Impacts of climate change/variability on the streamflow in the Yellow River Basin, China

Changes of streamflow reflect combined effects of climate, soil and vegetation in the basin scale. This study was conducted to investigate the response of streamflow to the climate changes/variability in different scales of the Yellow River Basin (YRB). The spatial distribution and temporal trends were explored for precipitation and potential evapotranspiration (PE) during 1961-2000 to illustrate climate change/variability and impacts of climate change/variability on streamflow were explained by investigating the relationship of precipitation, PE and streamflow in the YRB. The results presented that: (i) precipitation and PE exhibited different spatial distribution patterns and temporal trends in different regions, and most stations showed negative trends for precipitation in the basin; (ii) the relationship of streamflow with precipitation and PE showed high nonlinearity, and the magnitudes and patterns of streamflow response to precipitation and PE displayed different patterns varied with the dry conditions in different region or years; and (iii) the precipitation elasticity of streamflow (?_P) was 1.80, 1.08, 1.78 and 1.95 in Lanzhou, Toudaoguai, Huayuankou and Lijin respectively, while the PE elasticity of streamflow (?_ET) was −3.41, −4.40, −4.52 and −4.20 in above four scales, respectively, from which can be seen that streamflow was more sensitive to precipitation in wet region than in arid region and inversely it was more sensitive to PE in arid regions than in wet regions. Furthermore, precipitation elasticity of streamflow calculated from the partial correlation presented a reasonable result to show the combined effect of precipitation and PE on streamflow.     

Response of a stream in disequilibrium to timber harvest

Timber was harvested on South Fork of Thomas Creek, White Mountains of Arizona, USA, for the first time in 1978–1979. This caused significant increases in annual flow volumes and annual instantaneous peak flows. North Fork remained untouched, but both streams were in disequilibrium before harvest time. Due to wetter years during the postharvest period, North Fork also experienced some flow increases, but the difference was not significant. Flow increases cause increased erosion in disequilibrium channels. While in South Fork channel cross sections enlarged by 10% since preharvest time, those in North Fork enlarged by only 2.5%. The number of knickpoints tripled in South Fork, which was about double that in North Fork. Knickpoint development resulted in destruction of the natural control structures (log steps and transverse gravel bars) in South Fork (47%), while in North Fork they increased by 23%. Knickpoints are scarps on the channel bed that have the appearance of gully headcuts. The tripling of the number of knickpoints signifies that adjustment processes of the bed profile are intensified drastically in South Fork. The geomorphic changes signify that due to increases in discharge, the extent of disequilibrium is exacerbated in South Fork. Yet, volumes of erosion are relatively small, as will be sediment volumes leaving the watershed at a given time, because of the stepwise sediment transport occurring in this ephemeral stream.     

ERRORS IN ESTIMATED STREAMFLOW PARAMETERS AND STORAGES FOR UNGAUGED CATCHMENTS1

ABSTRACT: For a set of 81 catchments in southeast Victoria, Australia, predictive equations were developed by least squares regression of the mean and coefficient of variation of annual Streamflow against a variety of rainfall and physiographic parameters. The data were also divided into subsets according to catchment size, subregion, or record length of investigate if the relationships differed significantly between subsets. Only the catchment area and some rainfall statistical parameters were found to be significant. Streamflow parameters predicted by the regression equations were used to estimate storage requirements in ungauged catchments. The influence of errors in the Streamflow parameters on the storage error was examined.     

Predicting streamflow response to fire-induced landcover change: implications of parameter uncertainty in the MIKE SHE model

Fire is a primary agent of landcover transformation in California semi-arid shrubland watersheds, however few studies have examined the impacts of fire and post-fire succession on streamflow dynamics in these basins. While it may seem intuitive that larger fires will have a greater impact on streamflow response than smaller fires in these watersheds, the nature of these relationships has not been determined. The effects of fire size on seasonal and annual streamflow responses were investigated for a medium-sized basin in central California using a modified version of the MIKE SHE model which had been previously calibrated and tested for this watershed using the Generalized Likelihood Uncertainty Estimation methodology. Model simulations were made for two contrasting periods, wet and dry, in order to assess whether fire size effects varied with weather regime. Results indicated that seasonal and annual streamflow response increased nearly linearly with fire size in a given year under both regimes. Annual flow response was generally higher in wetter years for both weather regimes, however a clear trend was confounded by the effect of stand age. These results expand our understanding of the effects of fire size on hydrologic response in chaparral watersheds, but it is important to note that the majority of model predictions were largely indistinguishable from the predictive uncertainty associated with the calibrated model - a key finding that highlights the importance of analyzing hydrologic predictions for altered landcover conditions in the context of model uncertainty. Future work is needed to examine how alternative decisions (e.g., different likelihood measures) may influence GLUE-based MIKE SHE streamflow predictions following different size fires, and how the effect of fire size on streamflow varies with other factors such as fire location.     

