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
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.
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.
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.
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.
Flow regimes are critical for determining physical and biological processes in rivers, and their classification and regionalization traditionally seeks to link patterns of flow to physiographic, climate and other information. There are many approaches to, and rationales for, catchment classification, with those focused on streamflow often seeking to relate a particular response characteristic to a physical property or climatic driver. Rationales include such topics as Prediction in Ungauged Basins (PUB), helping with experimental approaches, and providing guidance for model selection in poorly understood hydrological systems. While scale and time are important considerations for classification, the Annual Daily Hydrograph (ADH) is a first-order easily visualized integrated expression of catchment function, and over many years is a distinct hydrological signature. In this study, we use t-SNE, a state-of-the-art technique of dimensionality reduction, to classify 17110 ADHs for 304 reference catchments in mountainous Western North America. t-SNE is chosen over other conventional methods of dimensionality reduction (e.g. PCA) as it presents greater separability of ADHs, which are projected on a 2D map where the similarities are evaluated according to their map distance. We then utilize a Deep Learning encoder to upgrade the non-parametric t-SNE to a parametric approach, enhancing its capability to address ‘unseen’ samples. Results showed that t-SNE was an effective classifier as it successfully clustered ADHs of similar flow regimes on the 2D map. In addition, many compact clusters on the 2D map in the coastal Pacific Northwest suggest information redundancy in the local hydrometric network. The t-SNE map provides an intuitive way to visualize the similarity of high-dimensional data of ADHs, groups catchments with like characteristics, and avoids the reliance on subjective hydrometric indicators.
The hydroclimatology of Northern South America responds to strongly-coupled dynamics of oceanic and terrestrial surface-atmosphere exchange, as moisture evaporated from these sources interact to produce continental rainfall. However, the relative contributions of these two source types through the annual cycle have been described only in modeling studies, with no observational tools used to corroborate these predictions. The use of isotopic techniques to study moisture sources has been common in assessing changes in the water cycle and in climate dynamics, as isotopes allow tracking the connection between evaporation, transpiration, and precipitation, as well as the influence of large scale hydroclimatic phenomena, such as the seasonal Inter Tropical Convergence Zone migration. We characterize the isotopic composition of moisture sources becoming precipitation in the Andes and Caribbean regions of Colombia, using stable isotopes data (δ18O, δ2H) from the Global Network of Isotopes in Precipitation (1971-2016) and contrasting it with moisture trajectory tracking from the FLEXPART model, using input from ERA-Interim reanalysis to compute the relative contribution of oceanic and terrestrial sources through the annual cycle. Our results indicate that most precipitation in the region comes from terrestrial sources including recycling (>30 % for all months), Orinoco (up to 28 % monthly for April), and the northern Amazon (up to 17 % monthly for June, July, and August); followed by oceanic sources including the Tropical South Pacific (up to 30 % monthly in October, November, December) and Tropical North Atlantic (up to 30 % monthly for January). These outcomes highlight the utility of combining stable isotopes in precipitation and modeling techniques to discriminate terrestrial and oceanic sources of precipitation. Further, our results highlight the need to assess the hydrological consequences of land cover change in South America, particularly in a country like Colombia where water, food and energy security all depend directly on precipitation. .
Aaron Smith1, Doerthe Tetzlaff1,2,3, Marco Maneta4,Chris Soulsby3,21IGB Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin, Berlin, Germany2Humboldt University Berlin, Berlin, Germany3Northern Rivers Institute, School of Geosciences, University of Aberdeen, UK4Department of Geosciences, University of Montana, Missoula, Montana, USACorrespondence to: Aaron Smith (email@example.com)
Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co-locating water quality observations with established stream gauges. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin - one of the U.S.’s most nutrient polluted basins - using the established Agro-IBIS model. While real-world observations are limited in space and time, particularly for nitrate, the synthetic data set allows for sufficiently long periods to train machine learning models and assess their performance. Using the synthetic data, we established baseline 1-day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48-3.3 mg/L). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional sensors. Synthetic data enable us to quantitatively assess the expected value of an additional nitrate sensor being deployed, which is, of course, not possible if we are limited to the present observational network. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at all possible locations. Finally, we assessed the co-benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for AI to make short-term predictions and provide an unbiased assessment of the marginal benefit and co-benefits to an expanded sensor network. While we use water quantity in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.
With global warming and rapid urbanization, urban agglomerations over the Loess Plateau (LP) are suffering from various urban disasters. Urbanization has aggravated the decreasing trends of extreme precipitation in Taiyuan and Xi’an urban agglomerations (UAs) and enhanced the increasing trends of extreme precipitation in Luoyang, Hohhot and Xining UAs during 1979–2018. Meanwhile, the number of light rain days decreases in almost all the cities, indicating the sensitivity of light rain days to urbanization. The climate change is a primary contributor to the change of urban precipitation during 1980–2000. However, the urbanization contribution has been increasing gradually since 2000, and the urbanization further amplifies the trend of extreme precipitation caused by the climate change. In terms of the physical mechanisms, the rapid increasing surface temperature and aerosol particles are closely related to the urban precipitation. Our findings provide a systematic understanding of the urbanization effects on the extreme precipitation over the LP and may play an important role in the mitigation of urban disasters.
