Water Resource Planning
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Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 89
Yuan Liu ◽  
Dongchun Yan ◽  
Anbang Wen ◽  
Zhonglin Shi ◽  
Taili Chen ◽  

In this study, the temporal and spatial patterns of rainfall in the Longchuan River basin from 1977 to 2017 were analyzed, to assess the feature of precipitation. Based on the daily precipitation time series, the Lorenz curve, precipitation concentration index (PCI), precipitation concentration degree (PCD), and the precipitation concentration period (PCP) were used to evaluate the precipitation distribution characteristics. The PCI, PCD and PCP in five categories, defined by the fixed thresholds, were proposed to investigate the concentrations, and the average values indicated the higher concentrations in the higher intensities. The indices showed strong irregularity of daily and monthly precipitation distributions in this basin. The decrease in the PCD revealed an increase in the proportion of precipitation in the dry season. The rainy days of slight precipitation in the upper and lower basins with significant downward trends (−13.13 d/10 a, −7.78 d/10 a) led to longer dry spells and an increase in the risk of drought, even severe in the lower area. In the upper basin, the increase in rainfall erosivity was supported by the upward trend in the PCIw of heavy precipitation and the simple daily intensity index (SDII) of extreme precipitation. Moreover, the PCP of light precipitation, moderate precipitation, and heavy precipitation concentrated earlier at the end of July. The results of this study can provide beneficial reference information to water resource planning, reservoir operation, and agricultural production in the basin.

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 181
Sudip Basack ◽  
Ghritartha Goswami ◽  
Sumanpran Sonowal ◽  
Moses Karakouzian

Saline water intrusion into freshwater aquifers is a major geohydraulic problem relevant to coastal environment. Apart from contaminating the fresh groundwater resources, the saltwater intrusion alters the geotechnical properties of the aquifer materials, affecting the coastal water resource planning and management. The present study focuses on an in-depth laboratory investigation of the influence of saltwater submergence on the geohydraulic properties of sand. The fine sand sample was submerged under saline water of specified concentrations for specific periods, and the alteration in their engineering properties has been studied. It is observed that the specific gravity, dry density, and permeability of fine sand is significantly affected by the period of submergence and saline concentration. The specific gravity of sand particles was observed to increase almost linearly with period of submergence and saline concentration. While the sand dry density decreased fairly linearly with the period of submergence, the same is not being affected significantly by saline concentration. The permeability of sand increased nonlinearly with both period of submergence and saline concentration; for a submergence period of 14 days and saline concentration of 30,000 ppm, the permeability increased to a maximum value.

2021 ◽  
Vol 930 (1) ◽  
pp. 012040
G A P Eryani ◽  
I M S Amerta ◽  
M W Jayantari

Abstract In water resource planning, information on water availability is needed. Nowadays, data on water availability is still difficult to obtain. With technology in the form of a rainfall-runoff simulation model that can predict water availability in the Unda watershed. It can add information about the potential for water in the Unda watershed. It can be used to prepare water resources management in the Unda watershed so that the existing potential can be used sustainably. Based on the rainfall simulation model results in the Unda watershed, it can be concluded that after running the initial model and calibration. The results are obtained R2 value was 0.68 and increased by 9.81% to 0.754. Both the initial model and the calibration model show an efficient R2 value, NASH value increases by 49.93% to 0.713, which includes satisfactory criteria, RMSE value of 1.135 and decreased by 49.47% to 0.758, and the PBIAS value was 44.70% which was classified as unsatisfactory and decreased from 80.24% to 24.80% at the time of calibration which was classified as satisfactory. In general, the overall simulation results are quite good for representing the watershed’s efficient hydrological process.

