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1
Bartlett, M. S., E. Daly, J. J. McDonnell, A. J. Parolari e A. Porporato. "Stochastic rainfall-runoff model with explicit soil moisture dynamics". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, n. 2183 (novembre 2015): 20150389. http://dx.doi.org/10.1098/rspa.2015.0389.
Testo completoAbstract (sommario):
Stream runoff is perhaps the most poorly represented process in ecohydrological stochastic soil moisture models. Here we present a rainfall-runoff model with a new stochastic description of runoff linked to soil moisture dynamics. We describe the rainfall-runoff system as the joint probability density function (PDF) of rainfall, soil moisture and runoff forced by random, instantaneous jumps of rainfall. We develop a master equation for the soil moisture PDF that accounts explicitly for a general state-dependent rainfall-runoff transformation. This framework is then used to derive the joint rainfall-runoff and soil moisture-runoff PDFs. Runoff is initiated by a soil moisture threshold and a linear progressive partitioning of rainfall based on the soil moisture status. We explore the dependence of the PDFs on the rainfall occurrence PDF (homogeneous or state-dependent Poisson process) and the rainfall magnitude PDF (exponential or mixed-exponential distribution). We calibrate the model to 63 years of rainfall and runoff data from the Upper Little Tennessee watershed (USA) and show how the new model can reproduce the measured runoff PDF.
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Moore, R. J. "The PDM rainfall-runoff model". Hydrology and Earth System Sciences 11, n. 1 (17 gennaio 2007): 483–99. http://dx.doi.org/10.5194/hess-11-483-2007.
Testo completoAbstract (sommario):
Abstract. The Probability Distributed Model, or PDM, has evolved as a toolkit of model functions that together constitute a lumped rainfall-runoff model capable of representing a variety of catchment-scale hydrological behaviours. Runoff production is represented as a saturation excess runoff process controlled by the absorption capacity (of the canopy, surface and soil) whose variability within the catchment is characterised by a probability density function of chosen form. Soil drainage to groundwater is controlled by the water content in excess of a tension threshold, optionally inhibited by the water content of the receiving groundwater store. Alternatively, a proportional split of runoff to fast (surface storage) and slow (groundwater) pathways can be invoked with no explicit soil drainage function. Recursive solutions to the Horton-Izzard equation are provided for routing flows through these pathways, conveniently considered to yield the surface runoff and baseflow components of the total flow. An alternative routing function employs a transfer function that is discretely-coincident to a cascade of two linear reservoirs in series. For real-time flow forecasting applications, the PDM is complemented by updating methods based on error prediction and state-correction approaches. The PDM has been widely applied throughout the world, both for operational and design purposes. This experience has allowed the PDM to evolve to its current form as a practical toolkit for rainfall-runoff modelling and forecasting.
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Todini, E. "The ARNO rainfall—runoff model". Journal of Hydrology 175, n. 1-4 (febbraio 1996): 339–82. http://dx.doi.org/10.1016/s0022-1694(96)80016-3.
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Basha, H. A. "Simple Nonlinear Rainfall-Runoff Model". Journal of Hydrologic Engineering 5, n. 1 (gennaio 2000): 25–32. http://dx.doi.org/10.1061/(asce)1084-0699(2000)5:1(25).
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Buchtele, Josef. "Runoff changes simulated using a rainfall-runoff model". Water Resources Management 7, n. 4 (1993): 273–87. http://dx.doi.org/10.1007/bf00872285.
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Swathi, V., K. Srinivasa Raju, Murari R. R. Varma e S. Sai Veena. "Automatic calibration of SWMM using NSGA-III and the effects of delineation scale on an urban catchment". Journal of Hydroinformatics 21, n. 5 (18 luglio 2019): 781–97. http://dx.doi.org/10.2166/hydro.2019.033.
Testo completoAbstract (sommario):
Abstract The study aims at calibration of the storm water management model (SWMM) with non-dominated sorting genetic algorithm-III (NSGA-III) for urban catchment in Hyderabad, India. The SWMM parameters calibrated were Manning's roughness coefficient (N), depression storage for pervious and impervious areas (DP and Di), sub-catchment width (W), curve number (CN), drying time (dry) of soil and percentage of imperviousness (I). The efficacy of calibration was evaluated by comparing the observed and simulated peak flows and runoff using goodness-of-fit indices. The calibration takes into consideration eight event rainfalls resulting in eight calibrated sets. Weights of goodness-of-fit indices were estimated and the best calibrated set was further validated for five continuous rainfalls/runoffs. Simulated runoff volume and peak runoff over the five continuous rainfalls deviated by 7–22% and 2–20% with respect to observed data. Results indicated that parameters calibrated for an event rainfall could be used for continuous rainfall-runoff modelling. The effect of catchment delineation scale on runoff was also studied. The study indicated that output of the model was sensitive to variation in parameter values of infiltration and imperviousness.
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Furumai, H., H. K. P. K. Jinadasa, M. Murakami, F. Nakajima e R. K. Aryal. "Model description of storage and infiltration functions of infiltration facilities for urban runoff analysis by a distributed model". Water Science and Technology 52, n. 5 (1 settembre 2005): 53–60. http://dx.doi.org/10.2166/wst.2005.0108.
Testo completoAbstract (sommario):
Although there have been simulation researches focusing on reduction of stormwater peak flow by introduced infiltration facilities, model simulation of dynamic runoff behavior is still limited for frequently occurring rainfall events with weak intensity. Therefore, dynamic simulation was carried out in two urban drainages with infiltration facilities incorporated with a distributed model using two methods for describing functions of infiltration facilities. A method adjusting effective rainfall model gave poor simulation of runoff behavior in light rainfalls. Another method considering dynamic change of storage capacity as well as infiltration rate gave satisfactory estimation of the runoff in both drainages. In addition, assumption of facility clogging improved the agreement between measured and simulated hydrographs in small and medium-sized rainfall. Therefore, the proposed method might be useful for quantifying the secondary effects of the infiltration facilities on groundwater recharge and urban non-point pollutant trapping as well as runoff reduction.
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Vaze, J., D. A. Post, F. H. S. Chiew, J. M. Perraud, J. Teng e N. R. Viney. "Conceptual Rainfall–Runoff Model Performance with Different Spatial Rainfall Inputs". Journal of Hydrometeorology 12, n. 5 (1 ottobre 2011): 1100–1112. http://dx.doi.org/10.1175/2011jhm1340.1.
Testo completoAbstract (sommario):
Abstract Different methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall–runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall–runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the “distributed” application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall–runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required.
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Lee, Kang, Joo, Kim, Kim e Lee. "Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique". Water 11, n. 4 (23 aprile 2019): 850. http://dx.doi.org/10.3390/w11040850.
Testo completoAbstract (sommario):
The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoff simulations. We propose a blending technique using a rainfall ensemble and runoff simulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoff models that use rainfall ensembles as input data to perform a runoff analysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808–3.354 dBR. We derived a runoff hydrograph with greatly reduced uncertainty by applying the blending technique to the runoff simulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoff models.
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Lee, Hyo-Sang, Min-Woo Jeon, Daniela Balin e Michael Rode. "Application of Rainfall Runoff Model with Rainfall Uncertainty". Journal of Korea Water Resources Association 42, n. 10 (30 ottobre 2009): 773–83. http://dx.doi.org/10.3741/jkwra.2009.42.10.773.
