scholarly journals A distributed soil moisture, temperature and infiltrometer dataset for permeable pavements and green spaces

2019 ◽  
Author(s):  
Axel Schaffitel ◽  
Tobias Schuetz ◽  
Markus Weiler
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Field Capacity ◽  
Data Repository ◽  
High Temporal Resolution ◽  
Urban Water ◽  
Energy Fluxes ◽  
Infiltration Capacity ◽  
Permeable Pavements ◽  
Measuring Period

Abstract. Knowledge on water and energy fluxes is a key for urban planning and design. Nevertheless, hydrological data for urban environments is sparse and as a result, many processes are still poorly understood and thus inadequately represented within models. We contribute to reduce this shortcoming by providing a dataset, which includes time series of soil moisture and soil temperature measured underneath 18 different permeable pavements (PPs) and 4 urban greenspaces located within the city of Freiburg (Germany). Time series were recorded with a high temporal resolution of 10 min with a total of 65 individual soil moisture sensors and cover a measuring period of 2 entire years (Nov. 2016 – Oct. 2018). The recorded time series contain valuable information on the soil hydrological behavior and demonstrate the effect of surface properties and surrounding urban structures on soil temperatures. In addition, we performed double-ring infiltration experiments, which in combination with the soil moisture measurements yielded soil hydrological parameters for the PPs including porosity, field capacity and infiltration capacity. We present this unique dataset, which is a valuable source of information for studying urban water and energy cycles. We encourage its usage in various ways e.g. for model calibration and validation purposes, to study thermal regimes of cities and to derive urban water and energy fluxes. The dataset is freely available at the FreiDok plus data repository at https://freidok.uni-freiburg.de/data/149321 and https://doi.org/10.6094/UNIFR/149321 (Schaffitel et al., 2019).

scholarly journals A distributed soil moisture, temperature and infiltrometer dataset for permeable pavements and green spaces

2020 ◽  
Vol 12 (1) ◽  
pp. 501-517
Author(s):  
Axel Schaffitel ◽  
Tobias Schuetz ◽  
Markus Weiler
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Green Spaces ◽  
Data Repository ◽  
High Temporal Resolution ◽  
Urban Water ◽  
Energy Fluxes ◽  
Infiltration Capacity ◽  
Urban Green Spaces ◽  
Permeable Pavements

Abstract. Knowledge of water and energy fluxes is key for urban planning and design. Nevertheless, hydrological data from urban environments are sparse, and, as a result, many processes are still poorly understood and thus inadequately represented within models. We contribute to reducing this shortfall by providing a dataset that includes time series of soil moisture and soil temperature measured underneath 18 different permeable pavements (PPs) and 4 urban green spaces located within the city of Freiburg (Germany). Time series were recorded with a high temporal resolution of 10 min using a total of 65 individual soil moisture sensors and covering a measurement period of 2 years (November 2016–October 2018). The recorded time series contain valuable information on the soil hydrological behavior of PPs and demonstrate the effect of surface properties and surrounding urban structures on soil temperatures. In addition, we performed double-ring infiltration experiments, which in combination with the soil moisture measurements yielded soil hydrological parameters for the PPs, including porosity, field capacity and infiltration capacity. We present this unique dataset, which is a valuable source of information for studying urban water and energy cycles. We encourage its usage in various ways, e.g., for model calibration and validation purposes, study of thermal regimes of cities, and derivation of urban water and energy fluxes. The dataset is freely available from the FreiDok plus data repository at https://freidok.uni-freiburg.de/data/151573 and https://doi.org/10.6094/UNIFR/151573 (Schaffitel et al., 2019).

scholarly journals Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes

2017 ◽  
Vol 21 (1) ◽  
pp. 345-355 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Stylianos Georgiadis ◽  
Ida Bülow Gregersen ◽  
Karsten Arnbjerg-Nielsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Future Climate ◽  
High Temporal Resolution ◽  
Urban Water ◽  
Precipitation Time Series ◽  
Event Scale ◽  
The Impact

