A volumetric water budget of Devils Lake (USA): non-stationary precipitation–runoff relationships in an amplifier terminal lake

Devils Lake, a terminal lake in eastern North Dakota, rose more than 9 m between 1992 and 2013, producing a 286% increase in lake area, and causing more than US$1 billion in direct damages. An annual volumetric lake water budget is developed from monthly hydroclimatological variables for the period 1951–2010 to investigate the rapid lake expansion. The lake is an amplifier terminal lake in which long-term climatic changes are amplified by positive feedback mechanisms, causing the lake to transition from a precipitation-dominated to a runoff-dominated water budget. Factors specific to the Devils Lake Basin further amplify this positive feedback relationship. These include principles of fill–spill hydrology that operate between individual sub-basins within the closed basin, and between the innumerable wetland complexes within each sub-basin. These factors create a pronounced non-stationary precipitation–runoff relationship in the basin during both long-term wetting and drying phases.     

Study on the simulation of transparency of Lake Taihu under different hydrodynamic conditions

It was indicated in this study that there were negative relations between the concentrations of suspended solid (SS) and transparency according to the analysis of measured data of Lake Taihu. Their relations in pervious studies were reviewed, which showed that the changes of transparency in Lake Taihu could be reflected by simulating suspended solid concentration (SSC). Measured data showed that the changes of SSC with wind speed were similar at different water depths. SSC increased with the increasing of wind speed. Both wave and lake current of Lake Taihu had positive relations with SSC. However, wave was the main factor affecting sediment suspension, while flow took the second place. In this study, a numerical model coupling lake current, wave and SSC of Lake Taihu was developed. In the SS model, the combined effects of wave and current were included. The amounts of suspended and deposited sediments near the lake bed surface layer were treated separately. The stochastic characteristics of turbulent flow pulsation near lake beds were also considered, and the start-up conditions of sediment suspension were introduced to the model. The model elucidated the mutual exchange processes between sediment particles in SS and active sediments within and on the bed surface layer. Simulated results showed that lake current had relatively significant effects on the SSC at littoral areas of Lake Taihu, while SSC at the central area of the lake was mainly influenced by wave. The changes of transparency with SSC were simulated for Lake Taihu using this model. Calculated results were validated by measured data with good fitness, which indicated that the model is basically suitable for the simulation and prediction of transparency of Lake Taihu.     

An enhanced rainfall–runoff model with coupled canopy interception

The translation of rainfall to runoff is significantly affected by canopy interception. Therefore, a realistic representation of the role played by vegetation cover when modelling the rainfall–runoff system is essential for predicting water, sediment, and nutrient transport on hillslopes. Here, we developed a new mathematical model to describe the dynamics of interception, infiltration, and overland flow on canopy-covered sloping land. Based on the relationship between rainfall intensity and the maximum interception rate, the interception process was modelled under two simplified scenarios (i.e., r_e ≤ Int_m and r_e > Int_m). Parameterization of the model was based on consideration of both vegetation condition and soil properties. By analysing the given examples, we found that Int_m reflects the capacity of the canopy to store the precipitation, k reveals the ability of the canopy to retain the intercepted water, and the processes of infiltration and runoff generation are impacted dramatically by Int_m and k. To evaluate the model, simulated rainfall experiments were conducted in 2 years (2016 and 2017) across six cultivation plots at Changwu State Key Agro-Ecological Experimental Station of the Chinese Loess Plateau. The parameters were obtained by fitting the unit discharge (simulated rainfall experiments in 2016) using the least squares method, and estimation formulas for parameters pertaining to vegetation/soil factors (measured in 2016) were constructed via multiple nonlinear regressions. By matching the simulated results and unit discharge (simulated rainfall experiments in 2017), the validity of the model was verified, and a reasonable precision (average R² = .86 and average root mean square error = 6.45) was obtained. The model developed in this research creatively incorporates the canopy interception process to complement the modelling of rainfall infiltration and runoff generation during vegetation growth and offers an improved hydrological basis to analyse matter transport during rainfall events.     

