CHIRPS product.

The Revised Curve Number Rainfall–Runoff Methodology for an Improved Runoff Prediction

The Curve Number (CN) rainfall–runoff model is a widely used method for estimating the amount of rainfall and runoff, but its accuracy in predicting runoff has been questioned globally due to its failure to produce precise predictions. The model was developed by the United States Department of Agriculture (USDA) and Soil Conservation Services (SCS) in 1954, but the data and documentation about its development are incomplete, making it difficult to reassess its validity. The model was originally developed using a 1954 dataset plotted by the USDA on a log–log scale graph, with a proposed linear correlation between its two key variables (Ia and S), given by Ia = 0.2S. However, instead of using the antilog equation in the power form (Ia = S0.2) for simplification, the Ia = 0.2S correlation was used to formulate the current SCS-CN rainfall–runoff model. To date, researchers have not challenged this potential oversight. This study reevaluated the CN model by testing its reliability and performance using data from Malaysia, China, and Greece. The results of this study showed that the CN runoff model can be formulated and improved by using a power correlation in the form of Ia = Sλ. Nash–Sutcliffe model efficiency (E) indexes ranged from 0.786 to 0.919, while Kling–Gupta Efficiency (KGE) indexes ranged from 0.739 to 0.956. The Ia to S ratios (Ia/S) from this study were in the range of [0.009, 0.171], which is in line with worldwide results that have reported that the ratio is mostly 5% or lower and nowhere near the value of 0.2 (20%) originally suggested by the SCS.

Development of a New Event-Based Rainfall-Runoff Equation Based on Average Rainfall Intensity During an Event

Event-based rainfall-runoff models are practical tools commonly used to predict catchments’ response to a rainfall event. However, one of the main concerns is that the characteristics of rain events are neglected in the model development. This paper develops a novel event-based rainfall-runoff equation to incorporate rainfall characteristics into account. The performance of the new equation is evaluated based on the root mean square error, Nash–Sutcliffe efficiency coefficient, and per cent bias for 13,339 rainfall-runoff events between 2005 and 2020 over 23 catchments across New Zealand and Australia with oceanic, mediterranean, tropical, subtropical, and semiarid climates. Compared to the previous event-based models, the new equation shows an improvement in runoff estimation in almost all case studies. Furthermore, considering the new equation is simple, efficient, and takes the rain event duration into account, the new equation has the potential to become a robust alternative method to the conventional curve number method in hydrological engineering projects.

Calibration of Watershed Conceptual Models Using Local and Global ptimization

Many issues related to water resources require the solution of optimization problems. Optimization problems can be quite difficult such as the calibration of many conceptual watershed models. Conceptual watershed models are formulated using empirical relationships between hydrological variables observed in nature or field experiments .The success of automatic calibration depends mainly on the choice of optimization method. Most early attempts to calibrate watershed models have been based on local-search optimization methods. Local optimization methods are not designed to handle the presence of multiple regions of attraction, multi-local optima, insensitivities and parameter interdependencies, and other problems encountered in the calibration of watersheds models. It is therefore imperative that global optimization procedures that are capable of dealing with these various difficulties be employed. In this study, one local search optimization method and one global search method are used to calibrate the parameters a simple tenparameter rainfall-runoff model. The local search method is the well-known Rosenbrock's direct search method; the global search method is the newly developed Shuffled Complex Evolution (SCE) method . The results revealed that Shuffle Complex Evolution optimization method is more superior to Rosenbrok’s direct search method. The results confirmed the finding of many watershed modellers about the dependency of Rosenbrock’s method on the choice of initial search points. It further adds that Rosenbrock’s method is only effective when the initial search points are taken within 5 % or less from the true optimum parameter set. The results indicate that a proper choice of optimization methods can enhance the possibility of obtaining unique and conceptually realistic parameter estimate.

Application of Neural Networks for Hydrologic Process Understanding at a Midwestern Watershed

The Shell Creek Watershed (SCW) is a rural watershed in Nebraska with a history of chronic flooding. Beginning in 2005, a variety of conservation practices have been employed in the watershed. Those practices have since been credited with attenuating flood severity and improving water quality in SCW. This study investigated the impacts of 13 different controlling factors on flooding at SCW by using an artificial neural network (ANN)-based rainfall-runoff model. Additionally, flood frequency analysis and drought severity analysis were conducted. Special emphasis was placed on understanding how flood trends change in light of conservation practices to determine whether any relation exists between the conservation practices and flood peak attenuation, as the strategic conservation plan implemented in the watershed provides a unique opportunity to examine the potential impacts of conservation practices on the watershed. The ANN model developed in this study showed satisfactory discharge–prediction performance, with a Kling–Gupta Efficiency (KGE) value of 0.57. It was found that no individual controlling variable used in this study was a significantly better predictor of flooding in SCW, and therefore all 13 variables were used as inputs, which resulted in the satisfactory ANN model discharge–prediction performance. Furthermore, it was observed that after conservation planning was implemented in SCW, the magnitude of anomalous peak flows increased, while the magnitude of annual peak flows decreased. However, more comprehensive assessment is necessary to identify the relative impacts of conservation practices on flooding in the basin.

