Hydrological Modeling in Data-Scarce Catchments: The Kilombero Floodplain in Tanzania

Näschen, Kristian; Diekkrüger, Bernd; Leemhuis, Constanze; Steinbach, Stefanie; Seregina, Larisa; Thonfeld, Frank; Linden, Roderick van der

doi:10.3390/w10050599

Cited by 49 publications

(93 citation statements)

References 79 publications

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“…The SWAT model for this study is based on the details already given by Näschen et al [2]. The issue of data scarcity in the study region is solved by using freely available geodata in combination with data gathered from local partners in Tanzania (Table 2, [48]).…”

Section: Input Datamentioning

confidence: 99%

“…The station is currently being renewed and new stations are being set up in the catchment, nonetheless there is no time series and no rating curve for the new stations available up to today. Therefore, the historical discharge data was used to set up the hydrological model [2]. In addition, LULC maps were produced from Landsat imagery for four different time steps.…”

Section: Input Datamentioning

confidence: 99%

“…Recent developments in sub-Saharan Africa (SSA) show an increasing trend of conversion of natural land cover into arable land [1][2][3][4][5][6][7][8][9][10][11]. Drivers of change are manifold and can be directly linked to human activities such as population growth, economic development, and globalization [11,12].…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Land Use/Land Cover Change (LULCC) on Water Resources in a Tropical Catchment in Tanzania under Different Climate Change Scenarios

et al. 2019

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Many parts of sub-Saharan Africa (SSA) are prone to land use and land cover change (LULCC). In many cases, natural systems are converted into agricultural land to feed the growing population. However, despite climate change being a major focus nowadays, the impacts of these conversions on water resources, which are essential for agricultural production, is still often neglected, jeopardizing the sustainability of the socio-ecological system. This study investigates historic land use/land cover (LULC) patterns as well as potential future LULCC and its effect on water quantities in a complex tropical catchment in Tanzania. It then compares the results using two climate change scenarios. The Land Change Modeler (LCM) is used to analyze and to project LULC patterns until 2030 and the Soil and Water Assessment Tool (SWAT) is utilized to simulate the water balance under various LULC conditions. Results show decreasing low flows by 6-8% for the LULC scenarios, whereas high flows increase by up to 84% for the combined LULC and climate change scenarios. The effect of climate change is stronger compared to the effect of LULCC, but also contains higher uncertainties. The effects of LULCC are more distinct, although crop specific effects show diverging effects on water balance components. This study develops a methodology for quantifying the impact of land use and climate change and therefore contributes to the sustainable management of the investigated catchment, as it shows the impact of environmental change on hydrological extremes (low flow and floods) and determines hot spots, which are critical for environmental development.Sustainability 2019, 11, 7083 2 of 28 increased agricultural outputs such as food and timber production [9,13]. Several water-related targets of the Sustainable Development Goals (SDGs) are at risk due to land conversions into arable land, especially with regard to SDG 6 (Clean Water and Sanitation) and SDG 15 (Life on Land) [13,14].Several studies investigated the impact of LULCC and climate change on water resources separately [3,15] or simultaneously [16,17]. The results of the studies differ due to several reasons e.g., the type of LULCC, the regional focus, or the time period and model chosen to simulate climate change. However, many studies indicate an increased exposure to hydro-climatic extremes in Eastern Africa [18][19][20][21]. This study exemplarily analyzes LULCC compared to climate change in the Kilombero Catchment in Tanzania and how these affect water resources. The catchment itself is subject to aforementioned LULCC [1,13] and pressure on land resources in the valley is fostered by government plans to implement the Southern Agricultural Growth Corridor of Tanzania (SAGCOT) [22], which is accompanied by a growing population and migration of pastoralists into the valley [23]. SAGCOT follows a green growth approach covering three development clusters in Kilombero, Ihemi, and Mbarali, comprising one third of the mainland of Tanzania [1,24,25]. On the one hand the key clusters are charac...

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Section: Input Datamentioning

confidence: 99%

Section: Input Datamentioning

confidence: 99%

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The Impact of Land Use/Land Cover Change (LULCC) on Water Resources in a Tropical Catchment in Tanzania under Different Climate Change Scenarios

et al. 2019

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“…The studies of hydrological modelling play a crucial role in planning water resources, projecting hydraulic structures, and evaluating environmental impacts [1][2][3][4]. The estimation of accurate streamflow is required for the estimation of floods, development of agricultural strategies, and planning of hydraulic structures.…”

