2c:null 2d:["$","title",null,{"children":"Environmental Flows | Western Power | Observable"}] 32:T4e1,html` Environmental Flows* are the flows of water which remain in the natural course of a river following the construction of a hydroelectric power station or other infrastructure which diverts some of the flow from its normal course. Constant and sufficient Environmental Flows are crucial in maintaining the habitats and ecology of the plants and animals which live in and around the river as well as the eco-services which depend on the river such as livestock grazing and fisheries.

In the case of the Ngonye Falls hydroelectric project, flows will be diverted out of the Zambezi river and into a canal before passing through the generating turbines and then back into the normal course of the river. The distance along the river where there will be reduced flows is approximately 8 km. Water will be diverted using a low weir (overtoppable wall) across the width of the river. The Environmental Flows will be constantly released through the weir structure to maintain the health of the 8 km downstream reach.

* Environmental Flows are also known as EFlows, Environmental Water Requirements (EWRs), Environmental Flow Requirements (EFRs) and Environmental Compensation Flows.

`33:T579,html` The Environmental Flow requirements for the project have been determined through a comprehensive set of studies undertaken by an independent consultant, EcoTone Freshwater Consultants. EcoTone undertook field surveys at the project site during October 2015 and 2017 (low flow conditions) and February 2018 (high flow conditions).

Significance of expected Impact	Level	Assessment Level	Confidence
Low	1	Desktop	Very Low
Low to Moderate	2	Rapid	Low to Medium
Moderate to High	3	Intermediate	Medium
High	4	Comprehensive	High
The assessment level for Environmental Flow determination depends on the significance of the expected impact.

The Environmental Flow determination completed by EcoTone was undertaken at an Intermediate Level, with the inclusion of six hydraulic site measurements, dry and wet season ecological observations and calibrated hydrology. `34:T611,html`

2) Determine Present Ecological State of the Hydrological Resource Units

The current, pre-project functional ecological integrity of each resource unit was measured by completing field surveys of each unit to determine the state of the general habitat, water quality, riparian vegetation, aquatic macroinvertibrates ('river insects') and fish. Based on these surveys each resource unit was assigned an EcoStatus Category ranging from Natural (A) to Critically Modified (F).

Riparian vegetation assessment. The conditions and species composition of the riparian vegetation for four of the reaches were determined.
Aquatic macroinvertebrate assessment. The invertebrate specialist surveyed the aquatic macroinvertebrates occurring within each resource unit using the South African Scoring System version 5 methodology and sampling equipment and techniques This included a comprehensive assessment of the frequency distribution of different flow-depths and substrate units.
Fish assessment. The fish specialist sampled fish in all suitable aquatic habitats near the EFR site using an electro-fish shocker and nets.
Water quality assessment. Water quality was assessed at all sites during each visit for in situ variables, main cations and anions and nutrients. Inferences regarding water quality were also made from the diatom community assessment at each site.

`35:T1285,html` EcoStatus Categories

A	Unmodified/natural. Close to natural or close too predevelopment conditions within the natural variability of the system drivers: hydrology, physico-chemical and geomorphology. The habitat template and biological components can be considered close to natural or to pre-development conditions. The resilience of the system has not been compromised.
A/B	Close to natural condition most of the time. Conditions may rarely and temporarily decrease below the upper boundary of a B category.
B	Largely natural with few modifications. A small change in the attributes of natural habitats and biota may have taken place in terms of frequencies of occurrence and abundance. Ecosystem functions and resilience are essentially unchanged.
B/C	Close to largely natural most of the time. Conditions may rarely and temporarily decrease below the upper boundary of a C category.
C	Moderately modified. Loss and change of natural habitat and biota have occurred in terms of frequencies of occurrence and abundance. Basic ecosystem functions are still predominantly unchanged. The resilience of the system to recover from human impacts has not been lost and it is ability to recover to a moderately modified condition following disturbance has been maintained.
C/D	Close to moderately modified condition most of the time. Conditions may rarely and temporarily decrease below the upper boundary of a D category.
D	Largely modified. A large change or loss of natural habitat, biota and basic ecosystem functions have occurred. The resilience of the system to sustain this category has not been compromised and the ability to deliver ecological goods and services has been maintained.
D/E	Close to largely modified condition most of the time. Conditions may rarely and temporarily decrease below the upper boundary of an E category. The resilience of the system is often under severe stress and may be lost permanently if adverse impacts continue.
E	Seriously modified. The change in the natural habitat template, biota and basic ecosystem functions are extensive. Only resilient biota may survive, and it is highly likely that invasive and problem (pest) species may dominate. The resilience of the system is severely compromised as is the capacity to provide ecological goods and services. However, geomorphological conditions are largely intact but extensive restoration may be required to improve the system's hydrology and physico-chemical conditions.
E/F	Close to seriously modified condition most of the time. Conditions may rarely and temporarily decrease below the upper boundary of an F category. The resilience of the system is frequently under severe stress and may be lost permanently if adverse impacts continue.
F	Critically/Extremely modified. Modifications have reached a critical level and the system has been modified completely with an almost complete change of the natural habitat template, biota and basic ecosystem functions. Ecological goods and services have largely been lost This is likely to include severe catchment changes as well as hydrological, physico-chemical and geomorphological changes. In the worst instances the basic ecosystem functions have been destroyed and the changes are irreversible. Restoration of the system to a synthetic but sustainable condition acceptable for human purposes and to limit downstream impacts is the only option.