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K. N., Vidya. "Runoff assessment by Storm water management model (SWMM)- A new approach". Journal of Applied and Natural Science 13, SI (19 luglio 2021): 142–48. http://dx.doi.org/10.31018/jans.v13isi.2813.
Testo completoAbstract (sommario):
The present study investigated the storm wise runoff collected in farm pond with the runoff estimated by Storm Water Management Model (SWMM) and Soil Conservation Service (SCS-CN) models. The SWMM and SCS-CN models estimated runoff depth storm wise. The runoff depths correspond to the catchment area given the runoff volume from the catchment. The runoff depth estimated from the Storm Water Management Model and Soil Conservation Service model was compared against the depth of runoff estimated from the Water balance model. For small rainfall depths, the runoff estimated from the Storm Water Management Model was at par with the actual runoff volume stored at the pond. It is necessary to know the watershed runoff contribution to the river or streams due to rainfall in order to determine environmental risk or flood potential. In larger rainfall depth, the runoff volume estimated from the SWMM model was less than the stored runoff volume at Farm Pond. The Soil Conservation Service Model gave better results for larger rainfall depth compared to Storm Water Management Model. SWMM was able to simulate runoff depth for small rainfall depths of 2mm. The peak runoff depths were produced by rainfall depths of 35.5mm. Initial abstractions of the study area for antecedent moisture content i.e. AMC I, AMCII and AMCIII are 53.2, 23.91 and 10.43mm, respectively. The comparison showed that both SWMM and SCS-CN models gave better runoff quantification results.
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Zhou, Yan, Zhongmin Liang, Binquan Li, Yixin Huang, Kai Wang e Yiming Hu. "Seamless Integration of Rainfall Spatial Variability and a Conceptual Hydrological Model". Sustainability 13, n. 6 (23 marzo 2021): 3588. http://dx.doi.org/10.3390/su13063588.
Testo completoAbstract (sommario):
Rainfall is an important input to conceptual hydrological models, and its accuracy would have a considerable effect on that of the model simulations. However, traditional conceptual rainfall-runoff models commonly use catchment-average rainfall as inputs without recognizing its spatial variability. To solve this, a seamless integration framework that couples rainfall spatial variability with a conceptual rainfall-runoff model, named the statistical rainfall-runoff (SRR) model, is built in this study. In the SRR model, the exponential difference distribution (EDD) is proposed to describe the spatial variability of rainfall for traditional rain gauging stations. The EDD is then incorporated into the vertically mixed runoff (VMR) model to estimate the statistical runoff component. Then, the stochastic differential equation is adopted to deal with the flow routing under stochastic inflow. To test the performance, the SRR model is then calibrated and validated in a Chinese catchment. The results indicate that the EDD performs well in describing rainfall spatial variability, and that the SRR model is superior to the Xinanjiang model because it provides more accurate mean simulations. The seamless integration framework considering rainfall spatial variability can help build a more reasonable statistical rainfall-runoff model.
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Koivusalo, Harri, e Tuomo Karvonen. "Modeling Surface Runoff". Hydrology Research 26, n. 3 (1 giugno 1995): 205–22. http://dx.doi.org/10.2166/nh.1995.0012.
Testo completoAbstract (sommario):
The objective of this study was to compare approaches to modeling surface runoff due to summer and autumn storms on a cultivated field. The data consisted of measurements performed every 15 minutes during rainfall-surface runoff events in 1993. A transfer function model was formulated using measured rainfall or rainfall excess as an input and surface runoff as an output. The physical models were based on the kinematic wave approximation of the Saint Venant equations. Surface runoff was assumed to flow first as an overland flow on a level field and second in rills. The results showed that the transfer function model using rainfall excess as an input, and the implicitly solved rill flow model performed the best with respect to the fitness coefficients, which denoted the efficiency of the model. The testing of the models using fixed parameter combinations indicated that an event based parameter estimation was not applicable in verifying the models to changing conditions.
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Kovář, P., e V. Kadlec. "Use of the KINFIL rainfall-runoff model on the Hukava catchment". Soil and Water Research 4, No. 1 (11 febbraio 2009): 1–9. http://dx.doi.org/10.17221/22/2008-swr.
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The paper reports on the flood events on the forested Hukava catchment. It describes practical implementation of the KINFIL rainfall-runoff model. This model has been used for the reconstruction of the rainfall-runoff events and thus for the calibration of its parameters. The model was subsequently used to simulate the design discharges with an event duration of t<sub>d</sub> = 30, 60, and 300 min in the period of recurrence of 100 years, and during the scenario simulations of the land use change when 40% and 80% of the forest in the catchment had been cleared out and then replaced by permanent grasslands. The implementation of the KINFIL model supported by GIS proved to be a proper method for the flood runoff assessment on small catchments, during which different scenarios of the land use changes were tested.
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Yang, Xu, Xue-Yi You, Min Ji e Ciren Nima. "Influence factors and prediction of stormwater runoff of urban green space in Tianjin, China: laboratory experiment and quantitative theory model". Water Science and Technology 67, n. 4 (1 febbraio 2013): 869–76. http://dx.doi.org/10.2166/wst.2012.600.
Testo completoAbstract (sommario):
The effects of limiting factors such as rainfall intensity, rainfall duration, grass type and vegetation coverage on the stormwater runoff of urban green space was investigated in Tianjin. The prediction equation of stormwater runoff was established by the quantitative theory with the lab experimental data of soil columns. It was validated by three field experiments and the relative errors between predicted and measured stormwater runoff are 1.41, 1.52 and 7.35%, respectively. The results implied that the prediction equation could be used to forecast the stormwater runoff of urban green space. The results of range and variance analysis indicated the sequence order of limiting factors is rainfall intensity > grass type > rainfall duration > vegetation coverage. The least runoff of green land in the present study is the combination of rainfall intensity 60.0 mm/h, duration 60.0 min, grass Festuca arundinacea and vegetation coverage 90.0%. When the intensity and duration of rainfall are 60.0 mm/h and 90.0 min, the predicted volumetric runoff coefficient is 0.23 with Festuca arundinacea of 90.0% vegetation coverage. The present approach indicated that green space is an effective method to reduce stormwater runoff and the conclusions are mainly applicable to Tianjin and the semi-arid areas with main summer precipitation and long-time interval rainfalls.
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Sun, H., P. S. Cornish e T. M. Daniell. "Digital Elevation Hydrological Modelling in a Small Catchment in South Australia". Hydrology Research 34, n. 3 (1 giugno 2003): 161–78. http://dx.doi.org/10.2166/nh.2003.0002.
Testo completoAbstract (sommario):
A rainfall runoff model based on a digital elevation model (DEM) was applied to a small catchment in Happy Valley, South Australia to predict catchment storm runoff. The DEM was used to partition the catchment into several thousand irregular shaped elements. These elements, with an average size of 825 m2 each, form an interconnected one-dimensional flow network for runoff routing. The rainfall runoff model is a kinematic flow model which combines the solving of flow continuity equation and the Manning's equation to generate surface and subsurface runoff. This study improves on the existing rainfall runoff model in several areas. It adds spatial rainfall averaging methods to derive spatial rainfalls for catchment modelling; and it improves the catchment soil moisture representation by developing a boundary wetness index, and relates this index to antecedent catchment flow to derive spatial catchment moisture distribution. Improved runoff predictions were obtained as a result of the improvement in spatial data input and spatial soil moisture representation. The study identifies these improvements as the key areas for better runoff prediction. It demonstrates that where prediction results showed larger than expected variance, it is frequently caused by the inability to derive good spatially distributed input data rather than parameter estimation errors.