A wavelet based approach for combining the outputs of different rainfall–runoff models

The rainfall–runoff modelling being a stochastic process in nature is dependent on various climatological variables and catchment characteristics and therefore numerous hydrological models have been developed to simulate this complex process. One approach to modelling this complex non-linear rainfall–runoff process is to combine the outputs of various models to get more accurate and reliable results. This multi-model combination approach relies on the fact that various models capture different features of the data, and hence combination of these features would yield better result. This study for the first time presented a novel wavelet based combination approach for estimating combined runoff The simulated daily output (Runoff) of five selected conventional rainfall–runoff models from seven different catchments located in different parts of the world was used in current study for estimating combined runoff for each time period. Five selected rainfall–runoff models used in this study included four data driven models, namely, the simple linear model, the linear perturbation model, the linearly varying variable gain factor model, the constrained linear systems with a single threshold and one conceptual model, namely, the soil moisture accounting and routing model. The multilayer perceptron neural network method was used to develop combined wavelet coupled models to evaluate the effect of wavelet transformation (WT). The performance of the developed wavelet coupled combination models was compared with their counterpart simple combination models developed without WT. It was concluded that the presented wavelet coupled combination approach outperformed the existing approaches of combining different models without applying input WT. The study also recommended that different models in a combination approach should be selected on the basis of their individual performance.     

Comparison of an artificial neural network and a conceptual rainfall–runoff model in the simulation of ephemeral streamflow

ABSTRACT

The rainfall–runoff process is governed by parameters that can seldom be measured directly for use with distributed models, but are rather inferred by expert judgment and calibrated against historical records. Here, a comparison is made between a conceptual model (CM) and an artificial neural network (ANN) for their ability to efficiently model complex hydrological processes. The Sacramento soil moisture accounting model (SAC-SMA) is calibrated using a scheme based on genetic algorithms and an input delay neural network (IDNN) is trained for variable delays and hidden layer neurons which are thoroughly discussed. The models are tested for 15 ephemeral catchments in Crete, Greece, using monthly rainfall, streamflow and potential evapotranspiration input. SAC-SMA performs well for most basins and acceptably for the entire sample with R² of 0.59–0.92, while scoring better for high than low flows. For the entire dataset, the IDNN improves simulation fit to R² of 0.70–0.96 and performs better for high flows while being outmatched in low flows. Results show that the ANN models can be superior to the conventional CMs, as parameter sensitivity is unclear, but CMs may be more robust in extrapolating beyond historical record limits and scenario building.

EDITOR M.C. Acreman; ASSOCIATE EDITOR not assigned     

Rainfall–runoff–inundation analysis of the 2010 Pakistan flood in the Kabul River basin

Abstract

Pakistan has suffered a devastating flood disaster in 2010. In the Kabul River basin (92 605 km²), large-scale riverine and flash floods caused destructive damage with more than 1100 casualties. This study analysed rainfall–runoff and inundation in the Kabul River basin with a newly developed model that simulates the processes of rainfall–runoff and inundation simultaneously based on two-dimensional diffusion wave equations. The simulation results showed a good agreement with an inundation map produced based on MODIS for large-scale riverine flooding. In addition, the simulation identified flash flood-affected areas, which were confirmed to be severely damaged based on a housing damage distribution map. Since the model is designed to be used even immediately after a disaster, it can be a useful tool for analysing large-scale flooding and to provide supplemental information to agencies for relief operations.

Editor Z.W. Kundzewicz

Citation Sayama, T., Ozawa, G., Kawakami, T., Nabesaka, S. and Fukami, K., 2012. Rainfall–runoff–inundation analysis of the 2010 Pakistan flood in the Kabul River basin. Hydrological Sciences Journal, 57 (2), 298–312.     

Application of non-stationary Poisson model prediction on seismic belts in northwestern of China

Earthquake probability prediction is based on earthquakes occurred in a certain seismo-tectonic region to predict the probable times and probability of certain magnitude segment earthquake or the earthquake whose mag-nitude is larger than certain magnitude low limit in the coming certain period, this was extensively applied to earthquake risk analysis and earthquake forecast. The main characteristics of the method are that when earthquake statistical model was founded according to the occurred …     

Suitability of a lumped rainfall–runoff model for flashy tropical watersheds in New Caledonia

ABSTRACT

The GR4H lumped hourly rainfall–runoff model was assessed for its integration in a ridge-to-reef modelling framework. Particular attention was paid to rainfall representation, robustness of parameter estimates and ability to reproduce the main runoff features. The study was conducted in four tropical mountainous watersheds in New Caledonia, which are exposed to intense rainfall events, large annual climatic variations triggered by El Niño oscillation, and wildfires. The inverse distance and elevation weighting algorithm outperformed other classical rainfall interpolation methods under data-limited conditions. The time span of data needed for robust calibration was site specific and varied from 6–7 years to 10 years, which may be linked to El Niño events and to wildfires. With sufficient data, simulation quality was equivalent during the calibration and validation periods. The GR4H model was generally able to simulate both flash floods and large annual variations. The model was more reliable when simulating wet years and watersheds not subject to land-cover changes.     

