Basin-scale performance of a semidistributed rainfall-runoff model for hydrological predictions and water resources assessment of large rivers: The Congo River

2014 ◽  
Vol 50 (2) ◽  
pp. 1174-1188 ◽  
Author(s):  
Raphael M. Tshimanga ◽  
Denis A. Hughes
Keyword(s):  
Water Resources ◽  
Large Rivers ◽  
Rainfall Runoff ◽  
Congo River ◽  
Basin Scale ◽  
Water Resources Assessment ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Resources Assessment

Perhitungan ketersediaan air permukaan di Indonesia berdasarkan data satelit

2018 ◽  
Vol 13 (2) ◽  
pp. 115-130 ◽  
Author(s):  
Radhika Radhika ◽  
Rendy Firmansyah ◽  
Waluyo Hatmoko
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Water Availability ◽  
River Flow ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Satellite Technology ◽  
Rainfall Runoff Model ◽  
The Tropics ◽  
Runoff Model

Information on water availability is vital in water resources management. Unfortunately, information on the condition of hydrological data, either river flow data, or rainfall data is very limited temporally and spatially. With the availability of satellite technology, rainfall in the tropics can be monitored and recorded for further analysis. This paper discusses the calculation of surface water availability based on rainfall data from TRMM satellite, and then Wflow, a distributed rainfall-runoff model generates monthly time runoff data from 2003 to 2015 for all river basin areas in Indonesia. It is concluded that the average surface water availability in Indonesia is 88.3 thousand m3/s or equivalent to 2.78 trillion m3/ year. This figure is lower than the study of Water Resources Research Center 2010 based on discharge at the post estimated water that produces 3.9 trillion m3/year, but very close to the study of Aquastat FAO of 2.79 trillion m3 / year. The main benefit of this satellite-based calculation is that at any location in Indonesia, potential surface water can be obtained by multiplying the area of the catchment and the runoff height.

A WebGIS-based system for rainfall-runoff prediction and real-time water resources assessment for Beijing

2009 ◽  
Vol 35 (7) ◽  
pp. 1517-1528 ◽  
Author(s):  
Yangwen Jia ◽  
Hongli Zhao ◽  
Cunwen Niu ◽  
Yunzhong Jiang ◽  
Hong Gan ◽  
...  
Keyword(s):  
Water Resources ◽  
Real Time ◽  
Rainfall Runoff ◽  
Runoff Prediction ◽  
Water Resources Assessment ◽  
Resources Assessment

scholarly journals Model analysis of forest thinning impacts on the water resources during hydrological drought periods

2021 ◽  
Author(s):  
Hiroki Momiyama ◽  
Tomo'omi Kumagai ◽  
Tomohiro Egusa
Keyword(s):  
Water Resources ◽  
Meteorological Data ◽  
Rainfall Runoff ◽  
Forest Thinning ◽  
Flow Duration ◽  
Mountain Catchment ◽  
Data Generator ◽  
Flow Duration Curves ◽  
Rainfall Runoff Model ◽  
Runoff Model

In Japan, there has recently been an increasing call for forest thinning to conserve water resources from forested mountain catchments in terms of runoff during prolonged drought periods of the year. How their water balance and the resultant runoff are altered by forest thinning is examined using a combination of 8-year hydrological observations, 100-year meteorological data generator output, and a semi-process-based rainfall-runoff model. The rainfall-runoff model is developed based on TOPMODEL assuming that forest thinning has an impact on runoff primarily through an alteration in canopy interception. The main novelty in this analysis is that the availability of the generated 100-year meteorological data allows the investigations of the forest thinning impacts on mountain catchment water resources under the most severer drought conditions. The model is validated against runoff observations conducted at a forested mountain catchment in the Kanto region of Japan for the period 2010–2017. It is demonstrated that the model reproduces temporal variations in runoff and evapotranspiration at inter- and intra-annual time scales, resulting in well reproducing the observed flow duration curves. On the basis of projected flow duration curves for the 100-year, despite the large increase in an annual total runoff with ordinary intensifying thinning, low flow rates, i.e., water resources from the catchment in the drought period in the year, in both normal and drought years were impacted by the forest thinning to a lesser extent. Higher catchment water retention capacity appreciably enhanced the forest thinning effect on increasing available water resources.

scholarly journals Coupling Tank Model and Lars-Weather Generator in Assessments of the Impacts of Climate Change on Water Resources

2019 ◽  
Vol 27 (1) ◽  
pp. 14-24 ◽  
Author(s):  
Naser Mohammadzadeh ◽  
Bahman Jabbarian Amiri ◽  
Leila Eslami Endergoli ◽  
Shirin Karimi
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Weather Generator ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
The Caspian Sea ◽  
Tank Model ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract With the aim of assessing the impact of climate change on surface water resources, a conceptual rainfall-runoff model (the tank model) was coupled with LARS-WG as a weather generator model. The downscaled daily rainfall, temperature, and evaporation from LARS-WG under various IPCC climate change scenarios were used to simulate the runoff through the calibrated Tank model. A catchment (4648 ha) located in the southern basin of the Caspian Sea was chosen for this research study. The results showed that this model has a reasonable predictive capability in simulating minimum and maximum temperatures at a level of 99%, rainfall at a level of 93%, and radiation at a level of 97% under various scenarios in agreement with the observed data. Moreover, the results of the rainfall-runoff model indicated an increase in the flow rate of about 108% under the A1B scenario, 101% under the A2 scenario, and 93% under the B1 scenario over the 30-year time period of the discharge prediction.

scholarly journals Genetic programming for hydrological applications: to model or forecast that is the question

2021 ◽  
Author(s):  
Herath Mudiyanselage Viraj Vidura Herath ◽  
Jayashree Chadalawada ◽  
Vladan Babovic
Keyword(s):  
Water Resources ◽  
Genetic Programming ◽  
Data Science ◽  
Model Building ◽  
Building Blocks ◽  
Rainfall Runoff ◽  
New Paradigm ◽  
Distributed Modelling ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract Genetic programming (GP) is a widely used machine learning (ML) algorithm that has been applied in water resources science and engineering since its conception in the early 1990s. However, similar to other ML applications, the GP algorithm is often used as a data fitting tool rather than as a model building instrument. We find this a gross underutilization of the GP capabilities. The most unique and distinct feature of GP that makes it distinctly different from the rest of ML techniques is its capability to produce explicit mathematical relationships between input and output variables. In the context of theory-guided data science (TGDS) which recently emerged as a new paradigm in ML with the main goal of blending the existing body of knowledge with ML techniques to induce physically sound models. Hence, TGDS has evolved into a popular data science paradigm, especially in scientific disciplines including water resources. Following these ideas, in our prior work, we developed two hydrologically informed rainfall-runoff model induction toolkits for lumped modelling and distributed modelling based on GP. In the current work, the two toolkits are applied using a different hydrological model building library. Here, the model building blocks are derived from the Sugawara TANK model template which represents the elements of hydrological knowledge. Results are compared against the traditional GP approach and suggest that GP as a rainfall-runoff model induction toolkit preserves the prediction power of the traditional GP short-term forecasting approach while benefiting to better understand the catchment runoff dynamics through the readily interpretable induced models.

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