Evaporation Calculations Research Articles

Prediction of zinc evaporation in the snout of high-temperature hot-dip zinc-aluminum-magnesium coating line

The internal flow field of snout affects the quality of hot-dip zinc products significantly. The prediction of zinc evaporation is a key issue of analyzing the atmosphere flow in the snout. In this paper, simulations of the atmosphere flow with zinc evaporation considered are carried out in the snout of a high temperature hot-dip zinc-aluminum-magnesium coating line. In order to eliminate the false diffusion in numerical calculation of zinc evaporation, a strategy of high-order scheme combined with local orthogonal grids at the zinc vaporization boundary is proposed. The simulation is validated by available experimental data. The effects of operation parameters such as the temperature of snout wall, the width and the speed of the strip on the zinc vapor flow are studied. It is found that with the increased temperature gradient between the snout wall and strip wall, the concentration of zinc vapor in the snout is also increased. Therefore, the method of heating the snout wall can reduce the temperature gradient between the snout wall and strip wall, so it is beneficial to reduce the evaporation of zinc liquid in the snout.

Monte-Carlo calculations of evaporation and fission in excited spallation reaction fragments

Monte-Carlo calculations of evaporation and fission in excited spallation reaction fragments

The Impact of Preprocessing Approaches on Neural Network Performance: A Case Study on Evaporation in Adana, a Mediterranean Climate

The application of artificial intelligence (AI) technologies is quickly expanding in water management. Additionally, the artificial neural network methodology has an advantage over traditional statistical approaches in that it does not need assumptions about the distribution of data and variables. These methods can be used if there is a large enough data collection and criteria relevant to the nature of the problem. Preprocessing data before utilizing it improves the performance of the AI model. Evaporation matters in water management, agriculture processes and soil science. It is critical to ensure proper estimation of evaporation losses for effective water resource planning and management particularly in drought-prone areas such as Adana. Artificial intelligence approaches can be applied successfully in evaporation calculation. In this research, we used the Standard scaler, power transformer, robust scaler quantile transformer (Uniform) and quantile transformer (Normal), and min-max scaler preprocessing techniques to preprocess the multilayer perceptron neural network (MLPNN). We also trained the MLPNN using unprocessed data and compared it to the results of the preprocessed model. In the setup of the model, daily temperature, pressure, wind, sunny hours, and humidity parameters covering the years 2018-2021 were presented as input to the MLPNN model. Consequently, we pinpoint that all preprocessing approaches produce better outcomes than unscaled. Although all models produced statistically high accuracy predictions according to statistical criteria, the MLPNN model established without transformation (test phase: r2: 0.93, NSE : 0.927, SMAPE: 10.77, RMSE: 1.2, MAE: 0.9) exhibited the lowest accuracy. The evaporation prediction model that was developed using the MLPNN-based standard scalar optimization algorithm exhibited the highest level of accuracy (test phase: r2: 0.978, NSE: 0.977, SMAPE: 5.93, RMSE: 0.68, MAE: 0.48). Power Transformer (test phase: r2: 0.978, NSE: 0.977, SMAPE: 5.81, RMSE: 0.67, MAE: 0.49) showed second-degree promising results. It can be concluded from these results that the estimation of meteorological variables requires the scaling and presentation of data in a uniform structure. Therefore, improving efficiency and productivity in water management or agricultural processes can be enhanced by making more accurate evaporation estimates.

Revisiting the greenhouse effect – a hydrological perspective

ABSTRACT Quantification of the greenhouse effect is a routine procedure in the framework of hydrological calculations of evaporation. According to the standard practice, this is made considering the water vapour in the atmosphere, without any reference to the concentration of carbon dioxide (CO2), which, however, in the last century has increased from 300 to about 420 ppm. As the formulae used for the greenhouse effect quantification were introduced 50–90 years ago, we examine whether these are still representative or not, based on eight sets of observations, distributed across a century. We conclude that the observed increase of the atmospheric CO2 concentration has not altered, in a discernible manner, the greenhouse effect, which remains dominated by the quantity of water vapour in the atmosphere, and that the original formulae used in hydrological practice remain valid. Hence, there is no need for adaptation of the original formulae due to increased CO2 concentration.

