Pub Date : 2023-10-23DOI: 10.5194/hess-27-3733-2023
Luca Carraro
Abstract. Spatially explicit mathematical models are key to a mechanistic understanding of environmental processes in rivers. Such models necessitate extended information on networks' morphology, which is often retrieved from geographic information system (GIS) software, thus hindering the establishment of replicable script-based workflows. Here I present rivnet, an R package for GIS-free extraction and analysis of river networks based on digital elevation models (DEMs). The package exploits TauDEM's flow direction algorithm in user-provided or online accessible DEMs, and allows for computing covariate values and assigning hydraulic variables across any network node. The package is designed so as to require minimal user input while allowing for customization for experienced users. It is specifically intended for application in models of ecohydrological, ecological or biogeochemical processes in rivers. As such, rivnet aims to make river network analysis accessible to users unfamiliar with GIS-based and geomorphological methods and therefore enhance the use of spatially explicit models in rivers.
{"title":"Technical note: Seamless extraction and analysis of river networks in R","authors":"Luca Carraro","doi":"10.5194/hess-27-3733-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3733-2023","url":null,"abstract":"Abstract. Spatially explicit mathematical models are key to a mechanistic understanding of environmental processes in rivers. Such models necessitate extended information on networks' morphology, which is often retrieved from geographic information system (GIS) software, thus hindering the establishment of replicable script-based workflows. Here I present rivnet, an R package for GIS-free extraction and analysis of river networks based on digital elevation models (DEMs). The package exploits TauDEM's flow direction algorithm in user-provided or online accessible DEMs, and allows for computing covariate values and assigning hydraulic variables across any network node. The package is designed so as to require minimal user input while allowing for customization for experienced users. It is specifically intended for application in models of ecohydrological, ecological or biogeochemical processes in rivers. As such, rivnet aims to make river network analysis accessible to users unfamiliar with GIS-based and geomorphological methods and therefore enhance the use of spatially explicit models in rivers.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"R-17 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135366892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-20DOI: 10.5194/hess-27-3701-2023
Barbara Herbstritt, Benjamin Gralher, Stefan Seeger, Michael Rinderer, Markus Weiler
Abstract. Methodological advancements have been made in in situ observations of water stable isotopes that have provided valuable insights into ecohydrological processes. The continuous measurement capabilities of laser-based analyzers allow for high temporal resolutions and non-destructive minimally invasive study designs of such in situ approaches. However, isotope analyzers are expensive, heavy, and require shelter and access to electrical power, which impedes many in situ assays. Therefore, we developed a new inexpensive technique to collect discrete water vapor samples in the field via diffusion-tight inflatable bags that can later be analyzed in the lab. In a series of structured experiments, we tested different procedural settings, bag materials, and closure types for diffusion tightness during storage as well as for practical handling during filling and extraction. To facilitate reuse of sampling bags, we present a conditioning procedure using ambient air as primer. In order to validate our method, direct measurements through hydrophobic in situ probes were compared to repeated measurements of vapor sampled with our bags from the same source. All steps are summarized in a detailed standard operating procedure (SOP). This procedure represents the preparation and measurement of calibration and validation vapor standards necessary for processing of unknown field-collected vapor samples in the foreseen application. By performing pertinent calibration procedures, accuracy was better than 0.4 ‰ for δ18O and 1.9 ‰ for δ2H after 1 d of storage. Our technique is particularly suitable when used in combination with minimally invasive water vapor sampling in situ probes that have already been employed for soils and tree xylem. It is an important step towards minimally invasive monitoring of stable isotope distributions and also time series in virtually undisturbed soils and trees without the need to have an analyzer in the field. It is therefore a promising tool for many applications in ecohydrology and meteorology.