`36:T6f3,html` Ecological Importance and Sensitivity Categories

Very High	Rivers that are considered ecologically important and sensitive on a national or even international level. The biodiversity of these systems is usually very sensitive to flow and habitat modifications. They play a major role in moderating the quantity and quality of water of major rivers.
High	Rivers that are ecologically important and sensitive. The biodiversity of these systems may be sensitive to flow and habitat modifications. They play a role in moderating the quantity and quality of water of major rivers.
Moderate	Rivers that are ecologically important and sensitive on a provincial or local scale. The biodiversity of these systems is not usually sensitive to flow and habitat modifications. They play a small role in moderating the quantity and quality of water of major rivers.
Low / Marginal	Rivers that are not ecologically important and sensitive at any scale. The biodiversity of these systems is ubiquitous and not sensitive to flow and habitat modifications. They play an insignificant role in moderating the quantity and quality of water of major rivers.

`37:T715,html` Current EcoStatus & Ecological Importance and Sensitivity

The table below summarises the results of the Present Ecological State and Ecological Importance and Sensitivity assessments. These categories were used to inform the amount of Environmental Flows required by each resource unit to maintain the resulting EcoStatus, after construction of the power station at an acceptable level.~

Section	EcoStatus	Ecological Importance & Sensitivity
Channel A	A	High
Channel C	A	High
Channel D	A	High
Channel E	A	High
Channel F/G	A	High
Gorge	B	Moderate

`38:T52b,html`

4) Determine the Environemtnal Flow Requirements

The New ‘Revised Desktop Reserve Model’ (new RDRM) was applied for Environmental Flow determination at each of the critical cross-sections. This approach integrates hydrological, hydraulic and ecological data to establish a modified flow regime that is expected to achieve the required level of ecological functioning relative to the Present Ecological State and depending on the Ecological Importance and Sensitivity.

The RDRM works by calculating, for each resource unit and for a range of flows, the proportion of the habitat which falls into each of six habitat categories according to flow depth and flow speed. To calculate these speed-depth habitat categories, an acoustic doppler current profile (ADCP) survey was undertaken for each of the critical cross-sections for each resource unit.

Water Speed / Depth	Shallow	Intermediate	Deep
Fast	Fast-Shallow	Fast-Intermediate	Fast-Deep
Slow	Slow-Shallow	Slow-Intermediate	Slow-Deep

`39:T42b,html` Once the relationship between changes in flow and changes in the proportion of each habitat category has been established then the impact on the critical indicator species identified earlier in the assessment can be determined.

For example, some fish and macroinvertebrate species require certain water depths and speeds at different times of their lifecycle for feeding, breeding and shelter. The RDRM assesses the minimum flow requirement, in each resource unit to maintain sufficient proportions of the required habitat depth-speed categories to ensure the survival of these species. The indicator species are selected to act as proxies for the overall health of the entire ecosystem.