Hydro-PE: gridded datasets of historical and future Penman–Monteith potential evaporation for the United Kingdom

Abstract. We present two new potential evaporation datasets for the United Kingdom: a historical dataset, Hydro-PE HadUK-Grid, which is derived from the HadUK-Grid gridded observed meteorology (1969–2021), and a future dataset, Hydro-PE UKCP18 RCM, which is derived from UKCP18 regional climate projections (1980–2080). Both datasets are suitable for hydrological modelling and provide Penman–Monteith potential evapotranspiration parameterised for short grass, with and without a correction for interception on days with rainfall. The potential evapotranspiration calculations have been formulated to closely follow the methodology of the existing Meteorological Office Rainfall and Evaporation Calculation System (MORECS) potential evapotranspiration, which has historically been widely used by hydrological modellers in the United Kingdom. The two datasets have been created using the same methodology to allow seamless modelling from past to future. Hydro-PE HadUK-Grid shows good agreement with MORECS in much of the United Kingdom, although Hydro-PE HadUK-Grid is higher in the mountainous regions of Scotland and Wales. This is due to differences in the underlying meteorology, in particular the wind speed, which are themselves due to the different spatial scales of the data. Hydro-PE HadUK-Grid can be downloaded from https://doi.org/10.5285/9275ab7e-6e93-42bc-8e72-59c98d409deb (Brown et al., 2022) and Hydro-PE UKCP18 RCM can be downloaded from https://doi.org/10.5285/eb5d9dc4-13bb-44c7-9bf8-c5980fcf52a4 (Robinson et al., 2021).

Yield and Water Use Efficiency of Drip Irrigation of Pepper

Drip irrigation is gaining importance in mitigating the consequences of water scarcity even in regions with abundant rainfall. The transition from surface to subsurface drip irrigation is accompanied by numerous problems. To overcome these issues, shallow subsurface drip irrigation can be potentially used as an effective drought control tool that brings additional benefits compared to conventional surface drip irrigation techniques. This research investigated the effects of different calculations of daily crop water requirements, reference evapotranspiration (ETo), and pan evaporation (Eo) on the yield and water use efficiency of pepper irrigated with a surface and shallow subsurface drip irrigation system. The experiment was conducted in field conditions in the Vojvodina region, the northern part of Serbia. The irrigation scheduling was based on the water balance approach. The calculated evapotranspiration rate was about 400 mm for the pepper growing period, regardless of the calculation method. The highest yield of pepper and evapotranspiration water use efficiency was obtained on the Eo variant with surface drip irrigation. However, irrigation water use efficiency showed no statistical significance concerning the calculation of evapotranspiration and irrigation type. The results indicated that both calculation methods and irrigation types can be used in pepper production, but priority should be given to pan-evaporation-based calculation.

Simulations of laminar methane flames doped with iron nitrate/1-butanol aerosol in a novel matrix burner

A novel matrix burner enabled the investigation of aerosol-doped laminar low-pressure flames. Iron nitrate dissolved in 1-butanol was used as a typical model system, also found in the flame spray pyrolysis for synthesis of iron oxide. The state of the aerosol entering the flame front was quantified by single-droplet evaporation calculations. Three-dimensional simulations were conducted to quantify thermal losses and the impact of buoyancy on the deviation from an ideal one-dimensional approximation. Highly resolved simulations of the burner matrix confirmed the compact, external mixing zone and the flatness of the flame in the low-pressure operation. Simulations based on the one-dimensional approximation demonstrated the suitability of the experimental setup for reaction kinetics investigations. The results were compared and validated by temperature measurements and probing mass spectrometry. Based on investigations of the particle-producing flame, a hypothesis about the origin of gas-borne nanoparticles in the spray-flame synthesis process was derived. This work demonstrates the suitability of the novel matrix burner for the investigation of reaction kinetics in aerosol doped, quasi-premixed, flat flames using one-dimensional, laminar flame simulations.

Advances in observation and calculation of lake evaporation

Advances in observation and calculation of lake evaporation

Water stable isotopes reveal the hydrological response of Costa Rican glacial lakes to climate variability

Lakes are generally considered a key information source for understanding paleoclimate. Here, we use stable water isotopes (δ2H and δ18O) in a high-elevation tropical glacial lake to inform about modern climate variability and local hydrological conditions. We report a data set of water isotopes in precipitation and lake water collected between June 2015 and November 2020 in the Chirripó National Park, Costa Rica. Using a linear resistance model, the isotopic information coupled with lake water temperature and hydrometeorological data allowed the calculation of seasonal evaporation to inflow ratios (E/I) for Lake Ditkevi. Isotopic variations (δ18O and d-excess) reflect seasonal changes in the evaporative conditions, with E/I ratios ranging from 2.7 ± 1.3% to 10.8 ± 5.4%. Significant correlations between lake d-excess, lake water temperature, and sea surface temperature anomalies in El Niño 3.4 region revealed the sensitive hydrological response of this high elevation tropical glacial lake to the cyclic deviations in the ocean-atmosphere domain of warm/cold El Niño Southern Oscillation episodes. Our findings contribute to i) a better understanding of the climatic controls on high-elevation precipitation and surface water systems in the tropics and ii) inform paleoclimate reconstructions in Central America.

