Smart drainage management to limit summer drought damage in Nordic agriculture under the circular economy conceptSyed Md Touhidul Mustafa 1, *, Kedar Ghag1, Anandharuban Panchanathan2, Bishal Dahal 1, Amirhossein Ahrari1, Toni Liedes 3, Hannu Marttila1, Tamara Avellán1, Mourad Oussalah2, Björn Klöve 1, & Ali Torabi Haghighi11Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FIN90014, Oulu, Finland2Center for Machine Vision and Signal Analysis, University of Oulu, Finland3Intelligent Machines and Systems, University of Oulu, PO Box 4200, 90014 Oulu, Finland

A document by Mariane Chittolina. Click on the document to view its contents.

Beavers influence hydrology by constructing woody dams. Using a before after control impact experimental design, we quantified the effects of a beaver dam sequence on the flow regime of a stream in SW England. Building upon our previous research (Puttock et al., 2021), we consider the mechanisms that underpin flow attenuation in beaver wetlands. Rainfall-driven hydrological events were extracted between 2009 and 2020, for the impacted (n=612) and control (n=634) catchments, capturing events seven years before and three years after beaver occupancy, at the impacted site. General additive models were used to describe average hydrograph geometry across all events. After beaver occupancy, Lag times increased by 55.9% and declined by 17.5% in impacted and control catchments, respectively. Flow duration curve analysis showed a larger reduction in frequency of high flows, following beaver dam construction, with declines of Q5 exceedance levels of 33% and 15% for impact and control catchments, respectively. Using event total rainfall to predict peak flow, five generalised linear models were fitted to test the hypothesis that beaver dams attenuate flow, to a greater degree, with larger storm magnitude. The best performing model showed we can have high confidence that beaver dams attenuated peak flows, with increasing magnitude, up to between 0.5-2.5 m 3 s -1 for the 94 th percentile of event total rainfall; but we cannot confidently detect attenuation beyond the 97 th percentile. Increasing flow attenuation, with event magnitude, is attributed to transient floodplain storage in low gradient/profile floodplain valleys. These findings support the assertion that beaver dams restore attenuated flows. However, with long-term datasets of extreme hydrological events lacking, it is challenging to predict the effect of beaver dams during extreme events with high precision. Beaver dams will have spatially variable impacts on hydrological processes, requiring further investigation to quantify responses to dams across differing landscapes and scales.

A document by Hanshe Lim. Click on the document to view its contents.

Modelling the response of hydrological processes to the changing climate requires the use of a chain of numerical models, each of which contributes some degree of uncertainty to the final outputs. As a result, hydrological projections, despite the progressive increase in the accuracy of the models along the chain, can still display high levels of uncertainty, especially at small temporal and spatial scales. The randomness intrinsic to climate phenomena, known as internal climate variability, is also a component contributing to the uncertainty of the hydrological projections. Unlike the uncertainties emerging from the climate and hydrological models, the internal climate variability is irreducible. In this work, we quantify and partition the uncertainty of hydrological processes in two mountainous catchments in Switzerland, emerging from climate models and internal variability, across a broad range of scales. To that end, we used high-resolution ensembles of climate and hydrological data, produced by a two-dimensional weather generator (AWE-GEN-2d) and a distributed hydrological model (Topkapi-ETH). We quantified the uncertainty in hydrological projections towards the end of the century through the estimation of the values of signal-to-noise ratios (STNR). We found small STNR values (<-1) in the projection of annual streamflow for most sub-catchments in both study sites that are dominated by the large natural variability of precipitation (explains ~70% of total uncertainty). Furthermore, we investigated in detail specific hydrological components that are critical in the model chain. For example, snowmelt or liquid precipitation exhibits robust change signals, which translates into high STNR values for streamflow during warm seasons and at higher elevations, together with a larger contribution of climate model uncertainty, suggesting that an improvement of the involved models has the potential of significantly narrowing the uncertainty. In contrast, extreme flows show low STNR values due to large internal climate variability across all elevations, which limits the possibility of narrowing their estimation uncertainty in a warming climate.

