Alpine Waters edited by Ulrich Bundi

Abstract

A snowmelt runoff model is derived for relatively small rivers. The model involves the main components of the catchment water budget, physiographical and some other factors: water equivalent of snow cover, precipitation, antecedent moisture content, daily snowmelt, non-uniformity of snow cover, retention capacity of the basin, and percentage of forest area. The model structure includes calculations of the daily values of snowmelt excess and the transformation of these values into discharges at the outlet of the basin based on meteorological observations and appropriate distribution functions. Both calculations are made separately for open and forest areas. The parameters of the model were derived by optimization methods. The linear model based on the superposition principle is used to transform the discharges of a small river into total inflow into a large reservoir. The combined model was used to forecast for five days in advance daily mean inflows into the Gorky and Kuibyshev reservoirs (on the River Volga), using the observed and forecast discharges of the small rivers as input.     

Challenges of hydrological analysis for water resource development in semi-arid mountainous regions: case study in Iran

Abstract

This study modified the BTOPMC (Block-wise TOPMODEL with the Muskingum-Cunge routing method) distributed hydrological model to make it applicable to semi-arid regions by introducing an adjustment coefficient for infiltration capacity of the soil surface, and then applied it to two catchments above the dams in the Karun River basin, located in semi-arid mountain ranges in Iran. The application results indicated that the introduced modification improved the model performance for simulating flood peaks generated by infiltration excess overland runoff at a daily time scale. The modified BTOPMC was found to fulfil the need to reproduce important signatures of basin hydrology for water resource development, such as annual runoff, seasonal runoff, low flows and flood flows. However, it was also very clear that effective model use was significantly constrained by the scarcity of ground-gauged precipitation data. Considerable efforts to improve the precipitation data acquisition should precede water resource development planning.

Editor D. Koutsoyiannis     

Comparison of forecasting performance of nonlinear models of hydrological time series

Mean monthly flows of the Tatry alpine mountain region in Slovakia are predominantly fed by snowmelt in the spring and convective precipitation in the summer. Therefore their regime properties exhibit clear seasonal patterns. Positive deviations from these trends have substantially different features than the negative ones. This provides intuitive justification for the application of nonlinear two-regime models for modelling and forecasting of these time series. Nonlinear time series structures often have lead to good fitting performances, however these do not guarantee an equally good forecasting performance. In this paper therefore the forecasting performance of several nonlinear time series models is compared with respect to their capabilities of forecasting monthly and seasonal flows in the Tatry region. A new type of regime-switching models is also proposed and tested. The best predictive performance was achieved for a new model subclass involving aggregation operators.     

THE GENERATION OF SPRING PEAK FLOWS BY SHORT-TERM METEOROLOGICAL EVENTS

Abstract

Spring peak flows recorded over a 25-year period in Benton Creek, a small forested watershed in northern Idaho, were studied in their relation to meteorological events. More peak flows were generated by rain-on-snow than by clear-weather snowmelt; the two types of peaks differ in magnitude and in other characteristics. Two rather simple techniques were used to calculate the generative capacity of a rainfall-temperature event and the hypothetical outflow of water from a snowpack during rainy and rainfree periods. Similar data on spring peaks on two regional subbasins were considered also.     

Snow cover and runoff modelling in a high mountain catchment with scarce data: effects of temperature and precipitation parameters

Snowmelt is an important source of runoff in high mountain catchments. Snowmelt modelling for alpine regions remains challenging with scarce gauges. This study simulates the snowmelt in the Karuxung River catchment in the south Tibetan Plateau using an altitude zone based temperature‐index model, calibrates the snow cover area and runoff simulation during 2003–2005 and validates the model performance via snow cover area and runoff simulation in 2006. In the snowmelt and runoff modelling, temperature and precipitation are the two most important inputs. Relevant parameters, such as critical snow fall temperature, temperature lapse rate and precipitation gradient, determine the form and amount of precipitation and distribution of temperature and precipitation in hydrological modelling of the sparsely gauged catchment. Sensitivity analyses show that accurate estimation of these parameters would greatly help in improving the snowmelt simulation accuracy, better describing the snow‐hydrological behaviours and dealing with the data scarcity at higher elevations. Specifically, correlation between the critical snow fall temperature and relative humidity and seasonal patterns of both the temperature lapse rate and the precipitation gradient should be considered in the modelling studies when precipitation form is not logged and meteorological observations are only available at low elevation. More accurate simulation of runoff involving snowmelt, glacier melt and rainfall runoff will improve our understanding of hydrological processes and help assess runoff impacts from a changing climate in high mountain catchments. Copyright © 2013 John Wiley & Sons, Ltd.     

Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis

In this study, a new method of stand-alone short-term spring snowmelt river flood forecasting was developed based on wavelet and cross-wavelet analysis. Wavelet and cross-wavelet analysis were used to decompose flow and meteorological time series data and to develop wavelet based constituent components which were then used to forecast floods 1, 2, and 6 days ahead. The newly developed wavelet forecasting method (WT) was compared to multiple linear regression analysis (MLR), autoregressive integrated moving average analysis (ARIMA), and artificial neural network analysis (ANN) for forecasting daily stream flows with lead-times equal to 1, 2, and 6 days. This comparison was done using data from the Rideau River watershed in Ontario, Canada. Numerical analysis was performed on daily maximum stream flow data from the Rideau River station and on meteorological data (rainfall, snowfall, and snow on ground) from the Ottawa Airport weather station. Data from 1970 to 1997 were used to train the models while data from 1998 to 2001 were used to test the models. The most significant finding of this research was that it was demonstrated that the proposed wavelet based forecasting method can be used with great accuracy as a stand-alone forecasting method for 1 and 2 days lead-time river flood forecasting, assuming that there are no significant trends in the amplitude for the same Julian day year-to-year, and that there is a relatively stable phase shift between the flow and meteorological time series. The best forecasting model for 1 day lead-time was a wavelet analysis model. In testing, it had the lowest RMSE value (13.8229), the highest R² value (0.9753), and the highest EI value (0.9744). The best forecasting model for 2 days lead-time was also a wavelet analysis model. In testing, it had the lowest RMSE value (31.7985), the highest R² value (0.8461), and the second highest EI value (0.8410). It was also shown that the proposed wavelet based forecasting method is not particularly accurate for longer lead-time forecasting such as 6 days, with the ANN method providing more accurate results. The best forecasting model for 6 days lead-time was an ANN model, with the wavelet model not performing as well. In testing, the wavelet model had an RMSE of 57.6917, an R² of 0.4835, and an EI of 0.4366.     

Snow in Lebanon: a preliminary study of snow cover over Mount Lebanon and a simple snowmelt model / Etude préliminaire du couvert neigeux et modèle de fonte des neige pour le Mont Liban

Abstract

The physical properties of snow, including apparent density, snow cover distribution and snowmelt in the Nahr El Kelb basin (Mount Lebanon), were studied in order to design a simple empirical snowmelt model. In February 2001, snow covered an area of 1600 km² on Mount Lebanon, representing a water equivalent of 1.1 x 10⁹ m³. The snow surface area was calculated by combining TM5 images with a digital elevation model, and field observations made every three days, from 1400 to 2300 m altitude. The depletion of snow cover was measured from the end of December 2000 to the end of June 2001. The snowmelt was measured from surface depletion on a degree-day basis. A simple model relating the daily snowmelt to the product of wind speed and average positive daily air temperature, is presented and discussed. For Mount Lebanon, this model gave a better approximation of snowmelt than a simple degree-day model.     

Long-range forecasts of River Po discharges based on predictable solar activity and a fuzzy neural network model / Prévisions à long terme des débits du Fleuve Pô basées sur l’activité solaire prévisible et sur un modèle de réseau de neurones flou

Abstract

Abstract Is it possible to make seasonal and interannual forecasts of hydrological variables if one cannot predict next week’s rainfall? Contrary to common view, some scientists support the hypothesis that variations in mean global temperature and precipitation are controlled more by external forcing (solar variability and volcanic eruptions) than by increasing atmospheric concentration of greenhouse gases. Temperature and precipitation are connected with special phases of the 11-year sunspot cycle, which coincide with significant accumulation of energetic solar eruptions. Because of the possibility of identifying years with many solar eruptions, the attractive prospect emerges of the long-term hydrological forecasting based on cycles of solar activity. Starting from this assumption, an expert system was built based on a fuzzy neural network model for seasonal and interannual forecasting of the Po River discharge. It was found that indices of solar activity and of global circulation are sufficient to yield useful forecasts of hydrological variables.     

