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Zarei, M. & Destouni, G. (2024). Research Gaps and Priorities for Terrestrial Water and Earth System Connections From Catchment to Global Scale. Earth's Future, 12(1), Article ID e2023EF003792.
Open this publication in new window or tab >>Research Gaps and Priorities for Terrestrial Water and Earth System Connections From Catchment to Global Scale
2024 (English)In: Earth's Future, E-ISSN 2328-4277, Vol. 12, no 1, article id e2023EF003792Article, review/survey (Refereed) Published
Abstract [en]

The out-of-sight groundwater and visible but much less extensive surface waters on land constitute a linked terrestrial water system around the planet. Research is crucial for our understanding of these terrestrial water system links and interactions with other geosystems and key challenges of Earth System change. This study uses a scoping review approach to discuss and identify topical, methodological and geographical gaps and priorities for research on these links and interactions of the coupled ground- and surface water (GSW) system at scales of whole-catchments or greater. Results show that the large-scale GSW system is considered in just a small part (0.4%-0.8%) of all studies (order of 105 for each topic) of either groundwater or surface water flow, storage, or quality at any scale. While relatively many of the large-scale GSW studies consider links with the atmosphere or climate (8%-43%), considerably fewer address links with: (a) the cryosphere or coastal ocean as additional interacting geosystems (5%-9%); (b) change drivers/pressures of land-use, water use, or the energy or food nexus (2%-12%); (c) change impacts related to health, biodiversity or ecosystem services (1%-4%). Methodologically, use of remote sensing data and participatory methods is small, while South America and Africa emerge as the least studied geographic regions. The paper discusses why these topical, methodological and geographical findings indicate important research gaps and priorities for the large-scale coupled terrestrial GSW system and its roles in the future of the Earth System. The water on the land surface (surface water) and that beneath it (groundwater), along with the water that is continuously and increasingly used and managed in human societies, are connected and constitute a coherent natural-social water system around the world. Many unknowns and open questions remain for how the small-scale variations add up to large-scale variability and change of this water system on land, as an integral part of the whole Earth System. Relevant research is crucial for reducing the unknowns and answering the questions, and this study's scoping review aims to assess how they have been addressed in published research so far. The aim is to identify key research gaps and priorities for further research on how the integrated water system on land functions and evolves on large scales, from whole hydrological catchments and in multiple catchments around the world up to global scale. The scoping review results show key research gaps and priorities to be the coupling of surface water and groundwater on land, and the interactions of this coupled water system with other parts and major challenges of the Earth System. Geographically, the gaps and priorities emerge as particularly large and urgent for South America and Africa. Coupling of the ground-surface water system is a key gap in terrestrial water research, particularly at large scalesResearch on terrestrial water interactions with other geospheres and key challenges of Earth System change is rare but impactfulMajor geographic gaps in research on the large-scale coupled terrestrial water system emerge for South America and Africa

Keywords
scoping review, terrestrial water system, Earth System, coupled natural-social system, geospheres, societal challenges
National Category
Environmental Sciences
Identifiers
urn:nbn:se:su:diva-225422 (URN)10.1029/2023EF003792 (DOI)001134671000001 ()2-s2.0-85181491510 (Scopus ID)
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-17Bibliographically approved
Panahi, M., Khosravi, K., Rezaie, F., Ferreira, C. S. S., Destouni, G. & Kalantari, Z. (2023). A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms. Earth's Future, 11(11), Article ID e2023EF003749.
Open this publication in new window or tab >>A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms
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2023 (English)In: Earth's Future, E-ISSN 2328-4277, Vol. 11, no 11, article id e2023EF003749Article in journal (Refereed) Published
Abstract [en]

Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large-scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation-wide flood susceptibility mapping, this modeling approach considers ten geo-environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo-environmental input factors. In general, accurate nation-wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well-performing and cost-effective for the case of Sweden, calling for further application and testing in other world regions.