Modeling Flow and Sediment Transport in a River System Using an Artificial Neural Network

A river system is a network of intertwining channels and tributaries, where interacting flow and sediment transport processes are complex and floods may frequently occur. In water resources management of a complex system of rivers, it is important that instream discharges and sediments being carried by streamflow are correctly predicted. In this study, a model for predicting flow and sediment transport in a river system is developed by incorporating flow and sediment mass conservation equations into an artificial neural network (ANN), using actual river network to design the ANN architecture, and expanding hydrological applications of the ANN modeling technique to sediment yield predictions. The ANN river system model is applied to modeling daily discharges and annual sediment discharges in the Jingjiang reach of the Yangtze River and Dongting Lake, China. By the comparison of calculated and observed data, it is demonstrated that the ANN technique is a powerful tool for real-time prediction of flow and sediment transport in a complex network of rivers. A significant advantage of applying the ANN technique to model flow and sediment phenomena is the minimum data requirements for topographical and morphometric information without significant loss of model accuracy. The methodology and results presented show that it is possible to integrate fundamental physical principles into a data-driven modeling technique and to use a natural system for ANN construction. This approach may increase model performance and interpretability while at the same time making the model more understandable to the engineering community.     

Evaluating the cumulative effects of alteration on New England wetlands

In New England, patterns of glacial deposition strongly influence wetland occurrence and function. Many wetlands are associated with permeable deposits and owe their existence to groundwater discharge. Whether developed on deposits of high or low permeability, wetlands are often associated with streams and appear to play an important role in controlling and modifying streamflow. Evidence is cited showing that some wetlands operate to lessen flood peaks, and may have the seasonal effect of increasing spring discharges and depressing low flows. Wetlands overlying permeable deposits may be associated with important aquifers where they can produce slight modifications in water quality and head distribution within the aquifer. Impacts to wetlands undoubtedly will affect these functions, but the precise nature of the effect is difficult to predict. This is especially true of incremental impacts to wetlands, which may, for example, produce a change in streamflow disproportionate to wetland area in the drainage basin, i.e., a nonlinear effect as defined by Preston and Bedford (1988). Additional research is needed before hydrologic function can be reliably correlated with physical properties of wetlands and landscapes.A model is proposed to structure future research and explore relationships between hydrologic function and physical properties of wetlands and landscapes. The model considers (1) the nature of the underlying deposits (geologic type), (2) location in the drainage basin (topographic position), (3) relationship to the principal zone of saturation (hydrologic position), and (4) hydrologic character of the organic deposit.     

Regression modeling of streamflow, baseflow, and runoff using geographic information systems

Regression models for predicting total streamflow (TSF), baseflow (TBF), and storm runoff (TRO) are needed for water resource planning and management. This study used 54 streams with >20 years of streamflow gaging station records during the period October 1971 to September 2001 in Pennsylvania and partitioned TSF into TBF and TRO. TBF was considered a surrogate of groundwater recharge for basins. Regression models for predicting basin-wide TSF, TBF, and TRO were developed under three scenarios that varied in regression variables used for model development. Regression variables representing basin geomorphological, geological, soil, and climatic characteristics were estimated using geographic information systems. All regression models for TSF, TBF, and TRO had R(2) values >0.94 and reasonable prediction errors. The two best TSF models developed under scenarios 1 and 2 had similar absolute prediction errors. The same was true for the two best TBF models. Therefore, any one of the two best TSF and TBF models could be used for respective flow prediction depending on variable availability. The TRO model developed under scenario 1 had smaller absolute prediction errors than that developed under scenario 2. Simplified Area-alone models developed under scenario 3 might be used when variables for using best models are not available, but had lower R(2) values and higher or more variable prediction errors than the best models.     

Geospatial Analysis of Climatic and Geomorphic Interactions Influencing Stream Discharge, Appalachian Mountains, USA

Streamflow values are commonly synthesized for locations where flow measurement stations are lacking or where only intermittent measurements are available. In an Appalachian Mountains dataset comprised of 29 watersheds, the most appropriate among geomorphic, geologic, and hydrogeologic datasets were selected for use in prediction of streamflow at watershed scale. A statistical model was developed using principal components analysis (PCA) and cluster analysis (CA) for. Using CA on variables derived from the PCA, an optimum set of variables was derived for predicting streamflow. Results indicate there are two categories of watersheds in the study area. The first is strongly correlated with climatic variables (precipitation, temperature, elevation, and groundwater recharge). The second is strongly correlated with two geomorphic variables (watershed slope and percentage of forested area). The spatial distribution of cluster classifications shows that watersheds dominated by the climatic component are located along the Allegheny Front while watersheds dominated by the geomorphic component are located in the Allegheny Plateau and Valley and Ridge physiographic provinces. These variations between the Allegheny Plateau and Valley and Ridge physiographic provinces suggest that, to accurately model streamflow, modeling needs be done based on natural physiographic boundaries rather than political boundaries. In this physiographic setting, elevation seems to be a major control.