Process understanding of the interaction between streamflow, groundwater and water usages under drought are hampered by a limited number of gauging stations, especially in tributaries. Recent technological advances facilitate the application of non-commercial measurement devices for monitoring environmental systems. The Dreisam River in the South-West of Germany was affected by several hydrological drought events from 2015 to 2020, when parts of the main stream and tributaries fell dry. A flexible longitudinal water quality and quantity monitoring network was set up in 2018. Among other measurements it employs an image based method with QR codes as fiducial marker. In order to assess under which conditions the QR-code based water level loggers (WLL) deliver data according to scientific standards, we present a comparison to conventional capacitive based WLL. The results from 20 monitoring stations reveal that the riverbed was dry for > 50 \% at several locations and even for > 70 \% at most severely affected locations during July and August 2020, with the north western parts of the catchment being especially concerned. Thus, the highly variable longitudinal drying patterns of the stream reaches could be monitored. The image-based method was found to be a valuable asset for identification of confounding factors and validation of zero level occurrences. Nevertheless, a simple image processing approach (based on an automatic thresholding algorithm) did not compensate for errors due to natural conditions and technical setup. Our findings highlight that the complexity of measurement environments is a major challenge when working with image-based methods.

We examined changes in catchment-scale annual and seasonal evapotranspiration after 50% strip thinning, using runoff data from headwater catchments. The short-term water balance (STWB) method between periods from 8 to 100 days was applied to the treated (KT) and control (KC) catchments. The estimated evapotranspiration during the pre- and post-thinning periods were 840 and 910 and 780 and 860 mm/year in the catchments KT and KC, respectively. According to a paired catchment analysis of estimated evapotranspiration, monthly evapotranspiration increased from 3 to 20 mm from June to December, while it decreased from 7 to 31 mm from January to May. The estimated annual and monthly evapotranspiration was compatible with the values monitored in the plot-scale interception, canopy transpiration, and ground surface evapotranspiration. Our findings showed that the decreases in evapotranspiration due to 50% thinning were similar or different in different methods of measurement when compared with thinning in the other catchments around the world. The STWB model can evaluate the effects of timber harvesting on changes in evapotranspiration (ET), including the reproduction of seasonal patterns of ET.

Concentration-discharge (C-Q) relationships can provide insight into how catchments store and transport solutes, but analysis is often limited to long-term behavior assessed from infrequent grab samples. Increasing availability of high-frequency sensor data has shown that C-Q relationships can vary substantially across temporal scales, and in response to different hydrologic drivers. Here, we present four years of dissolved organic carbon (DOC) and nitrate-nitrogen (NO3-N) sensor data from a snowmelt- dominated catchment in the Rocky Mountains of Colorado. We assessed both the direction (enrichment vs. dilution) and hysteresis in C-Q relationships across a range of time scales, from interannual to sub-daily. Both solutes exhibited a seasonal flushing response, with concentrations initially increasing as solute stores are mobilized by the melt pulse, but then declining as these stores are depleted. The high-frequency data revealed that the seasonal melt pulse was composed of numerous individual daily melt pulses. The solute response to daily melt pulses was relatively chemostatic, suggesting mobilization and depletion to be progressive rather than episodic processes. In contrast, rainfall-induced pulses produced short-lived but substantial enrichment responses, suggesting they may activate alternative solute sources or transport pathways. The results clearly demonstrate that solute responses to individual events may differ significantly from the longer-term behavior these events combine to generate, something which only becomes apparent when high-frequency data are used.  

Accurate simulation of plant water use across agricultural ecosystems is essential for various applications, including precision agriculture, quantifying groundwater recharge, and optimizing irrigation rates. Previous approaches to integrating plant water use data into hydrologic models have relied on evapotranspiration (ET) observations. Recently, the flux variance similarity approach has been developed to partition ET to transpiration (T) and evaporation, providing an opportunity to use T data to parameterize models. To explore the value of T/ET data in improving hydrologic model performance, we examined multiple approaches to incorporate these observations for vegetation parameterization. We used ET observations from 5 eddy covariance towers located in the San Joaquin Valley, California, to parameterize orchard crops in an integrated land surface – groundwater model. We find that a simple approach of selecting the best parameter sets based on ET and T performance metrics works best at these study sites. Selecting parameters based on performance relative to observed ET creates an uncertainty of 27% relative to the observed value. When parameters are selected using both T and ET data, this uncertainty drops to 24%. Similarly, the uncertainty in potential groundwater recharge drops from 63% to 58% when parameters are selected with ET or T and ET data, respectively. Additionally, using crop type parameters results in similar levels of simulated ET as using site-specific parameters. Different irrigation schemes create high amounts of uncertainty and highlight the need for accurate estimates of irrigation when performing water budget studies.