Keywords
large-scale flood prediction, nation-wide flood susceptibility mapping, convolutional neural network, gray wolf optimizer, imperialist competitive algorithm, Sweden
National Category
Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:su:diva-224827 (URN)10.1029/2023EF003749 (DOI)2-s2.0-85177475476 (Scopus ID)
Available from: 2023-12-29 Created: 2023-12-29 Last updated: 2024-02-13Bibliographically approved
Li, W., Reichstein, M., O, S., May, C., Destouni, G., Migliavacca, M., . . . Orth, R. (2023). Contrasting Drought Propagation Into the Terrestrial Water Cycle Between Dry and Wet Regions. Earth's Future, 11(7), Article ID e2022EF003441.
Open this publication in new window or tab >>Contrasting Drought Propagation Into the Terrestrial Water Cycle Between Dry and Wet Regions
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2023 (English)In: Earth's Future, E-ISSN 2328-4277, Vol. 11, no 7, article id e2022EF003441Article in journal (Refereed) Published
Abstract [en]

Drought's intensity and duration have increased in many regions over the last decades. However, the propagation of drought-induced water deficits through the terrestrial water cycle is not fully understood at a global scale. Here we study responses of monthly evaporation (ET) and runoff to soil moisture droughts occurring between 2001 and 2015 using independent gridded datasets based on machine learning-assisted upscaling of satellite and in-situ observations. We find that runoff and ET show generally contrasting drought responses across climate regimes. In wet regions, runoff is strongly reduced while ET is decoupled from soil moisture decreases and enhanced by sunny and warm weather typically accompanying soil moisture droughts. In drier regions, ET is reduced during droughts due to vegetation water stress, while runoff is largely unchanged as precipitation deficits are typically low in these regions and ET decreases are buffering runoff reductions. While these water flux drought responses are controlled by the large-scale climate regimes, they are additionally modulated by local vegetation characteristics. Land surface models capture the observed water cycle responses to drought in the case of runoff, but not for ET where the ET deficit (surplus) is overestimated (underestimated), related to a misrepresentation of the general soil moisture-evaporation interplay. In summary, our study illustrates how the joint analysis of machine learning-enhanced Earth observations can advance the understanding of global eco-hydrological processes, as well as the validation of land surface models.

National Category
Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:su:diva-221235 (URN)10.1029/2022EF003441 (DOI)001023466000001 ()2-s2.0-85165473155 (Scopus ID)
Available from: 2023-09-19 Created: 2023-09-19 Last updated: 2023-09-19Bibliographically approved
Althoff, D. & Destouni, G. (2023). Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff. One Earth, 6(9), 1246-1257
Open this publication in new window or tab >>Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff
2023 (English)In: One Earth, ISSN 2590-3330, E-ISSN 2590-3322, Vol. 6, no 9, p. 1246-1257Article in journal (Refereed) Published
Abstract [en]

The partitioning of precipitation water input on land between green (evapotranspiration) and blue (runoff) water fluxes distributes the annually renewable freshwater resource among sectors and ecosystems. The patterns and main drivers of this partitioning are not fully understood around the global land area. We decipher the worldwide patterns and key determinants of this water flux partitioning and investigate its predictability based on a global machine learning model. Available data for 3,614 hydrological catchments and model application to the global land area agree in showing mostly larger green than blue water flux. Possible expansion/intensification of irrigated and/or rainfed agriculture to feed a growing human population, along with climate warming, will tend to increase this flux partitioning asymmetry, jeopardizing blue water security. The developed machine learning model presents a promising predictive tool for future blue and green water availability under various forthcoming climate and land-use change scenarios around the world.

National Category
Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:su:diva-223429 (URN)10.1016/j.oneear.2023.08.002 (DOI)001080458500001 ()2-s2.0-85170663877 (Scopus ID)
Available from: 2023-11-01 Created: 2023-11-01 Last updated: 2023-11-01Bibliographically approved
Cantoni, J., Kalantari, Z. & Destouni, G. (2023). Legacy contributions to diffuse water pollution: Data-driven multi-catchment quantification for nutrients and carbon. Science of the Total Environment, 879, Article ID 163092.
Open this publication in new window or tab >>Legacy contributions to diffuse water pollution: Data-driven multi-catchment quantification for nutrients and carbon
2023 (English)In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 879, article id 163092Article in journal (Refereed) Published
Abstract [en]