Flood Runoff Simulation under Changing Environment, Based on Multiple Satellite Data in the Jinghe River Basin of the Loess Plateau, China

Understanding the hydrological surface condition changes, climate change and their combined impacts on flood runoff are critical for comprehending the hydrology under environmental changes and for solving future flood management challenges. This study was designed to examine the relative contributions of the hydrological surface condition changes and climate change in the flood runoff of a 45,421-km2 watershed in the Loess Plateau region. Statistical analytical methods, including Kendall’s trend test, the Theisen median trend analysis, and cumulative anomaly method, were used to detect trends in the relationship between the climatic variables, the normalized difference vegetation index (NDVI), land use/cover change (LUCC) data, and observed flood runoff. A grid-cell distributed rainfall–runoff model was used to detect the quantitative hydrologic responses to the climatic variability and land-use change. We found that climatic variables were not statistically significantly different (p > 0.05) over the study period. From 1985 to 2013, the cropland area continued to decrease, while the forest land, pastures, and residential areas increased in the Jinghe River Basin. Affected by LUCC and climate change, the peak discharges and flood volumes decreased by 8–22% and 5–67%, respectively. This study can provide a reference for future land-use planning and flood runoff control policy formulation and for revision in the study area.

A COMPARISON OF SIMULATED RUNOFF BASED ON GROUND RAIN GAUGES AND PERSIANN-CDR SATELLITE PRECIPITATION RECORDS USING SWAT MODEL

Abstract. Easy access to valid climatic data has always played a fundamental role in progressing hydrological studies. That is why numerous satellite-based precipitation products (SPPs) have been generated in the contemporary era. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is considered one of the most popular climatic databases which started to produce daily rainfall data with 0.25° × 0.25° temporal and spatial resolutions in 1983. The aim of this research is to evaluate how well PERSIANN-CDR has performed in a rainfall-runoff modeling application over the period of 1994 to 2015. In this regard, using Soil & Water Assessment Tool (SWAT), two rainfall-runoff models based on Ground-based Rain Gauge stations (GRGs) and PERSIANN-CDR precipitation records were developed for the Chelgerd sub-basin, which is the main branch of the Zayandeh-Roud Basin in central Iran, in order to analyze how accurate the simulated runoff by PERSIANN-CDR database is. Comparing the developed SWAT model calibration results using the satellite database precipitation (NS = 0.78, P-Factor = 0.52, and R-Factor = 0.41) to calibration results of the developed model based on GRGs (NS = 0.81, P-Factor = 0.54, and R-Factor = 0.42) showed that although PERSIANN-CDR precipitation magnitudes were typically less than GRGs records, accuracy indicators of simulated runoffs to Ghale-Shahrokh were almost the same.

Analisis Debit Banjir Rencana Daerah Tangkapan Air Waduk Tugu Menggunakan HEC-HMS

Over the course construction of Tugu Reservoir, it is proposed to build a spillway gate to increase its performance as a flood control infrastructure. The construction planning of spillway gate requires a calculation of design flood discharge for Tugu Reservoir Catchment Area. The latest calculation was carried out during its construction planning in 2010. Therefore, it is necessary to evaluate and recalculate the design flood discharge using the latest data. This study aims to model design flood discharge of Tugu Reservoir Catchment Area using HEC-HMS software. This software is able to simulate rainfall-runoff modeling in a catchment area. Based on field conditions, Tugu Reservoir catchment has parameters, curve number value of 79, impervious value of 5%, and a lag time of 4.81 minutes. The result of the HEC-HMS modeling shows that the design flood discharge of Tugu Reservoir for Q100 is 369,30 m3/s; Q1000 is 656,70 m3/s; and QPMF is 995.30 m3/s. Based on the test using Creager Graph, the design flood discharge for Tugu Reservoir is still in the reasonable category with C value below 100. The result of the HEC-HMS modeling is not much different from the calculation result of Tugu Reservoir Construction Planning in 2010.

On the application of rainfall projections from a convection-permitting climate model to lumped catchment models

Climate change is predicted to increase rainfall intensity in tropical regions. Convection permitting (CP) climate models have been developed to address deficiencies in conventional climate models that use parameterised convection. However, to date, precipitation projections from CP climate models have not been used in conjunction with hydrological models to explore potential impacts of explicit modelling of convective rainfall on river flows in the tropics. Here we apply the outputs of a continental scale CP climate model as inputs to lumped rainfall-runoff models in Africa for the first time. Applied to five catchments in the Lake Victoria Basin, we show that the CP climate model produces greater river flows than an equivalent model using parameterised convection in both the current and future (c. 2100) climate. However, the location of the catchments near to Lake Victoria results in limited changes in extreme rainfall and river flows relative to changes in mean rainfall and river flows. Application of CP model rainfall data from an area where rainfall extremes change more than the change in mean rainfall to the rainfall-runoff model does not result in significant changes in river flows. Instead, this is shown to be a result of the rainfall-runoff model structure and parameterisation, which we posit is due to large-scale storage in the catchments associated with wetland cover, that buffers the impact of rainfall extremes. Based on an assessment of hydrological attributes (wetland coverage, water table depth, topography, precipitation, evapotranspiration and river flow) using global-scale datasets for the catchments in this research, this buffering may be extensive across humid regions. Application of CP climate model data to lumped catchment models in these areas are unlikely to result in significant increases in extreme river flows relative to increases in mean flows.

