scholarly journals Establishment and Analysis of Spatiotemporal Variation Hydrological Model of Distributed Rainfall and Evaporation in Biliu River Basin

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qi Liu ◽  
Xiaolong Zhao ◽  
Hongyan Wang ◽  
Yongfeng Sun
Keyword(s):  
River Basin ◽  
River Runoff ◽  
Irrigation Water ◽  
Water Demand ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Precipitation Process ◽  
Model Parameters ◽  
Runoff Simulation ◽  
Irrigation Water Demand

The Biliu River originates from the southern foot of Qinling Mountain in Gaizhou city, with an elevation of 1047 m, and is the largest river in Dalian. The hydrological elements mainly include rainfall, runoff, temperature, evaporation, and other time series associated with the hydrological cycle. Among them, runoff is the most visible output performance, and the direct source of runoff is during rainfall. This paper establishes a reservoir scheduling model that considers the influence of multiple uncertainty factors and analyzes the influence of mixed uncertainty on reservoir scheduling and Xingli’s objectives based on probability box theory. In terms of uncertainties, the uncertainty of hydrological model parameters and the randomness of precipitation processes are mainly considered, with the former having an impact on river runoff simulation and the latter having an impact on both river runoff simulation and crop irrigation water demand. In the case of the Jing River basin, for example, the results show that, compared to the stochasticity of the precipitation process, the variation in precipitation has a significant effect on irrigation water demand in maize, followed by the frequency of precipitation, and the interaction between the two is not significant.

scholarly journals The relationship between irrigation water demand and drought in the Yellow River basin

2016 ◽  
Vol 374 ◽  
pp. 129-136 ◽  
Author(s):  
Yu Wang ◽  
Weihao Wang ◽  
Shaoming Peng ◽  
Guiqin Jiang ◽  
Jian Wu
Keyword(s):  
River Basin ◽  
Irrigation Water ◽  
Water Demand ◽  
Yellow River ◽  
Yellow River Basin ◽  
Irrigation District ◽  
The Yellow River ◽  
Irrigation Water Demand ◽  
Irrigation Districts ◽  
The Yellow River Basin

Abstract. In order to organize water for drought resistance reasonably, we need to study the relationship between irrigation water demand and meteorological drought in quantitative way. We chose five typical irrigation districts including the Qingtongxia irrigation district, Yellow River irrigation districts of Inner Mongolia in the upper reaches of the Yellow River, the Fen river irrigation district and the Wei river irrigation district in the middle reaches of the Yellow River and the irrigation districts in the lower reaches of the Yellow River as research area. Based on the hydrology, meteorology, groundwater and crop parameters materials from 1956 to 2010 in the Yellow River basin, we selected reconnaissance drought index (RDI) to analyze occurrence and evolution regularity of drought in the five typical irrigation districts, and calculated the corresponding irrigation water demand by using crop water balance equation. The relationship of drought and irrigation water demand in each typical irrigation district was studied by using grey correlation analysis and relevant analysis method, and the quantitative relationship between irrigation water demand and RDI was established in each typical irrigation district. The results showed that the RDI can be applied to evaluate the meteorological drought in the typical irrigation districts of the Yellow River basin. There is significant correlation between the irrigation water demand and RDI, and the grey correlation degree and correlation coefficient increased with increasing crops available effective rainfall. The irrigation water demand of irrigation districts in the upstream, middle and downstream of the Yellow River basin presented different response degrees to drought. The irrigation water demand increased 105 million m3 with the drought increasing one grade (RDI decreasing 0.5) in the Qingtongxia irrigation district and Yellow River irrigation districts of Inner Mongolia. The irrigation water demand increased 219 million m3 with the drought increasing one grade in the Fen river irrigation district and Wei river irrigation district. The irrigation water demand increased 622 million m3 with the drought increasing one grade in the downstream of Yellow River irrigation districts.

scholarly journals Climate Normalized Spatial Patterns of Evapotranspiration Enhance the Calibration of a Hydrological Model

2022 ◽  
Vol 14 (2) ◽  
pp. 315
Author(s):  
Julian Koch ◽  
Mehmet Cüneyd Demirel ◽  
Simon Stisen
Keyword(s):  
Spatial Pattern ◽  
River Basin ◽  
Spatial Patterns ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Model Parameters ◽  
Climate Control ◽  
Spatial Efficiency ◽  
Senegal River ◽  
Senegal River Basin

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement has been supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often driven by underlying climate gradients, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated patterns. To address this, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as a modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. We apply the mesoscale Hydrological Model (mHM) to model the hydrological cycle of the Senegal River basin. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. As objective functions we applied the Kling-Gupta-Efficiency (KGE) for Q and the Spatial Efficiency (SPAEF) for ET. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial pattern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern, i.e., a 30% decrease in SPAEF. Both calibrations reached comparable performance of Q, i.e., KGE around 0.7. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.

scholarly journals Assessing effects of climate change on irrigation water demand in the Lombok River Basin, Indonesia