The output from the RDRM is the minimum flows required to maintain each of the resource units at a particular EcoStatus. For example the RDRM specifies the flows required, according to the calendar month and proportional to the whole river flow, to maintain the habitat and ecology of the Channel A reach with an overall 'B', 'C' or 'D' etc EcoStatus. `3a:T9c4,{ var data = [ { x:[569,569], y:[0,100], line:{color:'rgba(0,0,0,1)',width:1}, mode:'lines+text', textfont:{size:12, color:'black'}, text:['',' Median Flow'], textposition:'bottom-right', }, { y: natural_split.map(e=>e.A*100), x: natural_split.map(e=>e.Flow), stackgroup: 'one', line:{width:.5}, name:'A', marker: {color: colr(1)}, }, { y: natural_split.map(e=>e.C*100), x: natural_split.map(e=>e.Flow), stackgroup: 'one', line:{width:.5}, name:'C', marker: {color: colr(5)}, }, { y: natural_split.map(e=>e.D*100), x: natural_split.map(e=>e.Flow), stackgroup: 'one', line:{width:.5}, name:'D', marker: {color: colr(3)}, }, { y: natural_split.map(e=>e.E*100), x: natural_split.map(e=>e.Flow), line:{width:.5}, stackgroup: 'one', name:'E', marker: {color: colr(1)}, }, { y: natural_split.map(e=>e.FG*100), x: natural_split.map(e=>e.Flow), stackgroup: 'one', line:{width:.5}, name:'F/G', marker: {color: colr(5)}, }, ]; var layout = { annotations: [ { x: Math.log10(300), y: 20, text: 'Channel A', font:{color:'black'}, showarrow:false, }, { x: Math.log10(800), y: 27, text: 'C', font:{color:'black'}, showarrow:false, }, { x: Math.log10(1500), y: 26.5, text: 'D', font:{color:'black'}, showarrow:false, }, { x: Math.log10(3000), y: 26, text: 'E', font:{color:'black'}, showarrow:false, }, { x: Math.log10(6000), y: 80, text: 'Channel F/G', font:{color:'black'}, showarrow:false, }, ], width:width, height: 300, font: font, showlegend: false, margin: margin(true), legend: { x: 1, xanchor: 'right', y: 1 }, title: plotTitle('Natural Flow Split'), xaxis: { showgrid: false, zeroline: false, ticks:'outside', type:'log', title:'Flow (m\u00B3/s)' }, yaxis: { title: '', zeroline: false, showline: false, separatethousands:true, title:'%' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{staticPlot: true}); return html`

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` }3b:T94d,{ var data = [ { y: natural_split.map(e=>e.A*100), x: natural_split.map(e=>e.Exceedance*100), stackgroup: 'one', line:{width:.5}, name:'A', marker: {color: colr(1)}, }, { y: natural_split.map(e=>e.C*100), x: natural_split.map(e=>e.Exceedance*100), stackgroup: 'one', line:{width:.5}, name:'C', marker: {color: colr(5)}, }, { y: natural_split.map(e=>e.D*100), x: natural_split.map(e=>e.Exceedance*100), stackgroup: 'one', line:{width:.5}, name:'D', marker: {color: colr(3)}, }, { y: natural_split.map(e=>e.E*100), x: natural_split.map(e=>e.Exceedance*100), line:{width:.5}, stackgroup: 'one', name:'E', marker: {color: colr(1)}, }, { y: natural_split.map(e=>e.FG*100), x: natural_split.map(e=>e.Exceedance*100), stackgroup: 'one', line:{width:.5}, name:'F/G', marker: {color: colr(5)}, }, ]; var layout = { annotations: [ { x: (90), y: 20, text: 'Channel A', font:{color:'black'}, showarrow:false, }, { x: (70), y: 34, text: 'C', font:{color:'black'}, showarrow:false, }, { x: (50), y: 33, text: 'D', font:{color:'black'}, showarrow:false, }, { x: (30), y: 30, text: 'E', font:{color:'black'}, showarrow:false, }, { x: (10), y: 80, text: 'Channel F/G', font:{color:'black'}, showarrow:false, }, ], width:width, height: 280, font: font, showlegend: false, margin: { l: 90, r: 90, b: 80, t: 0}, legend: { x: 1, xanchor: 'right', y: 1 }, xaxis: { showgrid: false, zeroline: false, ticks:'outside', title:'Time Exceedance %', range:[100,0] }, yaxis: { title: '', zeroline: false, showline: false, separatethousands:true, title:'%' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{staticPlot: true}); return html`

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Natural proportion of total river flow which pass through each of the critical cross-sections by total river flow (top) and by time exceedance (bottom).