{"title":"Technical note: Discrete in situ vapor sampling for subsequent lab-based water stable isotope analysis","authors":"Barbara Herbstritt, Benjamin Gralher, Stefan Seeger, Michael Rinderer, Markus Weiler","doi":"10.5194/hess-27-3701-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3701-2023","url":null,"abstract":"Abstract. Methodological advancements have been made in in situ observations of water stable isotopes that have provided valuable insights into ecohydrological processes. The continuous measurement capabilities of laser-based analyzers allow for high temporal resolutions and non-destructive minimally invasive study designs of such in situ approaches. However, isotope analyzers are expensive, heavy, and require shelter and access to electrical power, which impedes many in situ assays. Therefore, we developed a new inexpensive technique to collect discrete water vapor samples in the field via diffusion-tight inflatable bags that can later be analyzed in the lab. In a series of structured experiments, we tested different procedural settings, bag materials, and closure types for diffusion tightness during storage as well as for practical handling during filling and extraction. To facilitate reuse of sampling bags, we present a conditioning procedure using ambient air as primer. In order to validate our method, direct measurements through hydrophobic in situ probes were compared to repeated measurements of vapor sampled with our bags from the same source. All steps are summarized in a detailed standard operating procedure (SOP). This procedure represents the preparation and measurement of calibration and validation vapor standards necessary for processing of unknown field-collected vapor samples in the foreseen application. By performing pertinent calibration procedures, accuracy was better than 0.4 ‰ for δ18O and 1.9 ‰ for δ2H after 1 d of storage. Our technique is particularly suitable when used in combination with minimally invasive water vapor sampling in situ probes that have already been employed for soils and tree xylem. It is an important step towards minimally invasive monitoring of stable isotope distributions and also time series in virtually undisturbed soils and trees without the need to have an analyzer in the field. It is therefore a promising tool for many applications in ecohydrology and meteorology.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135618044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-20DOI: 10.5194/hess-27-3687-2023
Meghan Halabisky, Dan Miller, Anthony J. Stewart, Amy Yahnke, Daniel Lorigan, Tate Brasel, Ludmila Monika Moskal
Abstract. Accurate, unbiased wetland inventories are critical to monitor and protect wetlands from future harm or land conversion. However, most wetland inventories are constructed through manual image interpretation or automated classification of multi-band imagery and are biased towards wetlands that are easy to directly detect in aerial and satellite imagery. Wetlands that are obscured by forest canopy, that occur ephemerally, and that have no visible standing water are, therefore, often missing from wetland maps. To aid in the detection of these cryptic wetlands, we developed the Wetland Intrinsic Potential (WIP) tool, based on a wetland-indicator framework commonly used on the ground to detect wetlands through the presence of hydrophytic vegetation, hydrology, and hydric soils. Our tool uses a random forest model with spatially explicit input variables that represent all three wetland indicators, including novel multi-scale topographic indicators that represent the processes that drive wetland formation, to derive a map of wetland probability. With the ability to include multi-scale topographic indicators that help identify cryptic wetlands, the WIP tool can identify areas conducive to wetland formation while providing a flexible approach that can be adapted to diverse landscapes. For a study area in the Hoh River watershed in western Washington, USA, classification of the output probability with a threshold of 0.5 provided an overall accuracy of 91.97 %. Compared to the National Wetlands Inventory, the classified WIP tool output identified over 2 times the wetland area and reduced errors of omission from 47.5 % to 14.1 % but increased errors of commission from 1.9 % to 10.5 %. The WIP tool is implemented as an ArcGIS toolbox using a combination of R and Python scripts.