Legacy pollutants are increasingly proposed as possible reasons for widespread failures to improve water quality, despite the implementation of stricter regulations and mitigation measures. This study investigates this possibility, using multi-catchment data and relatively simple, yet mechanistically-based, source distinction relationships between water discharges and chemical concentrations and loads. The relationships are tested and supported by the available catchment data. They show dominant legacy contributions for total nitrogen (TN), total phosphorus (TP) and total organic carbon (TOC) across catchment locations and scales, from local to country-wide around Sweden. Consistently across the study catchments, close relationships are found between the legacy concentrations of TN and TOC and the land shares of agriculture and of the sum of agriculture and forests, respectively. The legacy distinction and quantification capabilities provided by the data-driven approach of this study could guide more effective pollution mitigation and should be tested in further research for other chemicals and various sites around the world.

Keywords
Legacy sources, Eutrophication, Water browning, Streams, Groundwater, Land use
National Category
Earth and Related Environmental Sciences
Identifiers
urn:nbn:se:su:diva-217103 (URN)10.1016/j.scitotenv.2023.163092 (DOI)000972221300001 ()37001269 (PubMedID)2-s2.0-85151265064 (Scopus ID)
Available from: 2023-05-24 Created: 2023-05-24 Last updated: 2023-05-24Bibliographically approved
Kan, J.-C., Ferreira, C., Destouni, G., Pan, H., Passos, M. V., Barquet, K. & Kalantari, Z. (2023). Predicting agricultural drought indicators: ML approaches across wide-ranging climate and land use conditions. Ecological Indicators, 154, Article ID 110524.
Open this publication in new window or tab >>Predicting agricultural drought indicators: ML approaches across wide-ranging climate and land use conditions
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2023 (English)In: Ecological Indicators, ISSN 1470-160X, E-ISSN 1872-7034, Vol. 154, article id 110524Article in journal (Refereed) Published
Abstract [en]

Agricultural drought can severely reduce crop yields, lead to large economic losses and health impacts. Combined climate and land use variations determine key indicators of agricultural drought, including soil moisture and the Palmer drought severity index (PDSI). This study investigated the use of machine learning (ML) methods for predicting these indicators over Sweden, spanning steep climate and land use gradients. Three data arrangement methods (multi-features, temporal, and spatial) were used and compared in combination with seven ML/deep learning (DL) models (random forest (RF), decision tree, multivariate linear regression, support vector regression, autoregressive integrated moving average (AMIRA), artificial neural network, and convolutional neural network). Seven investigated features, obtained from Google Earth Engine, were used in the ML/DL modeling (soil moisture, PDSI, precipitation, evapotranspiration, elevation, slope and soil texture). The temporal ARIMA model (found most suitable for local scale prediction) and the multi-features RF model (more suitable for national-scale prediction) emerged as best performing for soil moisture prediction (with MAE of 9.1 and 11.95, and R2 of 0.79 and 0.59, respectively). All models generally performed better in predicting the soil moisture than the PDSI indicator of drought. For drought indicator prediction and mapping, previous-year average monthly soil moisture emerged as the most important feature, combined with the four additional corresponding features of PDSI, precipitation, evapotranspiration and elevation.

Keywords
Drought, Soil moisture, Palmer drought severity index, Climate, Land use, Machine learning, Sweden
National Category
Biological Sciences Earth and Related Environmental Sciences
Identifiers
urn:nbn:se:su:diva-221398 (URN)10.1016/j.ecolind.2023.110524 (DOI)001034589400001 ()2-s2.0-85163201022 (Scopus ID)
Available from: 2023-09-20 Created: 2023-09-20 Last updated: 2023-09-20Bibliographically approved
Hambäck, P. A., Dawson, L., Geranmayeh, P., Jarsjö, J., Kačergytė, I., Peacock, M., . . . Blicharska, M. (2023). Tradeoffs and synergies in wetland multifunctionality: A scaling issue. Science of the Total Environment, 862, Article ID 160746.
Open this publication in new window or tab >>Tradeoffs and synergies in wetland multifunctionality: A scaling issue
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2023 (English)In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 862, article id 160746Article, review/survey (Refereed) Published
Abstract [en]