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Hearman, A. J., e C. Hinz. "Sensitivity of point scale surface runoff predictions to rainfall resolution". Hydrology and Earth System Sciences 11, n. 2 (5 marzo 2007): 965–82. http://dx.doi.org/10.5194/hess-11-965-2007.
Testo completoAbstract (sommario):
Abstract. This paper investigates the effects of using non-linear, high resolution rainfall, compared to time averaged rainfall on the triggering of hydrologic thresholds and therefore model predictions of infiltration excess and saturation excess runoff at the point scale. The bounded random cascade model, parameterized to three locations in Western Australia, was used to scale rainfall intensities at various time resolutions ranging from 1.875 min to 2 h. A one dimensional, conceptual rainfall partitioning model was used that instantaneously partitioned water into infiltration excess, infiltration, storage, deep drainage, saturation excess and surface runoff, where the fluxes into and out of the soil store were controlled by thresholds. The results of the numerical modelling were scaled by relating soil infiltration properties to soil draining properties, and in turn, relating these to average storm intensities. For all soil types, we related maximum infiltration capacities to average storm intensities (k*) and were able to show where model predictions of infiltration excess were most sensitive to rainfall resolution (ln k*=0.4) and where using time averaged rainfall data can lead to an under prediction of infiltration excess and an over prediction of the amount of water entering the soil (ln k*>2) for all three rainfall locations tested. For soils susceptible to both infiltration excess and saturation excess, total runoff sensitivity was scaled by relating drainage coefficients to average storm intensities (g*) and parameter ranges where predicted runoff was dominated by infiltration excess or saturation excess depending on the resolution of rainfall data were determined (ln g*<2). Infiltration excess predicted from high resolution rainfall was short and intense, whereas saturation excess produced from low resolution rainfall was more constant and less intense. This has important implications for the accuracy of current hydrological models that use time averaged rainfall under these soil and rainfall conditions and predictions of larger scale phenomena such as hillslope runoff and runon. It offers insight into how rainfall resolution can affect predicted amounts of water entering the soil and thus soil water storage and drainage, possibly changing our understanding of the ecological functioning of the system or predictions of agri-chemical leaching. The application of this sensitivity analysis to different rainfall regions in Western Australia showed that locations in the tropics with higher intensity rainfalls are more likely to have differences in infiltration excess predictions with different rainfall resolutions and that a general understanding of the prevailing rainfall conditions and the soil's infiltration capacity can help in deciding whether high rainfall resolutions (below 1 h) are required for accurate surface runoff predictions.
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Brazil, Larry E. "Rainfall runoff model development and applications". Eos, Transactions American Geophysical Union 68, n. 34 (1987): 715. http://dx.doi.org/10.1029/eo068i034p00715.
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Franz, Delbert. "Rainfall runoff model development and applications". Eos, Transactions American Geophysical Union 68, n. 34 (1987): 716. http://dx.doi.org/10.1029/eo068i034p00716-01.
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Capkun, G., A. C. Davison e A. Musy. "A robust rainfall-runoff transfer model". Water Resources Research 37, n. 12 (dicembre 2001): 3207–16. http://dx.doi.org/10.1029/2001wr000295.
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ADHIKARI, RN, S. CHATTARAJAN, US PATTNAIK e MM SRJVASTAVA. "Rainfall-runoff relationship based on the model of runoff formation at the natural storage". MAUSAM 40, n. 3 (28 aprile 2022): 81–84. http://dx.doi.org/10.54302/mausam.v40i3.2132.
Testo completoAbstract (sommario):
An attempt is mad~ to establish a relationship between rainfall and runoff. The basic input data are (i) rainfall, (ii) run off and (iii) evapotranspiration. The moisture content prior to rainfall under consideration and after the termination of rainfall is computed by water balance technique this method is applied in small agricultural catchments in Soil Conservation Research Farm at Ballary, Karnataka, which is categorised its semiarid zone of black soil region. The relationship between rainfall and runoff under different initial moisture content and rainfall intensities are found out. Attempts are also made to get relationship between moisture condition of the catchment after the end of rainfall and runoff with rainfall intensities as an additional factor. The estimated runoff obtained from various equations are compared with the observed runoff. The rainfall-runoff relationship with initial moisture content as third parameter gives encouraging results for estimation of runoff.
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Whyte, J. M., A. Plumridge e A. V. Metcalfe. "Comparison of predictions of rainfall-runoff models for changes in rainfall in the Murray-Darling Basin". Hydrology and Earth System Sciences Discussions 8, n. 1 (24 gennaio 2011): 917–55. http://dx.doi.org/10.5194/hessd-8-917-2011.
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Abstract. Management of water resources requires an appreciation for how climate change, in particular changes in rainfall, affects the volume of water available in runoff. While there are many studies that use hydrological models for this purpose, comparisons of predictions appear much less commonly in the literature. This paper aims to contribute to this discussion by proposing methods for evaluating the effect on daily runoff projections of rainfall-runoff models when historical daily rainfall inputs are scaled by factors that increase and decrease the rainfall. Considered are the widely used lumped conceptual model SIMHYD and a selection of time series models which feature lagged runoff and rainfall terms. In particular these are AutoRegressive with eXogenous input (ARX), a variant containing nonlinear autoregressive runoff terms (NARX), a model for the log transform of runoff, a finite impulse response model (FIR) and a two regime threshold autoregressive model with exogenous input (TARX). Results show that SIMHYD and the single regime time series models considered have very different behaviour under scaled input rainfall. Reasons for the discrepancy are discussed. The amplification of the rainfall change observed for SIMHYD is consistent with claims that a 1% change in rainfall leads to a 2–3% change in runoff in the Murray-Darling Basin.
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Murtiono, Ugro Hari. "Kajian Model Estimasi Volume Limpasan Permukaan, Debit Puncak Aliran, dan Erosi Tanah dengan Model Soil Conservation Service (SCS), Rasional Dan Modified Universal Soil Loss Equation (MUSLE) (Studi Kasus di DAS Keduang, Wonogiri)". Forum Geografi 22, n. 2 (20 dicembre 2008): 169. http://dx.doi.org/10.23917/forgeo.v22i2.4992.
Testo completoAbstract (sommario):
Hydrologic modelling has been developing and it is usefull for basic data in managing water resources. The aim of the reseach is to estimate volume runoff, maximum discharge, and soil erosion with SCS, Rational, and MUSLE models on Keduang Watershed. Explain the data analysis, and flow to get the data. SCS parameters model use are: runoff, rainfall, deferent between rainfall runoff. The deferent rainfall between runoff relationship kurva Runoff Coefisient (Curve Nunmber/CN). This Coefisient connected with Soil Hydrology Group (antecedent moisture content/AMC), landuse, and cultivation method. Rational parameters model use are: runoff coefisient, soil type, slope, land cover, rainfall intensity, and watershed areas. MUSLE parameters model use are: rainfall erosifity (RM), soil erodibility (K), slope length (L), slope (S), land cover (C), and soil conservation practice (P). The result shows that the conservation service models be applied Keduang Watershed, Wonogiri is over estimed abaut 29.54 %, Rational model is over estimed abaut 49.96 %, and MUSLE model is over estimed abaut 48.47 %.
