Streamflow Patterns Research Articles

Assessment of the Future Climate Change Projections on Streamflow Hydrology and Water Availability over Upper Xijiang River Basin, China

Hydrological models are widely applied for simulating complex watershed processes and directly linking meteorological, topographical, land-use, and geological conditions. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated at two monitoring stations, which improved model performance and increased the reliability of flow predictions in the Upper Xijiang River Basin. This study evaluated the potential impacts of climate change on the streamflow and water yield of the Upper Xijiang River Basin using Arc-SWAT. The model was calibrated (1991–1997) and validated (1998–2001) using the Sequential Uncertainty Fitting Algorithm (SUFI-2). Model calibration and validation suggest a good match between the measured and simulated monthly streamflow, indicating the applicability of the model for future daily streamflow predictions. Large negative changes of low flows are projected under future climate scenarios, exhibiting a 10% and 30% decrease in water yield over the watershed on a monthly scale. Overall, findings generally indicated that winter flows are expected to be affected the most, with a maximum impact during the January–April period, followed by the wet monsoon season in the May–September period. Water balance components of the Upper Xijiang River Basin are expected to change significantly due to the projected climate change that, in turn, will seriously affect the water resources and streamflow patterns in the future. Thus, critical problems, such as ground water shortages, drops in agricultural crop yield, and increases in domestic water demand are expected at the Xijiang River Basin.

Assessment of Cyclic and Asynchronous River Flow Patterns of the Angara – Yenisei and Volga – Kama Cascades as a Condition for a Possible HHP Operation Using a Compensated Energy Generation Mode

The assessment of cyclic river flow fluctuations of the Angara – Yenisei and Volga – Kama cascades was performed to reveal the streamflow variation patterns of the Angara, Yenisei, Volga, and Kama rivers, which can be further used to optimize the operating conditions of the hydroelectric power plants located within these cascades.

Runoff Changes from Urumqi Glacier No. 1 over the Past 60 Years, Eastern Tianshan, Central Asia

Glaciers are vital to water resources in the arid land of central Asia. Long-term runoff records in the glacierized area are particularly valuable in terms of evaluating glacier recession and water resource change on both a regional and global scale. The runoff records of streams draining basins with 46% current glacier cover, located at the Urumqi Glacier No. 1 in the source area of the Urumqi River in eastern Tianshan, central Asia, were examined for the purpose of assessing climatic and glacial influences on temporal patterns of streamflow for the period 1959–2018. Results suggest that runoff from the catchment correlates well with temperature and associated precipitation data. During the period 1993–2018, it increased by 114.39 × 104 m3, which was 1.7 times the average runoff during the period 1959–1992. A simple water balance model is introduced to calculate the different components of the runoff, including precipitation runoff from glacier surface and from nonglacial areas, glacier mass balance and glacial runoff. Thus, the long-term change of each component and its response to climate change are revealed. We found that the period 1997–2018 is likely to be the “peak water” (tipping point) of the glacial runoff resulting from shrinkage of glacier area.

Impacts of Climate Change and Land Use/Cover Change on Streamflow in Beichuan River Basin in Qinghai Province, China

Climate change (CC) and land use/cover change (LUCC) are the main drivers of streamflow change. In this study, the effects of CC and LUCC on streamflow regime as well as their spatial variability were examined by using the Distributed Hydrology Soil Vegetation Model (DHSVM) for the Beichuan River Basin in the northeast Tibetan Plateau. The results showed that CC increased annual and maximum streamflow in the upstream but decreased them in the downstream. CC also enhanced minimum streamflow in the whole river basin and advanced the occurrence of daily minimum streamflow. Temperature change exerted greater influence on streamflow regime than wind speed change did in most situations, but the impact of wind speed on streamflow reflected the characteristics of accumulative effects, which may require more attention in future, especially in large river basins. As for LUCC, cropland expansion and reservoir operation were the primary reasons for streamflow reduction. Cropland expansion contributed more to annual mean streamflow change, whereas reservoir operation greatly altered monthly streamflow pattern and extreme streamflow. Reservoir regulation also postponed the timing of minimum streamflow and extended durations of average, high, and low streamflow. Spatially, CC and LUCC played predominant roles in the upstream and the downstream, respectively.

