Search

A significant increase in surface water salinization in low-lying deltas is expected globally due to saline groundwater exfiltration driven by rising sea levels and decreasing freshwater availability. Sustaining fresh water-dependent agriculture in such areas will entail an increased demand for fresh water flushing. Unfortunately, the flushing of surface water is not operationally optimised and results in excessive use of scarce freshwater. To meet the increased demand for flushing, while minimizing the need for diverted freshwater, new operational designs are required. This paper presents a novel network model based approach that uses De Saint Venant (SV) and Advection Dispersion (AD) equations to optimize multiple objectives on water level and salinity control using a Nonlinear Model Predictive Control (NMPC). The resulting NMPC problem is solved with a receding horizon implementation, where the nonlinear program (NLP) at each iteration is solved using state-of-the-art large scale interior point solver (IPOPT). We evaluate the performance of the proposed approach and compare it to the traditional fixed flushing for a representative Dutch polder. Firstly, the approach is shown to be capable of controlling the water level and salinity level in the polder. Secondly, the results highlight that the network of canals, which were originally made for drainage, could not be made sufficiently fresh with current intake capacity. A simple design approach was used to identify appropriate new capacities for two of the gates that allow optimal flushing to guarantee the required water level and salinity constraints.

Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora of scaling-related challenges that remain unsolved. To the user, in light of model result interpretation, finer-resolution output might imply an increase in understanding of the complex interplay of heterogeneity within the hydrological system. Here we investigate spatial scaling in the form of varying spatial resolution by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model based on 454 basins from the large-sample CAMELS data set. Model instances are derived at three spatial resolutions, namely 3ĝ€¯km, 1ĝ€¯km, and 200ĝ€¯m. The results show that a finer spatial resolution does not necessarily lead to better streamflow estimates at the basin outlet. Statistical testing of the objective function distributions (Kling-Gupta efficiency (KGE) score) of the three model instances resulted in only a statistical difference between the 3ĝ€¯km and 200ĝ€¯m streamflow estimates. However, an assessment of sampling uncertainty shows high uncertainties surrounding the KGE score throughout the domain. This makes the conclusion based on the statistical testing inconclusive. The results do indicate strong locality in the differences between model instances expressed by differences in KGE scores of on average 0.22 with values larger than 0.5. The results of this study open up research paths that can investigate the changes in flux and state partitioning due to spatial scaling. This will help to further understand the challenges that need to be resolved for hyper-resolution hydrological modelling.

Plastics originating from land are mainly transported to the oceans by rivers. The total plastic transport from land to seas remains uncertain because of difficulties in measuring and the lack of standard observation techniques. A large focus in observations is on plastics floating on the water surface. However, an increasing number of observations suggest that large quantities of plastics are transported in suspension, below the water surface. Available underwater plastic monitoring methods use nets or fish traps that need to be deployed below the surface and are labor-intensive. In this research, we explore the use of echo sounding as an innovative low-cost method to quantify and identify suspended macroplastics. Experiments under controlled and natural conditions using a low-cost off-the-shelf echo sounding device show that plastic items can be detected and identified up to 7 m below the river surface. Eight different debris items (metal can, cup, bottles, food wrappers, food container) were characterized based on their reflection signature. Reflectance from plastic items diverged significantly from organic material and non-plastic anthropogenic debris. During a multi-day trial field expedition in the Guadalete river, Spain, we found that between 0.8 and 6.3 m depth considerable quantities of plastics are transported. As most plastic monitoring and removal strategies focus on the upper layer below the surface (up to approximately 1.5 m depth), a substantial share of the total plastic transport may be neglected. With this paper we 1) demonstrate that echo sounding is a promising tool for underwater plastic monitoring, and 2) emphasize the importance of an improved understanding of the existing plastic loads below the surface.

