Daily Runoff Research Articles

Land cover effect on water discharge, matter losses and surface temperature: Results of 20 years monitoring in the Šumava Mts

Three small catchments (each c. 2 km2) with different land cover (drained pasture, wetland and spruce forest) located in the Šumava Mountains in the Czech Republic (altitude 780–1026 m) were monitored continuously for 20 years. The aim of the study was to evaluate the aging of different types of land cover landscape in terms of soil acidification (loss of alkalines). Average annual water discharge from the pasture catchment was c. 60 % of annual precipitation, but 38 % and 34 % of annual precipitation from the wetland and the forest, respectively. The relatively highest water runoff was from the pasture in all months. Daily runoffs during periods of heavy rain were 26 mm from the pasture, 16 mm from the wetland and 7 mm from the forested catchment. Water discharged from the drained pasture had two times higher electrical conductivity than water from the wetland and forest due to higher concentrations of bicarbonate, nitrate, calcium, magnesium, sodium and potassium. In the wetland and forest catchments, the amounts of nitrate, sulphate, chloride, TC, TN, TP and ammonium in the precipitated water were higher than that of the discharged with water (soluble matter losses). The amount of soluble matter discharged from the pasture catchment (matter losses) was in all cases higher than the amount in the precipitated water. On sunny days in the vegetation season, the pasture catchment had higher surface temperature than the wetland and forest catchments, which had higher evapotranspiration. Thermal pictures from satellites (e.g. Landsat) or taken by airborne thermal cameras can be used for identification of landscape areas of higher water outflow and higher matter losses.

Plant Physiological Responses to Rising CO2 Modify Simulated Daily Runoff Intensity With Implications for Global‐Scale Flood Risk Assessment

AbstractClimate change is expected to increase the frequency of flooding events and, thus, the risks of flood‐related mortality and infrastructure damage. Global‐scale assessments of future flooding from Earth system models based only on precipitation changes neglect important processes that occur within the land surface, particularly plant physiological responses to rising CO2. Higher CO2 can reduce stomatal conductance and transpiration, which may lead to increased soil moisture and runoff in some regions, promoting flooding even without changes in precipitation. Here we assess the relative impacts of plant physiological and radiative greenhouse effects on changes in daily runoff intensity over tropical continents using the Community Earth System Model. We find that extreme percentile rates increase significantly more than mean runoff in response to higher CO2. Plant physiological effects have a small impact on precipitation intensity but are a dominant driver of runoff intensification, contributing to one half of the 99th and one third of the 99.9th percentile runoff intensity changes.

Comparison of Optimization Algorithms in Deep Learning-Based Neural Networks for Hydrological Forecasting: Case Study of Nam River Daily Runoff

Neural networks have been widely used in forecasting hydrometeorological variables such as rainfall-runoff simulation and statistical downscaling. Furthermore, deep learning approaches have been developed in recent decades. To improve the performance in deep learning, many optimization algorithms have been proposed in literature to estimate the weights of connections between nodes. An essential need exists to provide information about how the proposed optimization algorithms behave in different applications so that a reasonable algorithm can be selected for a particular application. In the current study, we used rainfall datasets of the Nam River basin to test several available algorithms used in runoff forecasting such as Stochastic Gradient Descent (SGD), Adaptive Gradient Algorithm (Adagrad), Root-Mean-Square prop (RMSprop), Adaptive Delta (Adadelta), Adaptive Moment Estimation (Adam), and Adam with Nesterov Momentum (Nadam). Among these, Adam and Nadam, which are the most recently developed, presented better performance in predicting runoff even though the difference was limited. This difference might be critical when using modeling procedures that must be repeated numerous times, such as deep learning. Further extensive studies might refine the parameter estimation algorithm and allow the use of deep learning for hydrometeorological applications with the tested recent optimization algorithms. Keywords: Neural Network, Optimization, Rainfall-Runoff, Simulation

Simulating Daily Runoff in Hydrologic Standard Basin Considering Agricultural Reservoir Operation

Simulating Daily Runoff in Hydrologic Standard Basin Considering Agricultural Reservoir Operation