Wetland area in agricultural landscapes has been heavily reduced to gain land for crop production, but in recent years there is increased societal recognition of the negative consequences from wetland loss on nutrient retention, biodiversity and a range of other benefits to humans. The current trend is therefore to re-establish wetlands, often with an aim to achieve the simultaneous delivery of multiple ecosystem services, i.e., multifunctionality. Here we review the literature on key objectives used to motivate wetland re-establishment in temperate agricultural landscapes (provision of flow regulation, nutrient retention, climate mitigation, biodiversity conservation and cultural ecosystem services), and their relationships to environmental properties, in order to identify potential for tradeoffs and synergies concerning the development of multifunctional wetlands. Through this process, we find that there is a need for a change in scale from a focus on single wetlands to wetlandscapes (multiple neighboring wetlands including their catchments and surrounding landscape features) if multiple societal and environmental goals are to be achieved. Finally, we discuss the key factors to be considered when planning for re-establishment of wetlands that can support achievement of a wide range of objectives at the landscape scale.

Keywords
Ecosystem services, Wetlandscapes, Ecohydrology, Climate mitigation, Biodiversity conservation, Cultural services
National Category
Earth and Related Environmental Sciences
Identifiers
urn:nbn:se:su:diva-218058 (URN)10.1016/j.scitotenv.2022.160746 (DOI)000992330200001 ()36513236 (PubMedID)2-s2.0-85144074450 (Scopus ID)
Available from: 2023-07-26 Created: 2023-07-26 Last updated: 2023-07-26Bibliographically approved
Panahi, D. M., Destouni, G., Kalantari, Z. & Zahabiyoun, B. (2022). Distinction of driver contributions to wetland decline and their associated basin hydrology around Iran. Journal of Hydrology: Regional Studies, 42, Article ID 101126.
Open this publication in new window or tab >>Distinction of driver contributions to wetland decline and their associated basin hydrology around Iran
2022 (English)In: Journal of Hydrology: Regional Studies, E-ISSN 2214-5818, Vol. 42, article id 101126Article in journal (Refereed) Published
Abstract [en]

Study region: Six wetland sites around Iran (Gavkhoni and Hur al-Azim wetlands, Gorgan Bay, and Namak, Urmia, and Maharloo & Bakhtegan lakes) and their associated hydrological basins.

Study focus: The aim was to distinguish the contributions of climatic and non-climatic changes (including land-use/land-cover, LULC) to areal decline in six Iranian wetlands. This was done using data-driven quantification methodology that combined comparative change correlation and Budyko-based analyses of evapotranspiration (ETb), and runoff (Rb) changes in the hydrological basin of each wetland, extended to consider explicitly climate-driven change in evaporation rate (Ew) from the wetland area and the shift from previous Ew to ETb caused by the wetland decline itself.

New hydrological insights for the region: Comparative correlation analysis revealed an overall stronger correlation of wetland decline with LULC changes (mainly cropland, urban land) than with changes in temperature (T) or precipitation (P) across all wetland sites. The extended Budyko-based analysis revealed that the predominant cause of wetland decline across all sites was increased ETb, with related decrease in Rb from basin to wetland, whereas changes in Ew and in wetland decline shifting Ew to ETb had only a weak influence. In line with the correlation analysis results, non-climatic drivers were revealed as causing ETb increases and Rb decreases, leading to wetland decline to a greater degree than climate change (T, P).

Keywords
Wetland decline, Climate change, Land use, cover change, Evapotranspiration, Runoff, Iran
National Category
Earth and Related Environmental Sciences
Identifiers
urn:nbn:se:su:diva-207935 (URN)10.1016/j.ejrh.2022.101126 (DOI)000822607000003 ()2-s2.0-85131693583 (Scopus ID)
Available from: 2022-08-19 Created: 2022-08-19 Last updated: 2022-08-19Bibliographically approved
Kåresdotter, E., Destouni, G., Ghajarnia, N., Lammers, R. B. & Kalantari, Z. (2022). Distinguishing Direct Human-Driven Effects on the Global Terrestrial Water Cycle. Earth's Future, 10(8), Article ID e2022EF002848.
Open this publication in new window or tab >>Distinguishing Direct Human-Driven Effects on the Global Terrestrial Water Cycle
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2022 (English)In: Earth's Future, E-ISSN 2328-4277, Vol. 10, no 8, article id e2022EF002848Article in journal (Refereed) Published
Abstract [en]