Soil water repellency (SWR) increases surface runoff and preferential flows. Thus, quantitative evaluation of SWR distribution is necessary to understand water movements. Because the variability of SWR distribution makes it difficult to measure directly, we developed a method for estimating an SWR distribution index, defined as the areal fraction of surface soil showing SWR (SWRarea). The theoretical basis of the method is as follows: (1) SWRarea is equivalent to the probability that a position on the soil surface is drier than the critical water content (CWC); SWR is present (droplets absorbed in >10 s) when the soil surface is drier than the CWC and absent when it is wetter. (2) CWC and soil moisture content (θ) are normally distributed independent variables. (3) Thus, based on probability theory, the cumulative normal distribution of θ – CWC (f(x)) can be obtained from the distributions of CWC and θ, and f(0), the cumulative probability that θ – CWC < 0, gives the SWRarea. To investigate whether the method gives reasonable results, we repeatedly measured θ at 0–5 cm depth and determined the water repellency of the soil surface at multiple points in fixed plots with different soils and topography in a humid-temperate forest. We then calculated the CWC from the observed θ–SWR relationship at each point. We tested the normality of the CWC and θ distributions and the correlation between CWC and θ. Then, we determined f(x) from the CWC and θ distributions and estimated the SWRarea on each measurement day. Although CWC and θ were both normally distributed, in many cases they were correlated. Nevertheless, the CWC–θ dependency had little effect on the estimation error, and f(x) explained 69% of the SWRarea variability. Our findings show that a stochastic approach is useful for estimating SWRarea.

Lateral saturated soil hydraulic conductivity, Ks,l, is the soil property governing subsurface water transfer in hillslopes, and the key parameter in many numerical models simulating hydrological processes both at the hillslope and catchment scales. Likewise, the hydrological connectivity of lateral flow paths plays a significant role in determining the intensity of the subsurface flow at various spatial scales. The objective of the study is to investigate the relationship between Ks,l and hydraulic connectivity at the hillslope spatial scale. Ks,l was determined by the subsurface flow rates intercepted by drains, and by water table depths observed in a well network. Hydraulic connectivity of the lateral flow paths was evaluated by the synchronicity among piezometric peaks, and between the latter and the peaks of drained flow. Soil moisture and precipitation data were used to investigate the influence of the transient hydrological soil condition on connectivity and Ks,l. It was found that the higher was the synchronicity of the water table response between wells, the lower was the time lag between the peaks of water levels and those of the drained subsurface flow. Moreover, the most synchronic water table rises determined the highest drainage rates. The relationships between Ks,l and water table depths were highly non-linear, with a sharp increase of the values for water table levels close to the soil surface. Estimated Ks,l values for the full saturated soil were in the order of thousands of mm h-1, suggesting the activation of macropores in the root zone. The Ks,l values determined at the peak of the drainage events were correlated with the indicators of synchronicity. The sum of the antecedent soil moisture and of the precipitation was correlated with the indicators of connectivity and with Ks,l. We suggest that, for simulating realistic processes at the hillslope scale, the hydraulic connectivity could be implicitly considered in hydrological modelling through an evaluation of Ks,l at the same spatial scale.

Salt marshes are hotspots of nutrient processing en route to sensitive coastal environments. While our understanding of these systems has improved over the years, we still have limited knowledge of the spatiotemporal variability of critical biogeochemical processes within salt marshes. Sea-level rise will continue to force change on salt marsh functioning, highlighting the urgency of filling this knowledge gap. Our study was conducted in a central California estuary experiencing extensive marsh drowning and relative sea-level rise, making it a model system for such an investigation. Here we instrumented three marsh positions with different degrees of inundation (6.7%, 8.9%, and 11.2% of the time for the upper, middle, and lower marsh positions, respectively), providing locations with varied geochemical characteristics and hydrological interaction at the site. We continuously monitored redox potential (Eh) at depths of 0.1, 0.3, and 0.5 m, subsurface water levels (WL), and temperature at each marsh position to understand how drivers of subsurface biogeochemical processes fluctuate across tidal cycles, using wavelet analyses to explain the interactions between Eh and WL. We found that tidal forcing significantly affects biogeochemical processes by imparting controls on Eh variability, likely driving subsurface hydro-biogeochemistry of the salt marsh. Wavelet coherence showed that the Eh-WL relationship is non-linear, and their lead-lag relationship is variable. We found that precipitation events perturb Eh at depth over timescales of hours, even though WL show relatively minimal change during events. This work highlights the importance of high-frequency measurements, such as Eh, to help explain factors that govern subsurface geochemistry and hydrological processes in salt marshes.