Variations in sediment runoff in the lower reaches and the delta of the Kuban River in the XX–XXI centuries

Data of network and expedition measurements and information about water management arrangements were used to study in detail the peculiarities of along-channel and long-term variations in the major characteristics of suspended sediment runoff in the lower reach and the delta of the Kuban River. For characteristics periods, the annual volumes of actual and estimated sediment runoff and the contribution of economic activity in its variations are evaluated and possible changes in sediment runoff characteristics in the XXI century are forecasted. The specific features, values, and causes of sediment runoff transformation in Kuban delta in the past and the present are analyzed; sediment balance in the delta is calculated.     

Hydrological model parameter (in)stability – “crash testing” the HBV model under contrasting flood seasonality conditions

This paper investigates the transferability of calibrated HBV model parameters under stable and contrasting conditions in terms of flood seasonality and flood generating processes (FGP) in five Norwegian catchments with mixed snowmelt/rainfall regimes. We apply a series of generalized (differential) split-sample tests using a 6-year moving window over (i) the entire runoff observation periods, and (ii) two subsets of runoff observations distinguished by the seasonal occurrence of annual maximum floods during either spring or autumn. The results indicate a general model performance loss due to the transfer of calibrated parameters to independent validation periods of ?5 to ?17%, on average. However, there is no indication that contrasting flood seasonality exacerbates performance losses, which contradicts the assumption that optimized parameter sets for snowmelt-dominated floods (during spring) perform particularly poorly on validation periods with rainfall-dominated floods (during autumn) and vice versa.     

Relation of deformation behavior with precipitation and groundwater of the Babaoshan fault in Beijing

Introduction The surface of the Earth is the main location where the fluid strongly interacts with solid, and where the atmosphere, hydrosphere, biosphere, and lithosphere strongly interact. Obviously, fault zones as the channels of fluid (water) flow are the focus area of this strong interaction. Earth-quakes, as the products of tectonic activity, occurred near or on the fault zones, can be regarded as one of the results of this strong interaction. Bolt (1999) pointed out that if there were …     

Analysis of hydrological seasonality across northern catchments using monthly precipitation–runoff polygon metrics

Abstract

Seasonality is an important hydrological signature for catchment comparison. Here, the relevance of monthly precipitation–runoff polygons (defined as scatter points of 12 monthly average precipitation–runoff value pairs connected in the chronological monthly sequence) for characterizing seasonality patterns was investigated to describe the hydrological behaviour of 10 catchments spanning a climatic gradient across the northern temperate region. Specifically, the research objectives were to: (a) discuss the extent to which monthly precipitation–runoff polygons can be used to infer active hydrological processes in contrasting catchments; (b) test the ability of quantitative metrics describing the shape, orientation and surface area of monthly precipitation–runoff polygons to discriminate between different seasonality patterns; and (c) examine the value of precipitation–runoff polygons as a basis for catchment grouping and comparison. This study showed that some polygon metrics were as effective as monthly average runoff coefficients for illustrating differences between the 10 catchments. The use of precipitation–runoff polygons was especially helpful to look at the dynamics prevailing in specific months and better assess the coupling between precipitation and runoff and their relative degree of seasonality. This polygon methodology, linked with a range of quantitative metrics, could therefore provide a new simple tool for understanding and comparing seasonality among catchments.

Editor Z.W. Kundzewicz; Associate editor K. Heal

Citation Ali, G., Tetzlaff, D., Kruitbos, L., Soulsby, C., Carey, S., McDonnell, J., Buttle, J., Laudon, H., Seibert, J., McGuire, K., and Shanley, J., 2013. Analysis of hydrological seasonality across northern catchments using monthly precipitation–runoff polygon metrics. Hydrological Sciences Journal, 59 (1), 56–72.     

Use of spatially distributed water table observations to constrain uncertainty in a rainfall–runoff model

The Generalised Likelihood Uncertainty Estimation (GLUE) methodology is used to investigate how distributed water table observations modify simulation and parameter uncertainty for the hydrological model TOPMODEL, applied to the Sæternbekken Minifelt catchment in Norway. Errors in simulating observed flows, continuously-logged borehole water levels and more extensive, spatially distributed water table depths are combined using Bayes' equation within a `likelihood measure' L. It is shown how the distributions of L for the TOPMODEL parameters change as the different types of observed data are considered. These distributions are also used to construct corresponding simulation uncertainty bounds for flows, borehole water levels, and water table depths within the spatially-extensive piezometer network. Qualitatively wide uncertainty bounds for water table simulations are thought to be consistent with the simplified nature of the distributed model.     