Prediction of Continuous Rainfall-Runoff Scenarios for Snow-Fed Watershed of Himachal Pradesh, India using Geographical Information System GIS based on Hydrological Modelling

Rainfall runoff modeling is one of the most complex hydrological modeling due to the involvement of different watershed physical parameters. It is essential for the analysis of watershed hydrological response towards the received precipitation under the influence of watershed variables. As it is a replica of watershed hydrological response, Rainfall-Runoff modeling is essential to evaluate the general characteristics of total surface runoff at catchment’s outlet. The main objective of this study was prediction of rainfall runoff using Hydrologic Engineering Center-Hydrologic Modeling System [HEC-HMS] model for Rampur watershed which was covered in Kullu and Shimla districts situated at Upper Sutlej sub-basin of Himachal Pradesh, India. Geo-spatial data of Digital Elevation Model,LandUse-LandCover (LU-LC) map, Soil map and Hydro-meteorological data of rainfall were collected from different open source web portals to utilise as input parameters for modeling. Soil Conservation Service-Curve Number (SCS-CN) for precipitation loss modeling, Soil Conservation Service–Dimensionless Unit Hydrograph [SCS-UH] for transform modeling, Muskingum method for Runoff routing were used as input parameters of respective components of HEC-HMS model. This article proposes a methodology for generating hourly rainfall data from daily rainfall data using Intensity-Duration-Frequencycurves in Meteorological model of HMS. The simulation results of runoff with 5 different return period scenarios are follows the return period frequency tendency, such as 1000 year return period runoff peak delivers 4729.5 m3/s, 500 year return period delivers a peak runoff of 4263.9 m3/s, 100 year return period gives a peak runoff of 3196.5 m3/s, 50 year return period runoff deliver 2745.3 m3/s and 10 year return period deliver 953.7 m3/s runoff at the outlet of Rampur watershed.

A Review on Climate Change and Hydrological Models

Various ongoing researches are there on topics like which model will give more compatible results with that of observed discharges. It was argued that even complex modeling does not provide better results. Rainfall-runoff models are classified based on model input and parameters and the extent of physical principles applied in the model. This paper discussed various climate change and Hydrological models. Majorly, there are three types of models such as: Empirical model, conceptual mode and physical model. Empirical model is observation oriented models which take only the information from the existing data without considering the features and processes of hydrological system and hence these models are also called data driven models. Conceptual model (parametric models) describes all of the component hydrological processes. And physical model is a mathematically idealized representation of the real phenomenon. Global Climate Models (GCMs) have grown from the Atmospheric General Circulation Models (AGCMs) broadly used for daily weather prediction. It has been used for a range of applications, including investigating interactions between processes of the climate system, simulating evolution of the climate system, and providing projections of future climate states under scenarios that might vary the evolution of the climate system. Hydrological models are mainly used for predicting system behavior and understanding various hydrological processes. A model consists of various parameters that define the characteristics of the model. SWAT is a semi-empirical and semi-physical model. Key w ords: Climate change, GCM, Hydrology, SWAT DOI: 10.7176/APTA/87-01 Publication date: January 31 st 2023

Rainfall-Runoff Modeling and Hydrological Responses to the Projected Climate Change for Upper Baro Basin, Ethiopia

Rainfall-Runoff Modeling and Hydrological Responses to the Projected Climate Change for Upper Baro Basin, Ethiopia

Extreme Event-based Rainfall-runoff Simulation Utilizing GIS Techniques in Irawan Watershed, Palawan, Philippines

River flow assessments and ecologically sustainable water management plans are now possible due to the advancement of sophisticated computer models. The US Army Corps of Engineers developed the HEC-HMS model, which can be used for various hydrological simulations. Rainfall-runoff modeling aids in estimating peak flows, which is critical for water resource management planning. On December 18, 2017, a heavy rainfall event in the ungauged Irawan basin in Puerto Princesa City, Palawan, Philippines, was simulated to determine the peak flow and amount of water. The current research aims to construct a rainfall-runoff simulation model. A specific hyetograph is used to make the hydrographs for the basin. This study utilizes ArcGIS and QGIS, which perform the geospatial analysis and provide the HEC-HMS model's hydrologic modeling inputs. The hydrological parameters were determined using soil type, land use, and land cover maps. Incorporating SCS loss, Clark unit hydrograph, and Muskingum flow routing, HEC-HMS was employed in the rainfall-runoff simulation. Rainfall data corresponding to the recorded streamflow was used to calibrate and validate the parameters. Several performance metrics, including Nash-Sutcliffe efficiency (NSE) and Percentage Bias (PBIAS), were utilized to evaluate the overall effectiveness of the system. An effective decision-making and warning system can be implemented using the developed model. Doi: 10.28991/CEJ-2023-09-01-017 Full Text: PDF