Existence of piedmont zone in a river bed is a critical parameter from among numerous variations of topographical, geological and geographical conditions that can significantly influence the river flow scenario. Downstream flow situation assessed by routing of upstream hydrograph may yield higher flow depth if existence of such high infiltration zone is ignored and therefore it is a matter of concern for water resources planning and flood management. This work proposes a novel modified hydrodynamic model that has the potential to accurately determine the flow scenario in presence of piedmont zone. The model has been developed using unsteady free surface flow equations, coupled with Green-Ampt infiltration equation as governing equation. For solution of the governing equations Beam and Warming implicit finite difference scheme has been used. The proposed model was first validated from the field data of Trout Creek River showing excellent agreement. The validated model was then applied to a hypothetical river reach commensurate with the size of major tributaries of Brahmaputra Basin of India. Results indicated a 10% and 14% difference in the maximum value of discharge and depth hydrograph in presence and absence of piedmont zone respectively. Overall this model was successfully used to accurately predict the effect of piedmont zone on the unsteady flow in a river.
Calibration of parameters of mathematical models is still a tough task in several engineering problems. Many of the models adopted for the numerical simulations of real phenomena, in fact, are of empirical derivation. Therefore, they include parameters which have to be calibrated in order to correctly reproduce the physical evidence. Thus, the success of a numerical model application depends on the quality of the performed calibration, which can be of great complexity, especially if the number of parameters is higher than one. Calibration is traditionally performed by engineers and researchers through manual trial-and-error procedures. However, since models themselves are increasingly sophisticated, it seems more proper to look at more advanced calibration procedures. In this work, in particular, an optimization technique for a multi-parameter calibration is applied to a two-phase depth-averaged model, already adopted in previous works to simulate morphodynamic processes, such as, for example, the dike erosion by overtopping.
One of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis.
The identification of the moment when direct flow ends and baseflow begins is one of the biggest challenges of hydrological cycle modeling. The objectives of this research were: to characterize the recession curves (RC) and to separate the components of the hydrograph in a compact model. The RC were extracted from time series in three subwatersheds in Mexico. An expo-linear model was adapted and fitted to the master recession curves to find the transition point of the hydrograph and separate the baseflow. The model discriminated the RC in two decreasing ratios: one linear associated to the direct flow, and one exponential linked to the baseflow. The transition point between these two flows was obtained analytically by equaling both ratios. The derivation of a model parameter allowed to find the maximum points in the hydrometric time series, which were the criterion to separate the baseflow. The application of this model is recommended in the analysis of RC with different magnitudes from the flexibility and attachment to the fundaments of exhaustion of a reservoir.
During hydrological research in a Chilean swamp forest, we noted a pattern of higher streamflows close to midday and lower ones close to midnight, the opposite of an evapotranspiration (Et)-driven cycle. We analyzed this diurnal streamflow signal (DSS), which appeared mid-spring (in the growing season). The end of this DSS coincided with a sustained rain event in autumn, which deeply affected stream and meteorological variables. A survey along the stream revealed that the DSS maximum and minimum values appeared 6 and 4 hours earlier, respectively, at headwaters located in the mountain forests/ plantations than at the control point in the swamp forest. Et in the swamp forest was higher in the morning and in the late afternoon, but this process could not influence the groundwater stage. Trees in the mountain headwaters reached their maximum Ets in the early morning and/or close to midday. Our results suggest that the DSS is a wave that moves from forests high in the mountains towards lowland areas, where Et is decoupled from the DSS. This signal delay seems to convert the link between streamflow and Et in an apparent, but spurious positive relationship. It also highlights the role of landscape heterogeneity in shaping hydrological processes.
The short-term predictions of annual and seasonal discharge derived by a modified TIPS (Tendency, Intermittency, Periodicity and Stochasticity) methodology are presented in this paper. The TIPS method (Yevjevich, 1984) is modified in such a way that annual time scale is used instead of daily. The reason of extracting a seasonal component from discharge time series represents an attempt to identify the long-term stochastic behaviour. The methodology is applied for modelling annual discharges at six gauging stations in the middle Danube River basin using the observed data in the common period from 1931 to 2012. The model performance measures suggest that the modelled time series are matched reasonably well. The model is then used for the short-time predictions for three annual step ahead (2013–2015). The annual discharge predictions of larger river basins for moderate hydrological conditions show reasonable matching with records expressed as the relative error from –8% to +3%. Irrespective of this, wet and dry periods for the aforementioned river basins show significant departures from annual observations. Also, the smaller river basins display greater deviations up to 26% of the observed annual discharges, whereas the accuracy of annual predictions do not strictly depend on the prevailing hydrological conditions.
The study examines possible water savings by replacing alfalfa with winter wheat in the Fergana Valley, located upstream of the Syrdarya River in Central Asia. Agricultural reforms since the 1990s have promoted this change in cropping patterns in the Central Asian states to enhance food security and social benefits. The water use of alfalfa, winter wheat/fallow, and winter wheat/green gram (double cropping) systems is compared for high-deficit, low-deficit, and full irrigation scenarios using hydrological modeling with the HYDRUS-1D software package. Modeling results indicate that replacing alfalfa with winter wheat in the Fergana Valley released significant water resources, mainly by reducing productive crop transpiration when abandoning alfalfa in favor of alternative cropping systems. However, the winter wheat/fallow cropping system caused high evaporation losses from fallow land after harvesting of winter wheat. Double cropping (i.e., the cultivation of green gram as a short duration summer crop after winter wheat harvesting) reduced evaporation losses, enhanced crop output and hence food security, while generating water savings that make more water available for other productive uses. Beyond water savings, this paper also discusses the economic and social gains that double cropping produces for the public within a broader developmental context.
The knowledge of snowpack distribution at a catchment scale is important to predict the snowmelt runoff. The objective of this study is to select and quantify the most important factors governing the snowpack distribution, with special interest in the role of different canopy structure. We applied a simple distributed sampling design with measurement of snow depth and snow water equivalent (SWE) at a catchment scale. We selected eleven predictors related to character of specific localities (such as elevation, slope orientation and leaf area index) and to winter meteorological conditions (such as irradiance, sum of positive air temperature and sum of new snow depth). The forest canopy structure was described using parameters calculated from hemispherical photographs. A degree-day approach was used to calculate melt factors. Principal component analysis, cluster analysis and Spearman rank correlation were applied to reduce the number of predictors and to analyze measured data. The SWE in forest sites was by 40% lower than in open areas, but this value depended on the canopy structure. The snow ablation in large openings was on average almost two times faster compared to forest sites. The snow ablation in the forest was by 18% faster after forest defoliation (due to the bark beetle). The results from multivariate analyses showed that the leaf area index was a better predictor to explain the SWE distribution during accumulation period, while irradiance was better predictor during snowmelt period. Despite some uncertainty, parameters derived from hemispherical photographs may replace measured incoming solar radiation if this meteorological variable is not available.