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Gholami, Vahid, e Mohammad Reza Khaleghi. "A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands". Journal of Forest Science 67, No. 4 (15 aprile 2021): 165–74. http://dx.doi.org/10.17221/90/2020-jfs.
Testo completoAbstract (sommario):
Simulation of the runoff-rainfall process in forest lands is essential for forest land management. In this research, a hydrologic modelling system (HEC-HMS) and artificial neural network (ANN) were applied to simulate the rainfall-runoff process (RRP) in forest lands of Kasilian watershed with an area of 68 square kilometres. The HMS model was performed using the secondary data of rainfall and discharge at the climatology and hydrometric stations, the Soil Conservation Service (SCS) for simulating a flow hydrograph, the curve number (CN) method for runoff estimation, and lag time method for flow routing. Further, a multilayer perceptron (MLP) network was used for simulating the rainfall-runoff process. HEC-HMS model was used to optimize the initial loss (IL) values in the rainfall-runoff process as an input. IL reflects the conditions of vegetation, soil infiltration, and antecedent moisture condition (AMC) in soil. Then, IL values and also incremental rainfall were applied as inputs into ANN to simulate the runoff values. The comparison of the results of simulating the RRP in two scenarios, using IL and without IL, showed that the IL parameter has a high effect in increasing the simulation performance of the rainfall-runoff process. Moreover, ANN predictions were more precise in comparison with those of the HMS model. Further, forest lands can significantly increase IL values and decrease runoff generation.
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Charlier, J. B., R. Moussa, P. Cattan, Y. M. Cabidoche e M. Voltz. "Modelling runoff at the plot scale taking into account rainfall partitioning by vegetation: application to stemflow of banana (<I>Musa</I> spp.) plant". Hydrology and Earth System Sciences Discussions 6, n. 3 (16 giugno 2009): 4307–47. http://dx.doi.org/10.5194/hessd-6-4307-2009.
Testo completoAbstract (sommario):
Abstract. Rainfall partitioning by vegetation modifies the intensity of rainwater reaching the ground, which affects runoff generation. Incident rainfall is intercepted by the plant canopy and then redistributed into throughfall and stemflow. Rainfall intensities at the soil surface are therefore not spatially uniform, generating local variations of runoff production that are disregarded in runoff models. The aim of this paper was to model runoff at the plot scale, accounting for rainfall partitioning by vegetation in the case of plants concentrating rainwater at the plant foot and promoting stemflow. We developed a lumped modelling approach, including a stemflow function that divided the plot into two compartments: one compartment including stemflow and the relative water pathways and one compartment for the rest of the plot. This stemflow function was coupled with a production function and a transfer function to simulate a flood hydrograph using the MHYDAS model. Calibrated parameters were a "stemflow coefficient", which compartmented the plot; the saturated hydraulic conductivity (Ks), which controls infiltration and runoff; and the two parameters of the diffusive wave equation. We tested our model on a banana plot of 3000 m2 on permeable Andosol (mean Ks=75 mm h−1) under tropical rainfalls, in Guadeloupe (FWI). Runoff simulations without and with the stemflow function were performed and compared to 18 flood events from 10 to 130 mm rainfall depth. Modelling results showed that the stemflow function improved the calibration of hydrographs according to the error criteria on volume and on peakflow and to the Nash and Sutcliffe coefficient. This was particularly the case for low flows observed during residual rainfall, for which the stemflow function allowed runoff to be simulated for rainfall intensities lower than the Ks measured at the soil surface. This approach also allowed us to take into account the experimental data, without needing to calibrate the runoff volume on Ks parameter. Finally, the results suggest a rainwater redistribution module should be included in distributed runoff models at a larger scale of the catchment.
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Charlier, J. B., R. Moussa, P. Cattan, Y. M. Cabidoche e M. Voltz. "Modelling runoff at the plot scale taking into account rainfall partitioning by vegetation: application to stemflow of banana (<i>Musa</i> spp.) plant". Hydrology and Earth System Sciences 13, n. 11 (12 novembre 2009): 2151–68. http://dx.doi.org/10.5194/hess-13-2151-2009.
Testo completoAbstract (sommario):
Abstract. Rainfall partitioning by vegetation modifies the intensity of rainwater reaching the ground, which affects runoff generation. Incident rainfall is intercepted by the plant canopy and then redistributed into throughfall and stemflow. Rainfall intensities at the soil surface are therefore not spatially uniform, generating local variations of runoff production that are disregarded in runoff models. The aim of this paper was to model runoff at the plot scale, accounting for rainfall partitioning by vegetation in the case of plants concentrating rainwater at the plant foot and promoting stemflow. We developed a lumped modelling approach, including a stemflow function that divided the plot into two compartments: one compartment including stemflow and the related water pathways and one compartment for the rest of the plot. This stemflow function was coupled with a production function and a transfer function to simulate a flood hydrograph using the MHYDAS model. Calibrated parameters were a "stemflow coefficient", which compartmented the plot; the saturated hydraulic conductivity (Ks), which controls infiltration and runoff; and the two parameters of the diffusive wave equation. We tested our model on a banana plot of 3000 m2 on permeable Andosol (mean Ks=75 mm h−1) under tropical rainfalls, in Guadeloupe (FWI). Runoff simulations without and with the stemflow function were performed and compared to 18 flood events from 10 to 140 rainfall mm depth. Modelling results showed that the stemflow function improved the calibration of hydrographs according to the error criteria on volume and on peakflow, to the Nash and Sutcliffe coefficient, and to the root mean square error. This was particularly the case for low flows observed during residual rainfall, for which the stemflow function allowed runoff to be simulated for rainfall intensities lower than the Ks measured at the soil surface. This approach also allowed us to take into account the experimental data, without needing to calibrate the runoff volume on Ks parameter. Finally, the results suggest a rainwater redistribution module should be included in distributed runoff models at a larger scale of the catchment.
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Rebora, N., L. Ferraris, J. von Hardenberg e A. Provenzale. "Rainfall downscaling and flood forecasting: a case study in the Mediterranean area". Natural Hazards and Earth System Sciences 6, n. 4 (12 luglio 2006): 611–19. http://dx.doi.org/10.5194/nhess-6-611-2006.
Testo completoAbstract (sommario):
Abstract. The prediction of the small-scale spatial-temporal pattern of intense rainfall events is crucial for flood risk assessment in small catchments and urban areas. In the absence of a full deterministic modelling of small-scale rainfall, it is common practice to resort to the use of stochastic downscaling models to generate ensemble rainfall predictions to be used as inputs to rainfall-runoff models. In this work we present an application of a new spatial-temporal downscaling procedure, called RainFARM, to an intense precipitation event predicted by the limited-area meteorological model Lokal Model over north-west Italy. The uncertainty in flood prediction associated with the small unresolved scales of forecasted precipitation fields is evaluated by using an ensemble of downscaled fields to drive a semi-distributed rainfall-runoff model.
28
Chou, Chien-ming. "Random Modeling of Daily Rainfall and Runoff Using a Seasonal Model and Wavelet Denoising". Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/917365.