A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain

Streamflow data are of prime importance to water-resources planning and management, and the accuracy of their estimation is very important for decision making. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) models have been evaluated and compared to find a method to improve streamflow estimation. For a more complete evaluation, the accuracy and ability of these streamflow estimation models was also established separately based on their performance during different periods of flows using regional flow duration curves (FDCs). Specifically, the FDCs were divided into five sectors: very low, low, medium, high and very high flow. This segmentation of flow allows analysis of the model performance for every important discharge event precisely. In this study, the models were applied in two catchments in Peninsular Spain with contrasting climatic conditions: Atlantic and Mediterranean climates. The results indicate that SWAT and ANNs were generally good tools in daily streamflow modelling. However, SWAT was found to be more successful in relation to better simulation of lower flows, while ANNs were superior at estimating higher flows in all cases.

Forecasting Daily Runoff by Extreme Learning Machine Based on Quantum-Behaved Particle Swarm Optimization

AbstractAccurate hydrologic time-series prediction plays an important role in modern water resource planning, water supply management, environmental protection, and power system operation. In gener...

Evaluating the Use of Nash-Sutcliffe Efficiency Coefficient in Goodness-of-Fit Measures for Daily Runoff Simulation with SWAT

AbstractHydrological models must be carefully calibrated to ensure that predictions are scientifically sound and reliable. As a goodness-of-fit measure, an ideal threshold of the Nash-Sutcliffe eff...

Applicability evaluation of the SWIM at river basins of the black soil region in Northeast China: A case study of the upper and middle Wuyuer River basin

In this paper, we selected the middle and upper reaches of the Wuyuer River basin in the black soil region of Northeast China as the study area. We adopted the soil and water integrated model (SWIM) and evaluated the parameter sensitivity using partial correlation coefficient. We calibrated and validated our simulation results based on the daily runoff data from Yi’an hydrological station at the outlet of the river basin and the evaporation data recorded by various weather stations from 1961 to 1997. Following evaluation of the modeling data against the observed data, we present the applicability of SWIM in the river basin of the black soil region, and discuss the resulting errors and their probable causes. Results show that in the periods of calibration and validation, the Nash-Sutcliffe efficiency (NSE) coefficients of the monthly and daily runoffs were not less than 0.71 and 0.55, and the relative errors were less than 6.0%. Compared to daily runoffs, the simulation result of monthly runoffs was better. Additionally, the NSE coefficients of the potential monthly evaporation were not less than 0.81. Together, the results suggest that the calibrated SWIM can be utilized in various simulation analyses of runoffs on a monthly scale in the black soil region of Northeast China. On the contrary, the model had some limitations in simulating runoffs from snowmelt and frozen soil. Meanwhile, the stimulation data deviated from the measured data largely when applied to the years with spring and summer floods. The simulated annual runoffs were considerably higher than the measured data in the years with abrupt increases in annual precipitation. However, the model is capable of reproducing the changes in runoffs during flood seasons. In summary, this model can provide fundamental hydrological information for comprehensive management of the Wuyuer River basin water environment, and its application can be potentially extended to other river basins in the black soil region.

Closure to “Development of a Direct Geomorphologic IUH Model for Daily Runoff Estimation in Ungauged Watersheds” by Seiyed Mossa Hosseini, Najmeh Mahjouri, and Samaneh Riahi

Closure to “Development of a Direct Geomorphologic IUH Model for Daily Runoff Estimation in Ungauged Watersheds” by Seiyed Mossa Hosseini, Najmeh Mahjouri, and Samaneh Riahi

Discussion of “Development of a Direct Geomorphologic IUH Model for Daily Runoff Estimation in Ungauged Watersheds” by Seiyed Mossa Hosseini, Najmeh Mahjouri, and Samaneh Riahi

Discussion of “Development of a Direct Geomorphologic IUH Model for Daily Runoff Estimation in Ungauged Watersheds” by Seiyed Mossa Hosseini, Najmeh Mahjouri, and Samaneh Riahi

Development of a Direct Geomorphologic IUH Model for Daily Runoff Estimation in Ungauged Watersheds