Stephen R. Sobie ◽  
Trevor Q. Murdock

Abstract Information about snow water equivalent in southwestern British Columbia is used for flood management, agriculture, fisheries, and water resource planning. This study evaluates whether a process-based, energy balance snow model supplied with high-resolution statistically downscaled temperature and precipitation data can effectively simulate snow water equivalent (SWE) in the mountainous terrain of this region. Daily values of SWE from 1951 to 2018 are simulated at 1 km resolution and evaluated using a reanalysis SWE product (SNODAS), manual snow survey measurements at 41 sites, and automated snow pillows at six locations in the study region. Simulated SWE matches observed inter-annual variability well (R2 > 0.8 for annual maximum SWE) but peak SWE biases of 20% to 40% occur at some sites in the study domain, and higher biases occur where observed SWE is very low. Modelled SWE displays lower bias compared to SNODAS reanalysis at most manual survey locations. Future projections for the study area are produced using 12 downscaled climate model simulations and used to illustrate the impacts of climate change on SWE at 1°C, 2°C, and 3°C of warming. Model results are used to quantify spring SWE changes at different elevations of the Whistler mountain ski resort, and the sensitivity of annual peak SWE in Metro Vancouver municipal watersheds to moderate temperature increases. The results illustrate both the potential utility of a process-based snow model, and identify areas where the input meteorological variables could be improved.

2021 ◽  
John Conallin ◽  
Nathan Ning ◽  
Jennifer Bond ◽  
Nicholas Pawsey ◽  
Lee Baumgartner ◽  

Abstract. Implementation failure is widely acknowledged as a major impediment to the success of water resource plans and policies, yet there are very few proactive approaches available for analysing potential implementation issues during the planning stage. The Motivations and Abilities (MOTA) framework was established to address this planning stage gap, by offering a multi-stakeholder, multi-level approach to evaluate the implementation feasibility of plans and policies. MOTA is a stepwise process focusing on the relationship between trigger, motivation, and ability. Here we outline the base model of the MOTA framework and review existing MOTA applications in assorted water resource management contexts. From our review we identify the strengths and limitations of the MOTA framework in various institutional implementation and social adoptability contexts. Our findings indicate that the existing MOTA base model framework has been successful in identifying the motivations and abilities of the stakeholders involved in a range of bottom-up water resource planning contexts, and in subsequently providing insight into the types of capacity- or consent-building strategies needed for effective implementation. We propose several complementary add-in applications to complement the base model, which specific applications may benefit from. Specifically, the incorporation of formal context and stakeholder analyses during the problem definition stage (Step 1), could provide a more considered basis for designing the latter steps within the MOTA analyses. In addition, the resolution of the MOTA analyses could be enhanced by developing more nuanced scoring approaches, or by adopting empirically proven ones from well-established published models. Through setting the base model application, additional add-in applications can easily be added to enhance different aspects of the analysis while still maintaining comparability with other MOTA applications. With a robust base model and a suite of add-in applications, there is great potential for the MOTA framework to become a staple tool for optimising implementation success in any water planning and policy-making context.

2021 ◽  
Kokeb Zena Besha ◽  
Tamene Adugna Demissie ◽  
Fekadu Fufa Feyessa

Abstract Understanding hydro-climatic trends in space and time is crucial for water resource planning and management, agricultural productivity and climate change mitigation of a region. This study examined the spatiotemporal variations in precipitation, reference evapotranspiration (ETo) and streamflow in a tropical watershed located in the central highlands of Ethiopia. Temporal trend implications were analyzed using the Mann-Kendall test, and Theil-Sen approach, whereas the inverse distance weighted interpolation method was applied for spatial trend variability analysis. The result showed that a significant decreasing trends in streamflow for the major rainy (Kiremt: Jun - Sept) season and annual time scales. At the same time, the annual and monthly ETo followed significantly increasing trends, but there has been a trendless time series for most of the months and annual mean precipitation series for the period 1986 - 2015. The study indicated that the spatial variability of annual and seasonal precipitation series decreased from north to south and west to east, while this was increased for ETo both for annual and seasonal time series over the study watershed. The contribution of rainfall and mean temperature to streamflow decline was insignificant. It is pointed out that river flow regime is weakly affected by climate changes, hence human activities are stronger in explaining the river flow trends of the watershed. Therefore, urgent calls on the needs for reducing human-induced impacts, and implementing appropriate watershed management, conservation measures and an efficient use of water resources.