Testo completoAbstract (sommario):
Instead of Fourier smoothing, this study applied wavelet denoising to acquire the smooth seasonal mean and corresponding perturbation term from daily rainfall and runoff data in traditional seasonal models, which use seasonal means for hydrological time series forecasting. The denoised rainfall and runoff time series data were regarded as the smooth seasonal mean. The probability distribution of the percentage coefficients can be obtained from calibrated daily rainfall and runoff data. For validated daily rainfall and runoff data, percentage coefficients were randomly generated according to the probability distribution and the law of linear proportion. Multiplying the generated percentage coefficient by the smooth seasonal mean resulted in the corresponding perturbation term. Random modeling of daily rainfall and runoff can be obtained by adding the perturbation term to the smooth seasonal mean. To verify the accuracy of the proposed method, daily rainfall and runoff data for the Wu-Tu watershed were analyzed. The analytical results demonstrate that wavelet denoising enhances the precision of daily rainfall and runoff modeling of the seasonal model. In addition, the wavelet denoising technique proposed in this study can obtain the smooth seasonal mean of rainfall and runoff processes and is suitable for modeling actual daily rainfall and runoff processes.
29
Herrnegger, M., H. P. Nachtnebel e K. Schulz. "From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution". Hydrology and Earth System Sciences Discussions 11, n. 12 (5 dicembre 2014): 13259–309. http://dx.doi.org/10.5194/hessd-11-13259-2014.
Testo completoAbstract (sommario):
Abstract. This paper presents a novel technique to calculate mean areal rainfall in a high temporal resolution of 60 min on the basis of an inverse conceptual rainfall–runoff model and runoff observations. Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. The most reliable hydrological information available refers to runoff, which in the presented work is used as input for a rainfall–runoff model. Thereby a conceptual, HBV-type model is embedded in an iteration algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value that corresponds to the observation. To verify the existence, uniqueness and stability of the inverse rainfall, numerical experiments with synthetic hydrographs as inputs into the inverse model are carried out successfully. The application of the inverse model with runoff observations as driving input is performed for the Krems catchment (38.4 km2), situated in the northern Austrian Alpine foothills. Compared to station observations in the proximity of the catchment, the inverse rainfall sums and time series have a similar goodness of fit, as the independent INCA rainfall analysis of Austrian Central Institute for Meteorology and Geodynamics (ZAMG). Compared to observations, the inverse rainfall estimates show larger rainfall intensities. Numerical experiments show, that cold state conditions in the inverse model do not influence the inverse rainfall estimates, when considering an adequate spin-up time. The application of the inverse model is a feasible approach to obtain improved estimates of mean areal rainfall. These can be used to enhance interpolated rainfall fields, e.g. for the estimation of rainfall correction factors, the parameterisation of elevation dependency or the application in real-time flood forecasting systems.
30
Kumar, Pushpendra, A. K. Lohani e A. K. Nema. "Rainfall Runoff Modeling Using MIKE 11 Nam Model". Current World Environment 14, n. 1 (25 aprile 2019): 27–36. http://dx.doi.org/10.12944/cwe.14.1.05.
Testo completoAbstract (sommario):
River basin planning and management are primarily based on the accurate assessment and prediction of catchment runoff. A continuous effort has been made by the various researchers to accurately assess the runoff generated from precipitation by developing various models. In this paper conceptual hydrological MIKE 11 NAM approach has been used for developing a runoff simulation model for Arpasub-basin of Seonath river basin in Chhattisgarh, India. NAM model has been calibrated and validated using discharge data at Kota gauging site on Arpa basin. The calibration and validation results show that this model is capable to define the rainfall runoff process of the basin and thus predicting daily runoff. The ability of the NAM model in rainfall runoff modelling of Arpa basin was assessed using Nash–Sutcliffe Efficiency Index (EI), coefficient of determination (R2) and Root Mean Square Error (RMSE). This study demonstrates the usefulness of the developed model for the runoff prediction in the Arpa basin which acts as a useful input for the integrated water resources development and management at the basin scale.
31
Máca, P., e P. Torfs. "The influence of temporal rainfall distribution in the flood runoff modelling". Soil and Water Research 4, Special Issue 2 (19 marzo 2010): S102—S110. http://dx.doi.org/10.17221/471-swr.
Testo completoAbstract (sommario):
The rainfall input is one of the main factors influencing the magnitude of the runoff response during a flood event. Its temporal and spatial distribution significantly contributes to the formation of hydrograph shape, peak discharge and flood volume. A novel approach to the evaluation of the role of the temporal rainfall pattern of hydrograph is presented in this contribution. The methodology shown is based on the coupling of the deterministic event based runoff model with the stochastic rainfall disaggregation model. The rainfall model simulates the hyetograph ensemble, which is the direct input to the calibrated event based runoff model. The event based runoff model calibration is based on the evaluation of real flood events. The rainfall ensemble is simulated according to the preservation of important statistical properties, which are estimated from the real rainfall data inputs. The proposed combination of two simulation techniques enables to generate the hydrograph ensemble upon a single flood event. The evaluation of the temporal rainfall distribution impact on the flood runoff response is performed through the determination of the selected rainfall runoff characteristics of the simulated hydrograph ensemble. The main result confirms the importance of the rainfall volume inputs and its temporal distribution on the flood runoff generation. The methodology shown enables to evaluate the potential of the real flood event to generate the flood event within the conditions of the small catchment scale.
32
Hu, Caihong, Shenglian Guo, Lihua Xiong e Dingzhi Peng. "A modified Xinanjiang model and its application in northern China". Hydrology Research 36, n. 2 (1 aprile 2005): 175–92. http://dx.doi.org/10.2166/nh.2005.0013.
Testo completoAbstract (sommario):
The Xinanjiang model has been widely used in the humid regions in southern China as a basic tool for rainfall–runoff simulation, flood forecasting and water resources planning and management. However, its performance in the arid and semi-arid regions of northern China is usually not so good as in the humid regions. A modified Xinanjiang model, in which runoff generation in the watershed is based on both infiltration excess and saturation excess runoff mechanisms, is presented and discussed. Three different watersheds are selected for assessing and comparing the performance of the Xinanjiang model, the modified Xinanjiang model, the VIC model and the TOPMODEL in rainfall–runoff simulation. It is found that the modified Xinanjiang model performs better than the Xinanjiang model, and the models considering the Horton and Dunne runoff generation mechanisms are slightly better than those models considering the single runoff generation mechanism in semi-arid areas. It is suggested that the infiltration excess runoff mechanism should be included in rainfall–runoff models in arid and semi-arid regions.
33
Corradini, C., e F. Melone. "Representation of Infiltration in Adaptive Rainfall – Runoff Models". Hydrology Research 23, n. 5 (1 ottobre 1992): 291–304. http://dx.doi.org/10.2166/nh.1992.0020.
Testo completoAbstract (sommario):
The reliability of the extended Time Compression Approximation (TCA), commonly adopted in watershed models in order to represent the infiltration associated with erratic rainfalls, is investigated. This approximation is considered as a component of an adaptive real-time flood forecasting model. The forecasted flows are compared with those obtained replacing the extended TCA with the Complex Storm Point Infiltration Model (CSPIM) recently proposed by Smith et al. (1993). The discharge forecasted through the infiltration component based on the numerical solution of Richards' equation is used as a bench mark. The models were applied to situations representative of real areas in Central Italy. The CSPIM based watershed model was found to provide excellent results. The TCA based model, in spite of the adaptive component, yielded poor results for various rainfall patterns. However, it seems to be a reasonable approximation when a uniform rainfall spatial distribution is involved.