AbstractThe aim of this paper is to propose a new geomorphologic method for deriving the complete shape of instantaneous unit hydrograph (IUH) in ungauged watersheds by calculating the mean travel time of excess rainfall in overland and channel segments. All required parameters for the calculations of travel time of sheet and channel flows for different hydrologic orders are determined based on geomorphologic data of a watershed. In the proposed model, IUH is obtained using the exponential probability density function (PDF) of mean travel time of possible flow paths in a watershed. In this method, the excess rainfall is determined using the National Resources Conservation Service) NRCS curve number (CN) method (SCS-CN). To investigate the performance of the proposed IUH model, it is applied for daily rainfall-runoff simulation in two separate watersheds with different climate conditions (i.e., cool-humid and semiarid) in Iran. Also, other geomorphologic IUH (GIUH) models are utilized as benchmark models f...

Investigating the uncertainty and transferability of parameters in SWAT model under climate change

ABSTRACTThere is an implicit assumption in most work that the parameters calibrated based on observations remain valid for future climatic conditions. However, this might not be true due to parameter instability. This paper investigates the uncertainty and transferability of parameters in a hydrological model under climate change. Parameter transferability is investigated with three parameter sets identified for different climatic conditions, which are: wet, intermediate and dry. A parameter set based on the baseline period (1961–1990) is also investigated for comparison. For uncertainty analysis, a k-simulation set approach is proposed instead of employing the traditional optimization method which uses a single best-fit parameter set. The results show that the parameter set from the wet sub-period performs the best when transferred into wet climate condition, while the parameter set from the baseline period is the most appropriate when transferred into dry climate condition. The largest uncertainty of simulated daily high flows for 2011–2040 is from the parameter set trained in the dry sub-period, while that of simulated daily medium and low flows lies in the parameter set from the intermediate calibration sub-period. For annual changes in the future period, the uncertainty with the parameter set from the intermediate sub-period is the largest, followed by the wet sub-period and dry sub-period. Compared with high and medium flows/runoffs, the uncertainty of low flows/runoffs is much smaller for both simulated daily flows and annual runoffs. For seasonal runoffs, the largest uncertainty is from the intermediate sub-period, while the smallest is from the dry sub-period. Apart from that, the largest uncertainty can be observed for spring runoffs and the lowest one for autumn runoffs. Compared with the traditional optimization method, the k-simulation set approach shows many more advantages, particularly being able to provide uncertainty information to decision support for watershed management under climate change.EDITOR Z.W. Kundzewicz ASSOCIATE EDITOR not assigned

Daily Runoff Forecasting Model Based on ANN and Data Preprocessing Techniques

There are many models that have been used to simulate the rainfall-runoff relationship. The artificial neural network (ANN) model was selected to investigate an approach of improving daily runoff forecasting accuracy in terms of data preprocessing. Singular spectrum analysis (SSA) as one data preprocessing technique was adopted to deal with the model inputs and the SSA-ANN model was developed. The proposed model was compared with the original ANN model without data preprocessing and a nonlinear perturbation model (NLPM) based on ANN, i.e., the NLPM-ANN model. Eight watersheds were selected for calibrating and testing these models. Comparative study shows that the learning and training ability of ANN models can be improved by SSA and NLPM techniques significantly, and the performance of the SSA-ANN model is much better than the NLPM-ANN model, with high foresting accuracy. The SSA-ANN1 model, which only considers rainfall as model input, was compared with the SSA-ANN2 model, which considers both rainfall and previous runoff as model inputs. It is shown that the Nash-Sutcliffe criterion of the SSA-ANN2 model is much higher than that of the SSA-ANN1 model, which means that the proper selection of previous runoff data as rainfall-runoff model inputs can significantly improve model performance since they usually are highly auto-correlated.

Application of Time Sequence Model Based on Excluded Seasonality in Daily Runoff Prediction

To build a time sequence prediction model with excluded seasonality out of the sequence with prominent seasonal features, primary treatment of seasonality exclusion shall firstly be done, to obtain the pass of stationary test; and then, by integrating autocorrelogram, partial autocorrelogram and AIC codes, ARMA model shall be identified and be able to pass residual correlation test and the optimum hydrological probability distribution function shall be finally determined. In accordance with the daily runoff of the Three Gorges from 1950-2009, the predicted daily runoff in 2010 via R software has small error compared with the observed daily runoff, obtaining good prediction performance.