Increased surface temperatures (0.7℃ per decade) in the Arctic affects polar ecosystems by reducing the extent and duration of annual snow cover. Monitoring of these important ecosystems needs detailed information on snow cover properties (depth and density) at resolutions (< 100 m) that influence ecological habitats and permafrost thaw. As arctic snow is strongly influenced by vegetation, an ecotype map at 10 m resolution was added to a method with the Random Forest (RF) algorithm previously developed for alpine environments and applied here over an arctic landscape for the first time. The topographic parameters used in the RF algorithm were Topographic Position Index (TPI) and up-wind slope index (Sx), which were estimated from the freely available Arctic DEM at 2 m resolution. Ecotypes with taller vegetation with moister soils were found to have deeper snow because of the trapping effect. Using feature importance with RF, snow depth distributions were predicted from topographic and ecosystem parameters with a root mean square error = 8 cm (23%) (R² = 0.79) at 10 m resolution for an arctic watershed (1 500 km²) in western Nunavut, Canada.

The snowpack regime influences the timing of soil water available for transpiration and synchrony with the evapotranspiration (ET) energy demand (air temperature, VPD, and shortwave radiation). Variability of snowmelt timing, soil water availability, and the energy demand results in heterogeneous ET rates throughout a watershed. In this study, we assess how ET and growing season length vary across five sites on an elevational gradient in the Dry Creek Watershed, ID, USA. We compared trends of daily and annual ET between 2012 and 2017 to environmental parameters of soil moisture, air temperature, vapor pressure deficit, snow cover, and precipitation and evaluate how ET varies between sites and what influences annual ET at each site. We observed three trends in ET across the watershed. The first trend is at the low elevation site where the snow cover is not continuous throughout the winter and rain is the dominant precipitation form. The first day of the growing season and ET occurs early in the season when the energy demand is low and soil water is available. Annual ET at the low elevation site is a balance between spring precipitation providing soil water into the summer season and limiting the ET energy demand. The second trend occurs at the middle elevation site located in the rain-snow transition. At this site, ET increases with snow depth and spring precipitation extending the soil water availability into the summer season. At the higher elevation sites, ET is aligned with the energy demand and limited by growing season length. At the high elevation sites, decreasing snow depth and spring precipitation and increasing spring air temperatures result in greater annual ET rates. The observations from this study highlight the influence of environmental parameters and the potential sensitivity of ET to climate change.

This study investigates the impacts of climate change on the hydrology and soil thermal regime of ten sub-arctic watersheds (northern Manitoba, Canada) using the Variable Infiltration Capacity (VIC) model. We utilize statistically downscaled and bias-corrected forcing datasets based on 17 general circulation model (GCM) - representative concentration pathways (RCP) scenarios from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to run the VIC model for three 30-year periods: a historical baseline (1981–2010), and future projections (2021–2050: 2030s and 2041–2070: 2050s), under representative concentration pathways (RCPs) 4.5 and 8.5. The CMIP5 Multi-Model Ensemble (MME) mean-based VIC simulations indicate a 15–20% increase and 10% decrease in the projected annual precipitation and snowfall, respectively over the southern portion of the basin and >20% rainfall increase over the higher latitudes of the domain by the 2050s. Snow accumulation is projected to decline across all sub-basins, particularly in the lower latitudes. Projected uncertainties in major water balance components (i.e., evapotranspiration, surface runoff, and streamflow) are more substantial in the wetland and lake-dominated Grass and Gunisao watersheds than their eight counterparts. Future warming increases soil temperatures >2.5°C by the 2050s, resulting in 40–50% more baseflow. Further analyses of soil temperature trends at three different depths show the most pronounced warming in the top soil layer (1.6°C 30-year-1 in the 2050s), whereas baseflow increases substantially by 19.7% and 46.3% during the 2030s and 2050s, respectively. These results provide crucial information on the potential future impacts of warming soil temperatures on the hydrology of sub-arctic watersheds in north-central Canada and similar hydro-climatic regimes.