Linking the Recent Glacier Retreat and Depleting Streamflow Patterns with Land System Changes in Kashmir Himalaya, India

This study reports the changes in glacier extent and streamflow similar to many Himalayan studies, but takes the unusual step of also linking these to downstream land use changes in Kashmir Valley. This study assessed changes in the area, snout, and equilibrium line altitude (ELA) of four parts of the Kolahoi Glacier using earth observation data from 1962 to 2018. Changes in the discharge of the two streams flowing out from Kolahoi Glacier into the Jhelum basin were also assessed between 1972 and 2018. Additionally, satellite data was used to track the downstream land system changes concerning agriculture, orchards, and built-up areas between 1980 and 2018. This analysis suggested a cumulative deglaciation of 23.6% at a rate of 0.42% per year from 1962 to 2018. The snout of two larger glaciers, G1 and G2, retreated at a rate of 18.3 m a−1 and 16.4 m a−1, respectively, from 1962 to 2018, although the rate of recession accelerated after 2000. Our analysis also suggested the upward shift of ELA by ≈120 m. The streamflows measured at five sites showed statistically significant depleting trends that have been a factor in forcing extensive land system changes downstream. Although the area under agriculture in Lidder watershed shrunk by 39%, there was a massive expansion of 176% and 476% in orchards and built-up areas, respectively, from 1980 to 2018. The conversion of irrigation-intensive agriculture lands (rice paddy) to less water-intensive orchards is attributed to economic considerations and depleting streamflow.

Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting

In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting.

Evolution of streamflow patterns in Goulburn-Broken catchment during 1884–2018 and its implications for floodplain management

Abstract Development of water resources infrastructure largely decoupled floodplains and wetlands from rivers due to alteration of streamflow patterns. However, there is limited understanding of undesirable changes in streamflow patterns due to flow regulation. This study aimed to examine effects of flow regulation and rainfall variation on: 1) long-term flow, 2) upstream and downstream flow, and 3) seasonal flow patterns in the Goulburn-Broken catchment in Victoria, Australia, and discuss the implications for floodplains and wetlands. The non-parametric Mann-Kendall test and other statistical methods were used to analyse changes in long-term (1884–2018), upstream and downstream (1977–2018), and seasonal flow patterns of Goulburn River. Results showed that average annual discharge of Goulburn River decreased over 59% during the post-change period (1957–2018) as compared to pre-change (1884–1956) discharge mainly due to flow regulation and diversion (>86%). This reduced frequency of overbank flows from once every 1.6 years in the pre-change period to once every 5 years in the post-change period. This extended disconnection of floodplains from the Goulburn River has had detrimental effects on biota. Finally, generation of adequate overbank flows and maintaining natural variability of high and low flows with a riverine functional flow approach is recommended to rejuvenate floodplains and wetlands.

Landscape pattern at the class level regulates the stream water nitrogen and phosphorus levels in a Chinese subtropical agricultural catchment

The landscape patch amount and configuration at the class level profoundly affect stream nitrogen (N) and phosphorus (P) concentrations in agricultural catchments, but their relationships and contributions to stream NP levels remain unclear. Here, we observed the stream water NP concentrations, stream flow, and landscape pattern at the class level in 11 catchments in a Chinese subtropical agricultural catchment during 2011–2017 to quantify their relationships and contributions. Pearson correlation analysis suggested that landscape patch amount and configuration at the class level significantly affected stream water NP levels, and stepwise multiple linear regression models showed that the landscape pattern of a single land use could effectively predict the NP levels. The determination coefficients (R2) of the total nitrogen (TN) formulas were 0.539–0.811 and smaller than those of the total phosphorus (TP) formulas (0.360–0.672). The best fit was obtained for the TN concentration by using the percentage of land use (PLAND) and fractal dimension index (FRAC) of ponds as independent variables (R2 = 0.811, p < 0.01), while that for the TP concentration was obtained when the edge density (ED) of rivers was used (R2 = 0.672, p < 0.01). Variance partitioning analysis was applied to further quantify the contributions of land use classes to the NP levels. Ponds and tea fields contributed the most to the stream TN and TP levels (30.2 % and 31.1 %, respectively). The PLAND and FRAC of ponds and FRAC of tea fields were the key landscape variables determining stream water NP levels. This study provides detailed instructions and suggestions for reasonable landscape planning to control stream water NP pollution at the practical level.