Application and evaluation of a snowmelt runoff model in the Tamor River basin,Eastern Himalaya using a Markov Chain Monte Carlo (MCMC) data assimilation approach

Previous studies have drawn attention to substantial hydrological changes taking place in mountainous watersheds where hydrology is dominated by cryospheric processes. Modelling is an important tool for understanding these changes but is particularly challenging in mountainous terrain owing to scarcity of ground observations and uncertainty of model parameters across space and time. This study utilizes a Markov Chain Monte Carlo data assimilation approach to examine and evaluate the performance of a conceptual, degree‐day snowmelt runoff model applied in the Tamor River basin in the eastern Nepalese Himalaya. The snowmelt runoff model is calibrated using daily streamflow from 2002 to 2006 with fairly high accuracy (average Nash–Sutcliffe metric ~0.84, annual volume bias < 3%). The Markov Chain Monte Carlo approach constrains the parameters to which the model is most sensitive (e.g. lapse rate and recession coefficient) and maximizes model fit and performance. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall compared with simulations using observed station precipitation. The average snowmelt contribution to total runoff in the Tamor River basin for the 2002–2006 period is estimated to be 29.7 ± 2.9% (which includes 4.2 ± 0.9% from snowfall that promptly melts), whereas 70.3 ± 2.6% is attributed to contributions from rainfall. On average, the elevation zone in the 4000–5500 m range contributes the most to basin runoff, averaging 56.9 ± 3.6% of all snowmelt input and 28.9 ± 1.1% of all rainfall input to runoff. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall versus snowmelt compared with simulations using observed station precipitation. Model experiments indicate that the hydrograph itself does not constrain estimates of snowmelt versus rainfall contributions to total outflow but that this derives from the degree‐day melting model. Lastly, we demonstrate that the data assimilation approach is useful for quantifying and reducing uncertainty related to model parameters and thus provides uncertainty bounds on snowmelt and rainfall contributions in such mountainous watersheds. Copyright © 2013 John Wiley & Sons, Ltd.     

CURRENT RESEARCH IN BRITISH SNOWMELT RIVER FLOODING

ABSTRACT

An adaptation of the degree-day method has been used to analyse a number of snowmelt events on two catchments as a first step in a programme of research on snowmelt river flooding in Britain. The analysis indicates that the degree-day factor varies during events and between events on the same catchment. A snowmelt event is seen as consisting of three phases, an initial lag phase, a phase of nearly constant degree-day factor and a recession phase. The degree-day factor in the constant part of each event has significant correlation with the total flood volume on both catchments and with liquid precipitation during the snowmelt on one catchment only. Separate procedures are considered necessary for forecasting the initial lag phase and runoff during the recession.     

Investigation of the model complexity required in runoff simulation at different time scales / Etude de la complexité de modélisation requise pour la simulation d'écoulement à différentes échelles temporelles

Abstract

The “optimal” model complexity is defined as the minimum watershed model structure required for realistic representation of runoff processes. This paper examines the effects of model complexity at different time scales, daily and hourly. Two watershed models with different levels of complexity were constructed and their capability to simulate runoff from a watershed was evaluated. Both models were tested on the same watershed using identical meteorological input, thereby assuring that any difference between model outputs is due only to their model structure. It is demonstrated that, at a daily time scale, a simple model gives good results. For the mountain situation, in which snowmelt is a dominant influence, the nonlinearity of the runoff processes is moderate, and therefore a simple model works well. The model produced good results over a period of 28 years of continuous simulation. However, this simpler model was inadequate when tested on an hourly time scale due to greater nonlinear effects, especially when modelling high-intensity rainfall events. Therefore, the hourly simulation benefited from the more complex model structure. These model results show that optimal watershed model complexity depends on temporal resolution, namely the simulation period and the computational time step. It was shown that certain process representations and model parameters that appeared unimportant during the long-term simulation had significant effects on the short-term extreme event model simulation.     

System dynamics model for predicting floods from snowmelt in North American prairie watersheds

This study uses a system dynamics approach to explore hydrological processes in the geographic locations where the main contribution to flooding is coming from the snowmelt. Temperature is identified as a critical factor that affects watershed hydrological processes. Based on the dynamic processes of the hydrologic cycle occurring in a watershed, the feedback relationships linking the watershed structure, as well as the climate factors, to the streamflow generation were identified prior to the development of a system dynamics model. The model is used to simulate flood patterns generated by snowmelt under temperature change in the spring. Model structure captures a vertical water balance using five tanks representing snow, interception, surface, subsurface and groundwater storage. Calibration and verification results show that temperature change and snowmelt play a key role in flood generation. Results indicate that simulated values match observed data very well. The goodness‐of‐fit between simulated and observed peak flow data is measured using coefficient of efficiency, coefficient of determination and square of the residual mass curve coefficient. For the Assiniboine River all three measures were in the interval between 0·92 and 0·96 and for the Red River between 0·89 and 0·97. The model is capable of capturing the essential dynamics of streamflow formation. Model input requires a set of initial values for all state variables and the time series of daily temperature and precipitation information. Data from the Red River Basin, shared by Canada and the USA, are used in the model development and testing. Copyright © 2002 John Wiley & Sons, Ltd.     