{"title":"The Wetland Intrinsic Potential tool: mapping wetland intrinsic potential through machine learning of multi-scale remote sensing proxies of wetland indicators","authors":"Meghan Halabisky, Dan Miller, Anthony J. Stewart, Amy Yahnke, Daniel Lorigan, Tate Brasel, Ludmila Monika Moskal","doi":"10.5194/hess-27-3687-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3687-2023","url":null,"abstract":"Abstract. Accurate, unbiased wetland inventories are critical to monitor and protect wetlands from future harm or land conversion. However, most wetland inventories are constructed through manual image interpretation or automated classification of multi-band imagery and are biased towards wetlands that are easy to directly detect in aerial and satellite imagery. Wetlands that are obscured by forest canopy, that occur ephemerally, and that have no visible standing water are, therefore, often missing from wetland maps. To aid in the detection of these cryptic wetlands, we developed the Wetland Intrinsic Potential (WIP) tool, based on a wetland-indicator framework commonly used on the ground to detect wetlands through the presence of hydrophytic vegetation, hydrology, and hydric soils. Our tool uses a random forest model with spatially explicit input variables that represent all three wetland indicators, including novel multi-scale topographic indicators that represent the processes that drive wetland formation, to derive a map of wetland probability. With the ability to include multi-scale topographic indicators that help identify cryptic wetlands, the WIP tool can identify areas conducive to wetland formation while providing a flexible approach that can be adapted to diverse landscapes. For a study area in the Hoh River watershed in western Washington, USA, classification of the output probability with a threshold of 0.5 provided an overall accuracy of 91.97 %. Compared to the National Wetlands Inventory, the classified WIP tool output identified over 2 times the wetland area and reduced errors of omission from 47.5 % to 14.1 % but increased errors of commission from 1.9 % to 10.5 %. The WIP tool is implemented as an ArcGIS toolbox using a combination of R and Python scripts.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135616050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-20DOI: 10.5194/hess-27-3719-2023
Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, Julia Lutz
Abstract. Intensity–duration–frequency (IDF) statistics describing extreme rainfall intensities in Norway were analysed with the purpose of investigating how the shape of the curves is influenced by geographical conditions and local climate characteristics. To this end, principal component analysis (PCA) was used to quantify salient information about the IDF curves, and a Bayesian linear regression was used to study the dependency of the shapes on climatological and geographical information. Our analysis indicated that the shapes of IDF curves in Norway are influenced by both geographical conditions and 24 h precipitation statistics. Based on this analysis, an empirical model was constructed to predict IDF curves in locations with insufficient sub-hourly rain gauge data. Our new method was also compared with a recently proposed formula for estimating sub-daily rainfall intensity based on 24 h rain gauge data. We found that a Bayesian inference of a PCA representation of IDF curves provides a promising strategy for estimating sub-daily return levels for rainfall.
{"title":"A principal-component-based strategy for regionalisation of precipitation intensity–duration–frequency (IDF) statistics","authors":"Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, Julia Lutz","doi":"10.5194/hess-27-3719-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3719-2023","url":null,"abstract":"Abstract. Intensity–duration–frequency (IDF) statistics describing extreme rainfall intensities in Norway were analysed with the purpose of investigating how the shape of the curves is influenced by geographical conditions and local climate characteristics. To this end, principal component analysis (PCA) was used to quantify salient information about the IDF curves, and a Bayesian linear regression was used to study the dependency of the shapes on climatological and geographical information. Our analysis indicated that the shapes of IDF curves in Norway are influenced by both geographical conditions and 24 h precipitation statistics. Based on this analysis, an empirical model was constructed to predict IDF curves in locations with insufficient sub-hourly rain gauge data. Our new method was also compared with a recently proposed formula for estimating sub-daily rainfall intensity based on 24 h rain gauge data. We found that a Bayesian inference of a PCA representation of IDF curves provides a promising strategy for estimating sub-daily return levels for rainfall.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135618368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.5194/hess-27-3663-2023
Thedini Asali Peiris, Petra Döll