Abstract. Urban water infrastructure has very long planning horizons, and planning is thus very dependent on reliable estimates of the impacts of climate change. Many urban water systems are designed using time series with a high temporal resolution. To assess the impact of climate change on these systems, similarly high-resolution precipitation time series for future climate are necessary. Climate models cannot at their current resolutions provide these time series at the relevant scales. Known methods for stochastic downscaling of climate change to urban hydrological scales have known shortcomings in constructing realistic climate-changed precipitation time series at the sub-hourly scale. In the present study we present a deterministic methodology to perturb historical precipitation time series at the minute scale to reflect non-linear expectations to climate change. The methodology shows good skill in meeting the expectations to climate change in extremes at the event scale when evaluated at different timescales from the minute to the daily scale. The methodology also shows good skill with respect to representing expected changes of seasonal precipitation. The methodology is very robust against the actual magnitude of the expected changes as well as the direction of the changes (increase or decrease), even for situations where the extremes are increasing for seasons that in general should have a decreasing trend in precipitation. The methodology can provide planners with valuable time series representing future climate that can be used as input to urban hydrological models and give better estimates of climate change impacts on these systems.

scholarly journals Webcam network and image database for studies of phenological changes of vegetation and snow cover in Finland, image time series from 2014–2016

2017 ◽  
Author(s):  
Mikko Peltoniemi ◽  
Mika Aurela ◽  
Kristin Böttcher ◽  
Pasi Kolari ◽  
John Loehr ◽  
...  
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Low Cost ◽  
Time Lapse ◽  
Observation Time ◽  
Data Repository ◽  
High Temporal Resolution ◽  
Digital Surveillance ◽  
Spatial Coverage ◽  
The Status

Abstract. In recent years, monitoring of the status of ecosystems using low-cost web (IP) or time lapse cameras has received wide interest. Networked cameras can provide information about snow cover and vegetation status with a broad spatial coverage and high temporal resolution, and serve as ground truths to earth observations, and be useful for gap-filling of cloudy areas in earth observation time series. Networked cameras can also play an important role in supplementing laborious phenological field surveys and citizen-science projects, which also suffer from observer-dependent observation bias. We established a network of digital surveillance cameras for automated monitoring of phenological activity of vegetation and snow cover in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1–3 cameras. Here, we document the network, basic camera information and access to images (see, https://doi.org/10.5281/zenodo.777952) in the permanent data repository (https://www.zenodo.org/communities/phenology_camera/). Individual DOI-referenced image time series from cameras are consisted of half-hourly images collected between 2014 and 2016. Additionally, we present example colour index time series derived from image time series from two contrasting sites.

scholarly journals Fluxes from Soil Moisture Measurements (FluSM v1.0). A Data-driven Water Balance Framework for Permeable Pavements

2020 ◽  
Author(s):  
Axel Schaffitel ◽  
Tobias Schuetz ◽  
Markus Weiler
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Water Cycle ◽  
Input Parameter ◽  
Data Driven ◽  
Minor Role ◽  
Water Fluxes ◽  
Parameter Uncertainties ◽  
Infiltration Capacity ◽  
Permeable Pavements

Abstract. Water fluxes at the soil-atmosphere interface are a key information for studying the terrestrial water cycle. However, measuring and modelling water fluxes in the vadose zone poses great challenges. While direct measurements require costly lysimeters, common soil hydrologic models rely on a correct parametrization, a correct representation of the involved processes and on the selection of correct initial and boundary conditions. In contrast to lysimeter measurements, soil moisture measurements are relatively cheap and easy to perform. Using such measurements, data-driven approaches offer the possibility to derive water fluxes directly. Here we present FluSM (Fluxes from Soil Moisture measurements), which is a simple, parsimonious and robust data-driven water balancing framework. FluSM requires only one single input parameter (the infiltration capacity) and is especially valuable for cases where the application of Richards based models is critical. Since Permeable Pavements (PPs) present such a case, we apply FluSM on a recently published soil moisture dataset to obtain the water balance of 15 different PPs over a period of two years. Consistent with findings from previous studies, our results show that vertical drainage dominates the water balance of PPs, while surface runoff plays only a minor role. An additional uncertainty analysis demonstrates the ability of the FluSM-approach for water balance studies, since input and parameter uncertainties have only small effects on the characteristics of the derived water balances. Due to the lack of data on the hydrologic behavior of PPs under field conditions, our results are of special interest for urban hydrology.

