Representation of crop phenology and associated management practices in the SWAT+ model using global datasets for large scale hydrological applications

2021 ◽  
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Ann van Griensven ◽  
Jonas Jägermeyr
Keyword(s):  
Remote Sensing ◽  
Management Practices ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Hydrological Models ◽  
Multiple Cropping ◽  
Crop Phenology ◽  
Sensing Data ◽  
Cropping Seasons ◽  
Global Datasets

<p>Even though cropland cultivation covers over 40% of the planet’s ice free land surface, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, harvesting). For instance, the SWAT+ model represents agricultural land by default in a generic way where the timing of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and sub-tropical regions such as the sub-Saharan Africa where crop growth dynamics are mainly controlled by rainfall rather than temperature.</p><p>In this study, we present an approach on how to reasonably incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a SWAT+ model for North Eastern Africa. We evaluate the influence of the crop phenology representation on simulations of Leaf Area Index (LAI) and Evapotranspiration (ET) using LAI remote sensing data derived from Proba-V satellite and WaPOR ET data respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations especially for regions with a single cropping cycle.  However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in global hydrological models.</p>

scholarly journals Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

2022 ◽  
Vol 26 (1) ◽  
pp. 71-89
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Jonas Jägermeyr ◽  
Ann van Griensven
Keyword(s):  
Remote Sensing ◽  
Management Practices ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Hydrological Models ◽  
Multiple Cropping ◽  
Crop Phenology ◽  
Cropping Season ◽  
Cropping Seasons ◽  
Global Datasets

Abstract. To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, and harvesting). The SWAT+ (Soil and Water Assessment Tool) model represents agricultural land by default in a generic way, where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and subtropical regions such as sub-Saharan Africa, where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for northeastern Africa. We evaluate the influence of the crop phenology representation on simulations of leaf area index (LAI) and evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR (Water Productivity through Open access of Remotely sensed derived data) ET data, respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations, especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural hydrologic response units (HRUs), which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in areas where they occur using regional to global hydrological models.

scholarly journals Improved Representation of Agricultural Land Use and Crop Management for Large Scale Hydrological Impact Simulation in Africa using SWAT+

2021 ◽  
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Jonas Jägermeyr ◽  
Ann van Griensven
Keyword(s):  
Management Practices ◽  
Agricultural Land ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Hydrological Models ◽  
Multiple Cropping ◽  
Crop Phenology ◽  
Cropping Season ◽  
Cropping Seasons ◽  
Global Datasets

Abstract. To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, harvesting). The SWAT+ model represents agricultural land by default in a generic way where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and sub-tropical regions such as the sub-Saharan Africa where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for Northeast Africa. We evaluate the influence of the crop phenology representation on simulations of Leaf Area Index (LAI) and Evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR ET data respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural Hydrologic Response Units (HRUs) which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in regional to global hydrological models.

scholarly journals Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

2021 ◽  
Vol 13 (10) ◽  
pp. 2014
Author(s):  
Celina Aznarez ◽  
Patricia Jimeno-Sáez ◽  
Adrián López-Ballesteros ◽  
Juan Pablo Pacheco ◽  
Javier Senent-Aparicio
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Ecosystem Services ◽  
Water Resources ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Erosion Control ◽  
Sensing Data ◽  
Extreme Precipitation Events ◽  
The Impact

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

scholarly journals FIRST RESULTS OF THE LEM BENCHMARK DATABASE FOR AGRICULTURAL APPLICATIONS

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 251-256
Author(s):  
I. D. Sanches ◽  
R. Q. Feitosa ◽  
B. Montibeller ◽  
P. M. Achanccaray Diaz ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Management Practices ◽  
Cropping Systems ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Multiple Cropping ◽  
Tropical Regions ◽  
Benchmark Database ◽  
First Results ◽  
Agricultural Applications

Abstract. Applying remote sensing technology to map and monitor agriculture and its impacts can greatly contribute for the proper development of this activity, promoting efficient food, fiber and energy production. For that, not only remote sensing images are needed, but also ground truth information, which is a key factor for the development and improvement of methodologies using remote sensing data. While a variety of images are current available, inclusive cost-free images, field reference data is scarcer. For agricultural applications, especially in tropical regions such as Brazil, where the agriculture is very dynamic and diverse (recent agricultural frontiers, crop rotations, multiple cropping systems, several management practices, etc.), and cultivated over a vast territory, this task is not trivial. One way of boosting the researches in agricultural remote sensing is to stimulate people to share their data, and to foster different groups to use the same dataset, so distinct methods can be properly compared. In this context, our group created the LEM Benchmark Database (a project funded by the ISPRS Scientific Initiative project - 2017) from the Luiz Eduardo Magalhães (LEM) municipality, Bahia State, Brazil. The database contains a set of pre-processed multitemporal satellite images (Landsat-8/OLI, Sentinel-2/MSI and SAR band-C Sentinel-1) and shapefiles of agricultural fields with their correspondent monthly land use classes, covering the period of one Brazilian crop year (2017–2018). In this paper we present the first results obtained with this database.

