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.

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The Upper Blue Nile (UBN) basin is less-explored in terms of drought studies as compared to other parts of Ethiopia and lacks a basin-specific drought monitoring system. This study compares six drought indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), Evapotranspiration Deficit Index (ETDI), Soil Moisture Deficit Index (SMDI), Aggregate Drought Index (ADI), and Standardized Runoff-discharge Index (SRI), and evaluates their performance with respect to identifying historic drought events in the UBN basin. The indices were calculated using monthly time series of observed precipitation, average temperature, river discharge, and modeled evapotranspiration and soil moisture from 1970 to 2010. The Pearson’s correlation coefficients between the six drought indices were analyzed. SPI and SPEI at 3-month aggregate period showed high correlation with ETDI and SMDI (r > 0.62), while SPI and SPEI at 12-month aggregate period correlate better with SRI. The performance of the six drought indices in identifying historic droughts: 1973–1974, 1983–1984, 1994–1995, and 2003–2004 was analyzed using data obtained from Emergency Events Database (EM-DAT) and previous studies. When drought onset dates indicated by the six drought indices are compared with that in the EM-DAT. SPI, and SPEI showed early onsets of drought events, except 2003–2004 drought for which the onset date was unavailable in EM-DAT. Similarly, ETDI, SMDI and SRI-3 showed early onset for two drought events and late onsets in one-drought event. In contrast, ADI showed late onsets for two drought events and early onset for one drought event. None of the six drought indices could individually identify the onsets of all the selected historic drought events; however, they may identify the onsets when combined by considering several input variables at different aggregate periods.

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Intensive tillage (IT) in potato crops is considered as one of the main non-point sources (NPS) of local water eutrophication in the Fuquene Lake of Colombia. Therefore, the local government has invested in several programs aiming at the adoption of principles of conservation tillage (CT) which would allow for developing and applying the agricultural best management practices (BMPs). The complexity of hydrological and geological heterogeneity makes the degree of benefit that CT has in different locations uncertain. In this study, the Soil and Water Assessment Tool (SWAT) was used to assess the impacts of changing IT for CT on nitrogen (N) and phosphorus (P) losses in surface water runoff from the potato crop in the Fuquene watershed. This is done at field and watershed levels. A two-year study quantified the changes in surface water runoff pollutants for three potato crop cycles under the traditional IT practice and CT practice - which included reducing tillage, green manure, and permanent soil cover - at twelve runoff plots installed in the Fuquene watershed (Quintero and Comerford, 2013). This information was used to build, calibrate and validate the SWAT model. The results suggest that CT for the Fuquene watershed can be reduced up to 26% of the sediment yield and 11% of the surface runoff compared with IT, which means an overall reduction of load. The main CT effect on nutrient losses in runoff is an increase in the total N and P (2% to 18% respectively) compared to IT. However, the results at watershed level showed different patterns from those obtained at field level. Despite the model uncertainties, the results show a possibility of using hydrological models to assess the effectiveness of various field management practices in agriculture.

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In this paper, evapotranspiration (ET) and leaf area index (LAI) were used to calibrate the SWAT model, whereas remotely sensed precipitation and other climatic parameters were used as forcing data for the 6300 km2 Day Basin, a tributary of the Red River in Vietnam. The efficacy of the Sequential Uncertainty Fitting (SUFI-2) parameter sensitivity and optimization model was tested with area specific remote sensing input parameters for every Hydrological Response Units (HRU), rather than with measurements of river flow representing a large set of HRUs, i.e., a bulk calibration. Simulated monthly ET correlations with remote sensing estimates showed an R² = 0.71, Nash-Sutcliffe Efficiency NSE = 0.65, and Kling Gupta Efficiency KGE = 0.80 while monthly LAI showed correlations of R2 = 0.59, NSE = 0.57 and KGE = 0.83 over a five-year validation period. Accumulated modelled ET over the 5-year calibration period amounted to 5713 mm compared to 6015 mm of remotely sensed ET, yielding a difference of 302 mm (5.3%). The monthly flow at two flow measurement stations were adequately estimated (R2 = 0.78 and 0.55, NSE = 0.71 and 0.63, KGE = 0.59 and 0.75 for Phu Ly and Ninh Binh, respectively). This outcome demonstrates the capability of SWAT model to obtain spatial and accurate simulation of eco-hydrological processes, also when rivers are ungauged and the water withdrawal system is complex.

