No abstract

River flow in cold climates is known to be one of the hydrological systems most affected by climate change, playing a central role in the sustainability of downstream socio-ecological systems. Numerous studies on the temporal and spatial variations of streamflow characteristics have been done, and a comprehensive study on the variation of hydrologic extremes is becoming increasingly important. This study evaluated the long-running changes in the magnitude, time, and inter-annual variability of hydrologic extremes, including high and low flow in 16 major Finnish rivers. We applied four new hydrologic extreme indices for summer-winter low flow ratio, spring-absolute high flow ratio, time-to-peak index, and increasing rate index during the snowmelt period to analyze the spatiotemporal variations of extreme streamflow from 1911 to 2020. The most detected trends in flow regimes have started in the last six decades and become more severe from 1991 to 2020, which is likely to be dominated by anthropogenic global warming. The results also indicated that alteration of low pulses in most rivers was associated with an increase (decrease) in winter (summer) flows, suggesting the annual minimum flow in summer frequently contradicts natural hydrologic regimes in Arctic rivers. Southern Finland has experienced higher variations in extreme hydrology over the last century. A new low flow regime was detected for southern rivers, characterized by frequent annual minimum flow in summer instead of winter. Moreover, the annual maximum flow before/after spring dictated a new high-flow regime characterized by frequent double peak flows in this region.

Genetic programming (GP) is an evolutionary regression method that has received considerable interest to model hydro-environmental phenomena recently. Considering the sparseness of hydro-meteorological stations on northern areas, this study investigates the benefits and downfalls of univariate streamflow modeling at high latitudes using GP and seasonal autoregressive integrated moving average (SARIMA). Furthermore, a new evolutionary time series model, called GP-SARIMA, is introduced to enhance streamflow forecasting accuracy at long-term horizons in a lake-river system. The paper includes testing the new model for one-step-ahead forecasts of daily mean, weekly mean, and monthly mean streamflow in the headwaters of the Oulujoki River, Finland. The results showed that a combination of correlogram and average mutual information (AMI) analysis might yield in the selection of the optimum lags that are needed to be used as the predictors of streamflow models. With Nash-Sutcliffe efficiency values of more than 99%, both GP and SARIMA models exhibited good performance for daily streamflow prediction. However, they were not able to precisely model the intramonthly snow water equivalent in the long-term forecast. The proposed ensemble model, which integrates the best GP and SARIMA models with the most efficient predictor, may eliminate one-fourth of root mean squared errors of standalone models. The GP-SARIMA also showed up to three times improvement in the accuracy of the standalone models based on the Nash-Sutcliff efficiency measure.

<p>Climate change and anthropogenic activities have always affected the hydrological condition of watersheds. The uniqueness of Nordic watersheds characteristics (systems of lakes and rivers dominant by cold climate) and land cover (drained and pristine forests and peatlands) results in different river regimes in these regions compared to the other parts of the world. Long extreme cold winters usually freeze the river and lakes deeply to some depth, while, during short Nordic summers, the river flows can be influenced by forest and forestry activities, especially drainage systems. In addition, the changing climate is another driver that impacts river flows, especially extreme hydrological events (floods and droughts). This study investigates the long-term flood frequency alteration in two snowmelt and rainfall-dominant seasons for several headwaters in Finland as a Nordic region. The long-term daily discharge, rainfall, snow depth, and temperature data for selected watersheds were analyzed. The monthly and annual changes in mean, maximum, and minimum of discharge and rainfall and their trends were assessed to detect the rain and snowmelt-dominated seasons. Then the flood frequencies are estimated using  EV (Extreme Value) method for both seasons in different periods. Investigating such changes provides a broad view of the current and long-term situation of the river systems, which can help for long-term water resources planning and hydrosystem developments.</p>