How the Environmental Lifting Condensation Level Affects the Sensitivity of Simulated Convective Storm Cold Pools to the Microphysics Parameterization

Abstract Several studies have documented the sensitivity of convective storm simulations to the microphysics parameterization, but there is less research documenting how these sensitivities change with environmental conditions. In this study, the influence of the lifting condensation level (LCL) on the sensitivity of simulated ordinary convective storm cold pools to the microphysics parameterization is examined. To do this, seven perturbed-microphysics ensembles with nine members each are used, where each ensemble uses a different base state with a surface-based LCL between 500 and 2000 m. A comparison of ensemble standard deviations of cold-pool properties shows a clear trend of increasing sensitivity to the microphysics as the LCL is raised. In physical terms, this trend is the result of lower relative humidities in high-LCL environments that increase low-level rain evaporational cooling rates, which magnifies differences in evaporation already present among the members of a given ensemble owing to the microphysics variations. Omitting supersaturation from the calculation of rain evaporation so that only the raindrop size distribution influences evaporation leads to more evaporation in the low-LCL simulations (owing to more drops), as well as a slightly larger spread in evaporational cooling amounts between members in the low-LCL ensembles. Cold pools in the low-LCL environments are also found to develop earlier and are initially more sensitive to raindrop breakup owing to a larger warm-cloud depth. Altogether, these results suggest that convective storms may be more predictable in low-LCL environments, and forecasts of convection in high-LCL environments may benefit the most from microphysics perturbations within an ensemble forecasting system. Significance Statement Computer simulations of thunderstorms can have grid spacings ranging from tens to thousands of meters. Because individual precipitation particles form on scales smaller than these grid spacings, the bulk effects of precipitation processes in models must be approximated. Past studies have found that models are sensitive to these approximations. In this study, we test whether the sensitivity to these approximations changes with the relative humidity in the lowest 1–2 km of the atmosphere. We found that increasing the relative humidity decreases the sensitivity of simulations to the precipitation process approximations. These results can inform meteorologists about the uncertainties surrounding computer-generated thunderstorm forecasts and suggest environmental conditions where using several computer models with different precipitation process approximations may be beneficial.

Analysis of the correction factors and coupling characteristics of multi-droplet evaporation

During the multi-droplet evaporation, the droplet interactions slow down the droplet evaporation. Historically, the evaporation processes have been studied mainly based on modifying the isolated droplet evaporation model. The actual multi-droplet evaporation can influence the three-dimensional flow field. However, it is not easy to replace the evaporation progress with the simplified model of the isolated droplet. Due to the complexity of the heat and mass transfer mechanism of the multi-droplet evaporation, the microscopic and accurate experiments are challenging to carry out, which leads to that the existing models are primarily derived from the theoretical aspects. In the present paper, the multi-droplet evaporation systems are simplified as different droplet arrays and reproduced based on the point source method (PSM) by introducing a correction factor to evaluate the inter-droplet interaction. The correction factor is numerically obtained, compared and validated. The parameter sensitivity of the correction factor is conducted systematically. In addition, the influence threshold is introduced to consider the droplet-gas coupling interactions. Thus, the local effects due to the transient evaporation of droplets are corrected. The results reveal that there exists a critical spacing used to determine whether the droplet interactions can be neglected. Additionally, droplets with larger sizes and smaller spacing dominate the inhibitory effect on the studied droplet. Finally, the correction for local effects will significantly reduce the droplet transient evaporation calculation error.

First-Principles Calculation of the Evaporation Field and Roll-up Effect of M (M = Fe, Cu, Si, and Mn) on the Fe (001) and Fe Step Structure.