Population growth is increasing the pressure on water resource availability. For useful assessment and planning for societal water availability impacts, it is imperative to disentangle the direct influences of human activities in the landscape from external climate-driven influences on water flows and their variation and change. In this study we used the water balance model, a gridded global hydrological model, to quantify and distinguish human-driven change components, modified by interventions such as dams, reservoirs, and water withdrawals for irrigation, industry, and households, from climate-driven change components on four key water balance variables in the terrestrial hydrological system (evapotranspiration, runoff, soil moisture, storage change). We also analyzed emergent effect patterns in and across different parts of the world, facilitating exploration of spatial variability and regional patterns on multiple spatial scales, from pixel to global, including previously uninvestigated parts of the world. Our results show that human activities drive changes in all hydrological variables, with different magnitudes and directions depending on geographical location. The differences between model scenarios with and without human activities were largest in regions with the highest population densities. In such regions, which also have relatively large numbers of dams for irrigation, water largely tends to be removed from storage and go to feed increased runoff and evapotranspiration fluxes. Our analysis considers a more complete set of hydrological variables than previous studies and can guide further research and management planning for future hydrological and water availability trends, including in relatively data-poor parts of the world.

Keywords
global hydrological modeling, human-water interactions, anthropogenic hydrological change, runoff, evapotranspiration, storage change
National Category
Earth and Related Environmental Sciences
Identifiers
urn:nbn:se:su:diva-209511 (URN)10.1029/2022EF002848 (DOI)000842669200001 ()2-s2.0-85137090494 (Scopus ID)
Available from: 2022-09-19 Created: 2022-09-19 Last updated: 2023-03-28Bibliographically approved
Vigouroux, G. & Destouni, G. (2022). Gap identification in coastal eutrophication research - Scoping review for the Baltic system case. Science of the Total Environment, 839, Article ID 156240.
Open this publication in new window or tab >>Gap identification in coastal eutrophication research - Scoping review for the Baltic system case
2022 (English)In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 839, article id 156240Article, review/survey (Refereed) Published
Abstract [en]

Coastal eutrophication is a major issue worldwide, also affecting the Baltic Sea and its coastal waters. Effective management responses to coastal eutrophication require good understanding of the interacting coastal pressures from land, the open sea, and the atmosphere, and associated coastal ecosystem impacts. In this study, we investigate how research on Baltic coastal eutrophication has handled these interactions so far and what key research gaps still remain. We do this through a scoping review, identifying 832 scientific papers with a focus on Baltic coastal eutrophication. These are categorized in terms of study focus, methods, and consideration of coastal system components and land-coast-sea interactions. The coastal component categories include coastal functions (including also socio-economic driver aspects), pressures that are natural (or mediated by a natural process or system) or directly anthropogenic, and management responses.

The classification results show that considerably more studies focus on coastal eutrophication pressures (52%) or impacts (39%) than on characterizing the coastal eutrophication itself (20%). Moreover, few studies investigate pressures and impacts together, indicating that feedbacks are understudied. Regarding methods, more studies focus on data collection (62%) than on linking and synthetic methods (44%; e.g., modelling), and very few studies use remote sensing (6%) or participatory (3%) methods. Coastal links with land and open sea are mentioned but much less investigated. Among the coastal functions, studies considering ecological aspects are dominant, but much fewer studies investigate human aspects and the coastal filter function. Among the coastal pressures, studies considering nutrient loads are dominant, but much fewer studies investigate the sources of these loads, especially long-lived legacy sources and possible solutions for their mitigation. Overall, few studies investigate synergies, trade-offs and incentives for various solutions to address cross-scale multi-solution management.

Keywords
Literature classi fication, Coastal system interactions, Pressures, Impacts, Management solutions, Land -coast -sea continuum
National Category
Earth and Related Environmental Sciences
Identifiers
urn:nbn:se:su:diva-207422 (URN)10.1016/j.scitotenv.2022.156240 (DOI)000809829000014 ()35644392 (PubMedID)
Available from: 2022-07-27 Created: 2022-07-27 Last updated: 2022-07-27Bibliographically approved
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