The hydrological regime of large river basins in Northern Eurasia in the XX–XXI centuries

The ability of present-day climate models to reproduce the mean annual regime of river runoff and its within-year distribution is evaluated for major Eurasian basins, including the basins of the Volga and Amur and the major Siberian rivers: the Ob, Yenisei, and Lena. Estimates are made for possible variations in seasonal runoff and characteristics of daily precipitation (the amount, rate, and probability) in drainage areas for the late XXI century. The analysis involved the use of the results of calculations by climatic general circulation models carried out under international Coupled Model Intercomparison Project.     

Evaluation of gridded precipitation data in the Hindu Kush–Karakoram–Himalaya mountainous area

The accurate measurement of precipitation is crucial for hydrological studies. This is especially true for the Hindu Kush–Karakoram–Himalaya (HKKH) mountain region, which is characterized by high spatiotemporal precipitation variability. The paucity of raingauges makes it difficult to measure precipitation in this region precisely. We conducted evaluation of TMPA 3B42V7 and APHRO 1101 in the HKKH area on a daily basis at a spatial resolution of 0.25°?×?0.25°, using 27 raingauges. Statistically, the largest error in the gridded data arose mainly from elevation, followed by volumetric error and Nash–Sutcliffe efficiency. Overall, the TMPA data have a poor correlation with ground observations in the HKKH area, especially for higher altitudes. The western areas are relatively more underestimated and the Karakoram shows higher frequency of bias in the TMPA retrievals. This method could help improve the satellite precipitation estimation algorithm as it considers local physiography and climatic factors.     

Simulating temporal variability in catchment response using a monthly rainfall–runoff model

Abstract

Temporal variability can result from shifts in climate, or from changes in the runoff response due to land- or water-use changes, and represents a potential source of uncertainty in calibrating hydrological models. Parameter values were determined using Monte Carlo parameter sampling methods for a monthly rainfall–runoff model (Pitman model) for different sub-periods on four catchments, with different types and degrees of temporal variability, in Australia and Africa. For some catchments, parameters were not dependent upon the sub-period used and fell within expected ranges given the relatively high degree of model equifinality. In other catchments, dependencies can be identified that are associated with signals contained within the sub-periods. While the Pitman model is relatively robust in the face of temporal variability, it is concluded that better simulations will always be obtained from calibration data that include signals representing the total variability in climate, land-use change and catchment responses.     

Calibration of a rainfall–runoff model at regional scale by optimising river discharge statistics: Performance analysis for the average/low flow regime

Traditional procedures for rainfall–runoff model calibration are generally based on the fit of individual values of simulated and observed hydrographs. We use here an alternative option that is carried out by matching, in the optimisation process, a set of streamflow statistics. Such an approach has the significant advantage to enable also a straightforward regional calibration of model parameters, based on the regionalisation of the selected statistics. The minimisation of the set of objective functions is carried out by using the AMALGAM algorithm, leading to the identification of behavioural parameter sets. The procedure is applied to a set of river basins located in central Italy: the basins are treated alternatively as gauged and ungauged and, as a term of comparison, the results obtained with a traditional time-domain calibration are also presented. With respect to previous applications of analogous procedures, we investigate here the identification of the target statistics depending on the purposes of the application, and in particular when the focus is on the reproduction of the low-flows. The results show that a suitable choice of the statistics to be optimised leads to interesting results in real world case studies as far as the reproduction of the different flow regimes is concerned.     

On the use of mean monthly runoff to predict the flow–duration curve in ungauged catchments

ABSTRACT

We examine the applicability of predicting the daily flow–duration curve (FDC) using mean monthly runoff represented in its stochastic form (MM_FDC) to aid in predictions in ungauged basins, using long-term hydroclimatic data at 73 catchments of humid climate, in the eastern USA. The analysis uses soil hydrological properties, soil moisture storage capacity and the predominant runoff generation mechanism. The results show that MM_FDC did not distinguish the shapes of the upper and lower thirds of the FDC. The upper third is where the precipitation pattern and the antecedent moisture conditions are dominant, while the lower third is where drought-induced low flows and the evapotranspiration effect are prevalent. It is possible to use the MM_FDC to predict the middle third of the FDC (exceedence probabilities between 33% and 66%). The method is constrained by the catchment flow variability (slope of FDC), which changes in accordance with landscape properties and the predominant runoff generation mechanism.     

Analysis of natural tracers and genetic runoff components in mixing models: Case study of small basins in Primor’e

An algorithm has been proposed for the diagnostics of the number of runoff genetic components and reliable chemical tracers in mixing models. The algorithm is applied to the analysis of data of hydrological—hydrochemical monitoring obtained during experimental studies on small watersheds in Elovyi Creek basin. The number of stable genetic runoff components and the list of tracers can vary from year to year depending on the state of moistening on the watershed.