Abstract. Almost no hydrological model takes into account that changes in evapotranspiration are affected by how vegetation responds to changing CO2 and climate. This severely limits their ability to quantify the impact of climate change on evapotranspiration and, thus, water resources. As the simulation of vegetation responses is both complex and very uncertain, we recommend a simple approach to considering (in climate change impact studies with hydrological models) the uncertainty that the vegetation response causes with respect to the estimation of future potential evapotranspiration (PET). To quantify this uncertainty in a simple manner, we propose running the hydrological model in two variants: with its standard PET approach and with a modified approach to compute PET. In the case of PET equations containing stomatal conductance, the modified approach can be implemented by adjusting the conductance. We introduce a modified approach for hydrological models that computes PET as a function of net radiation and temperature only, i.e., with the Priestley–Taylor (PT) equation. The new PT-MA approach is based on the work of Milly and Dunne (2016) (MD), who compared the change in non-water-stressed actual evapotranspiration (NWSAET) as computed by an ensemble of global climate models (GCMs), which simulate vegetation response as well as interactions between the atmosphere and the land surface, with various methods to compute PET change. Based on this comparison, MD proposed estimating the impact of climate change on PET as a function of only the change in net energy input at the land surface. PT-MA retains the impact of temperature on daily to interannual as well as spatial PET variations but removes the impact of the long-term temperature trend on PET such that long-term changes in future PET are driven by changes in net radiation only. We implemented PT-MA in the global hydrological model WaterGAP 2.2d and computed daily time series of PET between 1901 and 2099 using the bias-adjusted output of four GCMs. Increases in GCM-derived NWSAET between the end of the 20th and the end of the 21st century for Representative Concentration Pathway 8.5 (RCP8.5) are simulated well by WaterGAP if PT-MA is applied but are severely overestimated with the standard PT method. Application of PT-MA in WaterGAP results in smaller future decreases or larger future increases in renewable water resources (expressed as the variable RWR) compared with the standard PT method, except in a small number of grid cells where increased inflow from upstream areas due to increased upstream runoff leads to enhanced evapotranspiration from surface water bodies or irrigated fields. On about 20 % of the global land area, PT-MA leads to an increase in RWR that is more than 20 % higher than in the case of standard PT, while on more than 10 % of the global land area, the projected RWR decrease is reduced by more than 20 %. While the modified approach to compute PET is likely to avoid the o
{"title":"Improving the quantification of climate change hazards by hydrological models: a simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration","authors":"Thedini Asali Peiris, Petra Döll","doi":"10.5194/hess-27-3663-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3663-2023","url":null,"abstract":"Abstract. Almost no hydrological model takes into account that changes in evapotranspiration are affected by how vegetation responds to changing CO2 and climate. This severely limits their ability to quantify the impact of climate change on evapotranspiration and, thus, water resources. As the simulation of vegetation responses is both complex and very uncertain, we recommend a simple approach to considering (in climate change impact studies with hydrological models) the uncertainty that the vegetation response causes with respect to the estimation of future potential evapotranspiration (PET). To quantify this uncertainty in a simple manner, we propose running the hydrological model in two variants: with its standard PET approach and with a modified approach to compute PET. In the case of PET equations containing stomatal conductance, the modified approach can be implemented by adjusting the conductance. We introduce a modified approach for hydrological models that computes PET as a function of net radiation and temperature only, i.e., with the Priestley–Taylor (PT) equation. The new PT-MA approach is based on the work of Milly and Dunne (2016) (MD), who compared the change in non-water-stressed actual evapotranspiration (NWSAET) as computed by an ensemble of global climate models (GCMs), which simulate vegetation response as well as interactions between the atmosphere and the land surface, with various methods to compute PET change. Based on this comparison, MD proposed estimating the impact of climate change on PET as a function of only the change in net energy input at the land surface. PT-MA retains the impact of temperature on daily to interannual as well as spatial PET variations but removes the impact of the long-term temperature trend on PET such that long-term changes in future PET are driven by changes in net radiation only. We implemented PT-MA in the global hydrological model WaterGAP 2.2d and computed daily time series of PET between 1901 and 2099 using the bias-adjusted output of four GCMs. Increases in GCM-derived NWSAET between the end of the 20th and the end of the 21st century for Representative Concentration Pathway 8.5 (RCP8.5) are simulated well by WaterGAP if PT-MA is applied but are severely overestimated with the standard PT method. Application of PT-MA in WaterGAP results in smaller future decreases or larger future increases in renewable water resources (expressed as the variable RWR) compared with the standard PT method, except in a small number of grid cells where increased inflow from upstream areas due to increased upstream runoff leads to enhanced evapotranspiration from surface water bodies or irrigated fields. On about 20 % of the global land area, PT-MA leads to an increase in RWR that is more than 20 % higher than in the case of standard PT, while on more than 10 % of the global land area, the projected RWR decrease is reduced by more than 20 %. While the modified approach to compute PET is likely to avoid the o","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135780566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.5194/hess-27-3643-2023
Kaltrina Maloku, Benoit Hingray, Guillaume Evin