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements . The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements . The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

Plastics originating from land are mainly transported to the oceans by rivers. The total plastic transport from land to seas remains uncertain because of difficulties in measuring and the lack of standard observation techniques. A large focus in observations is on plastics floating on the water surface. However, an increasing number of observations suggest that large quantities of plastics are transported in suspension, below the water surface. Available underwater plastic monitoring methods use nets or fish traps that need to be deployed below the surface and are labor-intensive. In this research, we explore the use of echo sounding as an innovative low-cost method to quantify and identify suspended macroplastics. Experiments under controlled and natural conditions using a low-cost off-the-shelf echo sounding device show that plastic items can be detected and identified up to 7 m below the river surface. Eight different debris items (metal can, cup, bottles, food wrappers, food container) were characterized based on their reflection signature. Reflectance from plastic items diverged significantly from organic material and non-plastic anthropogenic debris. During a multi-day trial field expedition in the Guadalete river, Spain, we found that between 0.8 and 6.3 m depth considerable quantities of plastics are transported. As most plastic monitoring and removal strategies focus on the upper layer below the surface (up to approximately 1.5 m depth), a substantial share of the total plastic transport may be neglected. With this paper we 1) demonstrate that echo sounding is a promising tool for underwater plastic monitoring, and 2) emphasize the importance of an improved understanding of the existing plastic loads below the surface.

Nature-based solutions for water-resource challenges require advances in the science of ecohydrology. Current understanding is limited by a shortage of observations and theories that can further our capability to synthesize complex processes across scales ranging from submillimetres to tens of kilometres. Recent developments in environmental sensing, data, and modelling have the potential to drive rapid improvements in ecohydrological understanding. After briefly reviewing advances in sensor technologies, this paper highlights how improved measurements and modelling can be applied to enhance understanding of the following ecohydrological examples: interception and canopy processes, root uptake and critical zone processes, and up-scaled effects of land use on streamflow. Novel and improved sensors will enable new questions and experiments, while machine learning and empirical methods provide additional opportunities to advance science. The synergy resulting from the convergence of these parallel developments will provide new insight into ecohydrological processes and thereby help identify nature-based solutions to address water-resource challenges in the 21st century.

This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for real-time operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir operation problem in real time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal operation is required to compromise the three operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in real-time operation of a multi-reservoir system.

This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for real-time operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir operation problem in real time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal operation is required to compromise the three operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in real-time operation of a multi-reservoir system.

Nature-based solutions for water-resource challenges require advances in the science of ecohydrology. Current understanding is limited by a shortage of observations and theories that can further our capability to synthesize complex processes across scales ranging from submillimetres to tens of kilometres. Recent developments in environmental sensing, data, and modelling have the potential to drive rapid improvements in ecohydrological understanding. After briefly reviewing advances in sensor technologies, this paper highlights how improved measurements and modelling can be applied to enhance understanding of the following ecohydrological examples: interception and canopy processes, root uptake and critical zone processes, and up-scaled effects of land use on streamflow. Novel and improved sensors will enable new questions and experiments, while machine learning and empirical methods provide additional opportunities to advance science. The synergy resulting from the convergence of these parallel developments will provide new insight into ecohydrological processes and thereby help identify nature-based solutions to address water-resource challenges in the 21st century.

Land-use and land-cover changes are accelerating. Such changes can homogenize the water cycle and undermine planetary resilience. Policymakers and practitioners must consider water–vegetation interactions in their land-management decisions.

Plastic debris in water systems is a major challenge for our ecosystem, because it is extremely persistent in the environment. Apart from the importance of reducing the amount of plastic entering the ocean, clearing the rivers from debris is important for societal concerns, such as flood risks. Plastic waste accumulation at trash racks leads to a rise in upstream water level and may increase urban flood risk. Until now, most studies of riverine debris accumulation predominantly focused on organic accumulations at trash racks and bridge piers. In this study, flume experiments were used to study the behavior of plastic and mixed debris accumulations. One of the key findings from this study is that plastic debris causes a faster blockage than organic matter, as the plastic blockage contains fewer voids and therefore has a higher blockage density. In addition to the flume experiments, field measurements were performed in the Cikapundung River (Indonesia). This river is one of the tributaries of the Citarum River, which is considered one of the world’s most heavily polluted rivers. Combining the results of the flume experiments and field measurements demonstrated that a backwater rise of 1 m/h is plausible for a blocked trash rack in the Cikapundung River, illustrating the additional flood risk caused by plastic pollution. Our results emphasize the need for further quantifying riverine (plastic) debris and investigating its relation to changes in the water system behavior, including its influence on urban flood risk.