First-principles calculations were performed on the evaporation field of Fe, Cu, Mn, and Si in Fe (001) and on the evaporation field and roll-up effect of Fe, Cu, and Mn in the Fe (001) step structure. The larger the evaporation barrier energy tendency, at an electric field of 0 V/nm (absorption energy), the larger was the evaporation field. Electric field evaporation calculation results indicate that the order in which the electric field is easily evaporated is Mn > Cu > Fe > Si. The tendency that Mn and Cu evaporate more easily than does Fe and that the evaporation of Si is less probable is consistent with the experiment of a dilute element in steel. In the Fe (001) step structure, when the electric field is low, the roll-up effect where the evaporated atoms move on the step is large, and when the electric field is large, the roll-up effect is small. The roll-up effect of Cu was almost the same as that of Fe, and the roll-up effect of Mn was small because the chemical bond between Mn and Fe was weak.

Grid-based simulation of soil moisture in the UK: future changes in extremes and wetting and drying dates

Soil moisture, typically defined as the amount of water in the unsaturated soil layer, is a central component of the hydrological cycle. The potential impacts of climate change on soil moisture have been less specifically studied than those on river flows, despite soil moisture deficits/excesses being a factor in a range of natural hazards, as well as having obvious importance for agriculture. Here, 1 km grids of monthly mean soil moisture content are simulated using a national-scale grid-based hydrological model, more typically applied to look at changes in river flows across Britain. A comparison of the soil moisture estimates from an observation-based simulation, with soil moisture deficit data from an operational system developed by the UK Met Office (Meteorological Office Rainfall and Evaporation Calculation System; MORECS), shows relatively good correspondence in soil drying and wetting dates, and in the month when soils are driest. The UK Climate Projections 2018 Regional projections are then used to drive the hydrological model, to investigate changes in occurrence of indicative soil moisture extremes and changes in typical wetting and drying dates of soils across the country. Analyses comparing baseline (December 1981–November 2011) and future (December 2050–November 2080) time-slices suggest large increases in the spatial occurrence of low soil moisture levels, along with later soil wetting dates, although changes to soil drying dates are less clear. Such information on potential future changes in soil moisture is important to enable the development of appropriate adaptation strategies for a range of sectors vulnerable to soil moisture levels.

Anisotropic droplets with uniform internal structure prepared in batch-scale by combination of vortex mixing and phase separation

Structured fluids are attractive in material science owing to their softness, variability and excellent solubility. Anisotropic emulsions, with droplets composed by structured multiple phases, are a new class of colloidal materials due to their distinct advantages of chemical encapsulation and geometry variation. High uniformity of internal droplet structure is the premise of advanced application. A fabrication approach is investigated by combination of vortex mixing and phase separation (VP), i.e. homogenous emulsion is produced by vortex mixing two immiscible oils using a volatile co-solvent and then anisotropic emulsion is obtained by phase separation induced by the spontaneous volatilizing of co-solvent. Uniformity of droplet structure in binary, ternary and even quaternary systems is systematically studied. Numerical calculations of evaporation based on three-phase diagram are performed to illustrate the kinetic process of droplet compartmentalization. In addition, the uniform droplets are applied as templates to fabricate anisotropic porous particles. Uniformity of droplet geometry is witnessed by the standard deviation reduced more than ten times. Higher order structures including two-face Janus, three-face Cerberus, and even four faces verify the university of VP strategy. Nucleation, coalescence, Ostwald ripening, and phase separation are the main procedures of droplet compartmentalization, which is controllable based the phase diagram. Anisotropic porous particles fabricated show the significant advantages of uniform morphology and flexible regulation. The strategy would propel the application of anisotropic emulsions in fields of material synthesis, drug release, and micro-reactors.

Quantifying the contribution of SWAT modeling and CMIP6 inputting to streamflow prediction uncertainty under climate change