Lake Malawi’s threshold behaviour: A stakeholder-informed model to simulate sensitivity to climate change

Over 90% of Malawi’s electricity generation and irrigation depend on Lake Malawi outflows into the Shire River. Recent lake level declines have raised concerns over future climate change impacts, including the risk of no outflows if the Lake Malawi Outflow Threshold (LMOT) is passed. Addressing calls for model co-production, we iteratively engage stakeholders in data collection, and eliciting local system insights and management priorities, to inform the development of a Water Evaluation And Planning (WEAP) model for the Lake Malawi Shire River Basin. We use a simple model setup and manual calibration to allow for data sparsity and limited documentation of historical management decisions. The model satisfactorily captures limited observed streamflow patterns of Lake Malawi tributaries and lake level variations for the period 1960–2009, however, small errors in lake level simulation significantly affect simulation of monthly outflows. The riparian countries, Malawi, Tanzania and Mozambique contribute approximately 55%, 41% and 4% respectively to lake inflows (1960–2009 average). Forced with 29 bias-corrected global climate model projections (2021–2050) and assuming no change in current operating rules of key infrastructure, the WEAP model simulates wide-ranging changes. These include much higher lake levels that would cause downstream floods, and much lower lake levels, including 11 projections that fall below the LMOT. Both outcomes would have major implications for downstream hydropower and irrigation. Future water management plans require identification and evaluation of strategies that can address multi-year shifts in lake levels and the uncertainty inherent in future climate and hydrological model outputs.

A Classification of Streamflow Patterns Across the Coastal Gulf of Alaska

AbstractStreamflow controls many freshwater and marine processes, including salinity profiles, sediment composition, fluxes of nutrients, and the timing of animal migrations. Watersheds that border the Gulf of Alaska (GOA) comprise over 400,000 km2 of largely pristine freshwater habitats and provide ecosystem services such as reliable fisheries for local and global food production. Yet no comprehensive watershed‐scale description of current temporal and spatial patterns of streamflow exists within the coastal GOA. This is an immediate need because the spatial distribution of future streamflow patterns may shift dramatically due to warming air temperature, increased rainfall, diminishing snowpack, and rapid glacial recession. Our primary goal was to describe variation in streamflow patterns across the coastal GOA using an objective set of descriptors derived from flow predictions at the downstream‐most point within each watershed. We leveraged an existing hydrologic runoff model and Bayesian mixture model to classify 4,140 watersheds into 13 classes based on seven streamflow statistics. Maximum discharge timing (annual phase shift) and magnitude relative to mean discharge (amplitude) were the most influential attributes. Seventy‐six percent of watersheds by number showed patterns consistent with rain or snow as dominant runoff sources, while the remaining watersheds were driven by rain‐snow, glacier, or low‐elevation wetland runoff. Streamflow classes exhibited clear mechanistic links to elevation, ice coverage, and other landscape features. Our classification identifies watersheds that might shift streamflow patterns in the near future and, importantly, will help guide the design of studies that evaluate how hydrologic change will influence coastal GOA ecosystems.

Climate Associations with Headwater Streamflow in Managed Forests over 16 Years and Projections of Future Dry Headwater Stream Channels

Integrating climate-smart principles into riparian and upland forest management can facilitate effective and efficient land use and conservation planning. Emerging values of forested headwater streams can help forge these links, yet climate effects on headwaters are little studied. We assessed associations of headwater discontinuous streams with climate metrics, watershed size, and forest-harvest treatments. We hypothesized that summer streamflow would decrease in warm, dry years, with possible harvest interactions. We field-collected streamflow patterns from 65 discontinuous stream reaches at 13 managed forest sites in Western Oregon, USA over a 16-year period. We analyzed spatial and temporal variability in field-collected stream habitat metrics using non-metric multidimensional scaling ordination. Relationships between streamflow, climate metrics, basin size, and harvest treatments were analyzed with simple linear models and mixed models with repeated measures. Using past effects of climate variation on streamflow, we projected effects to 2085 under three future scenarios, then quantified implications on headwater networks for a case-study landscape. Ordination identified the percent dry length of stream reaches as a top predictor of spatial and temporal variation in discontinuous stream-habitat types. In our final multivariate model, the percent dry length was associated with heat: moisture index, mean minimum summer temperature, and basin area. Across future climate scenarios in years 2055–2085, a 4.5%–11.5% loss in headwater surface streamflow was projected; this resulted in 597–2058 km of additional dry channel lengths of headwater streams in our case study area, the range of the endemic headwater-associated Cascade torrent salamander (Rhyacotriton cascadae Good and Wake) in the Oregon Cascade Range, a species proposed for listing under the US Threatened and Endangered Act. Implications of our study for proactive climate-smart forest-management designs in headwaters include restoration to retain surface flows and managing over-ridge wildlife dispersal habitat from areas with perennial surface water flow, as stream reaches with discontinuous streamflow were projected to have reduced flows in the future with climate change projections.