` }3c:Tb0a,{ var data = [ { y: ewr_flows.filter(e=>e.Section=='A' && e.Category=='B').map(e=>e.Mod_Wet).concat(ewr_flows.filter(e=>e.Section=='A' && e.Category=='B').map(e=>e.Mod_Dry).reverse()), x: [1,2,3,4,5,6,7,8,9,10,11,12,12,11,10,9,8,7,6,5,4,3,2,1], mode: 'lines', fill:'toself', name:'B EcoStatus', line: {shape: 'spline',size:1, dash:'dot'}, marker: {color: 'rgb(40,140,40)'}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='A' && e.Category=='B').map(e=>e.Normal), x: [1,2,3,4,5,6,7,8,9,10,11,12], mode: 'lines', showlegend:false, name:'B EcoStatus', line: {shape: 'spline'}, marker: {color: 'rgb(40,140,40)'}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='A' && e.Category=='C').map(e=>e.Mod_Wet).concat(ewr_flows.filter(e=>e.Section=='A' && e.Category=='C').map(e=>e.Mod_Dry).reverse()), x: [1,2,3,4,5,6,7,8,9,10,11,12,12,11,10,9,8,7,6,5,4,3,2,1], mode: 'lines', fill:'toself', name:'C EcoStatus', line: {shape: 'spline',size:1, dash:'dot'}, marker: {color: colr(2)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='A' && e.Category=='C').map(e=>e.Normal), x: [1,2,3,4,5,6,7,8,9,10,11,12], mode: 'lines', showlegend:false, name:'C EcoStatus', line: {shape: 'spline'}, marker: {color: colr(2)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='A' && e.Category=='D').map(e=>e.Mod_Wet).concat(ewr_flows.filter(e=>e.Section=='A' && e.Category=='D').map(e=>e.Mod_Dry).reverse()), x: [1,2,3,4,5,6,7,8,9,10,11,12,12,11,10,9,8,7,6,5,4,3,2,1], mode: 'lines', fill:'toself', name:'D EcoStatus', line: {shape: 'spline',size:1, dash:'dot'}, marker: {color: colr(5)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='A' && e.Category=='D').map(e=>e.Normal), x: [1,2,3,4,5,6,7,8,9,10,11,12], mode: 'lines', showlegend:false, name:'D EcoStatus', line: {shape: 'spline'}, marker: {color: colr(5)}, type: 'scatter' }, ]; var layout = { width:width, height: 350, font: font, showlegend: true, legend: {x: 0,xanchor: 'left',y: 1}, margin: margin(true), title: plotTitle('Channel A Minimum Environmental Flows'), xaxis: { showgrid: false, zeroline: false, dtick: 10, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'], }, yaxis: { title: 'Flow (m\u00B3/s)', zeroline: false, showline: false, separatethousands:true, } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div; }3d:T5ef,{ var data = [ { y: ewr_flows.filter(e=>e.Section=='A' && e.Category=='B').map((e,i)=>Math.round(e.Normal/normal_A[i]*100)), x: [1,2,3,4,5,6,7,8,9,10,11,12,], mode: 'lines', name:'B EcoStatus', line: {shape: 'spline'}, marker: {color: 'rgb(40,140,40)'}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='A' && e.Category=='C').map((e,i)=>Math.round(e.Normal/normal_A[i]*100)), x: [1,2,3,4,5,6,7,8,9,10,11,12,], mode: 'lines', name:'C EcoStatus', line: {shape: 'spline'}, marker: {color: colr(2)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='A' && e.Category=='D').map((e,i)=>Math.round(e.Normal/normal_A[i]*100)), x: [1,2,3,4,5,6,7,8,9,10,11,12,], mode: 'lines', name:'D EcoStatus', line: {shape: 'spline'}, marker: {color: colr(5)}, type: 'scatter' }, ]; var layout = { width:width, height: 230, font: font, showlegend: false, margin: { l: 90, r: 90, b: 90, t: 0}, xaxis: { showgrid: false, zeroline: false, dtick: 10, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'], }, yaxis: { title: '% of Natural', zeroline: false, showline: false, separatethousands:true, } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div; }3e:Tb0c,{ var data = [ { y: ewr_flows.filter(e=>(e.Section=='D' && e.Category=='B') ).map(e=>e.Mod_Wet).concat(ewr_flows.filter(e=>e.Section=='D' && e.Category=='B').map(e=>e.Mod_Dry).reverse()), x: [1,2,3,4,5,6,7,8,9,10,11,12,12,11,10,9,8,7,6,5,4,3,2,1], mode: 'lines', fill:'toself', name:'B EcoStatus', line: {shape: 'spline',size:1, dash:'dot'}, marker: {color: 'rgb(40,140,40)'}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='D' && e.Category=='B').map(e=>e.Normal), x: [1,2,3,4,5,6,7,8,9,10,11,12], mode: 'lines', showlegend:false, name:'B EcoStatus', line: {shape: 'spline'}, marker: {color: 'rgb(40,140,40)'}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='D' && e.Category=='C').map(e=>e.Mod_Wet).concat(ewr_flows.filter(e=>e.Section=='D' && e.Category=='C').map(e=>e.Mod_Dry).reverse()), x: [1,2,3,4,5,6,7,8,9,10,11,12,12,11,10,9,8,7,6,5,4,3,2,1], mode: 'lines', fill:'toself', name:'C EcoStatus', line: {shape: 'spline',size:1, dash:'dot'}, marker: {color: colr(2)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='D' && e.Category=='C').map(e=>e.Normal), x: [1,2,3,4,5,6,7,8,9,10,11,12], mode: 'lines', showlegend:false, name:'C EcoStatus', line: {shape: 'spline'}, marker: {color: colr(2)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='D' && e.Category=='D').map(e=>e.Mod_Wet).concat(ewr_flows.filter(e=>e.Section=='D' && e.Category=='D').map(e=>e.Mod_Dry).reverse()), x: [1,2,3,4,5,6,7,8,9,10,11,12,12,11,10,9,8,7,6,5,4,3,2,1], mode: 'lines', fill:'toself', name:'D EcoStatus', line: {shape: 'spline',size:1, dash:'dot'}, marker: {color: colr(5)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='D' && e.Category=='D').map(e=>e.Normal), x: [1,2,3,4,5,6,7,8,9,10,11,12], mode: 'lines', showlegend:false, name:'D EcoStatus', line: {shape: 'spline'}, marker: {color: colr(5)}, type: 'scatter' }, ]; var layout = { width:width, height: 350, font: font, showlegend: true, legend: {x: 0,xanchor: 'left',y: 1}, margin: margin(true), title: plotTitle('Channel D Minimum Environmental Flows'), xaxis: { showgrid: false, zeroline: false, dtick: 10, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'], }, yaxis: { title: 'Flow (m\u00B3/s)', zeroline: false, showline: false, separatethousands:true, } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div; }3f:T5ef,{ var data = [ { y: ewr_flows.filter(e=>e.Section=='D' && e.Category=='B').map((e,i)=>Math.round(e.Normal/normal_D[i]*100)), x: [1,2,3,4,5,6,7,8,9,10,11,12,], mode: 'lines', name:'B EcoStatus', line: {shape: 'spline'}, marker: {color: 'rgb(40,140,40)'}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='D' && e.Category=='C').map((e,i)=>Math.round(e.Normal/normal_D[i]*100)), x: [1,2,3,4,5,6,7,8,9,10,11,12,], mode: 'lines', name:'C EcoStatus', line: {shape: 'spline'}, marker: {color: colr(2)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='D' && e.Category=='D').map((e,i)=>Math.round(e.Normal/normal_D[i]*100)), x: [1,2,3,4,5,6,7,8,9,10,11,12,], mode: 'lines', name:'D EcoStatus', line: {shape: 'spline'}, marker: {color: colr(5)}, type: 'scatter' }, ]; var layout = { width:width, height: 230, font: font, showlegend: false, margin: { l: 90, r: 90, b: 90, t: 0}, xaxis: { showgrid: false, zeroline: false, dtick: 10, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'], }, yaxis: { title: '% of Natural', zeroline: false, showline: false, separatethousands:true, } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div; }40:Tb14,{ var data = [ { y: ewr_flows.filter(e=>e.Section=='FG' && e.Category=='B').map(e=>e.Mod_Wet).concat(ewr_flows.filter(e=>e.Section=='FG' && e.Category=='B').map(e=>e.Mod_Dry).reverse()), x: [1,2,3,4,5,6,7,8,9,10,11,12,12,11,10,9,8,7,6,5,4,3,2,1], mode: 