Section: Introductionmentioning

confidence: 99%

Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin

Köyceğiz

Büyükyıldız

2019

Water

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Hydrologic models are important tools for the successful management of water resources. In this study, a semi-distributed soil and water assessment tool (SWAT) model is used to simulate streamflow at the headwater of Çarşamba River, located at the Konya Closed Basin, Turkey. For that, first a sequential uncertainty fitting-2 (SUFI-2) algorithm is employed to calibrate the SWAT model. The SWAT model results are also compared with the results of the radial-based neural network (RBNN) and support vector machines (SVM). The SWAT model performed well at the calibration stage i.e., determination coefficient (R 2 ) = 0.787 and Nash-Sutcliffe efficiency coefficient (NSE) = 0.779, and relatively lower values at the validation stage i.e., R 2 = 0.508 and NSE = 0.502. Besides, the data-driven models were more successful than the SWAT model. Obviously, the physically-based SWAT model offers significant advantages such as performing a spatial analysis of the results, creating a streamflow model taking into account the environmental impacts. Also, we show that SWAT offers the ability to produce consistent solutions under varying scenarios whereas it requires a large number of inputs as compared to the data-driven models. explicit knowledge of the physical behavior of the system [8]. In addition, adequate data should be provided for the training process in data-driven models. SWAT, a physically-based model frequently used by different disciplines, evaluates the watershed from a wider perspective [9][10][11][12][13][14]. The SWAT model is widely used in the simulation of the quality and quantity of surface and groundwater, in estimating the environmental impacts of different land use/land management practices and climate change, in calculating loads from pollutants, in evaluating best management practices, and in the simulation of various hydrological processes (runoff, infiltration, evapotranspiration, lateral flow, tile drainage, return flow, sediment etc) [15]. SWAT employs two different methods, the soil conservation services-curve number (SCS-CN) and the Green Ampt-MeinLarsen, for streamflow estimation [16][17][18][19]. Concurrent use of a digital elevation model (DEM), land use/land cover (LULC), and soil map alongside meteorological inputs also enables spatial analysis of the outputs produced by the model. As it includes physical inputs, the SWAT model yields successful results also in ungauged catchments [20,21].AI models such as support vector machines (SVM), artificial neural networks (ANN) and adaptive network-based fuzzy inference system (ANFIS) are widely used in estimating hydrological and meteorological phenomena. Tongal [22] used a chaotic approach (k-nearest neighbor-kNN) and neural networks (feed-forward neural networks, FFNN) the non-linear estimation of the streamflow of Yamula station in Kızılırmak Basin and found that the kNN model was more successful than the FFNN model for streamflow estimation. Buyukyildiz et al. [23] used five different methods, including support vector regression (SVR), artificial neur...

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“…The Kilombero Valley is one of the best characterised malaria transmission systems in Africa, having had some of the highest reported historical rates of malaria transmission [66]. Land use pressures related to population increases and overgrazing threaten the valley's biodiversity including its Puku antelope population, which is estimated to make up three-quarters of the world's population [67,68].…”

Section: Kilombero Valley United Republic Of Tanzaniamentioning

confidence: 99%

Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

2020

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Knowledge of the location and extent of surface water and inundated vegetation is vital for a range of applications including flood risk management, biodiversity monitoring, quantifying greenhouse gas emissions, and mapping water-borne disease risk. Here, we present a new tool, TropWet, which enables users of all abilities to map wetlands in herbaceous dominated regions based on simple unmixing of optical Landsat satellite imagery in the Google Earth Engine. The results demonstrate transferability throughout the African continent with a high degree of accuracy (mean 91% accuracy, st. dev 2.6%, n = 10,800). TropWet demonstrated considerable improvements over existing globally available surface water datasets for mapping the extent of important wetlands like the Okavango, Botswana. TropWet was able to provide frequency inundation maps as an indicator of malarial mosquito aquatic habitat extent and persistence in Barotseland, Zambia. TropWet was able to map flood extent comparable to operational flood risk mapping products in the Zambezi Region, Namibia. Finally, TropWet was able to quantify the effects of the El Niño/Southern Oscillation (ENSO) events on the extent of photosynthetic vegetation and wetland extent across Southern Africa. These examples demonstrate the potential for TropWet to provide policy makers with crucial information to help make national, regional, or continental scale decisions regarding wetland conservation, flood/disease hazard mapping, or mitigation against the impacts of ENSO.Landsat) with microwave systems (e.g., AMSR: Advanced Microwave Scanning Radiometer, SMAP: Soil Moisture Active Passive) [17,20]. Similarly, there is growing evidence that information from global navigation systems (GNSS-R: Global Navigation Satellite System Reflectometry) can provide timely and reliable classifications of wetlands by exploiting signals over both open water and vegetated water surfaces [21][22][23][24]. These approaches provide valuable tools for quantifying wetland dynamics at continental scales with important applications such as characterising greenhouse gas flux [17,18,24,25]. However, assessments of wetland extent and dynamics using these approaches tend to be generated at relatively coarse spatial resolutions (e.g., 25-36 km) and have limited applicability for informing decisions at a national or sub-national level, particularly related to more fine-scale environmental challenges such as those related to biodiversity, public health, and flood hazard.In terms of inundation mapping, perhaps the most mature area of research is the use of EO satellite imagery for mapping flood water hazards [2][3][4][5]7,[26][27][28][29][30][31][32]. Radar imagery provides one of the most reliable means of detecting flood water mainly due to the fact that this imagery is: (i) independent of cloud cover, (ii) relatively high resolution (e.g., Sentinel-1: 10 m), (iii) relatively high revisit times (e.g., Sentinel-1: 6-12 days), and (iv) there is a strong signal (low backscatter) over relatively smooth water surfaces. ...

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Hydrological Modeling in Data-Scarce Catchments: The Kilombero Floodplain in Tanzania

Cited by 49 publications

References 79 publications

The Impact of Land Use/Land Cover Change (LULCC) on Water Resources in a Tropical Catchment in Tanzania under Different Climate Change Scenarios

The Impact of Land Use/Land Cover Change (LULCC) on Water Resources in a Tropical Catchment in Tanzania under Different Climate Change Scenarios

Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin

Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

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