scholarly journals Modelling stream flow and quantifying blue water using modified STREAM model in the Upper Pangani River Basin, Eastern Africa

2013 ◽  
Vol 10 (12) ◽  
pp. 15771-15809 ◽  
Author(s):  
J. K. Kiptala ◽  
M. L. Mul ◽  
Y. Mohamed ◽  
P. van der Zaag
Keyword(s):  
River Basin ◽  
Water Use ◽  
Irrigation Water ◽  
Eastern Africa ◽  
Blue Water ◽  
Distributed Models ◽  
Low Flows ◽  
Catchment Areas ◽  
Spatially Distributed ◽  
Stream Model

Abstract. Effective management of all water uses in a river basin requires spatially distributed information of evaporative water use and the link towards the river flows. Physically based spatially distributed models are often used to generate this kind of information. These models require enormous amounts of data, if not sufficient would result in equifinality. In addition, hydrological models often focus on natural processes and fail to account for water usage. This study presents a spatially distributed hydrological model that has been developed for a heterogeneous, highly utilized and data scarce river basin in Eastern Africa. Using an innovative approach, remote sensing derived evapotranspiration and soil moisture variables for three years were incorporated as input data in the model conceptualization of the STREAM model (Spatial Tools for River basin Environmental Analysis and Management). To cater for the extensive irrigation water application, an additional blue water component was incorporated in the STREAM model to quantify irrigation water use (ETb(I)). To enhance model parameter identification and calibration, three hydrological landscapes (wetlands, hill-slope and snowmelt) were identified using field data. The model was calibrated against discharge data from five gauging stations and showed considerably good performance especially in the simulation of low flows where the Nash–Sutcliffe Efficiency of the natural logarithm (Eln) of discharge were greater than 0.6 in both calibration and validation periods. At the outlet, the Eln coefficient was even higher (0.90). During low flows, ETb(I) consumed nearly 50% of the river flow in the river basin. ETb(I) model result was comparable to the field based net irrigation estimates with less than 20% difference. These results show the great potential of developing spatially distributed models that can account for supplementary water use. Such information is important for water resources planning and management in heavily utilized catchment areas. Model flexibility offers the opportunity for continuous model improvement when more data become available.

scholarly journals Modelling stream flow and quantifying blue water using a modified STREAM model for a heterogeneous, highly utilized and data-scarce river basin in Africa

2014 ◽  
Vol 18 (6) ◽  
pp. 2287-2303 ◽  
Author(s):  
J. K. Kiptala ◽  
M. L. Mul ◽  
Y. A. Mohamed ◽  
P. van der Zaag
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Water Use ◽  
Irrigation Water ◽  
Eastern Africa ◽  
Blue Water ◽  
Distributed Models ◽  
Low Flows ◽  
Spatially Distributed ◽  
Stream Model

Abstract. Integrated water resources management is a combination of managing blue and green water resources. Often the main focus is on the blue water resources, as information on spatially distributed evaporative water use is not as readily available as the link to river flows. Physically based, spatially distributed models are often used to generate this kind of information. These models require enormous amounts of data, which can result in equifinality, making them less suitable for scenario analyses. Furthermore, hydrological models often focus on natural processes and fail to account for anthropogenic influences. This study presents a spatially distributed hydrological model that has been developed for a heterogeneous, highly utilized and data-scarce river basin in eastern Africa. Using an innovative approach, remote-sensing-derived evapotranspiration and soil moisture variables for 3 years were incorporated as input data into the Spatial Tools for River basin Environmental Analysis and Management (STREAM) model. To cater for the extensive irrigation water application, an additional blue water component (Qb) was incorporated in the STREAM model to quantify irrigation water use. To enhance model parameter identification and calibration, three hydrological landscapes (wetlands, hillslope and snowmelt) were identified using field data. The model was calibrated against discharge data from five gauging stations and showed good performance, especially in the simulation of low flows, where the Nash–Sutcliffe Efficiency of the natural logarithm (Ens_ln) of discharge were greater than 0.6 in both calibration and validation periods. At the outlet, the Ens_ln coefficient was even higher (0.90). During low flows, Qb consumed nearly 50% of the river flow in the basin. The Qb model result for irrigation was comparable to the field-based net irrigation estimates, with less than 20% difference. These results show the great potential of developing spatially distributed models that can account for supplementary water use. Such information is important for water resources planning and management in heavily utilized catchment areas. Model flexibility offers the opportunity for continuous model improvement when more data become available.

Assessment of Water Footprint for Koshi River Basin (KRB), Nepal

2020 ◽  
Author(s):  
Raj Deva Singh ◽  
Kumar Ghimire ◽  
Ashish Pandey
Keyword(s):  
River Basin ◽  
Water Use ◽  
Crop Production ◽  
Agricultural Land ◽  
Water Footprint ◽  
Green Water ◽  
Blue Water ◽  
Total Water ◽  
Green And Blue Water ◽  
Koshi River Basin