We present a systematic approach for salinity sensor placement in a polder network, where the objective is to estimate the unmeasured salinity levels in the main polder channels. We formulate this problem as optimization of the estimated salinity levels using root mean square error (RMSE) as the “goodness of fit” measure. Starting from a hydrodynamic and salt transport model of the Lissertocht catchment (a low-lying polder in the Netherlands), we use principal component analysis (PCA) to produce a low-order PCA model of the salinity distribution in the catchment. This model captures most of the relevant salinity dynamics and is capable of reconstructing the spatial and temporal salinity variation of the catchment. Just using three principal components (explaining 93% of the variance of the dataset) for the low-order PCA model, three optimally placed sensors with a greedy algorithm make the placement robust for modeling and measurement errors. The performance of the sensor placement for salinity reconstruction is evaluated against the detailed hydrodynamic and salt transport model and is shown to be close to the global optimum found by an exhaustive search with a RMSE of 82.2 mg/L.

Population growth, increasing energy demand and the depletion of fossil fuel reserves necessitate a search for sustainable alternatives for electricity generation. Hydropower could replace a large part of the contribution of gas and oil to the present energy mix. However, previous high-resolution estimates of hydropower potential have been local, and have yet to be applied on a global scale. This study is the first to formally present a detailed evaluation of the hydropower potential of each location, based on slope and discharge of each river in the world. The gross theoretical hydropower potential is approximately 52 PWh/year divided over 11.8 million locations. This 52 PWh/year is equal to 33% of the annually required energy, while the present energy production by hydropower plants is just 3% of the annually required energy. The results of this study: all potentially interesting locations for hydroelectric power plants, are available online.

The contents of this paper reflect the discussions that were held among the authors during and after the Workshop ‘Hyper-resolution global hydrological modelling: the next step’ held on 13–14 February 2014 in Utrecht, the Netherlands.

The contents of this paper reflect the discussions that were held among the authors during and after the Workshop ‘Hyper-resolution global hydrological modelling: the next step’ held on 13–14 February 2014 in Utrecht, the Netherlands.

The Actively Heated Fiber Optic (AHFO) method is shown to be capable of measuring soil water content several times per hour at 0.25 m spacing along cables of multiple kilometers in length. AHFO is based on distributed temperature sensing (DTS) observation of the heating and cooling of a buried fiber-optic cable resulting from an electrical impulse of energy delivered from the steel cable jacket. The results presented were collected from 750 m of cable buried in three 240 m colocated transects at 30, 60, and 90 cm depths in an agricultural field under center pivot irrigation. The calibration curve relating soil water content to the thermal response of the soil to a heat pulse of 10 W m−1 for 1 min duration was developed in the lab. This calibration was found applicable to the 30 and 60 cm depth cables, while the 90 cm depth cable illustrated the challenges presented by soil heterogeneity for this technique. This method was used to map with high resolution the variability of soil water content and fluxes induced by the nonuniformity of water application at the surface.

The Actively Heated Fiber Optic (AHFO) method is shown to be capable of measuring soil water content several times per hour at 0.25 m spacing along cables of multiple kilometers in length. AHFO is based on distributed temperature sensing (DTS) observation of the heating and cooling of a buried fiber-optic cable resulting from an electrical impulse of energy delivered from the steel cable jacket. The results presented were collected from 750 m of cable buried in three 240 m colocated transects at 30, 60, and 90 cm depths in an agricultural field under center pivot irrigation. The calibration curve relating soil water content to the thermal response of the soil to a heat pulse of 10 W m−1 for 1 min duration was developed in the lab. This calibration was found applicable to the 30 and 60 cm depth cables, while the 90 cm depth cable illustrated the challenges presented by soil heterogeneity for this technique. This method was used to map with high resolution the variability of soil water content and fluxes induced by the nonuniformity of water application at the surface.