2021 ◽  
Vol 11 (1) ◽  
Pakorn Ditthakit ◽  
Sirimon Pinthong ◽  
Nureehan Salaeh ◽  
Fadilah Binnui ◽  
Laksanara Khwanchum ◽  

AbstractEstimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e., GR2M model). Two regionalization methods (i.e., regression-based methods and distance-based methods) were investigated in this study. Three regression-based methods were selected including Multiple Linear Regression (MLR), Random Forest (RF), and M5 Model Tree (M5), and two distance-based methods included Spatial Proximity Approach and Physical Similarity Approach (PSA). Hydrological data and the basin's physical attributes were analyzed from 37 runoff stations in Thailand's southern basin. The results showed that using hydrological data for estimating the GR2M model parameters is better than using the basin's physical attributes. RF had the most accuracy in estimating regional GR2M model’s parameters by giving the lowest error, followed by M5, MLR, SPA, and PSA. Such regional parameters were then applied in estimating monthly runoff using the GR2M model. Then, their performance was evaluated using three performance criteria, i.e., Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The regionalized monthly runoff with RF performed the best, followed by SPA, M5, MLR, and PSA. The Taylor diagram was also used to graphically evaluate the obtained results, which indicated that RF provided the products closest to GR2M's results, followed by SPA, M5, PSA, and MLR. Our finding revealed the applicability of machine learning for estimating monthly runoff in the ungauged basins. However, the SPA would be recommended in areas where lacking the basin's physical attributes and hydrological information.

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-20
Mohammed Majeed Hameed ◽  
Mohamed Khalid AlOmar ◽  
Siti Fatin Mohd Razali ◽  
Mohammed Abd Kareem Khalaf ◽  
Wajdi Jaber Baniya ◽  

Reference evapotranspiration ET o   is one of the most significant factors in the hydrological cycle since it has a great influence on water resource planning and management, agriculture and irrigation management, and other processes in the hydrological sector. In this study, an efficient and local predictive model was established to forecast the monthly mean ET o   t over Turkey based on the data collected from 35 locations. For this purpose, twenty input combinations including hydrological and geographical parameters were introduced to three different approaches called multiple linear regression MLR , random forest RF , and extreme learning machine ELM . Moreover, in this study, large investigation was done, involving the establishment of 60 models and their assessment using ten statistical measures. The outcome of this study revealed that the ELM approach achieved high accurate estimation in accordance with the Penman–Monteith formula as compared to other models such as MLR and RF . Moreover, among the 10 statistical measures, the uncertainty at 95% U 95 indicator showed an excellent ability to select the best and most efficient forecast model. The superiority of ELM in the prediction of mean monthly ET o   over MLR and RF approaches is illustrated in the reduction of the U 95 parameter to 49.02% and 34.07% for RF and MLR models, respectively. Furthermore, it is possible to develop a local predictive model with the help of computer to estimate the ET o   using the simplest and cheapest meteorological and geographical variables with acceptable accuracy.

2021 ◽  
Vol 25 (8) ◽  
pp. 4319-4333
Kuk-Hyun Ahn

Abstract. Reliable estimates of missing streamflow values are relevant for water resource planning and management. This study proposes a multiple-dependence condition model via vine copulas for the purpose of estimating streamflow at partially gaged sites. The proposed model is attractive in modeling the high-dimensional joint distribution by building a hierarchy of conditional bivariate copulas when provided a complex streamflow gage network. The usefulness of the proposed model is firstly highlighted using a synthetic streamflow scenario. In this analysis, the bivariate copula model and a variant of the vine copulas are also employed to show the ability of the multiple-dependence structure adopted in the proposed model. Furthermore, the evaluations are extended to a case study of 54 gages located within the Yadkin–Pee Dee River basin in the eastern USA. Both results inform that the proposed model is better suited for infilling missing values. To be specific, the proposed multiple-dependence model shows the improvement of 9.2 % on average compared to the bivariate model from the historical case study. The performance of the vine copula is further compared with six other infilling approaches to confirm its applicability. Results demonstrate that the proposed model produces more reliable streamflow estimates than the other approaches. In particular, when applied to partially gaged sites with sufficient available data, the proposed model clearly outperforms the other models. Even though the model is illustrated by a specific case, it can be extended to other regions with diverse hydro-climatological variables for the objective of infilling.

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