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  • 1. Dile, Yihun Taddele
    et al.
    Karlberg, Louise
    Srinivasan, Raghavan
    Rockström, Johan
    Stockholm University, Faculty of Science, Stockholm Resilience Centre.
    INVESTIGATION OF THE CURVE NUMBER METHOD FOR SURFACE RUNOFF ESTIMATION IN TROPICAL REGIONS2016In: Journal of the American Water Resources Association, ISSN 1093-474X, E-ISSN 1752-1688, Vol. 52, no 5, p. 1155-1169Article in journal (Refereed)
    Abstract [en]

    This study tests the applicability of the curve number (CN) method within the Soil and Water Assessment Tool (SWAT) to estimate surface runoff at the watershed scale in tropical regions. To do this, surface runoff simulated using the CN method was compared with observed runoff in numerous rainfall-runoff events in three small tropical watersheds located in the Upper Blue Nile basin, Ethiopia. The CN method generally performed well in simulating surface runoff in the studied watersheds (Nash-Sutcliff efficiency [NSE] > 0.7; percent bias [PBIAS] < 32%). Moreover, there was no difference in the performance of the CN method in simulating surface runoff under low and high antecedent rainfall (PBIAS for both antecedent conditions: similar to 30%; modified NSE: similar to 0.4). It was also found that the method accurately estimated surface runoff at high rainfall intensity (e.g., PBIAS < 15%); however, at low rainfall intensity, the CN method repeatedly underestimated surface runoff (e.g., PBIAS > 60%). This was possibly due to low infiltrability and valley bottom saturated areas typical of many tropical soils, indicating that there is scope for further improvements in the parameterization/representation of tropical soils in the CN method for runoff estimation, to capture low rainfall-intensity events. In this study the retention parameter was linked to the soil moisture content, which seems to be an appropriate approach to account for antecedent wetness conditions in the tropics.

  • 2.
    Dile, Yihun Taddele
    et al.
    Stockholm University, Stockholm Environment Institute. Stockholm University, Faculty of Science, Stockholm Resilience Centre.
    Srinivasan, Raghavan
    Evaluation of CFSR climate data for hydrologic prediction in data-scarce watersheds: an application in the Blue Nile River Basin2014In: Journal of the American Water Resources Association, ISSN 1093-474X, E-ISSN 1752-1688, Vol. 50, no 5, p. 1226-1241Article in journal (Refereed)
    Abstract [en]

    Data scarcity has been a huge problem in modeling the water resources of the Upper Blue Nile basin, Ethiopia. Satellite data and different statistical methods have been used to improve the quality of conventional meteorological data. This study assesses the applicability of the National Centers for Environmental Prediction's Climate Forecast System Reanalysis (CFSR) climate data in modeling the hydrology of the region. The Soil and Water Assessment Tool was set up to compare the performance of CFSR weather with that of conventional weather in simulating observed streamflow at four river gauging stations in the Lake Tana basin — the upper part of the Upper Blue Nile basin. The conventional weather simulation performed satisfactorily (e.g., NSE ≥ 0.5) for three gauging stations, while the CFSR weather simulation performed satisfactorily for two. The simulations with CFSR and conventional weather yielded minor differences in the water balance components in all but one watershed, where the CFSR weather simulation gave much higher average annual rainfall, resulting in higher water balance components. Both weather simulations gave similar annual crop yields in the four administrative zones. Overall the simulation with the conventional weather performed better than the CFSR weather. However, in data-scarce regions such as remote parts of the Upper Blue Nile basin, CFSR weather could be a valuable option for hydrological predictions where conventional gauges are not available.

  • 3.
    Lam, Norris
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Nathanson, Marcus
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Lundgren, Niclas
    Rehnström, Robin
    Lyon, Steve W.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    A Cost-Effective Laser Scanning Method for Mapping Stream Channel Geometry and Roughness2015In: Journal of the American Water Resources Association, ISSN 1093-474X, E-ISSN 1752-1688, Vol. 51, no 5, p. 1211-1220Article in journal (Refereed)
    Abstract [en]

    This brief pilot study implements a camera-based laser scanning system that potentially offers a viable, cost-effective alternative to traditional terrestrial laser scanning (TLS) and LiDAR equipment. We adapted a low-cost laser ranging system (SICK LSM111) to acquire area scans of the channel and bed for a temporarily diverted stream. The 5mx2m study area was scanned at a 4mm point spacing which resulted in a point cloud density of 5,600 points/m(2). A local maxima search algorithm was applied to the point cloud and a grain size distribution of the stream bed was extracted. The 84th and 90th percentiles of this distribution, which are commonly used to characterize channel roughness, were 90mm and 109mm, respectively. Our example shows the system can resolve both large-scale geometry (e.g., bed slope and channel width) and small-scale roughness elements (e.g., grain sizes between about 30 and 255mm) in an exposed stream channel thereby providing a resolution adequate for the estimation of ecohydraulic roughness parameters such as Manning's n. While more work is necessary to refine our specific field-deployable system's design, these initial results are promising in particular for those working on a limited or fixed budget. This opens up a realm of laser scanning applications and monitoring strategies for water resources that may not have been possible previously due to cost limitations associated with traditional TLS systems.

  • 4.
    Lyon, Steve W.
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology. Stockholm University, Faculty of Science, Stockholm University Baltic Sea Centre, Baltic Nest Institute.
    Meidani, Roya
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    van der Velde, Ype
    Dahlke, Helen E.
    Swaney, Dennis P.
    Mörth, Carl-Magnus
    Stockholm University, Faculty of Science, Stockholm University Baltic Sea Centre, Baltic Nest Institute. Stockholm University, Faculty of Science, Department of Geological Sciences.
    Humborg, Christoph
    Stockholm University, Faculty of Science, Stockholm University Baltic Sea Centre, Baltic Nest Institute. Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Seasonal and Regional Patterns in Performance for a Baltic Sea Drainage Basin Hydrologic Model2015In: Journal of the American Water Resources Association, ISSN 1093-474X, E-ISSN 1752-1688, Vol. 51, no 2, p. 550-566Article in journal (Refereed)
    Abstract [en]

    This study evaluates the ability of the Catchment SIMulation (CSIM) hydrologic model to describe seasonal and regional variations in river discharge over the entire Baltic Sea drainage basin (BSDB) based on 31years of monthly simulation from 1970 through 2000. To date, the model has been successfully applied to simulate annual fluxes of water from the catchments draining into the Baltic Sea. Here, we consider spatiotemporal bias in the distribution of monthly modeling errors across the BSDB since it could potentially reduce the fidelity of predictions and negatively affect the design and implementation of land-management strategies. Within the period considered, the CSIM model accurately reproduced the annual flows across the BSDB; however, it tended to underpredict the proportion of discharge during high-flow periods (i.e., spring months) and overpredict during the summer low flow periods. While the general overpredictions during summer periods are spread across all the subbasins of the BSDB, the underprediction during spring periods is seen largely in the northern regions. By implementing a genetic algorithm calibration procedure and/or seasonal parameterization of subsurface water flows for a subset of the catchments modeled, we demonstrate that it is possible to improve the model performance albeit at the cost of increased parameterization and potential loss of parsimony.

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