Abstract. Analytical multiplicative random cascades (MRCs) are widely used for the temporal disaggregation of coarse-resolution precipitation time series. This class of models applies scaling models to represent the dependence of the cascade generator on the temporal scale and the precipitation intensity. Although determinant, the dependence on the external precipitation pattern is usually disregarded in the analytical scaling models. Our work presents a unified MRC modelling framework that allows the cascade generator to depend in a continuous way on the temporal scale, precipitation intensity and a so-called precipitation asymmetry index. Different MRC configurations are compared for 81 locations in Switzerland with contrasted climates. The added value of the dependence of the MRC on the temporal scale appears to be unclear, unlike what was suggested in previous works. Introducing the precipitation asymmetry dependence into the model leads to a drastic improvement in model performance for all statistics related to precipitation temporal persistence (wet–dry transition probabilities, lag-n autocorrelation coefficients, lengths of dry–wet spells). Accounting for precipitation asymmetry seems to solve this important limitation of previous MRCs. The model configuration that only accounts for the dependence on precipitation intensity and asymmetry is highly parsimonious, with only five parameters, and provides adequate performances for all locations, seasons and temporal resolutions. The spatial coherency of the parameter estimates indicates a real potential for regionalisation and for further application to any location in Switzerland.
{"title":"Accounting for precipitation asymmetry in a multiplicative random cascade disaggregation model","authors":"Kaltrina Maloku, Benoit Hingray, Guillaume Evin","doi":"10.5194/hess-27-3643-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3643-2023","url":null,"abstract":"Abstract. Analytical multiplicative random cascades (MRCs) are widely used for the temporal disaggregation of coarse-resolution precipitation time series. This class of models applies scaling models to represent the dependence of the cascade generator on the temporal scale and the precipitation intensity. Although determinant, the dependence on the external precipitation pattern is usually disregarded in the analytical scaling models. Our work presents a unified MRC modelling framework that allows the cascade generator to depend in a continuous way on the temporal scale, precipitation intensity and a so-called precipitation asymmetry index. Different MRC configurations are compared for 81 locations in Switzerland with contrasted climates. The added value of the dependence of the MRC on the temporal scale appears to be unclear, unlike what was suggested in previous works. Introducing the precipitation asymmetry dependence into the model leads to a drastic improvement in model performance for all statistics related to precipitation temporal persistence (wet–dry transition probabilities, lag-n autocorrelation coefficients, lengths of dry–wet spells). Accounting for precipitation asymmetry seems to solve this important limitation of previous MRCs. The model configuration that only accounts for the dependence on precipitation intensity and asymmetry is highly parsimonious, with only five parameters, and provides adequate performances for all locations, seasons and temporal resolutions. The spatial coherency of the parameter estimates indicates a real potential for regionalisation and for further application to any location in Switzerland.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135888526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.5194/hess-27-3621-2023
Ibrahim Nourein Mohammed, Elkin Giovanni Romero Bustamante, John Dennis Bolten, Everett James Nelson
Abstract. The National Aeronautics and Space Administration (NASA) has launched a new initiative, the Open-Source Science Initiative (OSSI), to enable and support science towards openness. The OSSI supports open-source software development and dissemination. In this work, we present NASAaccess, which is an open-source software package and web-based environmental modeling application for earth observation data accessing, reformatting, and presenting quantitative data products. The main objective of developing the NASAaccess platform is to facilitate exploration, modeling, and understanding of earth data for scientists, stakeholders, and concerned citizens whose objectives align with the new OSSI goals. The NASAaccess platform is available as software packages (i.e., the R and conda packages) as well as an interactive-format web-based environmental modeling application for earth observation data developed with Tethys Platform. NASAaccess has been envisioned as lowering the technical barriers and simplifying the process of accessing scalable distributed computing resources and leveraging additional software for data and computationally intensive modeling frameworks. Specifically, NASAaccess has been developed to meet the need for seamless earth observation remote-sensing and climate data ingestion into various hydrological modeling frameworks. Moreover, NASAaccess is also contributing to keeping interested parties and stakeholders engaged with environmental modeling, accessing the information available in various remote-sensing products. NASAaccess' current capabilities cover various NASA datasets and products that include the Global Precipitation Measurement (GPM) data products, the Global Land Data Assimilation System (GLDAS) land surface states and fluxes, and the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) Coupled Model Intercomparison Project Phase 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate change dataset products.
