monthly water balance
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2022 ◽  
Author(s):  
Maryam Khodadadi ◽  
Tarokh Maleki Roozbahani ◽  
Mercedeh Taheri ◽  
Fatemeh Ganji ◽  
Mohsen Nasseri

Abstract Against the paramount role of actual evapotranspiration (ET) in hydrological modeling, determining its values is mixed with different sources of uncertainties. In addition, estimation of ET with energy-based methods (e.g., METRIC) leads to different results with various acceptable initial and boundary conditions (such as land use and cold/hot pixels). The aim of the current research is to allow the uncertainty effects of ET as an interval-based input variable in hydrological modeling. The goal is achieved via feeding the uncertainty of computed ET values to the developed Interval-Based Water Balance (IBWB) model in terms of gray values. To this purpose, the comprehensive monthly water balance model (including surface and groundwater modules) has been revised to a new interval-based form. Moreover, the METRIC model has been used 20 times in each month of computational period to calculate the ET patterns with different hot/cold pixels to provide monthly ensemble ET values. For a comprehensive assessment, the selected water balance model has been calibrated with ensemble means of the computed ET with its classical type. The study area is a mountainous sub-basin of the Sefidrood watershed, Ghorveh-Dehgolan basin, with three alluvial aquifers in the North of Iran. Not only the paradigm shift from determinist to interval-based hydrologic structure improved the statistical metrics of the models’ responses, but also it decreased the uncertainty of the simulated streamflow and groundwater levels.


2021 ◽  
Vol 893 (1) ◽  
pp. 012078
Author(s):  
G I S L Faski ◽  
Ig L S Purnama ◽  
S Suprayogi

Abstrak Water balance serves to determine hydrological conditions in a watershed, one of which is by analyzing the surplus (excess water) and deficit (water shortage) that occurs. Extreme surpluses or deficits can cause hydrometeorological disasters, such as floods or droughts. This study aims to calculate the monthly water balance using the Thornthwaite-Mather method to determine variations in the incidence of surplus and deficit months in all three sub-watersheds in Bengkulu Watershed, namely Rindu Hati, Susup, and Bengkulu Hilir sub-watershed. The data used are monthly hydrometeorological data for 2009-2018 (10 years) were divided into two periods of water balance based on land use data. Water balance period 1 (2009-2013) uses 2009 land use data, while period 2 (2014-2018) uses 2014 land use data. The results show that the surplus, deficit, runoff, and discharge in the three sub-watersheds in the Bengkulu watershed are affected by rainfall. In general, the deficit incidents in all three sub-watersheds occur almost every three years. The Rindu Hati and Susup sub-watersheds have the same variations of surplus and deficit month incidents, while the Bengkulu Hilir sub-watershed is different, both in periods 1 and 2. It is not only the rainfall that affects the variation in the events of surplus and deficit in all three sub-watersheds of the Bengkulu watershed, but also the amount of water holding capacity (WHC). Therefore, the application of hydrometeorological data to analyze the water balance conditions in the Bengkulu watershed provides information on climate impact on water resources and environmental impact on flows in the watershed.


Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1048
Author(s):  
Harold Llauca ◽  
Waldo Lavado-Casimiro ◽  
Cristian Montesinos ◽  
William Santini ◽  
Pedro Rau

Quantification of the surface water offer is crucial for its management. In Peru, the low spatial density of hydrometric stations makes this task challenging. This work aims to evaluate the hydrological performance of a monthly water balance model in Peru using precipitation and evapotranspiration data from the high-resolution meteorological PISCO dataset, which has been developed by the National Service of Meteorology and Hydrology of Peru (SENAMHI). A regionalization approach based on Fourier Amplitude Sensitivity Testing (FAST) of the rainfall-runoff (RR) and runoff variability (RV) indices defined 14 calibration regions nationwide. Next, the GR2M model was used at a semi-distributed scale in 3594 sub-basins and river streams to simulate monthly discharges from January 1981 to March 2020. Model performance was evaluated using the Kling–Gupta efficiency (KGE), square root transferred Nash–Sutcliffe efficiency (NSEsqrt), and water balance error (WBE) metrics. The results show a very well representation of monthly discharges for a large portion of Peruvian sub-basins (KGE ≥ 0.75, NSEsqrt ≥ 0.65, and −0.29 < WBE < 0.23). Finally, this study introduces a product of continuous monthly discharge rates in Peru, named PISCO_HyM_GR2M, to understand surface water balance in data-scarce sub-basins.