Chirripó hydrological research site: Advancing stable isotope hydrology in the Central American Páramo

The Chirripó hydrological research site (CHRS) is located within the Chirripó National Park, Costa Rica (between 3100 and 3820 m asl) whereby ~100 km² are covered by Páramo, a high-elevation tropical grassland ecosystem. A lake district with approximately 30 lakes of glacial origin is also protected in this area. The CHRS has been monitored since April 2015 with the aim of establishing the first water isotope baseline for the Central American Páramo. At a regional scale, the water isotope ratios (δ²H and δ¹⁸O) in precipitation and surface water at CHRS are useful for describing the governing moisture transport from the Caribbean Sea and Pacific Ocean and the complex rainfall producing systems across the N–S mountain range of Central America. These data are also providing unique information about the evaporation and water balance conditions of tropical glacial lakes and the formation of orographic and convective precipitation in high-elevation tropical ecosystems. Current data sets from CHRS include continuous lake water temperature and meteorological conditions (i.e., precipitation amount, air temperature and relative humidity), as well as water stable isotopes in precipitation, stream water, and lake water (daily to biweekly sampling frequency). Stream water is collected at several locations across the topographic gradient whereas lake water is sampled in the three main lake systems of CHRS. CHRS serves as a reference site for conducting pilot isotopic research in high-elevation ecosystems to advance the atmospheric, hydrogeological and ecohydrological studies in these understudied biomes. All data from April 2015 to November 2020 are publicly available.     

Hydrological regime of remote catchments with extreme gradients under accelerated change: the Baker basin in Patagonia

Abstract

The Baker basin (27 000 km²) is located in one of the most pristine and remote areas of the planet. Its hydrological regime is poised to undergo dramatic changes in the near future due to hydropower development and climate change. The basin contains the second-largest lake in South America, and part of a major icefield. This study documents the natural baseline of the Baker River basin, discusses the main hydrological modes and analyses the potential for sustainable management. Annual precipitation varies several-fold from the eastern Patagonian steppes to the North Patagonian Icefield. The westernmost sub-basins are strongly governed by glacier melt with a peak discharge in the austral summer (January–March). The easternmost sub-basins have a much more seasonal response governed by quicker snowmelt in spring (November–December), while they exhibit low flows typical for semi-arid regions during summer and autumn. Topography, vegetation and wetlands may also influence streamflow. The strong spatio-temporal gradients and variability highlight the need for further monitoring, particularly in the headwaters, especially given the severe changes these basins are expected to undergo. The great diversity of hydrological controls and climate change pose significant challenges for hydrological prediction and management.

Editor Z.W. Kundzewicz

Citation Dussaillant, J.A., Buytaert, W., Meier, C., and Espinoza, F. 2012. Hydrological regime of remote catchments with extreme gradients under accelerated change: the Baker basin in Patagonia. Hydrological Sciences Journal, 57 (8), 1530–1542.     

Runoff reduction by forest growth in Hiji River basin,Japan / Diminution de l'écoulement causée par la croissance forestière dans le bassin versant du Fleuve Hiji,Japon

Abstract

Forest growth unfavourably reduces low flows and annual runoff in a basin in Japan. Annual precipitation and runoff of the watershed are summarized from observed daily rainfall and discharge, and annual evapotranspiration is estimated from the annual water balance. The water balance analysis shows obvious trends: reduced annual runoff and increased evapotranspiration over a 36-year period when forest growth increased the leaf area index. Between two periods, 1960–1969 and 1983–1992, mean annual runoff decreased 11%, from 1258 to 1118 mm, due to a 37% increase in evapotranspiration (precipitation minus runoff) from 464 to 637 mm. This increase in evapotranspiration cannot be attributed to changed evaporative demand, based on climatic variability over the 36-year period of record. Flow duration curves show reduced flows in response to forest growth. In particular, they suggest stronger absolute changes for higher flows but stronger proportional changes for medium and lower flows. A distributed model is applied to simulate the influences of five scenarios based on a 30% change in leaf area index and 5% change in soil storage capacity. From the simulation results, canopy growth appears to contribute much more to flow reduction than changes in soil storage capacity.     

