Representation of seasonal land use dynamics in SWAT+ for improved assessment of blue and green water consumption

This research article was published by Hydrology and Earth System Sciences, 2022In most (sub)-tropical African cultivated regions, more than one cropping season exists following the (one or two) rainy seasons. An additional cropping season is possible when irrigation is applied during the dry season, which could result in three cropping seasons. However, most studies using agro-hydrological models such as the Soil and Water Assessment Tool (SWAT) to map blue and green evapotranspiration (ET) do not account for these cropping seasons. Blue ET is a portion of crop evapotranspiration after irrigation application, while green ET is the evapotranspiration resulting from rainfall. In this paper, we derived dynamic and static trajectories from seasonal land use maps to represent the land use dynamics following the major growing seasons to improve simulated blue and green water consumption from simulated evapotranspiration in SWAT+. A comparison between the default SWAT+ set-up (with static land use representation) and a dynamic SWAT+ model set-up (with seasonal land use representation) is made by a spatial mapping of the ET results. Additionally, the SWAT+ blue and green ET were compared with the results from the four remote sensing data-based methods, namely SN (Senay), EK (van Eekelen), the Budyko method, and soil water balance method (SWB). The results show that ET with seasonal representation is closer to remote sensing estimates, giving higher performance than ET with static land use representation. The root mean squared error decreased from 181 to 69 mm yr−1, the percent bias decreased from 20 % to 13 %, and the Nash–Sutcliffe efficiency increased from −0.46 to 0.4. Furthermore, the blue and green ET results from the dynamic SWAT+ model were compared to the four remote sensing methods. The results show that the SWAT+ blue and green ET are similar to the van Eekelen method and performed better than the other three remote sensing methods. It is concluded that representation of seasonal land use dynamics produces better ET results, which provide better estimations of blue and green agricultural water consumption

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

This research article published by MDPI, 2020In 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

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Where does land use matter most? Contrasting land use effects on river quality at different spatial scales

Understanding the influence of land-use activities on river quality has been a key focus of river monitoring programs worldwide. However, defining which land-use spatial scale is relevant remains elusive. In this study, therefore, we contrasted the influence of land use on river quality using three types of land-use estimators, namely circular buffers around a monitoring site, circular buffers upstream of the monitoring site and the entire watershed area upstream of the monitoring site. The land-use percentage compositions within the Usa-Kikuletwa River catchment in northeastern Tanzania were quantified using Landsat-8 satellite images with a maximum mapping resolution of 30 m. Redundancy analysis models and generalized linear models were used to evaluate the influence of land use on macroinvertebrate assemblages and physico-chemical water quality at different spatial scales in the dry and wet seasons. Overall, a substantial fraction of variation in physico-chemical water quality, macroinvertebrate taxon richness, Chao-1 and TARISS (Tanzania River Scoring System) score could be explained by land use of the entire watershed area upstream of the monitoring site in the dry and wet seasons. However, macroinvertebrate abundances showed strong links with more local land-use patterns within 100 m and 2 km radii. Circular buffers upstream of monitoring sites were more informative for macroinvertebrate assemblages than circular buffers around the monitoring sites. However, the latter did correlate well with physico-chemical water quality variables. Land-use variables correlated across spatial scales (i.e., 100 m up to 2 km radii), but not with the land use in the entire watershed area above the monitoring site. Our results indicate that physico-chemical water quality variables and macroinvertebrates may respond differently to land-uses at different scales. More importantly, our results illustrate that the choice regarding spatial land-use metrics can bias conclusions of environmental impact studies in river systems.status: publishe

Where does land use matter most? Contrasting land use effects on river quality at different spatial scales