Large-scale climate teleconnections with South Korean streamflow variability

ABSTRACTLeading patterns of observed seasonal extreme and mean streamflow on the Korean peninsula were estimated using an empirical orthogonal teleconnection (EOT) technique. In addition, statistical correlations on a seasonal basis were calculated using correlation and regression analyses between the leading streamflow patterns and various climate indices based on atmospheric–ocean circulation. The spatio-temporal patterns of the leading EOT modes for extreme and mean streamflow indicate an upstream mode for the Han River, with increasing trends in summer, and a downstream mode for the Nakdong River, with oscillations mainly on inter-decadal time scales in winter. The tropical ENSO (El Niño Southern Oscillation) forcing for both extreme and mean streamflow is coherently associated with summer to winter streamflow patterns. The western North Pacific monsoon has a negative correlation with winter streamflow variability, and tropical cyclone indices also exhibit significant positive correlation with autumn streamflow. Leading patterns of autumn and winter streamflow time series show predictability up to two seasons in advance from the Pacific sea-surface temperatures.

Intensified hydroclimatic regime in Korean basins under 1.5 and 2°C global warming

AbstractThis study assesses the potential changes in regional hydroclimates over South Korea in response to 1.5 and 2.0°C of global warming above preindustrial levels based on multimodel ensemble projections forced by a representative concentration pathway (RCP4.5) scenario. The meteorological inputs, which are derived from five global climate models after removing systematic bias using quantile mapping, are fed into a distributed hydrological model, the variable infiltration capacity model, to estimate the hydrologic responses to different levels of greenhouse gas concentrations in future periods. The changes in seasonal mean precipitation differ between monsoon and intermonsoon seasons. An increase in summer precipitation and a decrease in winter precipitation commonly occur under 1.5 and 2.0°C of global warming, resulting in intensified precipitation seasonality. However, changes in spring and fall precipitation show opposite change signals or relatively little robustness (as measured by model agreement) in response to different degrees of warming. Spatial and seasonal changes in precipitation are directly transferred to runoff patterns, increasing the disparity between wet and dry seasons. Global warming also leads to changes in the distributions of daily precipitation and streamflow, and the projected changes systematically involve an increase in high‐intensity precipitation and a decrease in relatively low‐intensity precipitation. This behaviour tends to be amplified under 2.0°C in comparison to 1.5°C of global warming, with potential implications for increased water stress under a much warmer climate. More importantly, under 2.0°C of global warming, the magnitude of extremes such as the annual maximum day flow in Korean basins is likely to be enhanced. This study demonstrates that changes in precipitation characteristics can explicitly modulate runoff and subsequently streamflow patterns, suggesting positive benefits of half a degree less warming in terms of the frequency and intensity of extreme streamflow.

OMV Petrom starts up PolyFuel unit at Petrobrazi refinery in Romania

Detection and attribution of streamflow changes would be a standing point towards optimizing the management of basin-scale water resources. The Three Gorges Reservoir (TGR), the largest hydropower project worldwide, has recently experienced obvious inflow reduction, which has restricted its capability to achieve the designed electricity generation. This study considered the design period of TGR (1961–1990) as the baseline period for evaluating inflow variations during the subsequent impact period (1991–2015). Annual inflow reduced by 6%, and seasonal inflow significantly decreased by 16% in autumn, which is the main season for filling reservoir. A new stepwise procedure with the help of a water-use model, a lumped hydrological model, and a generic reservoir operation model is presented to unravel the causes of inflow reduction. Results revealed that the drier climate predominantly caused inflow decreases. Human activities have notably enhanced in recent decades, however, they have not posed significant threats to inflow deficiency. Climate variability, water consumption, and upstream reservoir operation contributed by 78%, 15%, and 7% to decrease annual inflow while they contributed by 63%, 4%, and 33% to reduce autumn inflow, respectively. The proposed procedure can quantitatively disentangle major human impacts including water consumption and reservoir regulation. Such information enriches policy makers' knowledge for framing delicacy management of regional human activities in response to changing streamflow pattern.