Hydrology ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 34
Author(s):  
Elisa Mammoliti ◽  
Davide Fronzi ◽  
Adriano Mancini ◽  
Daniela Valigi ◽  
Alberto Tazioli

Nowadays, the balance between incoming precipitation and stream or spring discharge is a challenging aspect in many scientific disciplines related to water management. In this regard, although advances in the methodologies for water balance calculation concerning each component of the water cycle have been achieved, the Thornthwaite–Mather method remains one of the most used, especially for hydrogeological purposes. In fact, in contrast to physical-based models, which require many input parameters, the Thornthwaite–Mather method is a simple, empirical, data-driven procedure in which the error associated with its use is smaller than that associated with the measurement of input data. The disadvantage of this method is that elaboration times can be excessively long if a classical MS Excel file is used for a large amount of data. Although many authors have attempted to automatize the procedure using simple algorithms or graphical user interfaces, some bugs have been detected. For these reasons, we propose a WebApp for monthly water balance calculation, called WaterbalANce. WaterbalANce was written in Python and is driven by a serverless computing approach. Two respective European watersheds are selected and presented to demonstrate the application of this method.


2020 ◽  
Vol 591 ◽  
pp. 125572
Author(s):  
Shujie Cheng ◽  
Lei Cheng ◽  
Pan Liu ◽  
Lu Zhang ◽  
Chongyu Xu ◽  
...  

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Hong X. Do ◽  
Joeseph P. Smith ◽  
Lauren M. Fry ◽  
Andrew D. Gronewold

2020 ◽  
Author(s):  
Zana Topalovic ◽  
Andrijana Todorovic ◽  
Jasna Plavsic

&lt;p&gt;Assessment of climate change impact on water resources is often based on hydrologic projections developed using monthly water balance models (MWBMs) forced by climate projections. These models are calibrated against historical data but are expected to provide accurate flow simulations under changing climate conditions. However, an evaluation of these models&amp;#8217; performance is needed to explore their applicability under changing climate conditions, assess uncertainties and eventually indicate model components that should be improved. This should be done in a comprehensive evaluation framework specifically tailored to evaluate applicability of MWBMs in changing climatic conditions.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;In this study, we evaluated performance of four MWBMs (abcd, Budyko, GR2M and WASMOD) used for hydrologic simulations in the arid Wimmera River catchment in Australia. This catchment is selected as a challenge for model application because it was affected by the Millennium drought, characterised by a decrease in precipitation and a dramatic drop in runoff. The model evaluation within the proposed framework starts with dividing the complete record period into five non-overlapping sub-periods, calibration and cross-validation (i.e., transfers) of the models. The Kling-Gupta efficiency coefficient is used for the calibration in each sub-period. Consistency in model performance, parameter estimates and simulated water balance components across the sub-periods is analysed. Model performance is quantified with statistical performance measures and errors in hydrological signatures. Because the relatively short monthly hydrologic series can lead to biased numerical performance indicators, the framework also includes subjective assessment of model performance and transferability.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The results show that model transfer between climatically contrasted sub-periods affect all statistical measures of model performance and some hydrologic signatures: standard deviation of flows, high flow percentile and percentage of zero flows. While some signatures are reproduced well in all transfers (baseflow index, lag 1 and lag 12 autocorrelations), suggesting their low informativeness about MWBM performance, many signatures are consistently poorly reproduced, even in the calibrations (seasonal distribution, most flow percentiles, streamflow elasticity). This means that good model performance in terms of statistical measures does not imply good performance in terms of hydrologic signatures, probably because the models are not conditioned to reproduce them. Generally, the greatest drop in performance of all the models is obtained in transfers to the driest period, although abcd and Budyko slightly outperformed GR2M and WASMOD. Subjective assessment of model performance largely corresponds to the numerical indicators.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Simulated water balance components, especially soil and groundwater storages and baseflow, significantly vary across the simulation periods. These results suggest that the model components and the parameters that control them are sensitive to the calibration period. Therefore, improved model conceptualisations (particularly partitioning of fast and slow runoff components) and enhanced calibration strategies that put more emphasis on parameters related to slow runoff are needed. More robust MWBM structures or calibration strategies should advance transferability of MWBMs, which is a prerequisite for effective water resources management under changing climate conditions.&lt;/p&gt;


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