Assessing the impacts of climate change on hydrologic systems is essential for establishing adaptive strategies for water resources management, flood risk control, mitigation, and ecological protection. Such assessments are typically performed by obtaining future levels of climate factors such as precipitation and temperature from climate models and coupling them with hydrological models to simulate future hydrological processes. Different algorithm choices for the hydrological cycle module lead to non-negligible uncertainties in the simulation results. Based on a case study of a small watershed located in the upper Huaihe River, China, we develop a SWAT model-based analyzing workflow that integrates two evapotranspiration calculation methods (Haegeraves and Penman-Monteith), two river routing models (Variable Storage and Muskingum), and four calibrated parameters sets. This workflow can identify the uncertainties from algorithms of the internal modules of the hydrological model, model parameterization, and climate model (i.e., General Circulation Models, GCMs, and climate scenarios in Shared Socioeconomic Pathways, SSPs) in the future runoff projections, and quantify their relative contributions. The results show that (1) the monthly runoff will increase in June and August and have little change in other months in 2021–2060, and increase in all months in 2061–2100; (2) annual mean runoff (Qm) and annual maximum runoff (Qp) will increase by 1.8% and 2.6% in the 2040s, and 14.7% and 18.6% in the 2080s; (3) the results of Bayesian model averaging (BMA) analysis indicates the probability of increase in Qm and Qp in all emission scenarios save the SSP 1–2.6 in the 2080s, and especially the probability of increase in Qp will be up to 97%; (4) for Qm, the evapotranspiration calculation method is the main source of uncertainty, accounting for more than half of the total uncertainty; and for Qp, GCM, SSP, and their interaction are the main sources of uncertainty with the contribution of about 70%. In contrast, the uncertainty arising from the parameterization of the hydrological model is small and can be ignored.

Revisiting the Schrage Equation for Kinetically Limited Evaporation and Condensation

Abstract The Schrage equation is commonly used in thermofluid engineering to model high-rate liquid–vapor phase change of pure fluids. Although shortcomings of this simple model were pointed out decades ago and more rigorous models have emerged from the kinetic theory community, Schrage's equation continues to be widely used. In this paper, we quantify the accuracy of the Schrage equation for evaporation and condensation of monatomic and polyatomic fluids at the low to moderately high flux operating conditions relevant to thermofluid engineering applications. As a high-accuracy reference, we numerically solve a Bhatnagar, Gross, and Krook (BGK)-like a model equation for polyatomic vapors that have previously been shown to produce accurate solutions to the Boltzmann transport equation. We observe that the Schrage equation overpredicts heat/mass fluxes by ∼15% for fluids with accommodation coefficients close to unity. For fluids with smaller accommodation coefficients, such as water, the Schrage equation yields more accurate flux estimates. We find that the Mott-Smith-like moment methods developed for liquid–vapor phase change are much more accurate than the Schrage equation, achieving heat/mass flux estimates that deviate by less than 1% (evaporation) and 4% (condensation) from the reference solution. In light of these results, we recommend using the moment method equations instead of the Schrage equation. We also provide tables with our high-accuracy numerical data for evaporation of any fluid and condensation of saturated water vapor, engineering equations fit our data, and code for moment method calculations of evaporation and condensation.

A Study on Evaporation Calculations of Agricultural Reservoirs in Hyper-Arid Areas

Free surface evaporation is an important process in regional water cycles and energy balance. The accurate calculation of free surface evaporation is of great significance for evaluating and managing water resources. In order to improve the accuracy of estimating reservoir evaporation in data-scarce arid regions, the applicability of the energy balance method was assessed to calculate water surface evaporation based on the evaporator and reservoir evaporation experiment. A correlation analysis was used to assess the major meteorological factors that affect water surface temperature to obtain the critical parameters of the machine learning models. The water surface temperature was simulated using five machine learning algorithms, and the accuracy of results was evaluated using the root mean square error (RMSE), correlation coefficient (r), mean absolute error (MAE), and Nash efficiency coefficient (NSE) between observed value and calculated value. The results showed that the correlation coefficient between the evaporation capacity of the evaporator, calculated using the energy balance method and the observed evaporation capacity, was 0.946, and the RMSE was 0.279. The r value between the calculated value of the reservoir evaporation capacity and the observed value was 0.889, and the RMSE was 0.241. The meteorological factors related to the change in water surface temperature were air temperature, air pressure, relative humidity, net radiation and wind speed. The correlation coefficients were 0.554, −0.548, −0.315, −0.227, and 0.141, respectively. The RMSE and MAE values of five models were: RF (0.464 and 0.336), LSSVM (0.468 and 0.340), LSTM (1.567 and 1.186), GA-BP (0.709 and 0.558), and CNN (1.113 and 0.962). In summary, the energy balance method could accurately calculate the evaporation of evaporators and reservoirs in hyper-arid areas. As an important calculation parameter, the water surface temperature is most affected by air temperature, and the RF algorithm was superior to the other algorithms in predicting water surface temperature, and it could be used to predict the missing data. The energy balance model and random forest algorithm can be used to accurately calculate and predict the evaporation from reservoirs in hyper-arid areas, so as to make the rational allocation of reservoir water resources.