scholarly journals Webcam network and image database for studies of phenological changes of vegetation and snow cover in Finland, image time series from 2014 to 2016

2018 ◽  
Vol 10 (1) ◽  
pp. 173-184 ◽  
Author(s):  
Mikko Peltoniemi ◽  
Mika Aurela ◽  
Kristin Böttcher ◽  
Pasi Kolari ◽  
John Loehr ◽  
...  
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Low Cost ◽  
Time Lapse ◽  
Observation Time ◽  
Data Repository ◽  
High Temporal Resolution ◽  
Digital Surveillance ◽  
Spatial Coverage ◽  
The Status

Abstract. In recent years, monitoring of the status of ecosystems using low-cost web (IP) or time lapse cameras has received wide interest. With broad spatial coverage and high temporal resolution, networked cameras can provide information about snow cover and vegetation status, serve as ground truths to Earth observations and be useful for gap-filling of cloudy areas in Earth observation time series. Networked cameras can also play an important role in supplementing laborious phenological field surveys and citizen science projects, which also suffer from observer-dependent observation bias. We established a network of digital surveillance cameras for automated monitoring of phenological activity of vegetation and snow cover in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1–3 cameras. Here, we document the network, basic camera information and access to images in the permanent data repository (http://www.zenodo.org/communities/phenology_camera/). Individual DOI-referenced image time series consist of half-hourly images collected between 2014 and 2016 (https://doi.org/10.5281/zenodo.1066862). Additionally, we present an example of a colour index time series derived from images from two contrasting sites.

scholarly journals Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes

2016 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Stylianos Georgiadis ◽  
Ida Bülow Gregersen ◽  
Karsten Arnbjerg-Nielsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Future Climate ◽  
High Temporal Resolution ◽  
Urban Water ◽  
Precipitation Time Series ◽  
Event Scale ◽  
The Impact

Abstract. Urban water infrastructure has very long planning horizons and planning is thus very dependent on reliable estimates on the impacts of climate change. Many urban water systems are designed using time series with a high temporal resolution. To assess the impact of climate change on these systems similarly high resolution precipitation time series for future climate are necessary. Climate models cannot at their current resolutions provide these time series at the relevant scales. Known methods for stochastic downscaling of climate change to urban hydrological scales have known shortcomings in constructing realistic climate changed precipitation time series at the sub-hourly scale. In the present study we present a deterministic methodology to perturb historical precipitation time series at minute scale to reflect non-linear expectations to climate change. The methodology shows good skill in meeting the expectations to climate change of extremes at event scale when evaluated at different timescales from the minute to the daily scale. The methodology also shows good skill with respect to representing expected changes to seasonal precipitation. The methodology is very robust to the actual magnitude of the expected changes as well as the direction of the changes (increase/decrease) even for situations where the extremes are increasing for seasons that in general should have a decreasing trend in precipitation. The methodology can provide planners with valuable time series representing future climate that can be used as input to urban hydrological models and give better estimates of climate change impacts on these systems.

Soil Moisture Deficits in South-Central Sweden

1989 ◽  
Vol 20 (2) ◽  
pp. 109-122 ◽  
Author(s):  
Lotta Andersson
Keyword(s):  
Soil Moisture ◽  
Field Capacity ◽  
Interannual Variations ◽  
Neutron Probe ◽  
South Central ◽  
Normal Ranges ◽  
Moisture Fluxes ◽  
In The Beginning ◽  
Probe Measurements ◽  
Different Parts