The hydraulic properties of coastal aquifer systems are relevant to various hydrogeological, hydro-ecological and engineering problems. This study presents an analytical solution for predicting groundwater head fluctuations induced by dual-tide in multilayered island aquifer systems, consisting of an unconfined aquifer on the top and any number of leaky aquifers below. The solution was derived via the methods of matrix differential calculus and separation of variables. It is more general than any existing analytical solutions for the tidal pressure propagation since the new solution can consider multilayered aquifer systems along with the effects of leakage and aquifer length. Using this solution, we illustrated potential errors that may occur due to neglecting one or more vital factors affecting groundwater fluctuations. Besides, we articulated the groundwater response to the dual-tide in complex coastal aquifers. Considering that some thin semipermeable layers may be ignored in practical field investigation, we also demonstrated the effects due to simplification of aquifer layers. The results showed that with the increase in the number of overlapped leaky layers, the tidal propagation in the bottom part of multilayered aquifer system approaches that in a single confined aquifer with the same transmissivity and storage.
This paper presents a taxonomy (hierarchical organization) of hydrological processes; specifically, runoff generation processes in natural watersheds. Over 120 process names were extracted from a literature review of papers describing experimental watersheds, perceptual models, and runoff processes in a range of hydro-climatic environments. Processes were arranged into a hierarchical structure, and presented as a spreadsheet and interactive diagram. For each process, additional information was provided: a list of alternative names for the same process, a classification into hydrological function (e.g. partitioning, flux, storage, release) and a unique identifier similar to a hashtag. The taxonomy provides a method to label and search hydrological knowledge, thereby facilitating synthesis and comparison of processes across watersheds.
Groundwater recharge in highly-fractured volcanic aquifers remains poorly understood in the humid tropics, whereby rapid demographic growth and unregulated land use change are resulting in extensive surface water pollution and a large dependency on groundwater extraction. Here we present a multi-tracer approach including δ18O-δ2H, 3H/3He, and noble gases within the most prominent multi-aquifer system of central Costa Rica, with the objective to assess dominant groundwater recharge characteristics and age distributions. We sampled wells and large springs across an elevation gradient from 868 to 2,421 m asl. Our results suggest relatively young apparent ages ranging from 0.0±3.2 up to 76.6±9.9 years. Helium isotopes R/RA (0.99 to 5.4) indicate a dominant signal from the upper mantle across the aquifer. Potential recharge elevations ranged from ~1,400 to 2,650 m asl, with recharge temperatures varying from ~11°C to 19°C with a mean value of 14.5±1.9°C. Recharge estimates ranged from 129±78 to 1,605±196 mm/yr with a mean value of 642±117 mm/yr, representing 20.1±4.0% of the total mean annual rainfall as effective recharge. The shallow unconfined aquifer is characterised by young and rapidly infiltrating waters, whereas the deeper aquifer units have relatively older waters. These results are intended to guide the delineation and mapping of critical recharge areas in mountain headwaters to enhance water security and sustainability in the most important headwater dependent systems of Costa Rica.
Typical applications of process- or physically-based models aim to gain a better process understanding or provide the basis for a decision-making process. To adequately represent the physical system, models should include all essential processes. However, model errors can still occur. Other than large systematic observation errors, simplified, misrepresented, inadequately parametrized or missing processes are potential sources of errors. This study presents a set of methods and a proposed workflow for analyzing errors of process-based models as a basis for relating them to process representations. The evaluated approach consists of three steps: (i) training a machine learning (ml) error-model using the input data of the process-based model and other available variables, (ii) estimation of local explanations (i.e., contributions of each variable to a individual prediction) for each predicted model error using SHapley Additive exPlanations (SHAP) in combination with principal component analysis, (iii) clustering of SHAP values of all predicted errors to derive groups with similar error generation characteristics. By analyzing these groups of different error-variable association, hypotheses on error generation and corresponding processes can be formulated. That can ultimately lead to improvements in process understanding and prediction. The approach is applied to a process-based stream water temperature model HFLUX in a case study for modelling an alpine stream in the Canadian Rocky Mountains. By using available meteorological and hydrological variables as inputs, the applied ml model is able to predict model residuals. Clustering of SHAP values results in three distinct error groups that are mainly related to shading and vegetation emitted longwave radiation. Model errors are rarely random and often contain valuable information. Assessing model error associations is ultimately a way of enhancing trust in implemented processes and of providing information on potential areas of improvement to the model.
The magnitude and spatial heterogeneity of snow deposition are difficult to model in mountainous terrain. Here, we investigated how snow patterns from a 32-year (1985 – 2016) snow reanalysis in the Tuolumne, Kings, and Sagehen Creek, California Sierra Nevada watersheds could be used to improve simulations of winter snow deposition. Remotely-sensed fractional snow-covered area (fSCA) from dates following peak-snowpack timing were used to identify dates from different years with similar fSCA, which indicated similar snow accumulation and depletion patterns. Historic snow accumulation patterns were then used to 1) relate snow accumulation observed by snow pillows to watershed-scale estimates of mean snowfall, and 2) estimate 90 m snow deposition. Finally, snow deposition fields were used to force snow simulations, the accuracy of which were evaluated versus airborne lidar snow depth observations. Except for water-year 2015, which had the shallowest snow estimated in the Sierra Nevada, normalized snow accumulation and depletion patterns identified from historic dates with spatially correlated fractional snow-covered area agreed on average, with absolute differences of less than 10%. Watershed-scale mean winter snowfall inferred from the relationship between historic snow accumulation patterns and snow pillow observations had a ±13% interquartile range of biases between 1985 and 2016. Finally, simulations using 1) historic snow accumulation patterns, and 2) snow accumulation observed from snow pillows, had snow depth coefficients of correlations and mean absolute errors that improved by 70% and 27%, respectively, as compared to simulations using a more common forcing dataset and downscaling technique. This work demonstrates the real-time benefits of satellite-era snow reanalyses in mountainous regions with uncertain snowfall magnitude and spatial heterogeneity.