On the Prediction of Evaporation in Arid Climate Using Machine Learning Model

Evaporation calculations are important for the proper management of hydrological resources, such as reservoirs, lakes, and rivers. Data-driven approaches, such as adaptive neuro fuzzy inference, are getting popular in many hydrological fields. This paper investigates the effective implementation of artificial intelligence on the prediction of evaporation for agricultural area. In particular, it presents the adaptive neuro fuzzy inference system (ANFIS) and hybridization of ANFIS with three optimizers, which include the genetic algorithm (GA), firefly algorithm (FFA), and particle swarm optimizer (PSO). Six different measured weather variables are taken for the proposed modelling approach, including the maximum, minimum, and average air temperature, sunshine hours, wind speed, and relative humidity of a given location. Models are separately calibrated with a total of 86 data points over an eight-year period, from 2010 to 2017, at the specified station, located in Arizona, United States of America. Farming lands and humid climates are the reason for choosing this location. Ten statistical indices are calculated to find the best fit model. Comparisons shows that ANFIS and ANFIS–PSO are slightly better than ANFIS–FFA and ANFIS–GA. Though the hybrid ANFIS–PSO (R2= 0.99, VAF = 98.85, RMSE = 9.73, SI = 0.05) is very close to the ANFIS (R2 = 0.99, VAF = 99.04, RMSE = 8.92, SI = 0.05) model, preference can be given to ANFIS, due to its simplicity and easy operation.

Thermal Stability Calculation and Experimental Investigation of Common Binary Chloride Molten Salts Applied in Concentrating Solar Power Plants

A computational study on thermal stability was conducted the first time, combining the modified quasi-chemical model, the Antoine equation, and the adiabatic flash evaporation calculation principle to design a method to calculate the system pressure-temperature (P-T) phase diagram of binary chloride molten salts. The evaporation temperature of the molten salt obtained by analyzing the P-T phase diagram of the eutectic molten salt clearly defined the upper limit of the optimal operating temperature of the mixed molten salt. The results indicated that the upper-temperature limits of NaCl-KCl, NaCl-CaCl2, KCl-CaCl2, NaCl-MgCl2, and KCl-MgCl2 are determined to be 1141 K, 1151 K, 1176 K, 1086 K, and 1068 K. The maximum working temperature was measured experimentally using a thermogravimetric analysis (TGA), and the relative error between the calculation and experiment was calculated. The maximum error between the calculated and experimental values of the maximum operating temperature was 6.02%, while the minimum was 1.29%, demonstrating the method’s high accuracy. Combined with the lowest eutectic temperature and the upper-temperature limits of binary chloride molten salts, the stable operating temperature ranges of NaCl-KCl, NaCl-CaCl2, KCl-CaCl2, NaCl-MgCl2, and KCl-MgCl2 are 891~1141 K, 750~1151 K, 874~1176 K, 732~1086 K, and 696~1086 K.

A Daily Water Balance Model Based on the Distribution Function Unifying Probability Distributed Model and the SCS Curve Number Method

A new daily water balance model is developed and tested in this paper. The new model has a similar model structure to the existing probability distributed rainfall runoff models (PDM), such as HyMOD. However, the model utilizes a new distribution function for soil water storage capacity, which leads to the SCS (Soil Conservation Service) curve number (CN) method when the initial soil water storage is set to zero. Therefore, the developed model is a unification of the PDM and CN methods and is called the PDM–CN model in this paper. Besides runoff modeling, the calculation of daily evaporation in the model is also dependent on the distribution function, since the spatial variability of soil water storage affects the catchment-scale evaporation. The generated runoff is partitioned into direct runoff and groundwater recharge, which are then routed through quick and slow storage tanks, respectively. Total discharge is the summation of quick flow from the quick storage tank and base flow from the slow storage tank. The new model with 5 parameters is applied to 92 catchments for simulating daily streamflow and evaporation and compared with AWMB, SACRAMENTO, and SIMHYD models. The performance of the model is slightly better than HyMOD but is not better compared with the 14-parameter model (SACRAMENTO) in the calibration, and does not perform as well in the validation period as the 7-parameter model (SIMHYD) in some areas, based on the NSE values. The linkage between the PDM–CN model and long-term water balance model is also presented, and a two-parameter mean annual water balance equation is derived from the proposed PDM–CN model.