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

The efficient use of reservoirs is an important part of water resources management. Regional monitoring of water storage and especially the understanding of seasonal variations are key issues for strategic risk management. Ground observations of reservoir storages are often unavailable due to for example lack of proper information transmission. Thus, it is of interest to develop the technology for reservoir storage monitoring for situations where no ground observation data is available. Remote sensing (RS) data is in the key role for the development of such technology. Earlier studies have shown that RS imagery can with satisfactory accuracy estimate water storages reservoirs. The aim of this study was to develop a methodology for monitoring storage volumes in reservoirs using strictly publicly available information. Red river (shared by China and Vietnam) was used as a case study. RS data (LANDSAT imagery and Digital Elevation Model {DEM)) was used for surface area estimations and development of area-volume relationships for all reservoir located in the Upper Red River. Further, the objective of this study was to develop means to estimate the impact of reservoir regulations on discharge in the river. Thus, a water systems model (WAFLEX) was developed for the Red River including observed storage volumes. This study found that use of only publicly available RS data could give satisfactory area-volume relationships for reservoirs in Upper Red River. The main restrictions for the use of this method are the spatial resolution of the imagery. Hence, small reservoirs need higher spatial resolution. Further, storage fluctuation could be estimated in reservoir with a quite low temporal resolution. However, the amount of observations was enough to develop an understanding on the impact of reservoir in Upper Red River. The WAFLEX model developed for Yuanjiang further showed that the reservoir fluctuation in the river does not impact the discharge downstream of the river signi, Patoaltaiden tehokas käyttö on yksi tärkeimpiä kysymyksiä kansainvälisten vesistöjen hallinnassa. Vesien varastoimisen seuranta ja kausivaihteluiden ymmärtäminen ovat avainasemassa riskien hallinnassa ja vesivarojen käytön suunnittelussa. Patoaltaiden tilavuuksista on usein harvoja havaintoja tai ovat hallinnollisista syistä saamattomissa. Tällaisia tilanteita ajatellen on tarpeen kehittää keinoja seurata altaiden tilavuusvaihteluita ilman paikallisten mittausten tarvetta. Kaukokartoitus on tälloin avainasemassa. Aiemmissa tutkimuksissa on todistettu kaukokartoituksen arvioivan vesitilavuuksia tyydyttävällä tarkkuudella. Tämän tutkimuksen tavoitteena oli kehittää altaiden tilavuusvaihteluiden seurantamenetelmä käyttämällä pelkästään julkisesti saatavilla olevaa materiaalia. Talloin tässä työssä ehdotettu menetelmä on käytettävissä maailmanlaajuisesti eikä vaadi tiukkaa yhteistyötä ja tiedonvälityskanavia muiden maiden kanssa. Punaista jokea (Red River) jonka jakaa Kiina ja Vietnam käytettiin tapaustutkimuksena. Kaukokartoitusdataa (LANDSAT kuvat ja OEM) käytettiin Punaisen joen yläjuoksun patoaltaiden pinta-alojen arviointiin ja tilavuus-pinta-ala suhteiden kehittämiseen. Lisäksi tavoitteena oli kehittää keino arvioida patoaltaiden tilavuusvaihtelun vaikutusta joen virtaamaan alajuoksussa kehittämällä vesitasemalli ja lisäämällä malliin havaitut allastilavuudet. Tutkimuksessa todettiin että käytetyillä kaukokartoituskuvilla voitiin ensinnäkin paikantaa isoimmat patoaltaat Punaisen joen yläjuoksussa ja lisäksi pinta-ala-tilavuussuhteiden tulos oli tyydyttävä. Ehdotetun menetelmän olennaisin rajoite todettiin olevan käytettyjen kuvien resoluutio. Vesitasemallilla todettiin Punaisen joen valtavirran allastilavuuden olevan liian pieni muuttaakseen joen virtaamaa.

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Les bas-fonds présentent un potentiel important pour l'intensification rizicole et la diversification des cultures en Afrique sub-saharienne. Mais la mauvaise maîtrise de l'eau constitue un obstacle majeur à leur mise en valeur. Le Consortium bas-fonds (IVC/CBF) regroupe des institutions nationales et internationales qui travaillent à promouvoir une utilisation plus intensive et plus durable des bas-fonds. Le second atelier scientifique du consortium bas-fonds s'est tenu en juin 1997. Il a permis de dégager les types d'aménagement adaptés aux différents bas-fonds, et de recenser les données nécessaires et les critères à prendre en compte pour faire le choix d'un aménagement. Ce compte rendu présente la synthèse des travaux et des discussions conduits pendant l'atelier ainsi qu'une étude de cas.

Les bas-fonds présentent un potentiel important pour l'intensification rizicole et la diversification des cultures en Afrique sub-saharienne. Mais la mauvaise maîtrise de l'eau constitue un obstacle majeur à leur mise en valeur. Le Consortium bas-fonds (IVC/CBF) regroupe des institutions nationales et internationales qui travaillent à promouvoir une utilisation plus intensive et plus durable des bas-fonds. Le second atelier scientifique du consortium bas-fonds s'est tenu en juin 1997. Il a permis de dégager les types d'aménagement adaptés aux différents bas-fonds, et de recenser les données nécessaires et les critères à prendre en compte pour faire le choix d'un aménagement. Ce compte rendu présente la synthèse des travaux et des discussions conduits pendant l'atelier ainsi qu'une étude de cas.