National-scale analysis of future river flow and soil moisture droughts: potential changes in drought characteristics

Abstract As urban areas and land-use constraints grow, there is increasing interest in utilizing urban spaces for food production. Several studies have uncovered significant potential for urban growing to supplement production of fruit and vegetables, focusing on one or two cities as case studies, whilst others have assessed the global scale potential. Here, we provide a national-scale analysis of the horticultural production potential of urban green spaces, which is a relevant scale for agri-food and urban development policy making using Great Britain (GB) as a case study. Urban green spaces available for horticultural production across GB are identified and potential yields quantified based on three production options. The distribution of urban green spaces within 26 urban towns and cities across GB are then examined to understand the productive potential compared to their total extent and populations. Urban green spaces in GB, at their upper limit, have the capacity to support production that is 8x greater than current domestic production of fruit and vegetables. This amounts to 38% of current domestic production and imports combined, or >400% if exotic fruits and vegetables less suited to GB growing conditions are excluded. Most urban green spaces nationally are found to fall within a small number of categories, with private residential gardens and amenity spaces making up the majority of space. By examining towns and cities across GB in further detail, we find that the area of green space does not vary greatly between urban conurbations of different sizes, and all are found to have substantial potential to meet the dietary needs of the local urban population. This study highlights that national policies can be suitably developed to support urban agriculture and that making use of urban green spaces for food production could help to enhance the resilience of the national-scale food system to shocks in import pathways, or disruptions to domestic production and distribution.

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Association between unemployment, income, education level, population size and air pollution in Czech cities: Evidence for environmental inequality? A pilot national scale analysis

Health & Place ◽

10.1016/j.healthplace.2012.04.011 ◽

2012 ◽

Vol 18 (5) ◽

pp. 1110-1114 ◽

Cited By ~ 23

Author(s):

Martin Branis ◽

Martina Linhartova

Keyword(s):

Air Pollution ◽

Population Size ◽

Education Level ◽

Level Population ◽

Scale Analysis ◽

Environmental Inequality ◽

National Scale

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Irrigation in JULES Land-Only Simulations over South and East Asia

10.5194/egusphere-egu21-12213 ◽

2021 ◽

Author(s):

Markus Todt ◽

Pier Luigi Vidale ◽

Patrick C. McGuire ◽

Omar V. Müller

Keyword(s):

Soil Moisture ◽

East Asia ◽

Land Surface ◽

River Flow ◽

Heat Fluxes ◽

Global Climate Models ◽

Irrigated Agriculture ◽

Irrigation Season ◽

Irrigation Scheme ◽

The Impact

Capturing soil moisture-atmosphere feedbacks in a weather or climate model requires realistic simulation of various land surface processes. However, irrigation and other water management methods are still missing in most global climate models today, despite irrigated agriculture being the dominant land use in parts of Asia. In this study, we test the irrigation scheme available in the land model JULES (Joint UK Land Environment Simulator) by running land-only simulations over South and East Asia driven by WFDEI (WATCH Forcing Data ERA-Interim) forcing data. Irrigation in JULES is applied on a daily basis by replenishing soil moisture in the upper soil layers to field capacity, and we use a version of the irrigation scheme that extracts water for irrigation from groundwater and rivers, which physically limits the amount of irrigation that can be applied. We prescribe irrigation for C3 grasses in order to simulate the effects of agriculture, albeit retaining the simpler, widely used 5-PFT (plant functional type) configuration in JULES. Irrigation generally increases soil moisture and evapotranspiration, which results in increasing latent heat fluxes and decreasing sensible heat fluxes. Comparison with combined observational/machine-learning products for turbulent fluxes shows that while irrigation can reduce biases, other biases in JULES, unrelated to irrigation, are larger than improvements due to the inclusion of irrigation. Irrigation also affects water fluxes within the soil, e.g. runoff and drainage into the groundwater level, as well as soil moisture outside of the irrigation season. We find that the irrigation scheme, at least in the uncoupled land-atmosphere setting, can rapidly deplete groundwater to the point that river flow becomes the main source of irrigation (over the North China Plain and the Indus region) and can have the counterintuitive effect of decreasing annual average soil moisture (over the Ganges plain). Subsequently, we will explore the impact of irrigation on regional climate by conducting coupled land-atmosphere simulations.

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Assessment of spatiotemporal characteristics of agro-meteorological drought events based on comparing Standardized Soil Moisture Index, Standardized Precipitation Index and Multivariate Standardized Drought Index

Journal of Water and Climate Change ◽

10.2166/wcc.2020.280 ◽

2020 ◽

Vol 11 (S1) ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Muhammad Imran Khan ◽

Xingye Zhu ◽

Muhammad Arshad ◽

Muhammad Zaman ◽

Yasir Niaz ◽

...