scholarly journals Using Farmer Decision Rules for Mapping Historical Land Use Change Patterns from 1954 to 2007 in Rural Northwestern Vietnam

Land ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 130
Author(s):  
Thanh Thi Nguyen ◽  
Melvin Lippe ◽  
Carsten Marohn ◽  
Tran Duc Vien ◽  
Georg Cadisch
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Management Practices ◽  
Anthropogenic Activities ◽  
Decision Rules ◽  
Remote Sensing Data ◽  
Rural Landscapes ◽  
Sensing Data ◽  
Assessment Period

The present study revealed how local socioecological knowledge elucidated during participatory rural appraisals and historical remote sensing data can be combined for analyzing land use change patterns from 1954 to 2007 in northwestern Vietnam. The developed approach integrated farmer decision rules on cropping preferences and location, visual and supervised classification methods, and qualitative information obtained during various forms of participatory appraisals. The integration of historical remote sensing data (aerial photo, Landsat, LISS III) with farmer decision rules showed the feasibility of the proposed method to explain crop distribution patterns for the assessment period of 53 years. Our approach is beneficial for data-limited environments, which is a prevalent situation for many developing regions. The derived land use and crop type dataset was used for understanding how anthropogenic activities altered the study area of the Chieng Khoi commune during the assessment period of five decades, and what potential impact this can have on the natural resource base. The newly developed approach offers a methodological pathway that can be easily transferred to local government authorities for a better understanding of cropping transitions and agricultural expansion trends in data-limited rural landscapes. The detected land use change patterns and upland cropping expansion of more than two hundred percent in 53 years not only revealed the consequences of the interactions and feedback between farmers and their land, but further highlighted the urgent need for implementing sustainable land management practices in the case study watershed of the Chieng Khoi commune and northwestern Vietnam in general.

Rice water requirements: local assessment based on remote sensing data in the Lower Mondego (Portugal)

2021 ◽  
Author(s):  
Isabel P. de Lima ◽  
Romeu G. Jorge ◽  
João L.M.P. de Lima
Keyword(s):  
Remote Sensing ◽  
Irrigation Water ◽  
Management Practices ◽  
Remote Sensing Data ◽  
Weather Data ◽  
Practical Consideration ◽  
Water Requirements ◽  
Rice Agriculture ◽  
Sensing Data ◽  
The Impact

<p>Irrigated rice agriculture, which is traditionally conducted applying continuous flooding, requires much more irrigation water than non-ponded crops. This can be a constraint in areas facing water scarcity issues, where the pursue for water resources optimization requires that water use efficiency is increased. Therefore, main local challenges for rice production are often to identify and apply more favorable and efficient irrigation and crop management practices, while guaranteeing high crop yields. For this purpose, the knowledge of rice crop water requirements is an important practical consideration. However, there are usually several limiting factors to obtain relevant data for the local conditions. Several recent approaches and methodologies based on remote sensing data, such as images obtained from satellites and Unmanned Aerial Systems (UAS), are offering attractive alternative routes to estimate crop evapotranspiration in a fast and easy way, including in rice fields.</p><p>For the rice producing area of the Lower Mondego region (Portugal), we report on exploring the usefulness of remote sensing tools for the local rice agriculture monitoring and management. Data include 25 land surface images of rice cultivated areas obtained from satellite Sentinel-2A during 2020, which together with weather data and crop parameters permits to calculate biophysical indicators and indices of vegetation water stress. Although remote sensing data available from satellite imagery presents some practical constraints (e.g. cloud cover, resolution), preliminary results from this study show that they allow to improve the characterization of the rice local cultivation conditions, therefore contributing to evaluate the impact of applying different irrigation and agriculture management practices, in particular those that have the potential to lead to significant savings of irrigation water.</p><p>This work was conducted under the umbrella of the international project MEDWATERICE (www.medwaterice.org) that focus on improving the sustainable use of water in the Mediterranean rice agro-ecosystem and aims to exploring the opportunity to apply water-saving, alternative, rice irrigation methods.</p>

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