Impact of Monthly Curve Number on Daily Runoff Estimation for Ozat Catchment in India

The Soil Conservation Service Curve Number (SCS-CN) is a well-established loss-rate model to estimate runoff. It combines watershed parameters and climatic factors in one entity curve number (CN). The CN exhibits an inherent seasonality beyond its spatial variability, which cannot be accounted for by the conventional methods. In the present study, an attempt has been made to determine the CN for different months of monsoon season with an objective to evaluate the impact of monthly CN on runoff estimation for Ozat catchment (Gujarat State, India). The standard CN and month wise CN were determined by three procedures, viz, the median, geometric mean and standard asymptotic fit using gauged rainfall and runoff. This study shows that the predictive capability of CN determination methods can be improved by using monthly CN. Refined Willmott’s index (dr) and mean absolute error (MAE) were used to assess and validate the performance of each method. The asymptotic fit CN method with monthly CN resulting dr from 0.46 to 0.49 and MAE from 1.13 mm to 1.18 mm was judged to be more consistent with the existing commonly used CN methods in terms of runoff estimation for the study area.

Incorporation of TOPMODEL into land surface model SSiB and numerically testing the effects of the corporation at basin scale

In order to examine and analyze the effects of integration of land surface models with TOPMODEL and different approaches for the integration on the model simulation of water and energy balances, the coupled models have been developed, which incorporate TOPMODEL into the Simplified Biosphere Model (SSiB) with different approaches(one divides a basin into a number of zones according to the distribution of topographic index, and the other only divides the basin into saturated and unsaturated zones). The coupled models are able to (but SSiB is not able to) take into account the impacts of topography variation and vertical heterogeneity of soil saturated hydraulic conductivity on horizontal distribution of soil moisture and in turn on water and energy balances within the basin(or a grid cell). By using the coupled models and SSiB model itself, the daily hydrological components such as runoffs are simulated and final results are analyzed carefully. Simulated daily results of hydrological components produced by both SSiB and coupled models show that (i) There is significant difference between results of soil wetness, its vertical distribution and seasonal variation, water and energy balance, and daily runoff in the basin predicted by SSiB and by the coupled models. The land surface model currently used such as SSiB is likely to misrepresent real feature of water and energy balances in the basin. (ii) Compared with the results for the basin predicted by SSiB, the coupled models predict more strong vertical and seasonal changes in soil wetness, higher evaporation and lower runoff, and improve the base flow simulation obviously. (iii) Comparing the results for the basin predicted by two coupled models with different integration approach and SSiB one by one, the results of daily runoffs and soil wetness predicted by the two coupled models are quite similar. It means, for the coupled models, the approach by dividing a region being considered into more subzones may have limited effects on improving runoff simulation results. The scheme only to divide the region into saturated and unsaturated zones may be a convenient and effective scheme. But then, if the results from the two coupled models for the basin are carefully compared, the simulated results by the coupled model with dividing the basin into more subzones will show higher evaporation and surface runoff but lower subsurface flow, lower total runoff, and lower ground water level averaged for five years.

Hydrologic Analysis of Rainfed Rice Areas Using a Simple Semi-distributed Water Balance Model

We analyzed the hydrologic characteristics of the Nong Saeng Basin (19.72 km2) in northeast Thailand. Because the land use in this basin is very complex, applying a fully distributed model would be extremely difficult. Therefore, we developed a semi-distributed hydrologic model for this basin. The hydrologic model comprised upland, paddy, and pond models. The new bucket model was applied for upland fields, and a modified tank model was used for the paddy and pond models. In addition, water movement between different land uses was considered. The results showed that the hydrologic model developed for the study basin performed extremely well if water movement between different land uses was considered. We simulated the water storage characteristics for two sub-catchments within the basin: sub-catchment 1 included few earthen weirs, whereas sub-catchment 2 included several earthen weirs. Owing to the earthen weirs, the maximum differences in ponding paddy water were 19,997 m3 in 2002 and 16,897 m3 in 2003, corresponding to 48% and 41% of the total volume of ponds in sub-catchment 1 (41,287 m3), respectively. If earthen weirs were to be constructed over the entire basin, the annual runoff from the basin would decrease by 2.0–3.2%. Although the decrease of the annual runoff is little, the maximum differences between the daily runoffs under real and simulated conditions in terms of percentage differences are −44.3% in September 2002 and −36.5% in September 2003, and it is found that the downstream impacts are quite large at the end of rainy season.