Soil–rock mixtures are widely encountered in geotechnical engineering projects. The instability and failure mechanism of grap-graded soil–rock mixtures under rainfall conditions has always been the focus of geological disaster research. To deeply explore the mechanism of seepage deformation of soil–rock mixtures, an indoor physical permeability test that considers soil–rock mixtures with different fine contents was conducted, and a particle-scale numerical simulation test of the permeability evolution was carried out using the coupling model of PFC3D and ABAQUS. The test results showed that the spatial distribution of fine particle loss along the height direction could be divided into three areas: top loss, middle uniform, and bottom loss area. The “island” effect of coarse particles, which is caused by excessive fine content and makes the fine particles bear more load, was eliminated with the loss of fine particles. In this preset working condition of coarse and fine particle diameters, setting FC to 35% may be the best way to fill the voids between the coarse particles. Particle migration leads to a change in the load-bearing skeleton structure, thereby causing seepage deformation. Therefore, the particle-scale numerical test method can better reproduce the seepage deformation process of grap-graded soil–rock mixtures.

Groundwater plays a crucial role in sustaining global food security but is being over-exploited in many basins of the world. Despite its importance and finite availability, local-scale monitoring of groundwater withdrawals required for sustainable water management practices is not carried out in most countries, including the United States. In this study, we combine publicly available datasets into a machine learning framework for estimating groundwater withdrawals over the state of Arizona. Here we include evapotranspiration, precipitation, crop coefficients, land use, well density, and watershed stress metrics for our predictions. We employ random forests to predict groundwater withdrawals from 2002-2020 at a 2 km spatial resolution using in-situ groundwater withdrawal data available for Arizona Active Management Areas (AMA) and Irrigation Non-Expansion Areas (INA) from 2002-2009 for training and 2010-2020 for validating the model respectively. The results show high training (R2≈ 0.86) and good testing (R2≈ 0.69) scores with normalized mean absolute error (NMAE) ≈ 0.64 and normalized root mean square error (NRMSE) ≈ 2.36 for the AMA/INA region. Using this method, we spatially extrapolate the existing groundwater withdrawal estimates to the entire state and observe the co-occurrence of both groundwater withdrawals and land subsidence in South-Central and Southern Arizona. Our model predicts groundwater withdrawals in regions where production wells are present on agricultural lands and subsidence is observed from Interferometric Synthetic Aperture Radar (InSAR), but withdrawals are not monitored. By performing a comparative analysis over these regions using the predicted groundwater withdrawals and InSAR-based land subsidence estimates, we observe a varying degree of subsidence for similar volumes of withdrawals in different basins. The performance of our model on validation datasets and its favorable comparison with independent water use proxies such as InSAR demonstrate the effectiveness and extensibility of our combined remote sensing and machine learning-based approach.

The climate of the Eurasia inland basin (EIB) is characterized by limited precipitation and high potential evapotranspiration; as such, water storage in the EIB is vulnerable to global warming and human activities. There is increasing evidence pointing to varying trends in water storage across different regions; however, a consistent conclusion on the main attributes of these trends is lacking. Based on the hydrological budget in a closed inland basin, the main attributes of changes in actual evapotranspiration (AET) and terrestrial water storage (TWS) were identified for the EIB and each closed basin. In the EIB and most of its closed basins, the TWS and AET showed significantly decreasing and non-significantly increasing trends, respectively. The primary cause underpinning the significantly decreasing TWS in the EIB was increasing AET. Approximately 70% of the increase in AET has been supplied by increased irrigation diversions and glacial melt runoff. At the basin scale, similar to the EIB, changes in AET were the predominant factor driving changes in TWS in most basins; the exception to this was the Balkhash Lake basin (BLB), Iran inland river basin (IIRB), Qaidam basin (QB), and Turgay River basin (TuRB). In these basins, changes in precipitation largely contributed to the TWS changes. The AET consumption of other water resources was the main factor contributing to AET changes in seven of 16 basins, including the Aral Sea, Caspian Sea, Junggar, Monglia Plateau, Qiangtang Plateau, and Tarim River basins. The increase in precipitation contributed more than 60% of increasing AET in four of 16 basins, particularly in the Helmand River basin and QB (>90%). Changes in precipitation and consumption by other water supply sources contributed to approximately half of the AET changes in the other five basins, including the Inner Mongolia Plateau, Issyk-Kul Sarysu, BLB, IIRB, and TuRB basins.