{"title":"Technical note: NASAaccess – a tool for access, reformatting, and visualization of remotely sensed earth observation and climate data","authors":"Ibrahim Nourein Mohammed, Elkin Giovanni Romero Bustamante, John Dennis Bolten, Everett James Nelson","doi":"10.5194/hess-27-3621-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3621-2023","url":null,"abstract":"Abstract. The National Aeronautics and Space Administration (NASA) has launched a new initiative, the Open-Source Science Initiative (OSSI), to enable and support science towards openness. The OSSI supports open-source software development and dissemination. In this work, we present NASAaccess, which is an open-source software package and web-based environmental modeling application for earth observation data accessing, reformatting, and presenting quantitative data products. The main objective of developing the NASAaccess platform is to facilitate exploration, modeling, and understanding of earth data for scientists, stakeholders, and concerned citizens whose objectives align with the new OSSI goals. The NASAaccess platform is available as software packages (i.e., the R and conda packages) as well as an interactive-format web-based environmental modeling application for earth observation data developed with Tethys Platform. NASAaccess has been envisioned as lowering the technical barriers and simplifying the process of accessing scalable distributed computing resources and leveraging additional software for data and computationally intensive modeling frameworks. Specifically, NASAaccess has been developed to meet the need for seamless earth observation remote-sensing and climate data ingestion into various hydrological modeling frameworks. Moreover, NASAaccess is also contributing to keeping interested parties and stakeholders engaged with environmental modeling, accessing the information available in various remote-sensing products. NASAaccess' current capabilities cover various NASA datasets and products that include the Global Precipitation Measurement (GPM) data products, the Global Land Data Assimilation System (GLDAS) land surface states and fluxes, and the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) Coupled Model Intercomparison Project Phase 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate change dataset products.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.5194/hess-27-3601-2023
Christoph Neukum, Angela Morales-Santos, Melanie Ronelngar, Aminu Bala, Sara Vassolo
Abstract. The Lake Chad basin, located in the centre of northern Africa, is characterized by strong climate seasonality with a pronounced short annual precipitation period and high potential evapotranspiration. Groundwater is an essential source for drinking-water supply, as well as for agriculture and groundwater-related ecosystems. Thus, assessment of groundwater recharge is very important although also difficult because of the strong effects of evaporation and transpiration, as well as the limited available data. A simple, generalized approach, which requires only limited field data, freely available remote sensing data, and well-established concepts and models, is tested for assessing groundwater recharge in the southern part of the basin. This work uses the FAO dual-Kc concept to estimate E and T coefficients at six locations that differ in soil texture, climate, and vegetation conditions. Measured values of soil water content and chloride concentrations along vertical soil profiles together with different scenarios for E and T partitioning and a Bayesian calibration approach are used to numerically simulate water flow and chloride transport using Hydrus-1D. Average groundwater recharge rates and the associated model uncertainty at the six locations are assessed for the 2003–2016 time period. Annual groundwater recharge varies between 6 and 93 mm and depends strongly on soil texture and related water retention and on vegetation. Interannual variability of groundwater recharge is generally greater than the uncertainty of the simulated groundwater recharge.