Attribution of decreasing annual and autumn inflows to the Three Gorges Reservoir, Yangtze River: Climate variability, water consumption or upstream reservoir operation?

Detection and attribution of streamflow changes would be a standing point towards optimizing the management of basin-scale water resources. The Three Gorges Reservoir (TGR), the largest hydropower project worldwide, has recently experienced obvious inflow reduction, which has restricted its capability to achieve the designed electricity generation. This study considered the design period of TGR (1961–1990) as the baseline period for evaluating inflow variations during the subsequent impact period (1991–2015). Annual inflow reduced by 6%, and seasonal inflow significantly decreased by 16% in autumn, which is the main season for filling reservoir. A new stepwise procedure with the help of a water-use model, a lumped hydrological model, and a generic reservoir operation model is presented to unravel the causes of inflow reduction. Results revealed that the drier climate predominantly caused inflow decreases. Human activities have notably enhanced in recent decades, however, they have not posed significant threats to inflow deficiency. Climate variability, water consumption, and upstream reservoir operation contributed by 78%, 15%, and 7% to decrease annual inflow while they contributed by 63%, 4%, and 33% to reduce autumn inflow, respectively. The proposed procedure can quantitatively disentangle major human impacts including water consumption and reservoir regulation. Such information enriches policy makers' knowledge for framing delicacy management of regional human activities in response to changing streamflow pattern.

Climate change, reforestation/afforestation, and urbanization impacts on evapotranspiration and streamflow in Europe

Abstract. Since the 1950s, Europe has undergone large shifts in climate and land cover. Previous assessments of past and future changes in evapotranspiration or streamflow have either focussed on land use/cover or climate contributions or on individual catchments under specific climate conditions, but not on all aspects at larger scales. Here, we aim to understand how decadal changes in climate (e.g. precipitation, temperature) and land use (e.g. deforestation/afforestation, urbanization) have impacted the amount and distribution of water resource availability (both evapotranspiration and streamflow) across Europe since the 1950s. To this end, we simulate the distribution of average evapotranspiration and streamflow at high resolution (1 km2) by combining (a) a steady-state Budyko model for water balance partitioning constrained by long-term (lysimeter) observations across different land use types, (b) a novel decadal high-resolution historical land use reconstruction, and (c) gridded observations of key meteorological variables. The continental-scale patterns in the simulations agree well with coarser-scale observation-based estimates of evapotranspiration and also with observed changes in streamflow from small basins across Europe. We find that strong shifts in the continental-scale patterns of evapotranspiration and streamflow have occurred between the period around 1960 and 2010. In much of central-western Europe, our results show an increase in evapotranspiration of the order of 5 %–15 % between 1955–1965 and 2005–2015, whereas much of the Scandinavian peninsula shows increases exceeding 15 %. The Iberian Peninsula and other parts of the Mediterranean show a decrease of the order of 5 %–15 %. A similar north–south gradient was found for changes in streamflow, although changes in central-western Europe were generally small. Strong decreases and increases exceeding 45 % were found in parts of the Iberian and Scandinavian peninsulas, respectively. In Sweden, for example, increased precipitation is a larger driver than large-scale reforestation and afforestation, leading to increases in both streamflow and evapotranspiration. In most of the Mediterranean, decreased precipitation combines with increased forest cover and potential evapotranspiration to reduce streamflow. In spite of considerable local- and regional-scale complexity, the response of net actual evapotranspiration to changes in land use, precipitation, and potential evaporation is remarkably uniform across Europe, increasing by ∼ 35–60 km3 yr−1, equivalent to the discharge of a large river. For streamflow, effects of changes in precipitation (∼ 95 km3 yr−1) dominate land use and potential evapotranspiration contributions (∼ 45–60 km3 yr−1). Locally, increased forest cover, forest stand age, and urbanization have led to significant decreases and increases in available streamflow, even in catchments that are considered to be near-natural.