34
Kobayashi, Kenichiro, Shigenori Otsuka e Kazuo Saito. "Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls". Natural Hazards and Earth System Sciences 16, n. 8 (9 agosto 2016): 1821–39. http://dx.doi.org/10.5194/nhess-16-1821-2016.
Testo completoAbstract (sommario):
Abstract. This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) are used as the input data to a rainfall–runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolutions. A distributed rainfall–runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km resolution JMA-NHM ensemble simulation are more appropriate than the 10 km resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s−1, a threshold value for flood control. The inflows with the 10 km resolution ensemble rainfall are all considerably smaller than the observations, while at least one simulated discharge out of 11 ensemble members with the 2 km resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls shows much better results than the original ensemble discharge simulations.
35
Vemula, Swathi, K. Srinivasa Raju e S. Sai Veena. "Modelling impact of future climate and land use land cover on flood vulnerability for policy support – Hyderabad, India". Water Policy 22, n. 5 (27 luglio 2020): 733–47. http://dx.doi.org/10.2166/wp.2020.106.
Testo completoAbstract (sommario):
Abstract The study analyses the impact of climate change and land use land cover (LULC) on runoff of Hyderabad city, India for the years 1995, 2005, 2016 and 2031. Flood vulnerability was evaluated for extreme historic and future rainfall events. Maximum daily rainfalls of 132, 181 and 165 mm that occurred in the decades of 1990–2000, 2001–2010 and 2011–2016 were considered for historic rainfall–runoff modelling. Complementarily in climate change, maximum daily rainfall of 266 mm predicted during 2020–2040 by Geophysical Fluid Dynamics Laboratory-Coupled Model 3 (GFDL-CM3) Representative Concentration Pathway (RCP) 2.6, was analysed for rainfall-runoff scenario in 2031. LULC was assessed from historic maps and the master plan of the city. Peak runoff was modelled in Storm Water Management Model (SWMM) for corresponding daily rainfall and LULC. The floodplain of the river Musi was modelled in Hydrological Engineering Center-River Analysis System (HEC-RAS). Results showed that changing rainfall and LULC increased peak runoff by three times, and flood depth in the river increased by 22% from 1995 to 2031. In 2016 and 2031, 48 and 51% of the city was highly vulnerable. Five detention basins were proposed to combat increasing runoff, due to which highly vulnerable areas reduced by 8% in 2016 and 9% in 2031.
36
Teng, Jin, Jai Vaze, Francis H. S. Chiew, Biao Wang e Jean-Michel Perraud. "Estimating the Relative Uncertainties Sourced from GCMs and Hydrological Models in Modeling Climate Change Impact on Runoff". Journal of Hydrometeorology 13, n. 1 (1 febbraio 2012): 122–39. http://dx.doi.org/10.1175/jhm-d-11-058.1.
Testo completoAbstract (sommario):
Abstract This paper assesses the relative uncertainties from GCMs and from hydrological models in modeling climate change impact on runoff across southeast Australia. Five lumped conceptual daily rainfall–runoff models are used to model runoff using historical daily climate series and using future climate series obtained by empirically scaling the historical climate series informed by simulations from 15 GCMs. The majority of the GCMs project a drier future for this region, particularly in the southern parts, and this is amplified as a bigger reduction in the runoff. The results indicate that the uncertainty sourced from the GCMs is much larger than the uncertainty in the rainfall–runoff models. The variability in the climate change impact on runoff results for one rainfall–runoff model informed by 15 GCMs (an about 28%–35% difference between the minimum and maximum results for mean annual, mean seasonal, and high runoff) is considerably larger than the variability in the results between the five rainfall–runoff models informed by 1 GCM (a less than 7% difference between the minimum and maximum results). The difference between the rainfall–runoff modeling results is larger in the drier regions for scenarios of big declines in future rainfall and in the low-flow characteristics. The rainfall–runoff modeling here considers only the runoff sensitivity to changes in the input climate data (primarily daily rainfall), and the difference between the hydrological modeling results is likely to be greater if potential changes in the climate–runoff relationship in a warmer and higher CO2 environment are modeled.
37
Kobayashi, K., S. Otsuka e K. Saito. "Ensemble flood forecasting to support dam water release operation using 10 and 2 km-resolution JMA Nonhydrostatic Model ensemble rainfalls". Natural Hazards and Earth System Sciences Discussions 3, n. 12 (18 dicembre 2015): 7411–56. http://dx.doi.org/10.5194/nhessd-3-7411-2015.
Testo completoAbstract (sommario):
Abstract. This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency Nonhydrostatic Model are used as the input data to a rainfall–runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolution. A distributed rainfall–runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km-resolution JMA-NHM ensemble simulation are more appropriate than the 10 km-resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s−1; a threshold value for flood control. The inflows with the 10 km-resolution ensemble rainfall are all considerably smaller than the observations, while, at least one simulated discharge out of 11 ensemble members with the 2 km-resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls show much better results than the original ensemble discharge simulations.
38
Herrnegger, M., H. P. Nachtnebel e K. Schulz. "From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution". Hydrology and Earth System Sciences 19, n. 11 (23 novembre 2015): 4619–39. http://dx.doi.org/10.5194/hess-19-4619-2015.
Testo completoAbstract (sommario):
Abstract. Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. In contrast, the most reliable hydrological information available refers to runoff, which in the presented work is used as input for an inverted HBV-type rainfall–runoff model that is embedded in a root finding algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value closely matching the observed runoff. The inverse model is applied and tested to the Schliefau and Krems catchments, situated in the northern Austrian Alpine foothills. The correlations between inferred rainfall and station observations in the proximity of the catchments are of similar magnitude compared to the correlations between station observations and independent INCA (Integrated Nowcasting through Comprehensive Analysis) rainfall analyses provided by the Austrian Central Institute for Meteorology and Geodynamics (ZAMG). The cumulative precipitation sums also show similar dynamics. The application of the inverse model is a promising approach to obtain additional information on mean areal rainfall. This additional information is not solely limited to the simulated hourly data but also includes the aggregated daily rainfall rates, which show a significantly higher correlation to the observed values. Potential applications of the inverse model include gaining additional information on catchment rainfall for interpolation purposes, flood forecasting or the estimation of snowmelt contribution. The application is limited to (smaller) catchments, which can be represented with a lumped model setup, and to the estimation of liquid rainfall.
39
Burian, S. J., e S. R. Durrans. "Evaluation of an artificial neural network rainfall disaggregation model". Water Science and Technology 45, n. 2 (1 gennaio 2002): 99–104. http://dx.doi.org/10.2166/wst.2002.0033.
Testo completoAbstract (sommario):
Previous research produced an artificial neural network (ANN) temporal rainfall disaggregation model. After proper training the model can disaggregate hourly rainfall records into sub-hourly time increments. In this paper we present results from continued evaluations of the performance of the ANN model specifically examining how the errors in the disaggregated rainfall hyetograph translate to errors in the prediction of the runoff hydrograph. Using a rainfall-runoff model of a hypothetical watershed we compare the runoff hydrographs produced by the ANN-predicted 15-minute increment rainfall pattern to runoff hydrographs produced by (1) the observed 15-minute increment rainfall pattern, (2) the observed hourly-increment rainfall pattern, and (3) the 15-minute increment rainfall pattern produced by a disaggregation model based on geometric similarity. For 98 test storms the peak discharges produced by the ANN model rainfall pattern had a median under-prediction of 16.6%. This relative error was less than the median under-prediction in peak discharge when using the observed 15-minute rainfall patterns aggregated to hourly increments (40.8%), and when using rainfall patterns produced by the geometric similarity rainfall disaggregation model (21.9%).
