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 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.

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 How Can We Represent Seasonal Land Use Dynamics in SWAT and SWAT+ Models for African Cultivated Catchments?

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1541
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Anna Msigwa ◽  
Hans C. Komakech ◽  
Boud Verbeiren ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Swat Model ◽  
Agricultural Practices ◽  
Area Index ◽  
Land Use Dynamics ◽  
Cropping Seasons ◽  
Decision Tables ◽  
Land Cover Maps

In SWAT and SWAT+ models, the variations in hydrological processes are represented by Hydrological Response Units (HRUs). In the default models, agricultural land cover is represented by a single growing cycle. However, agricultural land use, especially in African cultivated catchments, typically consists of several cropping seasons, following dry and wet seasonal patterns, and are hence incorrectly represented in SWAT and SWAT+ default models. In this paper, we propose a procedure to incorporate agricultural seasonal land-use dynamics by (1) mapping land-use trajectories instead of static land-cover maps and (2) linking these trajectories to agricultural management settings. This approach was tested in SWAT and SWAT+ models of Usa catchment in Tanzania that is intensively cultivated by implementing dominant dynamic trajectories. Our results were evaluated with remote-sensing observations for Leaf Area Index (LAI), which showed that a single growing cycle did not well represent vegetation dynamics. A better agreement was obtained after implementing seasonal land-use dynamics for cultivated HRUs. It was concluded that the representation of seasonal land-use dynamics through trajectory implementation can lead to improved temporal patterns of LAI in default models. The SWAT+ model had higher flexibility in representing agricultural practices, using decision tables, and by being able to represent mixed cropping cultivations.

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 The use of ALOS/PALSAR data for estimating sugarcane productivity

2014 ◽  
Vol 34 (6) ◽  
pp. 1245-1255 ◽  
Author(s):  
Michelle C. A. Picoli ◽  
Rubens A. C. Lamparelli ◽  
Edson E. Sano ◽  
Jansle V. Rocha
Keyword(s):  
Prediction Models ◽  
Multiple Linear Regression Model ◽  
Remote Sensing Data ◽  
Coefficient Of Determination ◽  
Alos Palsar ◽  
Sar Images ◽  
Long Period ◽  
Cropping Season ◽  
Cropping Seasons ◽  
Sugarcane Productivity

Some models have been developed using agrometeorological and remote sensing data to estimate agriculture production. However, it is expected that the use of SAR images can improve their performance. The main objective of this study was to estimate the sugarcane production using a multiple linear regression model which considers agronomic data and ALOS/PALSAR images obtained from 2007/08, 2008/09 and 2009/10 cropping seasons. The performance of models was evaluated by coefficient of determination, t-test, Willmott agreement index (d), random error and standard error. The model was able to explain 79%, 12% and 74% of the variation in the observed productions of the 2007/08, 2008/09 and 2009/10 cropping seasons, respectively. Performance of the model for the 2008/09 cropping season was poor because of the occurrence of a long period of drought in that season. When the three seasons were considered all together, the model explained 66% of the variation. Results showed that SAR-based yield prediction models can contribute and assist sugar mill technicians to improve such estimates.

scholarly journals Identification of Critical Diffuse Pollution Sources in an Ungauged Catchment by Using the Swat Model- A Case Study of Kolleru Lake, East Coast of India

2020 ◽  
pp. 53-68
Author(s):  
Meena Kumari Kolli ◽  
Christian Opp ◽  
Michael Groll
Keyword(s):  
Best Management Practices ◽  
Management Practices ◽  
Agricultural Land ◽  
Assessment Tool ◽  
Swat Model ◽  
Temporal Distribution ◽  
Agricultural Runoff ◽  
Freshwater Ecosystems ◽  
Diffuse Pollution ◽  
Lake Water Quality

Freshwater ecosystems are facing severe threats from human activities. As a consequence of this, they can get disturbed. In developing countries, like India, freshwater lakes are endangered primarily by agricultural activities, which often accelerate erosion and the runoff. The massive application of pesticides and chemical fertilizers to agricultural lands is one of the reasons for eutrophication in Kolleru Lake. The different natural and anthropogenic influences increase the highly complex ecosystem of the lake. Therefore, the objectives of this study are to ascertain the priority control areas, aiming at socio-economic development for the protection of the lake water quality by applying the Best Management Practices (BMPs).  For this purpose, the Soil and Water Assessment Tool (SWAT) was used to identify the critical areas of the lake's catchment in terms of pollution from agricultural runoff into the tributaries of the Kolleru Lake and the lake itself. The results demonstrated that the diffuse pollution load in the western and downstream watersheds the highest and that agricultural land was the primary pollutant source besides the accumulation of nutrients in the downstream areas. The differences in the sub-basin loads were observed in the catchment mainly depends on the topographic features, soil properties, land use, vegetation, and drainage patterns. From where the major outlet sub-basin has the highest accumulation of nitrate-nitrogen (NO3_N), and total phosphorus (TP) emissions were quantified. The temporal distribution of runoff and diffuse sources were estimated from 2008 – 2014. The runoff mainly governed diffuse pollution was found to be a significant contributing factor to the lake. Further, suggestions were provided for the implementation of agricultural management practices to minimize pollution levels.   Graphical Abstract:   (Own Source: Diagrammatic representation showing the interrelationship of the SWAT run model)