{"title":"Modelling groundwater recharge, actual evaporation, and transpiration in semi-arid sites of the Lake Chad basin: the role of soil and vegetation in groundwater recharge","authors":"Christoph Neukum, Angela Morales-Santos, Melanie Ronelngar, Aminu Bala, Sara Vassolo","doi":"10.5194/hess-27-3601-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3601-2023","url":null,"abstract":"Abstract. The Lake Chad basin, located in the centre of northern Africa, is characterized by strong climate seasonality with a pronounced short annual precipitation period and high potential evapotranspiration. Groundwater is an essential source for drinking-water supply, as well as for agriculture and groundwater-related ecosystems. Thus, assessment of groundwater recharge is very important although also difficult because of the strong effects of evaporation and transpiration, as well as the limited available data. A simple, generalized approach, which requires only limited field data, freely available remote sensing data, and well-established concepts and models, is tested for assessing groundwater recharge in the southern part of the basin. This work uses the FAO dual-Kc concept to estimate E and T coefficients at six locations that differ in soil texture, climate, and vegetation conditions. Measured values of soil water content and chloride concentrations along vertical soil profiles together with different scenarios for E and T partitioning and a Bayesian calibration approach are used to numerically simulate water flow and chloride transport using Hydrus-1D. Average groundwater recharge rates and the associated model uncertainty at the six locations are assessed for the 2003–2016 time period. Annual groundwater recharge varies between 6 and 93 mm and depends strongly on soil texture and related water retention and on vegetation. Interannual variability of groundwater recharge is generally greater than the uncertainty of the simulated groundwater recharge.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136013866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.5194/hess-27-3565-2023
Jessica A. Eisma, Gerrit Schoups, Jeffrey C. Davids, Nick van de Giesen
Abstract. High-quality citizen science data can be instrumental in advancing science toward new discoveries and a deeper understanding of under-observed phenomena. However, the error structure of citizen scientist (CS) data must be well-defined. Within a citizen science program, the errors in submitted observations vary, and their occurrence may depend on CS-specific characteristics. This study develops a graphical Bayesian inference model of error types in CS data. The model assumes that (1) each CS observation is subject to a specific error type, each with its own bias and noise, and (2) an observation's error type depends on the static error community of the CS, which in turn relates to characteristics of the CS submitting the observation. Given a set of CS observations and corresponding ground-truth values, the model can be calibrated for a specific application, yielding (i) number of error types and error communities, (ii) bias and noise for each error type, (iii) error distribution of each error community, and (iv) the single error community to which each CS belongs. The model, applied to Nepal CS rainfall observations, identifies five error types and sorts CSs into four static, model-inferred communities. In the case study, 73 % of CSs submitted data with errors in fewer than 5 % of their observations. The remaining CSs submitted data with unit, meniscus, unknown, and outlier errors. A CS's assigned community, coupled with model-inferred error probabilities, can identify observations that require verification and provides an opportunity for targeted re-training of CSs based on mistake tendencies.