A hybrid factorial stepwise-cluster analysis method for streamflow simulation – a case study in northwestern China

ABSTRACT

In this study, a hybrid factorial stepwise-cluster analysis (HFSA) method is developed for modelling hydrological processes. The HFSA method employs a cluster tree to represent the complex nonlinear relationship between inputs (predictors) and outputs (predictands) in hydrological processes. A real case of streamflow simulation for the Kaidu River basin is applied to demonstrate the efficiency of the HFSA method. After training a total of 24?108 calibration samples, the cluster tree for daily streamflow is generated based on a stepwise-cluster analysis (SCA) approach and is then used to reproduce the daily streamflows for calibration (1995–2005) and validation (2008–2010) periods. The Nash-Sutcliffe coefficients for calibration and validation are 0.68 and 0.65, respectively, and the deviations of volume are 1.68% and 4.11%, respectively. Results show that: (i) the HFSA method can formulate a SCA-based hydrological modelling system for streamflow simulation with a satisfactory fitting; (ii) the variability and peak value of streamflow in the Kaidu River basin can be effectively captured by the SCA-based hydrological modelling system; (iii) results from 2⁶ factorial experiments indicate that not only are minimum temperature and precipitation key drivers of system performance, but also the interaction between precipitation and minimum temperature significantly impacts on the streamflow. The findings are useful in indicating that the streamflow of the study basin is a mixture of snowmelt and rainfall water.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR G. Thirel     

Analysis of seasonal snowmelt contribution using a distributed energy balance model for a river basin in the Altai Mountains of northwestern China

Snowmelt water is a vital freshwater resource in the Altai Mountains of northwestern China. Yet its seasonal hydrological cycle characteristics could change under a warming climate and more rapid spring snowmelt. Here, we simulated snowmelt runoff dynamics in the Kayiertesi River catchment, from 2000 to 2016, by using an improved hydrological distribution model that relied on high-resolution meteorological data acquired from the National Centers for Environmental Prediction (Fnl-NCEP) that were downscaled using the Weather Research Forecasting model. Its predictions were compared to observed runoff data, which confirmed the simulations' reliability. Our results show the model performed well, in general, given its daily validation Nash–Sutcliffe efficiency (NSE) of 0.62 (from 2013 to 2015) and a monthly NSE score of 0.68 (from 2000 to 2010) for the studied river basin of the Altai Mountains. In this river basin catchment, snowfall accounted for 64.1% of its precipitation and snow evaporation for 49.8% of its total evaporation, while snowmelt runoff constituted 29.3% of the annual runoff volume. Snowmelt's contribution to runoff in the Altai Mountains can extend into non-snow days because of the snowmelt water retained in soils. From 2000 to 2016, the snow-to-rain ratio decreased rapidly, however, the snowmelt contribution remained relatively stable in the study region. Our findings provide a sound basis for making snowmelt runoff predictions, which could be used prevent snowmelt-induced flooding, as well as a generalizable approach applicable to other remote, high-elevation locations where high-density, long-term observational data are currently lacking. How snowmelt contributes to water dynamics and resources in cold regions is garnering greater attention. Our proposed model is thus timely perhaps, enabling more comprehensive assessments of snowmelt contributions to hydrological processes in those alpine regions characterized by seasonal snow cover.     

Estimating daily streamflow in the Congo Basin using satellite-derived data and a semi-distributed hydrological model

ABSTRACT

The application of remotely-sensed data for hydrological modeling of the Congo Basin is presented. Satellite-derived data, including TRMM precipitation, are used as inputs to drive the USGS Geospatial Streamflow Model (GeoSFM) to estimate daily river discharge over the basin from 1998 to 2012. Physically-based parameterization was augmented with a spatially-distributed calibration that enables GeoSFM to simulate hydrological processes such as the slowing effect of the Cuvette Centrale. The resulting simulated long-term mean of daily flows and the observed flow at the Kinshasa gauge were comparable (40 631 and 40 638 m³/s respectively), in the 7-year validation period (2004–2010), with no significant bias and a Nash-Sutcliffe model efficiency coefficient of 0.70. Modeled daily flows and aggregated monthly river outflows (compared to historical averages) for additional sites confirm the model reliability in capturing flow timing and seasonality across the basin, but sometimes fails to accurately predict flow magnitude. The results of this model can be useful in research and decision-making contexts and validate the application of satellite-based hydrological models driven for large, data-scarce river systems such as the Congo.