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Riparian zones have significant impact on nitrate removal despite their small areas. Most research on riparian zones have been implemented at small scales. Direct measurement at large scale is infeasible, thus using models is a good alternative. This study introduces a modified SWAT model, referred as SWAT_LS. Two modifications were implemented: (i) adding hydrological routing from upland areas to riparian zones; and (ii) adding a module to simulate denitrification in riparian zones based on the Riparian Nitrogen Model. SWAT_LS was applied to the Odense river basin in Denmark, a densely tile-drained agricultural river basin. Compared to SWAT, SWAT_LS provides an equally good performance for streamflow, and a significant improvement in nitrate predictions. SWAT_LS predicts that current riparian zones remove only 4–17% of nitrate loads because 70% of the riparian areas are bypassed due to subsurface drainage implementation. This ability would dramatically increase to 25–85% if riparian zones are entirely undrained.

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Land-use and land-cover changes are driving unprecedented changes in ecosystems and environmental processes at different scales. This study was aimed at identifying the potential land-use drivers in the Jedeb catchment of the Abbay basin by combining statistical analysis, field investigation and remote sensing. To do so, a land-use change model was calibrated and evaluated using the SITE (SImulation of Terrestrial Environment) modelling framework. SITE is cellular automata based multi-criteria decision analysis framework for simulating land-use conversion based on socio-economic and environmental factors. Past land-use trajectories (1986–2009) were evaluated using a reference Landsat-derived map (agreement of 84%). Results show that major land-use change drivers in the study area were population, slope, livestock and distances from various infrastructures (roads, markets and water). It was also found that farmers seem to increasingly prefer plantations of trees such as Eucalyptus by replacing croplands perhaps mainly due to declining crop yield, soil fertility and climate variability. Potential future trajectory of land-use change was also predicted under a business-as-usual scenario (2009–2025). Results show that agricultural land will continue to expand from 69.5% in 2009 to 77.5% in 2025 in the catchment albeit at a declining rate when compared with the period from 1986 to 2009. Plantation forest will also increase at a much higher rate, mainly at the expense of natural vegetation, agricultural land and grasslands. This study provides critical information to land-use planners and policy makers for a more effective and proactive management in this highland catchment.@en

To meet growing population demands for food and other agricultural commodities, agricultural land-use intensification and extensification seems to be increasing in the Abbay (Upper Blue Nile) basin in Ethiopia. However, the amount, location and degree of suitability of the basin for agriculture seem not well studied and/or documented. From global data sources, literature review and field investigation, a number of agricultural land suitability evaluation criteria were identified. These criteria were pre-processed as raster layers on a GIS platform and weights of criteria raster layers in determining suitability were computed using the analytic hierarchy process (AHP). A weighted overlay analysis method was used to compute categories of highly suitable, moderately suitable, marginally suitable and unsuitable lands for agriculture in the basin. It was found out that 53.8 % of the basin’s land coverage was highly suitable for agriculture and 23.2 % was moderately suitable. The marginally suitable and the unsuitable lands were at 11 and 12 % respectively. From the analysis, regions of the basin with high suitability as well as those with higher susceptibility for land degradation and soil erosion were identified.

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The increasing interest in larger spatial and temporal scale models and high resolution input data processing comes at a price of higher computational demand. This price is evidently even higher when common modeling routines such as calibration and uncertainty analysis are involved. Likewise, methods and techniques for reducing computation time in large scale socio-environmental modeling software is growing. Recent advancements in distributed computing such as Grid infrastructure have provided further opportunity to this effort. In the interest of gaining computational efficiency, we developed generic tools and techniques for enabling the Soil and Water Assessment Tool (SWAT) model application to run on the EGEE (Enabling Grids for E-science projects in Europe) Grid. Various program components/scripts were written to split a large scale hydrological model of the Soil and Water Assessment Tool (SWAT), to submit the split models to the Grid, and to collect and merge results into single output format. A three-step procedure was applied to take advantage of the Grid. Firstly, a python script was run in order to split the SWAT model into several sub-models. Then, individual sub-models were submitted in parallel for execution on the Grid. Finally, the outputs of the sub-basins were collected and the reach routing process was performed with another script executing a modified SWAT program. We conducted experimental simulations with multiple temporal and spatial scale hydrological models on the Grid infrastructure. Results showed that, in spite of computing overheads, parallel computation of socio-environmental models on the Grid is beneficial for model applications especially with large spatial and temporal scales. In the end, we conclude by recommending methods for further reducing computational overheads while running large scale model applications on the Grid.