Using tritium and 222Rn to estimate groundwater discharge and thawing permafrost contributing to surface water in permafrost regions on Qinghai-Tibet Plateau

On the Qinghai-Tibet Plateau, permafrost degradation and associated release of permafrost meltwater has undoubtedly modified the annual and seasonal streamflow patterns and influenced groundwater conditions. However, it is still unclear how extensively permafrost degradation has influenced surface and subsurface water systems due to very sparse observations. Isotope mass models combining tritium and 222Rn measurements are developed here to quantify the contributions of groundwater discharge and thawing permafrost to rivers and lakes in the Source Area of the Yellow River. Results demonstrate the spatial variability of permafrost thaw impacts and confirm significant influence on surface and subsurface runoff processes including effects on water yield capacity and water drainage connectivity. While results to date are conclusive, further work to refine the method and extend observations is warranted.

A general framework of design flood estimation for cascade reservoirs in operation period

The hydrological regimes of downstream reservoirs have been significantly altered due to the operation and regulation of upstream cascade reservoirs. The original design flood quantiles, namely “design flood in construction period”, do not consider anthropogenic impacts in reservoir operation period, and have led to enormous conflicts between flood control and conservation. In this study, the “design flood and flood limited water level in operation period” are defined for practical application. We establish a general framework to measure the spatiotemporal pattern of streamflow and to estimate design floods of cascade reservoirs in operation period. The multivariate t-copula and a genetic algorithm strategy are proposed to solve the curse of dimensionality encountered in the derivation of most likely regional composition. The Jinsha River and Yalong River cascade reservoir system in China, which consists of 13 large reservoirs with the total storage capacity of 74.06 billion m3 and hydropower capacity of 71.47 GW, is selected as a case study. Results indicate that: (1) The curse of dimensionality can be well addressed by applying multivariate t-copula to build high dimensional joint distribution and using the genetic algorithm to achieve the most likely regional composition. (2) Compared with the design floods in construction period, the design floods of downstream reservoirs in operation period have been significantly reduced due to the upstream reservoir regulation. The 1000-year design peak flood discharge, 3-day, 7-day and 30-day flood volumes of Xiangjiaba reservoir decrease by 38.7%, 37.4%, 34.2% and 13.8%, respectively. (3) The flood limited water level of these reservoirs can be raised without increasing flood control risks in operation period. The cascade reservoirs in the Jinsha River and Yalong River can generate 3.28 billion kW h more hydropower (or increase 4.3%) annually during flood season.

Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models

ABSTRACT Understanding streamflow patterns by incorporating climate signal information can contribute remarkably to the knowledge of future local environmental flows. Three machine learning models, the multivariate adaptive regression splines (MARS), the M5 Model Tree and the least squares support vector machine (LSSVM) are established to predict the streamflow pattern over the Mediterranean region of Turkey (Besiri and Baykan stations). The structure of the predictive models is built using synoptic-scale climate signal information and river flow data from antecedent records. The predictive models are evaluated and assessed using quantitative and graphical statistics. The correlation analysis demonstrates that the North Pacific (NP) and the East Central Tropical Pacific Sea Surface Temperature (Niño3.4) indices have a substantial influence on the streamflow patterns, in addition to the historical information obtained from the river flow data. The model results reveal the utility of the LSSVM model over the other models through incorporating climate signal information for modelling streamflow.

Environmental predictors of stream flow in semi-arid watersheds for biological assessments

Abstract The aim of this study is to test a spatially explicit statistical model to identify indicators of natural stream flow using readily available stream, climate and landscape data. Understanding flow behavior of unmonitored streams at different temporal scales using environmental indicators is of great interest considering the logistic constraints of providing comprehensive flow instrumentation for all stream reaches in a region. Our results have applications to assess human impact in watersheds, to study environmental changes in fresh water resources, and in the management of local ecosystem. This study uses classification and regression tree analysis to identify significant explanatory variables for a predictive model of stream flow in semi-arid watersheds of southern California, USA. The study collected 77 variables with 30 years record, for a set of 48 sites, interpolated to create raster files at 30 m spatial resolution. After applying Pearson correlation analyses to eliminate redundant variables, nine variables were found to have strong positive predictive value for estimating stream flow at ungauged sites. Nine prediction rasters portraying spatial variation of stream flow for the study region at three key index months during wet, dry, and average rainfall conditions. The predictive power of the variables was tested and cross validated over a subset of data not included when building the model. Model validation by site at monthly temporal resolution showed mixed results. While some sites where accurately predicted others did not. The comparison of observed vs predicted values by month suggest that this statistically based approach is able to predict the general patterns of stream flow at the regional scale, however it may be inaccurate in estimating actual flow values by month since the models tends to under-predict monthly discharge.