2021 ◽  
Vol 930 (1) ◽  
pp. 012061
Author(s):  
A W W Saputra ◽  
N A Zakaria ◽  
N W Chan
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Irrigation Water ◽  
Water Demand ◽  
Irrigation Efficiency ◽  
Command Area ◽  
Efficiency Improvement ◽  
Water Requirements ◽  
Irrigation Water Demand ◽  
The Impact

Abstract Irrigation water demand in the command area is affected by rainfall and climate conditions in the river basin. In climate change conditions, rainfall and temperature are predicted to increase and projected to impact irrigation water requirements significantly. Therefore, understanding the climate change effects on irrigation demand in the command area is significant to the river basin manager and planner for managing water resources effectively. This study aims to predict the impact of climate change and irrigation efficiency improvement on the irrigation water requirement in 2032-2040. This study used the CropWat model to estimate irrigation water requirements in 1995-2005 and 2032-2040. Irrigation water demand in the Dodokan watershed as a part of the Lombok river basin was computed using the historical rainfall and climate data from observation stations. Further, the observed data from 2006 to 2014 were projected into climate change in 2032-2040 as an input for the model to predict the demand in corresponding years. Result suggests that the change of annual irrigation water demand in the Dodokan watershed was expected to rise by 1.61% in 2032-2040 compared with 1995-2005, and irrigation efficiency improvement effort would decrease the demand -18.18% in the climate change period.

scholarly journals An Innovative Fog Catcher System Applied in the Andean Communities of Ecuador

2017 ◽  
Vol 60 (6) ◽  
pp. 1917-1923
Author(s):  
David V. Carrera-Villacrés ◽  
Iveth Carolina Robalino ◽  
Fabian F. Rodríguez ◽  
Washington R. Sandoval ◽  
Deysi L. Hidalgo ◽  
...  
Keyword(s):  
Sea Level ◽  
Water Deficit ◽  
Irrigation Water ◽  
Water Demand ◽  
Agricultural Production ◽  
Water Deficits ◽  
Irrigation Water Demand ◽  
The World ◽  
Precipitation Water ◽  
Local Water

Abstract. Fog catchers have been successfully applied in several countries around the world. In Ecuador, the Galte communities in the Andean region suffer from water deficits because they are located at an altitude higher than 3500 m above sea level. Rainfall in the area is relatively low, about 600 mm per year, with high evapotranspiration of approximately 615.74 mm per year. This study aimed to install fog catchers in Galte in 2014 and 2015 to help meet the communities’ water needs. The fog catcher system was designed to satisfy the irrigation water demand for local agricultural production, mainly maize, based on estimates using the Blaney-Criddle method. Every day throughout the year, each fog catcher collected 5 to 20 L of water per m2 of catcher area. The results indicate that the fog catcher system can meet about 5% of the local water demand for agricultural production. Keywords: Ecuador, Evaporation, Evapotranspiration, Precipitation, Water deficit.

scholarly journals Quantifying the Impacts of Compound Extremes on Agriculture and Irrigation Water Demand

2020 ◽  
Author(s):  
Iman Haqiqi ◽  
Danielle S. Grogan ◽  
Thomas W. Hertel ◽  
Wolfram Schlenker
Keyword(s):  
Heat Stress ◽  
Water Stress ◽  
Irrigation Water ◽  
Water Demand ◽  
Crop Production ◽  
Crop Yields ◽  
Fold Increase ◽  
The United States ◽  
Food Prices ◽  
Irrigation Water Demand

Abstract. Agricultural production and food prices are affected by hydroclimatic extremes. There has been a large literature measuring the impacts of individual extreme events (heat stress or water stress) on agricultural and human systems. Yet, we lack a comprehensive understanding of the significance and the magnitude of the impacts of compound extremes. Here, we combine a high-resolution weather product with fine-scale outputs of a hydrological model to construct functional indicators of compound hydroclimatic extremes for agriculture. Then, we measure the impacts of individual and compound extremes on crop yields focusing on the United States during the 1981–2015 period. Supported by statistical evidence, we confirm that wet heat is more damaging than dry heat for crops. We show that the average damage from heat stress has been up to four times more severe when combined with water stress; and the value of water experiences a four-fold increase on hot days. In a robust framework with only a few parameters of compound extremes, this paper also improves our understanding of the conditional marginal value (or damage) of water in crop production. This value is critically important for irrigation water demand and farmer decision-making – particularly in the context of supplemental irrigation and sub-surface drainage.

scholarly journals Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

2015 ◽  
Vol 19 (4) ◽  
pp. 2079-2100 ◽  
Author(s):  
N. Tangdamrongsub ◽  
S. C. Steele-Dunne ◽  
B. C. Gunter ◽  
P. G. Ditmar ◽  
A. H. Weerts
Keyword(s):  
Correlation Coefficient ◽  
River Basin ◽  
Hydrological Cycle ◽  
Water Storage ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rhine River ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35%. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.

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