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The Soil and Water Assessment Tool (SWAT) is an integrated river basin model that is widely applied within the Nile basin. Up to date, more than 20 peer-reviewed papers describe the use of SWAT for a variety of problems in the upper Nile basin countries, such as erosion modelling, land use and climate change impact modelling and water resources management. The majority of the studies are focused on locations in the tropical highlands in Ethiopia and around Lake Victoria. The popularity of SWAT is attributed to the fact that the tool is freely available and that it is readily applicable through the development of geographic information system (GIS) based interfaces and its easy linkage to sensitivity, calibration and uncertainty analysis tools. The online and free availability of basic GIS data that are required for SWAT made its applicability more straightforward even in data-scarce areas. However, the easy use of SWAT may not always lead to appropriate models which is also a consequence of the quality of the available free databases in these regions. In this paper, we aim at critically reviewing the use of SWAT in the context of the modelling purpose and problem descriptions in the tropical highlands of the Nile basin countries. To evaluate the models that are described in journal papers, a number of criteria are used to evaluate the model set-up, model performances, physical representation of the model parameters, and the correctness of the hydrological model balance. On the basis of performance indicators, the majority of the SWAT models were classified as giving satisfactory to very good results. Nevertheless, the hydrological mass balances as reported in several papers contained losses that might not be justified. Several papers also reported the use of unrealistic parameter values. More worrying is that many papers lack this information. For this reason, most of the reported SWAT models have to be evaluated critically. An important gap is the lack of attention that is given to the vegetation and crop processes. None of the papers reported any adaptation to the crop parameters, or any crop-related output such as leaf area index, biomass or crop yields. A proper simulation of the land cover is important for obtaining correct runoff generation, evapotranspiration and erosion computations. It is also found that a comparison of SWAT applications on the same or similar case study but by different research teams and/or model versions resulted in very different results. It is therefore recommended to find better methods to evaluate the representativeness of the distributed processes and parameters (especially when land use studies are envisaged) or predictions of the future through environmental changes. The main recommendation is that more details on the model set-up, the parameters and outputs should be provided in the journal papers or supplementary materials in order to allow for a more stringent evaluation of these models.

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The Upper Blue Nile is home to a large human and livestock population that live in diverse biophysical and socio-economic environment. The basin is increasingly experiencing multi-dimensional pressures including population growth, climate change and variability, deforestation, land/soil degradation, as well as increasing upstream-downstream tension on water use rights. Understanding the dynamic interactions of land use and water resources in the basin comes at the forefront of any effort to improving the livelihood and sustainability in the basin. As part of a study to develop a decision support system for an integrated natural resources management for the Upper Blue Nile basin, methods and techniques are developed to identify land use suitably based on various biophysical and socio-economic factors, and water resources availability in the basin. Possible biophysical and socio-economic land use change drivers on a mesoscale catchment, Jedeb, were identified using historical remote sensing data as well as primary data sources such as field observations, questionnaires and interviewing key stakeholder informants. Then, major land use change drivers were ranked using regression analysis (PCA). A hydrological model was setup using the Soil and Water Assessment Tool (SWAT) for the Jedeb catchment. Based on the identified high impact land use change drivers and additional factors such as agro-ecological zones and water resources availability (output from the hydrological model), a land use suitability analysis for the catchment was developed using the SITE (SImulation of Terrestrial Environments) generic land use change modeling framework. The land use and hydrological models exchange yearly simulation results to determine land use suitability analysis and impacts of land use change on components of the basin's hydrology, and vice-versa. Land use scenario was analyzed by assuming a 20% increase in population in the catchment. Preliminary results indicate a clear shift mainly from grassland land use type to cultivation land use type. It is concluded that the techniques and methodologies used in this study especially for integrating the two models can be used for a more realistic and thorough analysis of land use suitability and water resources dynamics and assessment study. Outputs of this study are used as inputs to develop a spatial decision support system for an integrated assessment and management of land use and water resources in the basin.

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The Soil and Water Assessment Tool (SWAT) has been used widely for large scale applications, reaching entire continents. Within the EU funded EnviroGrids project, a detailed application of SWAT on the Black Sea Basin is envisaged using high resolution data. In order to support the computation, the model is run on a computer grid. The use of the SWAT allowed for such computations with little adaptations to the source. A 3-step procedure is needed. In the first step, a program is run in order to split the model into several sub-models. Afterwards, the sub-models are run in parallel. In a last step, the outputs of the sub-basins are collected at a central computer and the routing is performed. High computations are also needed when simulations have to be repeated, such as for sensitivity, calibration and uncertainty analysis. In these cases, the simulations are repeated for different parameter sets. In this paper, we discuss the gridification of the algorithm "LH-OAT" that performs sensitivity analysis and has been linked to the SWAT model. The results show a clear improvement in calculation time. Nevertheless, it is concluded that the parallel computing of a distributed model is mainly beneficial for large scale applications with high resolution, while running the sensitivity analysis algorithm has more general and obvious benefit. In a next step, the gridification will be optimised depending on the application and the overheads that are due to submission and receiving of files, as well as potential waiting times for executions on the grid.@en