Evaluation of APEX for Daily Runoff and Sediment Yield from Three Plots in the Middle Huaihe River Watershed, China

This study was conducted to evaluate the performance of APEX using daily runoff and sediment yield (1982-1986) collected from three plots located in the middle Huaihe River watershed, China. A sensitivity analysis was conducted using an extended Fourier amplitude sensitivity test, followed by an automatic calibration procedure to calibrate the curve number for moisture condition 2 (CN2), curve number index coefficient (CNIC), conservation practice factor (PEC), and peak runoff rate - rainfall energy adjustment factor (APM). The percent errors of mean (PE) were within 20%, with Nash-Sutcliffe efficiency (EF) and R2 values all above 0.45 and 0.55 for both daily runoff and sediment yield for the three plots during the calibration period. For the validation period, the PE values were within 25%, and the EF and R2 values ranged from 0.41 to 0.84 and from 0.55 to 0.85, respectively. Scenario analyses were conducted for a 24-year period for (1) fallow (baseline), (2) mixed wood-grass with horizontal terraces (scenario 1), and (3) woodland with horizontal-level ditches (scenario 2). The long-term benefits of scenario 1 over the baseline were quantified as minimum surface runoff and sediment yield reductions of 37 mm year-1 (35%) and 22 Mg ha-1 year-1 (84%), respectively. The benefits of scenario 2 over the baseline were reductions of 37% surface runoff and 89% sediment yield. The results suggest that APEX is a useful tool for evaluating soil and water loss for different management practices in the middle Huaihe River watershed, China, and that construction of horizontal-level ditches and horizontal terraces, and enhancing vegetation, are efficient strategies for controlling runoff and sediment in the region.

Daily Runoff Simulation by an Integrated Catchment Model in the Middle and Lower Regions of the Changjiang Basin, China

To simulate daily runoff in the middle and lower regions of the Changjiang (Yangtze River) basin, we used an integrated, spatially distributed model consisting of the Hydrological Simulation Program—FORTRAN (HSPF) and two newly developed submodels, the paddy runoff model (PRM), simulating the runoff from paddy fields, and the lake discharge model (LDM), simulating the hydraulic effect of the Changjiang mainstream on the discharge from neighboring lakes. In the PRM, the ratios of the simulated monthly evapotranspiration relative to the observed potential evaporation were close to or exceeded 1.0 in both of the test subcatchments in the full ponding period with plentiful irrigation water, corresponding to many previous observations of evapotranspiraion rates from paddy fields. Whereas the original HSPF could not consider the backwater effect of the water level in the mainstream, the daily discharge from the lakes simulated by the LDM was dependent on the water level in the mainstream and corresponded to the...

Application of Conceptual Rainfall-Runoff Model METQ for Simulation of Daily Runoff and Water Level: The case of the Lake Burtnieks Watershed

Application of Conceptual Rainfall-Runoff Model METQ for Simulation of Daily Runoff and Water Level: The case of the Lake Burtnieks Watershed In this study a conceptual rainfall-runoff METQ model—the latest version METQ2007BDOPT—was applied to simulate the daily runoff and water level of the Lake Burtnieks watershed from 1990 to 1999. The model structure and parameters were basically the same as in the METQ98, with some additional improvements and semi-automatical calibration performance. Model calibration was done for four rivers and one lake gauging station. The results of calibration showed a good correlation between the measured and simulated daily discharges. The Nash-Sutcliffe efficiency R2 varied from 0.90 to 0.58 and correlation coefficient r from 0.95 to 0.83. The highest values of R2 = 0.90 and r = 0.95 were obtained for the River Salaca and the lowest R2 = 0.53 and r = 0.83 for Lake Burtnieks. We observed some relationships between the model parameter values and physiographic characteristic of the sub-catchments.