40
Brocca, L., S. Liersch, F. Melone, T. Moramarco e M. Volk. "Application of a model-based rainfall-runoff database as efficient tool for flood risk management". Hydrology and Earth System Sciences 17, n. 8 (6 agosto 2013): 3159–69. http://dx.doi.org/10.5194/hess-17-3159-2013.
Testo completoAbstract (sommario):
Abstract. A framework for a comprehensive synthetic rainfall-runoff database was developed to study catchment response to a variety of rainfall events. The framework supports effective flood risk assessment and management and implements simple approaches. It consists of three flexible components, a rainfall generator, a continuous rainfall-runoff model, and a database management system. The system was developed and tested at two gauged river sections along the upper Tiber River (central Italy). One of the main questions was to investigate how simple such approaches can be applied without impairing the quality of the results. The rainfall-runoff model was used to simulate runoff on the basis of a large number of rainfall events. The resulting rainfall-runoff database stores pre-simulated events classified on the basis of the rainfall amount, initial wetness conditions and initial discharge. The real-time operational forecasts follow an analogue method that does not need new model simulations. However, the forecasts are based on the simulation results available in the rainfall-runoff database (for the specific class to which the forecast belongs). Therefore, the database can be used as an effective tool to assess possible streamflow scenarios assuming different rainfall volumes for the following days. The application to the study site shows that magnitudes of real flood events were appropriately captured by the database. Further work should be dedicated to introduce a component for taking account of the actual temporal distribution of rainfall events into the stochastic rainfall generator and to the use of different rainfall-runoff models to enhance the usability of the proposed procedure.
41
Parisouj, Peiman, Esmaiil Mokari, Hamid Mohebzadeh, Hamid Goharnejad, Changhyun Jun, Jeill Oh e Sayed M. Bateni. "Physics-Informed Data-Driven Model for Predicting Streamflow: A Case Study of the Voshmgir Basin, Iran". Applied Sciences 12, n. 15 (25 luglio 2022): 7464. http://dx.doi.org/10.3390/app12157464.
Testo completoAbstract (sommario):
Accurate rainfall-runoff modeling is crucial for water resource management. However, the available models require more field-measured data to produce accurate results, which has been a long-term issue in hydrological modeling. Machine learning (ML) models have shown superiority in the hydrological field over statistical models. The primary aim of the present study was to advance a new coupled model combining model-driven models and ML models for accurate rainfall-runoff simulation in the Voshmgir basin in northern Iran. Rainfall-runoff data from 2002 to 2007 were collected from the tropical rainfall measuring mission (TRMM) satellite and the Iran water resources management company. The findings revealed that the model-driven model could not fully describe river runoff patterns during the investigated time period. The extreme learning machine and support vector regression models showed similar performances for 1-day-ahead rainfall–runoff forecasting, while the long short-term memory (LSTM) model outperformed these two models. Our results demonstrated that the coupled physically based model and LSTM model outperformed other models, particularly for 1-day-ahead forecasting. The present methodology could be potentially applied in the same hydrological properties catchment.
42
Jehanzaib, Muhammad, Muhammad Ajmal, Mohammed Achite e Tae-Woong Kim. "Comprehensive Review: Advancements in Rainfall-Runoff Modelling for Flood Mitigation". Climate 10, n. 10 (10 ottobre 2022): 147. http://dx.doi.org/10.3390/cli10100147.
Testo completoAbstract (sommario):
Runoff plays an essential part in the hydrological cycle, as it regulates the quantity of water which flows into streams and returns surplus water into the oceans. Runoff modelling may assist in understanding, controlling, and monitoring the quality and amount of water resources. The aim of this article is to discuss various categories of rainfall–runoff models, recent developments, and challenges of rainfall–runoff models in flood prediction in the modern era. Rainfall–runoff models are classified into conceptual, empirical, and physical process-based models depending upon the framework and spatial processing of their algorithms. Well-known runoff models which belong to these categories include the Soil Conservation Service Curve Number (SCS-CN) model, Storm Water Management model (SWMM), Hydrologiska Byråns Vattenbalansavdelning (HBV) model, Soil and Water Assessment Tool (SWAT) model, and the Variable Infiltration Capacity (VIC) model, etc. In addition, the data-driven models such as Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), Deep Neural Network (DNN), and Support Vector Machine (SVM) have proven to be better performance solutions in runoff modelling and flood prediction in recent decades. The data-driven models detect the best relationship based on the input data series and the output in order to model the runoff process. Finally, the strengths and downsides of the outlined models in terms of understanding variation in runoff modelling and flood prediction were discussed. The findings of this comprehensive study suggested that hybrid models for runoff modeling and flood prediction should be developed by combining the strengths of traditional models and machine learning methods. This article suggests future research initiatives that could help with filling existing gaps in rainfall–runoff research and will also assist hydrological scientists in selecting appropriate rainfall–runoff models for flood prediction and mitigation based on their benefits and drawbacks.
43
Nourani, Vahid, Samira Roumianfar e Elnaz Sharghi. "Using Hybrid ARIMAX-ANN Model for Simulating Rainfall - Runoff - Sediment Process Case Study". International Journal of Applied Metaheuristic Computing 4, n. 2 (aprile 2013): 44–60. http://dx.doi.org/10.4018/jamc.2013040104.
Testo completoAbstract (sommario):
The need for accurate modeling of rainfall-runoff-sediment processes has grown rapidly in the past decades. This study investigates the efficiency of black-box models including Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average with eXogenous input (ARIMAX) models for forecasting the rainfall-runoff-sediment process. According to the complex behavior of the rainfall-runoff-sediment time series, they include both linear and nonlinear components; therefore, employing a hybrid model which combines the advantages of both linear and non-linear models improves the accuracy of prediction. In this paper, a hybrid of ARIMAX-ANN model is applied to rainfall-runoff-sediment modeling of a watershed. At the first step of the hybrid modeling, the ARIMAX method is applied to forecast the linear component of the rainfall-runoff process and then in the second step, an ANN model is used to find the non-linear relationship among the residuals of the fitted linear ARIMAX model. Finally, total effective time series of runoff, obtained by the hybrid ARIMAX-ANN model are imposed as input to the proposed ANN model for prediction daily suspended sediment load of the watershed. The proposed model is more appropriate, as it uses the semi-linear relation for prediction of sediment load.
44
Skaugen, Thomas, e Christian Onof. "A rainfall-runoff model parameterized from GIS and runoff data". Hydrological Processes 28, n. 15 (2 agosto 2013): 4529–42. http://dx.doi.org/10.1002/hyp.9968.
Testo completo
45
Karabová, Beata, Anna E. Sikorska, Kazimierz Banasik e Silvia Kohnová. "Parameters determination of a conceptual rainfall-runoff model for a small catchment in Carpathians". Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation 44, n. 2 (1 dicembre 2012): 1–8. http://dx.doi.org/10.2478/v10060-011-0071-z.