scholarly journals Mass balance calibration and reservoir representations for large-scale hydrological impact studies using SWAT+

2020 ◽  
Vol 163 (3) ◽  
pp. 1307-1327 ◽  
Author(s):  
Celray James Chawanda ◽  
Jeffrey Arnold ◽  
Wim Thiery ◽  
Ann van Griensven
Keyword(s):  
Mass Balance ◽  
Large Scale ◽  
Management Practices ◽  
Swat Model ◽  
Regional Scale ◽  
Local Analysis ◽  
Climate Projections ◽  
Balance Calibration ◽  
Global Datasets ◽  
Irrigation Practices

AbstractClimate change (CC) has a high impact on hydrological processes which calls for reliable projections of CC hydrological impacts at large scales. However, there are several challenges in hydrological modelling at large scales. Large-scale models are often not adapted and evaluated at regional scale due to high computation time requirements or lack of information on human interactions, such as dam operations and irrigation practices at local scale. In this study, we present a regionalised methodology that uses a hydrological mass balance calibration (HMBC) and global datasets to represent reservoir and irrigation practices and apply these to a SWAT+ model for Southern Africa. We evaluate the influence of HMBC and the representation on irrigation and reservoirs on model performance and climate projections. We propose a generalised implementation of reservoirs using global datasets and decision tables to represent irrigation and reservoir management. Results show that inclusion of irrigation, reservoirs and HMBC leads to improved simulation of discharge and evapotranspiration with fewer iterations than a full parameter calibration. There is a substantial difference between projections made by the regionalised model and default model when looking at local impacts. We conclude that large-scale hydrological studies that involve local analysis and spatial mapping of results benefit from HMBC and representation of management practices. The proposed methodology can be scaled up and improve overall projections made by global models.

Impacts of climate and land use changes on the erosion processes in a Mediterranean agricultural catchment (Northern Morocco)

2021 ◽  
Author(s):  
Hamza Briak ◽  
Rachid Moussadek ◽  
Khadija Aboumaria ◽  
Fassil Kebede ◽  
Rachid Mrabet
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Best Management Practices ◽  
Sediment Yield ◽  
Soil Conservation ◽  
Management Practices ◽  
Agricultural Land ◽  
Swat Model ◽  
Baseline Scenario ◽  
Erosion Processes

<p>Recent studies on vulnerability to climate and land use change show a trend towards increased aridity accelerating soil erosion which is the primary factor to be considered by decision makers in the environmental field. Furthermore, to reduce the soil erosion intensity, it is required to clarify the sources zones of sediment yield where soil conservation works have to focus on. The model selected for this work is the Soil and Water Assessment Tool (SWAT) which is one of many models widely used to assess soil erosion risk and simulate conservation measures efficiency. In fact, the objective of this work is to evaluate the effects of different agricultural Best Management Practices (BMPs) on sediments using SWAT model in the Kalaya river basin located in the North of Morocco in order to recommend the most appropriate one. The model was calibrated and validated using observed data of flow and sediment concentration; the performance of the model was evaluated using statistical methods and the total soil erosion rate was estimated by this model in the study area. However, we concentrated on the representation of three interesting and most usable practices by the SWAT model: contouring, strip-cropping and terracing. The general parameters of the model have been modified to reflect the implementation of four different BMPs. The modification of these parameters was based on previous research and modeling efforts conducted in watersheds. Resulting sediment yield were compared with the result of simulation of the baseline scenario (existing conditions). In fact, effective measures to reduce sediment losses at the watershed level are organized according to their effectiveness, and these are terracing (28% reduction and the value is 15t/ha/y) followed by strip-cropping (9% reduction and the value is 5t/ha/y). On the other hand, measurements performed by the contouring are inappropriate for the study area because they have contributed to increasing the soil erosion (more than 31% of losses and the value is 17t/ha/y more than existing conditions). The mean annual values of sediment yields obtained for scenarios with and without BMPs were compared to assess the effectiveness of BMPs. Among all other practices, terracing was the most effective BMPs for reducing sediments which is perfectly recommended in the Mediterranean regions in general to avoid the risk of damage during intense rainfall. These results indicates that the use of terracing on agricultural land can potentially make improvements marked the control and limitation of soil erosion, and it also affords useful information for involved stakeholders in water and soil conservation activities for targeted management.</p>

scholarly journals Prediction of Surface Runoff Changes with Landuse-Land Cover Impact Using Remote Sensing Data and GIS Based ARcSWAT Model of Indrayani Watershed, Maharashtra, India