<p>Nepal is an agrarian country and almost one-third of Gross Domestic Product (GDP) is dependent on agricultural sector. Koshi river basin is the largest basin in the country and serves large share on agricultural production. Like another country, Nepalese agriculture holds largest water use in agriculture. In this context, it is necessary to reduce water use pressure. In this study, water footprint of different crop (rice, maize, wheat, millet, sugarcane, potato and barley) have been estimated for the year 2005 -2014 to get the average water footprint of crop production during study period. CROPWAT model, developed by Food and Agriculture Organization (FAO 2010b).</p><p>For the computation of the green and blue water footprints, estimated values of ET (the output of CROPWAT model) and yield (derived from statistical data) are utilised. Blue and green water footprint are computed for different districts (16 districts within KRB) / for KRB in different years (10 years from 2005 to 2014) and crops (considered 7 local crops). The water footprint of crops production for any district or basin represents the average of WF production of seven crops in the respective district or basin.</p><p>The study provides a picture of green and blue water use in crop production in the field and reduction in the water footprint of crop production by selecting suitable crops at different places in the field. The Crop, that has lower water footprint, can be intensified at that location and the crops, having higher water footprint, can be discontinued for production or measure for water saving technique needs to be implemented reducing evapotranspiration. The water footprint of agriculture crop production can be reduced by increasing the yield of the crops. Some measures like use of an improved variety of seed, fertilizer, mechanized farming and soil moisture conservation technology may also be used to increase the crop yields.</p><p>The crop harvested areas include both rainfed as well as irrigated land. Agricultural land occupies 22% of the study area, out of which 94% areas are rainfed whereas remaining 6% areas are under irrigation. The study shows 98% of total water use in crop production is due to green water use (received from rainfall) and remaining 2 % is due to blue water use received from irrigation (surface and ground water as source). Potato has 22% blue water proportion and contributes 85% share to the total blue water use in the basin. Maize and rice together hold 77% share of total water use in crops production. The average annual water footprint of crop production in KRB is 1248 cubic meter/ton having the variation of 9% during the period of 2005-2014. Sunsari, Dhankuta districts have lower water footprint of crop production. The coefficient of variation of water footprint of millet crop production is lower as compared to those of other crops considered for study whereas sugarcane has a higher variation of water footprint for its production.</p>

Multi-criterial calibration of a global hydrological model for the Mississippi basin: Exploring the effect of the number of calibration units

2020 ◽  
Author(s):  
HM Mehedi Hasan ◽  
Andreas Güntner ◽  
Somayeh Shadkam ◽  
Petra Döll
Keyword(s):  
River Basin ◽  
Hydrological Model ◽  
Grid Cell ◽  
Predictive Ability ◽  
Large River ◽  
Computational Effort ◽  
Distributed Models ◽  
Spatially Distributed ◽  
Grace Satellites ◽  
The Given

<p>The predictive ability of a hydrological model depends among others on how well the model is calibrated by model parameter adjustment. When calibrating spatially distributed models such as global hydrological models in which river basins are represented by laterally connected grid cells of mostly 0.5° latitude by 0.5° longitude, it is not appropriate and possible to adjust the parameters of each grid cell individually. This is mainly due to the lack of high-resolution observations but also due to the required computational effort. It needs to be investigated which spatial extent of calibration units for which parameters are uniformly adjusted, is optimal given the available observations and the characteristics of the region or river basin. To explore the effect of size and number of calibration units, the WaterGAP Global Hydrological Model (WGHM) was calibrated for a large river basin in North America, the Mississippi basin, successively dividing the basin into smaller calibration units, i.e., sub-basins, in order to examine the feasibility and value of reducing the size of calibration units for the given set of observations. Total water storage anomalies from GRACE satellites, snow cover from MODIS and in-situ streamflow were used as observations in an ensemble-based multi-criterial Pareto Optimization Calibration (POC) framework using the Borg-MOEA optimization algorithm.</p>

scholarly journals Directions and Extent of Flows Changes in Warta River Basin (Poland) in the Context of the Efficiency of Run-of-River Hydropower Plants and the Perspectives for Their Future Development

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 439
Author(s):  
Mariusz Sojka
Keyword(s):  
River Basin ◽  
Dominant Role ◽  
Flow Characteristics ◽  
Geological Structure ◽  
River Flows ◽  
Low Flows ◽  
Hydropower Plants ◽  
Sampling Series ◽  
Catchment Areas ◽  
Run Of River

This paper presents changes in the flow of 14 rivers located in the Warta River basin, recorded from 1951 to 2020. The Warta is the third-longest river in Poland. Unfortunately, the Warta River catchment area is one of the most water-scarce regions. It hosts about 150 hydropower plants with a capacity of up to 5 kW. The catchment areas of the 14 smaller rivers selected for the study differ in location, size, land cover structure and geological structure. The paper is the first study of this type with respect to both the number of analyzed catchments, the length of the sampling series and the number of analyzed flow characteristics in this part of Europe. The analysis of changes in the river flows was performed with reference to low minimum, mean and maximum monthly, seasonal and annual flows. Particular attention was paid to 1, 3, 7, 30 and 90-day low flows and durations of the flows between Q50 and Q90%. In addition, the duration of flows between Q50 and Q90% were analysed. Analysis of the direction and extent of particular flow types was performed by multitemporal analysis using the Mann–Kendall (MK) and Sen (S) tests. The analysis of multiannual flow sequences from the years 1951–2020 showed that the changes varied over the time periods and catchments. The most significant changes occurred in the low flows, while the least significant changes occurred in the high flows. From the point of view of the operation of the hydropower sector, these changes may be unfavourable and result in a reduction in the efficiency of run-of-river hydropower plants. It was established that local factors play a dominant role in the shaping of river flows in both positive and negative terms, for the efficiency of the hydropower plants.

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