Keyword(s):

Soil Moisture ◽

Drought Index ◽

Standardized Precipitation Index ◽

Precipitation Index ◽

Drought Indices ◽

Moisture Index ◽

Drought Characteristics ◽

Soil Moisture Index ◽

The Impact ◽

Standardized Drought Index

Abstract Drought indices that compute drought events by their statistical properties are essential stratagems for the estimation of the impact of drought events on a region. This research presents a quantitative investigation of drought events by analyzing drought characteristics, considering agro-meteorological aspects in the Heilongjiang Province of China during 1980 to 2015. To examine these aspects, the Standardized Soil Moisture Index (SSI), Standardized Precipitation Index (SPI), and Multivariate Standardized Drought Index (MSDI) were used to evaluate the drought characteristics. The results showed that almost half of the extreme and exceptional drought events occurred during 1990–92 and 2004–05. The spatiotemporal analysis of drought characteristics assisted in the estimation of the annual drought frequency (ADF, 1.20–2.70), long-term mean drought duration (MDD, 5–11 months), mean drought severity (MDS, −0.9 to −2.9), and mild conditions of mean drought intensity (MDI, −0.2 to −0.80) over the study area. The results obtained by MSDI reveal the drought onset and termination based on the combination of SPI and SSI, with onset being dominated by SPI and drought persistence being more similar to SSI behavior. The results of this study provide valuable information and can prove to be a reference framework to guide agricultural production in the region.

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Satellite soil moisture improves rainfall just where needed

10.5194/egusphere-egu2020-5303 ◽

2020 ◽

Author(s):

Luca Brocca ◽

Stefania Camici ◽

Christian Massari ◽

Luca Ciabatta ◽

Paolo Filippucci ◽

...

Keyword(s):

Soil Moisture ◽

River Discharge ◽

Large Scale ◽

River Flow ◽

Global Scale ◽

Rain Gauge ◽

Rainfall Runoff ◽

Global Precipitation Climatology Centre ◽

Satellite Rainfall ◽

Global Precipitation

Soil moisture is a fundamental variable in the water and energy cycle and its knowledge in many applications is crucial. In the last decade, some authors have proposed the use of satellite soil moisture for estimating and improving rainfall, doing hydrology backward. From this research idea, several studies have been published and currently preoperational satellite rainfall products exploiting satellite soil moisture products have been made available.The assessment of such products on a global scale has revealed an important result, i.e., the soil moisture based products perform better than state of the art products exactly over regions in which the data are needed: Africa and South America. However, over these areas the assessment against rain gauge observations is problematic and independent approaches are needed to assess the quality of such products and their potential benefit in hydrological applications. On this basis, the use of the satellite rainfall products as input into rainfall-runoff models, and their indirect assessment through river discharge observations is an alternative and valuable approach for evaluating their quality.For this study, a newly developed large scale dataset of river discharge observations over 500+ basins throughout Africa has been exploited. Based on such unique dataset, a large scale assessment of multiple near real time satellite rainfall products has been performed: (1) the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement), IMERG Early Run, (2) SM2RAIN-ASCAT (https://doi.org/10.5281/zenodo.3405563), and (3) GPM+SM2RAIN (http://doi.org/10.5281/zenodo.3345323). Additionally, gauge-based and reanalysis rainfall products have been considered, i.e., (4) the Global Precipitation Climatology Centre (GPCC), and (5) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. As rainfall-runoff model, the semi-distributed MISDc (Modello Idrologico Semi-Distribuito in continuo) model has been employed in the period 2007-2018 at daily temporal scale.First results over a part of the dataset reveal the great value of satellite soil moisture products in improving satellite rainfall estimates for river flow prediction in Africa. Such results highlight the need to exploit such products for operational systems in Africa addressed to the mitigation of the flood risk and water resources management.

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Soil moisture spatial heterogeneity and its remote sensing scale analysis in arid area

10.1117/12.898519 ◽

2011 ◽

Author(s):

Qing Zhang ◽

Kefa Zhou ◽

Wei Gao

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Spatial Heterogeneity ◽

Arid Area ◽

Scale Analysis

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Triple Collocation Evaluation of In Situ Soil Moisture Observations from 1200+ Stations as part of the U.S. National Soil Moisture Network

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0108.1 ◽

2020 ◽

Vol 21 (11) ◽

pp. 2537-2549

Author(s):

Trent W. Ford ◽

Steven M. Quiring ◽

Chen Zhao ◽

Zachary T. Leasor ◽

Christian Landry

Keyword(s):

United States ◽

Soil Moisture ◽

Land Surface ◽

Surface Model ◽

National Scale ◽

Model Soil ◽

Soil Moisture Data ◽

Spatial Coverage ◽

Soil Moisture Monitoring

AbstractSoil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.

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