{"title":"A Bayesian model for quantifying errors in citizen science data: application to rainfall observations from Nepal","authors":"Jessica A. Eisma, Gerrit Schoups, Jeffrey C. Davids, Nick van de Giesen","doi":"10.5194/hess-27-3565-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3565-2023","url":null,"abstract":"Abstract. High-quality citizen science data can be instrumental in advancing science toward new discoveries and a deeper understanding of under-observed phenomena. However, the error structure of citizen scientist (CS) data must be well-defined. Within a citizen science program, the errors in submitted observations vary, and their occurrence may depend on CS-specific characteristics. This study develops a graphical Bayesian inference model of error types in CS data. The model assumes that (1) each CS observation is subject to a specific error type, each with its own bias and noise, and (2) an observation's error type depends on the static error community of the CS, which in turn relates to characteristics of the CS submitting the observation. Given a set of CS observations and corresponding ground-truth values, the model can be calibrated for a specific application, yielding (i) number of error types and error communities, (ii) bias and noise for each error type, (iii) error distribution of each error community, and (iv) the single error community to which each CS belongs. The model, applied to Nepal CS rainfall observations, identifies five error types and sorts CSs into four static, model-inferred communities. In the case study, 73 % of CSs submitted data with errors in fewer than 5 % of their observations. The remaining CSs submitted data with unit, meniscus, unknown, and outlier errors. A CS's assigned community, coupled with model-inferred error probabilities, can identify observations that require verification and provides an opportunity for targeted re-training of CSs based on mistake tendencies.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135142219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Assessing eutrophication in lakes is of key importance, as this parameter constitutes a major aquatic ecosystem integrity indicator. The trophic state index (TSI), which is widely used to quantify eutrophication, is a universal paradigm in the scientific literature. In this study, a methodological framework is proposed for quantifying and mapping TSI using the Sentinel Multispectral Imager sensor and fieldwork samples. The first step of the methodology involves the implementation of stepwise multiple regression analysis of the available TSI dataset to find some band ratios, such as blue/red, green/red and red/red, which are sensitive to lake TSI. Trained with in situ measured TSI and match-up Sentinel images, we established the XGBoost of machine learning approaches to estimate TSI, with good agreement (R2= 0.87, slope = 0.85) and fewer errors (MAE = 3.15 and RMSE = 4.11). Additionally, we discussed the transferability and applications of XGBoost in three lake classifications: water quality, absorption contribution and reflectance spectra types. We selected XGBoost to map TSI in 2019–2020 with good-quality Sentinel-2 Level-1C images embedded in the ESA to examine the spatiotemporal variations of the lake trophic state. In a large-scale observation, 10 m TSI products from 555 lakes in China facing eutrophication and unbalanced spatial patterns associated with lake basin characteristics, climate and anthropogenic activities were investigated. The methodological framework proposed herein could serve as a useful resource for continuous, long-term and large-scale monitoring of lake aquatic ecosystems, supporting sustainable water resource management.
{"title":"Remote quantification of the trophic status of Chinese lakes","authors":"Sijia Li, Shiqi Xu, Kaishan Song, Tiit Kutser, Zhidan Wen, Ge Liu, Yingxin Shang, Lili Lyu, Hui Tao, Xiang Wang, Lele Zhang, Fangfang Chen","doi":"10.5194/hess-27-3581-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3581-2023","url":null,"abstract":"Abstract. Assessing eutrophication in lakes is of key importance, as this parameter constitutes a major aquatic ecosystem integrity indicator. The trophic state index (TSI), which is widely used to quantify eutrophication, is a universal paradigm in the scientific literature. In this study, a methodological framework is proposed for quantifying and mapping TSI using the Sentinel Multispectral Imager sensor and fieldwork samples. The first step of the methodology involves the implementation of stepwise multiple regression analysis of the available TSI dataset to find some band ratios, such as blue/red, green/red and red/red, which are sensitive to lake TSI. Trained with in situ measured TSI and match-up Sentinel images, we established the XGBoost of machine learning approaches to estimate TSI, with good agreement (R2= 0.87, slope = 0.85) and fewer errors (MAE = 3.15 and RMSE = 4.11). Additionally, we discussed the transferability and applications of XGBoost in three lake classifications: water quality, absorption contribution and reflectance spectra types. We selected XGBoost to map TSI in 2019–2020 with good-quality Sentinel-2 Level-1C images embedded in the ESA to examine the spatiotemporal variations of the lake trophic state. In a large-scale observation, 10 m TSI products from 555 lakes in China facing eutrophication and unbalanced spatial patterns associated with lake basin characteristics, climate and anthropogenic activities were investigated. The methodological framework proposed herein could serve as a useful resource for continuous, long-term and large-scale monitoring of lake aquatic ecosystems, supporting sustainable water resource management.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}