Some commonly used assumptions about climatically induced soil moisture fluxes within years and between different parts of a region were challenged with the help of a conceptual soil moisture model. The model was optimised against neutron probe measurements from forest and grassland sites. Five 10 yrs and one 105 yrs long climatic records, from the province of Östergötland, situated in south-central Sweden, were used as driving variables. It was concluded that some of the tested assumptions should not be taken for granted. Among these were the beliefs that interannual variations of soil moisture contents can be neglected in the beginning of the hydrological year and that soils usually are filled up to field capacity after the autumn recharge. The calculated climatic induced dryness was estimated to be rather insensitive to the choice of climatic stations within the region. Monthly ranges of soil moisture deficits (1883-1987) were shown to be skewed and it is therefore recommended to use medians and standard deviations in statistical analyses of “normal” ranges of soil moisture deficits.

scholarly journals Assessment of Automatically Monitored Water Levels and Water Quality Indicators in Rivers with Different Hydromorphological Conditions and Pollution Levels in Greece

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 86
Author(s):  
Angeliki Mentzafou ◽  
George Varlas ◽  
Anastasios Papadopoulos ◽  
Georgios Poulis ◽  
Elias Dimitriou
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Quality Indicators ◽  
High Frequency ◽  
Automatic Monitoring ◽  
Water Levels ◽  
High Temporal Resolution ◽  
Water Quality Indicators ◽  
Pollution Levels ◽  
Riverine Ecosystems

Water resources, especially riverine ecosystems, are globally under qualitative and quantitative degradation due to human-imposed pressures. High-temporal-resolution data obtained from automatic stations can provide insights into the processes that link catchment hydrology and streamwater chemistry. The scope of this paper was to investigate the statistical behavior of high-frequency measurements at sites with known hydromorphological and pollution pressures. For this purpose, hourly time series of water levels and key water quality indicators (temperature, electric conductivity, and dissolved oxygen concentrations) collected from four automatic monitoring stations under different hydromorphological conditions and pollution pressures were statistically elaborated. Based on the results, the hydromorphological conditions and pollution pressures of each station were confirmed to be reflected in the results of the statistical analysis performed. It was proven that the comparative use of the statistics and patterns of the water level and quality high-frequency time series could be used in the interpretation of the current site status as well as allowing the detection of possible changes. This approach can be used as a tool for the definition of thresholds, and will contribute to the design of management and restoration measures for the most impacted areas.

scholarly journals Responses of Hydrological Processes under Different Shared Socioeconomic Pathway Scenarios in the Huaihe River Basin, China

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1053
Author(s):  
Yuan Yao ◽  
Wei Qu ◽  
Jingxuan Lu ◽  
Hui Cheng ◽  
Zhiguo Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Climate Models ◽  
Global Climate Models ◽  
Hydrological Response ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Infiltration Capacity ◽  
Change Analysis ◽  
Average Annual Runoff

The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides more scenarios and reliable climate change results for improving the accuracy of future hydrological parameter change analysis. This study uses five CMIP6 global climate models (GCMs) to drive the variable infiltration capacity (VIC) model, and then simulates the hydrological response of the upper and middle Huaihe River Basin (UMHRB) under future shared socioeconomic pathway scenarios (SSPs). The results show that the five-GCM ensemble improves the simulation accuracy compared to a single model. The climate over the UMHRB likely becomes warmer. The general trend of future precipitation is projected to increase, and the increased rates are higher in spring and winter than in summer and autumn. Changes in annual evapotranspiration are basically consistent with precipitation, but seasonal evapotranspiration shows different changes (0–18%). The average annual runoff will increase in a wavelike manner, and the change patterns of runoff follow that of seasonal precipitation. Changes in soil moisture are not obvious, and the annual soil moisture increases slightly. In the intrayear process, soil moisture decreases slightly in autumn. The research results will enhance a more realistic understanding of the future hydrological response of the UMHRB and assist decision-makers in developing watershed flood risk-management measures and water and soil conservation plans.

scholarly journals Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

2021 ◽  
Vol 13 (4) ◽  
pp. 554
Author(s):  
A. A. Masrur Ahmed ◽  
Ravinesh C Deo ◽  
Nawin Raj ◽  
Afshin Ghahramani ◽  
Qi Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Climate Index ◽  
Climate Indices ◽  
High Temporal Resolution ◽  
Synoptic Scale ◽  
Time Step ◽  
Gated Recurrent Unit

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.

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