Accounting for Seasonal Land Use Dynamics to Improve Estimation of Agricultural Irrigation Water Withdrawals

Abstract

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.

Satellite‐Based Monitoring of Irrigation Water Use: Assessing Measurement Errors and Their Implications for Agricultural Water Management Policy

“…To support monitoring and management, it is critical that remote sensing models are able to estimate accurately the total amount of water abstracted by a farmer as opposed to only the consumptive rate of irrigation water use that does not consider inefficiencies associated with irrigation application or conveyance. As discussed in section 2.2, this is typically achieved by application adjustment factors to convert estimates of consumptive irrigation water use in to estimates of applied or abstracted rates of irrigation (e.g., Cheema et al, 2014;Fehri et al, 2019;Msigwa et al, In practice, these assumptions are unlikely to capture the true heterogeneity in field or farmer level irrigation efficiency. Several studies in our sample estimate crop irrigation requirements using either thermal-infrared or crop coefficient models and compare these estimates to in situ abstraction data (Abuzar et al, 2017;Campos et al, 2017;Elnmer et al, 2018;Foster et al, 2019;Gonçalves et al, 2020;Ma et al, 2018;Santos et al, 2010;Segovia-Cardozo et al, 2019;Vuolo et al, 2015;Wu et al, 2015).…”

“…To support monitoring and management, it is critical that remote sensing models are able to estimate accurately the total amount of water abstracted by a farmer as opposed to only the consumptive rate of irrigation water use that does not consider inefficiencies associated with irrigation application or conveyance. As discussed in section 2.2, this is typically achieved by application adjustment factors to convert estimates of consumptive irrigation water use in to estimates of applied or abstracted rates of irrigation (e.g., Cheema et al, 2014; Fehri et al, 2019; Msigwa et al, 2019; Van Eekelen et al, 2015; Vogels et al, 2020).…”

“…Typically, these factors are conditioned on the types of irrigation technology used by farmers in the region and do not vary between farmers operating the same technology or over time. For example, Msigwa et al (2019) and Van Eekelen et al (2015) both assume a constant irrigation application efficiency of 75% for case studies in southern Africa based on assumptions about prevailing irrigation technology mixes across their study areas.…”

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

“…In Sub-Saharan African countries like Tanzania, intensive agriculture that depends on the alternation between rainfed and irrigation serves as the main land use for both food security and economic growth [2]. The alternations in land use throughout the year, usually relying on the weather conditions, is referred to as land-use dynamics [3]. Land-use dynamics in agriculture often occur as multiple cropping cycles accompanied by different management practices, such as irrigation and fertilization.…”

“…Land-use dynamics in agriculture often occur as multiple cropping cycles accompanied by different management practices, such as irrigation and fertilization. In African cultivated catchments, agricultural land-use dynamics are usually attributed to the high variability in seasonal weather patterns (wet and dry seasons) throughout the year [3].…”