Hydrology under change: long-term annual and seasonal changes in small agricultural catchments in Norway

Abstract In agricultural catchments, hydrological processes are highly linked to particle and nutrient loss and can lead to a degradation of the ecological status of the water. Global warming and land use changes influence the hydrological regime. This effect is especially strong in cold regions. In this study, we used long-term hydrological monitoring data (22–26 years) from small agricultural catchments in Norway. We applied a Mann–Kendall trend and wavelet coherence analysis to detect annual and seasonal changes and to evaluate the coupling between runoff, climate, and water sources. The trend analysis showed a significant increase in the annual and seasonal mean air temperature. In all sites, hydrological changes were more difficult to detect. Discharge increased in autumn and winter, but this trend did not hold for all catchments. We found a strong coherence between discharge and precipitation, between discharge and snow water equivalent and discharge and soil water storage capacity. We detected different hydrological regimes of rain and snow-dominated catchments. The catchments responded differently to changes due to their location and inherent characteristics. Our results highlight the importance of studying local annual and seasonal changes in hydrological regimes to understand the effect of climate and the importance for site-specific management plans

GTAR:a new ensemble evolutionary autoregressive approach to model dissolved organic carbon

Abstract This article explores the forecasting capabilities of three classic linear and nonlinear autoregressive modeling techniques and proposes a new ensemble evolutionary time series approach to model and forecast daily dynamics in stream dissolved organic carbon (DOC). The model used data from the Oulankajoki River basin, a boreal catchment in Northern Finland. The models that were evolved used both accuracy and parsimony measures including autoregressive (AR), vector autoregressive (VAR), and self-exciting threshold autoregressive (SETAR). The new method, called genetic-based SETAR (GTAR), evolved through the integration of state-of-the-art genetic programming with SETAR. To develop the models, high-resolution DOC concentration and daily streamflow (as the external input for VAR) were measured at the same gauging station throughout the ice free season. The results showed that all the models characterize the DOC dynamics with an acceptable 1-day-ahead forecasting accuracy. Use of the streamflow time series as an exogenous variable did not increase the predictive accuracy of AR models. Moreover, the hybrid GTAR provided the best accuracy for the holdout testing data and proved to be a suitable approach for predicting DOC in boreal conditions

Combining in-situ fluorometry and distributed rainfall data provides new insights into natural organic matter transport dynamics in an urban river

Abstract Urbanization alters the quality and quantity of Dissolved Organic Matter (DOM) fluxes to rivers potentially leading to water quality problems and impaired ecosystem function. Traditional synoptic and point sampling approaches are generally inadequate for monitoring DOM source dynamics. To identify links between spatial heterogeneity in precipitation and DOM dynamics, we used a unique approach combining high spatial and temporal resolution precipitation datasets featuring point, catchment, and land-cover weighted precipitation to characterise catchment transport dynamics. These datasets were linked to fluorescence records from an urban stream (Bourn Brook, Birmingham, UK). Humic-like fluorescence (HLF: Ex. 365 nm, Em. 490 nm) and Tryptophan-like fluorescence (TLF: Ex. 285 nm, Em. 340 nm) were measured, (plus river flow and turbidity) at 5 min intervals for 10 weeks during Autumn 2017. The relationship between discharge (Q) and concentration (C) for TLF and HLF were strongly chemodynamic at low Q (<Q50) but TLF was chemostatic when Q exceeded this threshold. Figure of eight hysteresis was the most common response type for both HLF and TLF, indicating that DOM sources shift within and between events. Key drivers of DOM dynamics were identified using regression analysis and model outputs using point, catchment-averaged, and land-use weighted precipitation were compared. Antecedent rainfall was identified as the most important predictor (negative relationship) of TLF and HLF change suggesting DOM source exhaustion. Precipitation weighted by land cover showed that urbanization metrics were linked to increased TLF:HLF ratios and changes in hysteresis index. This study presents a novel approach of using land-cover weighted rainfall to enhance mechanistic understanding of DOM controls and sources. In contrast, catchment-average rainfall data have the potential to yield stronger understanding of TLF dynamics. This technique could be integrated with existing high resolution in-situ datasets to enhance our understanding of DOM dynamics in urban rivers

Geospatial artificial intelligence (GeoAI) in the integrated hydrological and fluvial systems modeling:review of current applications and trends

Abstract This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems

Coupling of water-carbon interactions during snowmelt in an Arctic Finland catchment

Abstract Snowmelt spring floods regulate carbon transport from land to streams. However, these coupled processes are rarely documented through high-resolution measurements focused on water-carbon interactions. We collated a state-of-the-art high-frequency data set throughout a snowmelt and early post snowmelt period, alongside regular samples of stream water, precipitation, and snowmelt isotopes (δ18O). Our study was conducted during the 2019 snowmelt and initial post snowmelt season in a subarctic, peatland influenced headwater catchment in Pallas, Northern Finland. We measured high-frequency dissolved organic carbon (DOC), and in-stream carbon dioxide (pCO2). We identified a change in hydrological processes as the snowmelt season progressed and the post snowmelt season began. We found (a) Overland flow dominated stream DOC dynamics in early snowmelt, while increased catchment connectivity opened new distal pathways in the later snowmelt period; (b) CO2 processes were initially driven by rapid bursts of CO2 from the meltwaters in snowmelt, followed by dilution and source limitation emerging post snowmelt as deep soil pathways replaced the snowpack as the main source of CO2; (c) stream carbon concentration shifted from being relatively balanced between CO2 and DOC during the early snowmelt period to being increasingly DOC dominated as snowmelt progressed due to changes in DOC and CO2 source supply. The study highlights the importance of using high-frequency measurements combined with high-frequency data analyses to identify changes in the processes driving water-carbon interactions. The degree to which water-carbon interactions respond to the continuation of Arctic water cycle amplification is central to delineating the evolving complexity of the future Arctic