Understanding the influence of land-use activities on river quality has been a key focus of river monitoring programs worldwide. However, defining which land-use spatial scale is relevant remains elusive. In this study, therefore, we contrasted the influence of land use on river quality using three types of land-use estimators, namely circular buffers around a monitoring site, circular buffers upstream of the monitoring site and the entire watershed area upstream of the monitoring site. The land-use percentage compositions within the Usa-Kikuletwa River catchment in northeastern Tanzania were quantified using Landsat-8 satellite images with a maximum mapping resolution of 30 m. Redundancy analysis models and generalized linear models were used to evaluate the influence of land use on macroinvertebrate assemblages and physico-chemical water quality at different spatial scales in the dry and wet seasons. Overall, a substantial fraction of variation in physico-chemical water quality, macroinvertebrate taxon richness, Chao-1 and TARISS (Tanzania River Scoring System) score could be explained by land use of the entire watershed area upstream of the monitoring site in the dry and wet seasons. However, macroinvertebrate abundances showed strong links with more local land-use patterns within 100 m and 2 km radii. Circular buffers upstream of monitoring sites were more informative for macroinvertebrate assemblages than circular buffers around the monitoring sites. However, the latter did correlate well with physico-chemical water quality variables. Land-use variables correlated across spatial scales (i.e., 100 m up to 2 km radii), but not with the land use in the entire watershed area above the monitoring site. Our results indicate that physico-chemical water quality variables and macroinvertebrates may respond differently to land-uses at different scales. More importantly, our results illustrate that the choice regarding spatial land-use metrics can bias conclusions of environmental impact studies in river system

Comparison of blue and green water fluxes for different land use classes in a semi-arid cultivated catchment using remote sensing

This research article published by Elsevier, 2021Study area Kikuletwa catchment, Upper Pangani River Basin, Tanzania. Study focus This study compared yearly blue and green water fluxes using four different methods: Senay’s method (SN) (Senay et al., 2016), van Eekelen method (EK) (van Eekelen et al., 2015), the Budyko method (Simons et al., 2020) and the Soil Water Balance (SWB) model (FAO and IHE Delft, 2019). The yearly blue and green water fluxes of different Land Use Land Cover (LULC) classes were estimated using an ensemble of seven global remote sensing-based evapotranspiration products (Ensemble ET) and the CHIRPS rainfall dataset. The Ensemble ET was created from seven global RS-based surface energy balance models (GLEAM, CMRS-ET, SSEBop, ALEXI, SEBS, ETMonitor and MOD16). New hydrological insights Our study found that the EK method was able to map blue and green water fluxes with realistic results for irrigated and non-irrigation cultivated areas. Budyko and SWB gave too high blue water fluxes for the non-irrigated agricultural areas, whereas the Budyko and SWB models were not able to show a clear difference in blue-water fluxes in irrigated versus non-irrigated areas. On the other hand, the SN method estimated no blue water fluxes in more than half of the identified irrigated areas. Three of the four methods estimate the highest blue water fluxes (318–582 mm/y) in forested areas, while the SWB model estimates the highest blue water fluxes in irrigated banana and coffee (278 mm/y). Overall, we conclude that the EK method yielded the most realistic spatial pattern of blue-water fluxes when compared to the irrigated land use map, whereas SWB could be considered after further calibration if higher temporal data resolution (e.g. monthly) is required

Accounting for seasonal land use dynamics to improve estimation of agricultural irrigation water withdrawals

The assessment of water withdrawals for irrigation is essential for managing water resources in cultivated tropical catchments. These water withdrawals vary seasonally, driven by wet and dry seasons. A land use map is one of the required inputs of hydrological models used to estimate water withdrawals in a catchment. However, land use maps provide typically static information and do not represent the hydrological seasons and related cropping seasons and practices throughout the year. Therefore, this study assesses the value of seasonal land use maps in the quantification of water withdrawals for a tropical cultivated catchment. We developed land use maps for the main seasons (long rains, dry, and short rains) for the semi-arid Kikuletwa catchment, Tanzania. Three Landsat 8 images from 2016 were used to develop seasonal land use land cover (LULC) maps: March (long rains), August (dry season), and October (short rains). Quantitative and qualitative observation data on cropping systems (reference points and questionnaires/surveys) were collected and used for the supervised classification algorithm. Land use classifications were done using 20 land use and land cover classes for the wet season image and 19 classes for the dry and short rain season images. Water withdrawals for irrigated agriculture were calculated using (1) the static land use map or (2) the three seasonal land use maps. Clear differences in land use can be seen between the dry and the other seasons and between rain-fed and irrigated areas. A difference in water withdrawals was observed when seasonal and static land use maps were used. The highest differences were obtained for irrigated mixed crops, with an estimation of 572 million m3/year when seasonal dynamic maps were used and only 90 million m3/year when a static map was used. This study concludes that detailed seasonal land use maps are essential for quantifying annual irrigation water use of catchment areas with distinct dry and wet seasonal dynamics.Water Resource