Testo completoAbstract (sommario):
Abstract Parameters determination of a conceptual rainfall-runoff model for a small catchment in Carpathians. One of the most important tasks in hydrology is to simulate and forecast hydrologic processes and variables. To achieve this, various linear and nonlinear hydrologic models were developed. One of the most commonly applied rainfall-runoff models is the Nash’s model of the Instantaneous Unit Hydrograph (IUH) (Nash, 1957) used jointly with the CN-NRCS method. Within this paper, the Nash’s model was applied to a small forested basin (Vištucký Creek, Slovakia) to reconstruct rainfall-runoff events based on the recorded precipitation. The Vištucký Creek catchment, located in the Little Carpathians, is a part of the flood protection management of regional sites in the Little Carpathians. Therefore, the object of this paper is, first, to determine the parameters of a conceptual rainfall-runoff model for the Vištucký creek catchment, second, to analyse how the selected characteristics of the model depend on the rainfall characteristics, and third, to compare obtained results with a similar study of Sikorska and Banasik (2010). The computer programme developed at the Dept. of Water Engineering (WULS-SGGW) was used to obtain the rainfall-runoff characteristics based on the Nash´s model. The derived characteristics were parameters of the Nash’s model (N, k, lag time) and rainfall-runoff characteristics (sum of total and effective precipitation, rainfall duration, runoff coefficient, time to IUH peak, value of IUH peak, goodness of fit). A relatively small effective precipitation from the rainfall events was derived. For the purpose of the analysis, a correlation between the lag time (and k parameter) and the sum of the total and effective precipitation was used. The use of the conceptual rainfall-runoff model (Nash´s model) for the small catchment in Carpathians was proved to give satisfactory results. The rainfall characteristics derived in this study are comparable to the results obtained by Spál et. al (2011), who used the same catchment in their analysis. Interestingly, our analysis indicated that there is a correlation between the rainfall duration and the lag time, what is opposite to the compared results of Sikorska and Banasik (2010).
46
Krajewski, Adam, Hyosang Lee, Leszek Hejduk e Kazimierz Banasik. "Predicted small catchment responses to heavy rainfalls with SEGMO and two sets of model parameters". Annals of Warsaw University of Life Sciences, Land Reclamation 46, n. 3 (1 ottobre 2014): 205–20. http://dx.doi.org/10.2478/sggw-2014-0017.
Testo completoAbstract (sommario):
Abstract Predicted small catchment responses to heavy rainfalls with SEGMO and two sets of model parameters. The study tests the ability of hydrological part of SEGMO (SedimentGraph Model), i.e. lumped parametric rainfall-runoff procedure of SEGMO to simulate design storm runoff in a Korean catchment. The aim of the investigation is to predict responses of small catchment of the Jeungpyeong river, located in central part of South Korea, with the area of 133.6 km2, to 100-year rainfall events, applying SEGMO and using two parallel approaches for model parameter estimation. The fi rst approach is based on catchment characteristics and USDA-SCS procedures, which is suitable for ungauged basins, and the other one is based on rainfall-runoff measurements. The way of estimation of model parameters has been demonstrated. Finally, the model outputs are compared. The difference in largest peak discharges obtained from SEGMO with the two sets of model parameters, i.e. when estimated on the base of catchment characteristics and USDA-SCS procedures, and on the base of rainfall-runoff measurements were relatively small, approaching 37%. This investigation can be seen as checking the uncertainties in model parameter estimation and their infl uence on fl ood fl ows.
47
Chan, Hsun-Chuan, Po-An Chen e Jung-Tai Lee. "Rainfall-Induced Landslide Susceptibility Using a Rainfall–Runoff Model and Logistic Regression". Water 10, n. 10 (29 settembre 2018): 1354. http://dx.doi.org/10.3390/w10101354.
Testo completoAbstract (sommario):
Conventional landslide susceptibility analysis adopted rainfall depth or maximum rainfall intensity as the hydrological factor. However, using these factors cannot delineate temporal variations of landslide in a rainfall event. In the hydrological cycle, runoff quantity reflects rainfall characteristics and surface feature variations. In this study, a rainfall–runoff model was adopted to simulate the runoff produced by rainfall in various periods of a typhoon event. To simplify the number of factors in landslide susceptibility analysis, the runoff depth was used to replace rainfall factors and some topographical factors. The proposed model adopted the upstream area of the Alishan River in southern Taiwan as the study area. The landslide susceptibility analysis of the study area was conducted by using a logistic regression model. The results indicated that the overall accuracy of predicted events exceeded 80%, and the area under the receiver operating characteristic curve (AUC) closed to 0.8. The results revealed that the proposed landslide susceptibility simulation performed favorably in the study area. The proposed model could predict the evolution of landslide susceptibility in various periods of a typhoon and serve as a new reference for landslide hazard prevention.
48
Freebairn, DM, e WC Boughton. "Hydrologic effects of crop residue management practices". Soil Research 23, n. 1 (1985): 23. http://dx.doi.org/10.1071/sr9850023.
Testo completoAbstract (sommario):
A daily water balance model of catchment behaviour was used with rainfall and runoff data from three 1 ha catchments over the period 1976-1981 inclusive to study the effects of different practices of crop residue management on volumes and peak rates of runoff. The practices studied were stubble burning, stubble incorporation, and stubble mulching. Rainfall and runoff data from the six-year study period were used to calibrate the catchment model to each of the management practices in turn. A 64-year record of daily rainfalls from a nearby meteorological station was used to estimate the long-term effects of the practices on the frequency distributions of runoff. An empirical relationship between peak rates of runoff and daily amounts of runoff was used with the daily water balance model to estimate the effects of the practices on peak rates of runoff. Burning of stubble produced the highest peak rates and amounts of runoff and mulching the lowest.
49
Kim, Changhwan, e Dae-Hong Kim. "Effects of Rainfall Spatial Distribution on the Relationship between Rainfall Spatiotemporal Resolution and Runoff Prediction Accuracy". Water 12, n. 3 (17 marzo 2020): 846. http://dx.doi.org/10.3390/w12030846.
Testo completoAbstract (sommario):
We studied how rainfall spatial distribution affects the relationship between rainfall spatiotemporal resolution and runoff prediction accuracy under real field conditions. We gathered radar rainfall and discharge data for three rainfall events. These rainfall-runoff events were then reproduced using a kinematic wave model. Modeling accuracy was estimated quantitatively using the Nash–Sutcliffe model efficiency coefficient and peak discharge ratio. Normalized root-mean-square error ( nRMSE ), skewness ( S k ), and second scaled spatial moment of catchment rainfall ( δ 2 ) were employed to quantify rainfall spatial distribution characteristics. By relating the accuracy of modeling results to the rainfall spatial characteristics using various rainfall spatiotemporal resolutions, we found that the modeling results converged to a value as the nRMSE , | S k | and | 1 − δ 2 | decreased. That is, rainfall spatial distributions affect the relationship between lower limit of rainfall spatiotemporal resolution for runoff models and runoff prediction accuracy.
50
Sharafati, A., e B. Zahabiyoun. "Rainfall Threshold Curves Extraction by Considering Rainfall-Runoff Model Uncertainty". Arabian Journal for Science and Engineering 39, n. 10 (27 giugno 2014): 6835–49. http://dx.doi.org/10.1007/s13369-014-1246-9.
Testo completo