2019 ◽  
pp. 06-13
Author(s):  
Narasayya Kamuju
Keyword(s):  
Surface Runoff ◽  
Agricultural Land ◽  
Assessment Tool ◽  
Swat Model ◽  
Soil Layer ◽  
Remote Sensing Data ◽  
North East ◽  
The North ◽  
Food And Agriculture ◽  
Data Layers

Modern Mathematical Models have been developed for studying the complex hydrological processes of a watershed and their direct relation to weather, topography, geology and land use. In this study the hydrology of Indrayani watershed located in Indrayani River basin at the North-East of Pune is modelled, using the Soil and water Assessment Tool (SWAT). It aims to simulate the surface runoff using a Temporal resolution of 10 years LandUse-LandCover (Lu-Lc) maps of 2003-04 and 2013-14. The ArcSWAT interface implemented in the ArcGIS software was used to delineate the study area and its sub-components, combine the data layers and edit the model database. The ArcSWAT model predicted Indrayani watershed hydrologic component of Surface Runoff with weather components of precipitation and temperature of 25 years along with Food and Agriculture Organisation soil layer. The SWAT model run in 2 Phases with Lu-Lc of 2003-04 as Phase-I and also model run using 2013-14 Lu-Lc as phase-II. The runoff predicted within a 10 year temporal changes of Lu-Lc, 919 mm of Surface Runoff obtained with Lu-Lc of 2003-04 and 767 mm of Surface Runoff predicted from 2013-14 Lu-Lc map layer respectively. The higher runoff predicted with Lu-Lc 2003-04, as such it has higher area of Agricultural land with lower urban area covered and less water bodies than 2013-14 Landuse-Lancover classes.

Lowland Rice Yield as Affected by Straw Incorporation and Inorganic Fertilizer Over Cropping Seasons in Fluvisol

2014 ◽  
Vol 3 (1) ◽  
pp. 129-141
Author(s):  
Brahima Koné ◽  
Zadi Florent ◽  
Gala bi Trazié Jeremie ◽  
Akassimadou Edja Fulgence ◽  
Konan Kouamé Firmin ◽  
...  
Keyword(s):  
Grain Yield ◽  
Rice Variety ◽  
Mineral Fertilizer ◽  
Lowland Rice ◽  
Inorganic Fertilizer ◽  
Cropping Season ◽  
Significant Difference ◽  
Straw Incorporation ◽  
Cropping Seasons ◽  
Fertilizer Effect

Grain yield stabilization of lowland rice over cropping seasons was explored using different compositions of inorganic fertilizers (NPK, NPKCa, NPKMg, NPKZn, NPKCaMg, NPKCaZn and NPKCaMgZn) and straw incorporation (3, 6, 9, 12 and 15 tha-1 ). No fertilizer and no straw amended plot was the control in a split-plot design with three replications laid in a Fluvisol of Guinea savanna in Centre Cote d’Ivoire. Three weeks old nursery rice variety NERICA L19 was transplanted. No significant difference of grain yield was observed between the different treatments excluding the highest yields recorded for treatments NPKMg (5.09 tha-1 ), NPKZn (5.15 tha-1 ) and NPKCaéMg (5.31 tha-1 ) compared with 12 (3.95 tha1 ) and 15 tha-1 (4.14 tha-1 ) as straw rates respectively. Grain yield declining trend was more pronounced for mineral fertilizer treatments showing twice greater depressive effect of cropping cycle compared with the straw especially, for treatments characterized by highest grain yield in the first cropping season and similar grain yields were recorded for both sources of nutrient in the third cropping cycle. Of slowness of nutrients releasing by straw, highest grain yield was expected for this soil amender within a longer period of cultivation whereas, unbalance soil micronutrients should be relevant to studious declining yield under inorganic fertilizer effect. Nevertheless, the straw rate of 12 tha-1 supplying 0.58% of NPK as mineral fertilizer equivalent can be recommended for sustaining lowland rice production in the studied agro-ecosystems unless for three cropping seasons.

Sign in / Sign up

Export Citation Format

Share Document