'lines', fill:'toself', name:'B EcoStatus', line: {shape: 'spline',size:1, dash:'dot'}, marker: {color: 'rgb(40,140,40)'}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='FG' && e.Category=='B').map(e=>e.Normal), x: [1,2,3,4,5,6,7,8,9,10,11,12], mode: 'lines', showlegend:false, name:'B EcoStatus', line: {shape: 'spline'}, marker: {color: 'rgb(40,140,40)'}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='FG' && e.Category=='C').map(e=>e.Mod_Wet).concat(ewr_flows.filter(e=>e.Section=='FG' && e.Category=='C').map(e=>e.Mod_Dry).reverse()), x: [1,2,3,4,5,6,7,8,9,10,11,12,12,11,10,9,8,7,6,5,4,3,2,1], mode: 'lines', fill:'toself', name:'C EcoStatus', line: {shape: 'spline',size:1, dash:'dot'}, marker: {color: colr(2)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='FG' && e.Category=='C').map(e=>e.Normal), x: [1,2,3,4,5,6,7,8,9,10,11,12], mode: 'lines', showlegend:false, name:'C EcoStatus', line: {shape: 'spline'}, marker: {color: colr(2)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='FG' && e.Category=='D').map(e=>e.Mod_Wet).concat(ewr_flows.filter(e=>e.Section=='FG' && e.Category=='D').map(e=>e.Mod_Dry).reverse()), x: [1,2,3,4,5,6,7,8,9,10,11,12,12,11,10,9,8,7,6,5,4,3,2,1], mode: 'lines', fill:'toself', name:'D EcoStatus', line: {shape: 'spline',size:1, dash:'dot'}, marker: {color: colr(5)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='FG' && e.Category=='D').map(e=>e.Normal), x: [1,2,3,4,5,6,7,8,9,10,11,12], mode: 'lines', showlegend:false, name:'D EcoStatus', line: {shape: 'spline'}, marker: {color: colr(5)}, type: 'scatter' }, ]; var layout = { width:width, height: 350, font: font, showlegend: true, legend: {x: 0,xanchor: 'left',y: 1}, margin: margin(true), title: plotTitle('Channel F/G Minimum Environmental Flows'), xaxis: { showgrid: false, zeroline: false, dtick: 10, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'], }, yaxis: { title: 'Flow (m\u00B3/s)', zeroline: false, showline: false, separatethousands:true, } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div; }41:T5f5,{ var data = [ { y: ewr_flows.filter(e=>e.Section=='FG' && e.Category=='B').map((e,i)=>Math.round(e.Normal/normal_FG[i]*100)), x: [1,2,3,4,5,6,7,8,9,10,11,12,], mode: 'lines', name:'B EcoStatus', line: {shape: 'spline'}, marker: {color: colr(2)}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='FG' && e.Category=='C').map((e,i)=>Math.round(e.Normal/normal_FG[i]*100)), x: [1,2,3,4,5,6,7,8,9,10,11,12,], mode: 'lines', name:'C EcoStatus', line: {shape: 'spline'}, marker: {color: 'rgb(40,140,40)'}, type: 'scatter' }, { y: ewr_flows.filter(e=>e.Section=='FG' && e.Category=='D').map((e,i)=>Math.round(e.Normal/normal_FG[i]*100)), x: [1,2,3,4,5,6,7,8,9,10,11,12,], mode: 'lines', name:'D EcoStatus', line: {shape: 'spline'}, marker: {color: colr(5)}, type: 'scatter' }, ]; var layout = { width:width, height: 230, font: font, showlegend: false, margin: { l: 90, r: 90, b: 90, t: 0}, xaxis: { showgrid: false, zeroline: false, dtick: 10, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'], }, yaxis: { title: '% of Natural', zeroline: false, showline: false, separatethousands:true, } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div; }42:T52e,{ var data = [ { y: ewr_overall.map(e=>e.Mod_Wet).concat(ewr_overall.map(e=>e.Mod_Dry).reverse()), x: [1,2,3,4,5,6,7,8,9,10,11,12,12,11,10,9,8,7,6,5,4,3,2,1], mode: 'lines', fill:'toself', name:'Mod Wet & Mod Dry', showlegend:false, line: {shape: 'spline',size:1, dash:'dot'}, marker: {color: colr(1)}, type: 'scatter' }, { y: ewr_overall.map(e=>e.Normal), x: [1,2,3,4,5,6,7,8,9,10,11,12], mode: 'lines', showlegend:false, name:'Normal Flow', line: {shape: 'spline'}, marker: {color: colr(1)}, type: 'scatter' }, ]; var layout = { width:width, height: 350, font: font, showlegend: false, legend: {x: 0,xanchor: 'left',y: 1}, margin: margin(true), title: plotTitle('Overall Environmental Flows'), xaxis: { showgrid: false, zeroline: false, dtick: 10, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'], }, yaxis: { title: 'Flow (m\u00B3/s)', zeroline: false, showline: false, separatethousands:true, } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div; }43:T8a2,{ let cap=`Median (P50) river discharges by month at the Ngonye Falls hydroelectric plant on the Zambezi river.` var data = [ { x:calmonths.map(mnth=>mnth.waterMonth), y:calmonths.map(mnth=>mnth.dischargeMedian+200), text:calmonths.map(mnth=>d3.format(',.2r')(mnth.dischargeMedian) + 'Mm3'), type: 'scatter', textposition:'top', mode:'text', hoverinfo: 'text', showlegend: false, } , { x:calmonths.map(mnth=>mnth.waterMonth), y:calmonths.map(mnth=>(mnth.canalDischargeMedian)), text:calmonths.map(mnth=>d3.format(',.2r')(mnth.canalDischargeMedian) + 'Mm3'), type: 'bar', name:'Generation Flow', hoverinfo: 'text', showlegend: true, marker: {color:colr_b(4,0.8)} }, { x:calmonths.map(mnth=>mnth.waterMonth), y:calmonths.map(mnth=>mnth.eFlowDischargeMedian), text:calmonths.map(mnth=>d3.format(',.2r')(mnth.eFlowDischargeMedian) + 'Mm3'), type: 'bar', name:'Environmental', mode:'lines+markers+text', hoverinfo: 'text', showlegend: true, marker: {color:makeTrans('#00AA00',0.5)} } , { x:calmonths.map(mnth=>mnth.waterMonth), y:calmonths.map(mnth=>mnth.spillDischargeMedian), text:calmonths.map(mnth=>d3.format(',.2r')(mnth.spillDischargeMedian) + 'Mm3'), type: 'bar', name:'Excess', mode:'lines+markers+text', hoverinfo: 'text', showlegend: true, marker: {color:colr_b(1,0.5)} } ] var layout = { width: width>900?870:width-30, height:300, margin: {t:10, b:30, l:70, r:30}, font: font, barmode:'stack', showlegend: true, legend: {x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'], }, yaxis: { title: 'Discharge ' + mm3, zeroline: false, tickformat:',.0f', showline: false, } }; const div=container(cap, width>900?890:width-10) plt.newPlot(div, data, layout,{displayModeBar: false}); return div }44:[{"id":"6cb6e657a7f88d181b73cd865eba2fe6423b5058b0a9f299deeab3f78c93167cd4af140a76feb6b61a9f9548c896bda0c5e7330699b6c757a9e322507d0fb771","href":"https://static.observableusercontent.com/files/6cb6e657a7f88d181b73cd865eba2fe6423b5058b0a9f299deeab3f78c93167cd4af140a76feb6b61a9f9548c896bda0c5e7330699b6c757a9e322507d0fb771","name":"ewr_sections@1.jpg","type":"image/jpeg","lastModified":1582783251922.988,"size":83780},{"id":"67d186397644f01fd27f9790743f00f22601702bf00c6713e840a93f72467c9be6eda48011f6128ae87887377a57004dd732019a6bf966ba7ff30ac47a8312d7","href":"https://static.observableusercontent.com/files/67d186397644f01fd27f9790743f00f22601702bf00c6713e840a93f72467c9be6eda48011f6128ae87887377a57004dd732019a6bf966ba7ff30ac47a8312d7","name":"rdrm_cannel_a.png","type":"image/png","lastModified":1582790480441.97,"size":88774},{"id":"3499228049d69ac526b726b54736a9c946cfd56723eb5e67541cd1d85b61dde7360fa027645ce509fee6e47642ff991a23dc54e24f902bb95dbbdc6d5748437f","href":"https://static.observableusercontent.com/files/3499228049d69ac526b726b54736a9c946cfd56723eb5e67541cd1d85b61dde7360fa027645ce509fee6e47642ff991a23dc54e24f902bb95dbbdc6d5748437f","name":"ewr_reach@1.jpg","type":"image/jpeg","lastModified":1582728902773.716,"size":69647}] 30:{"id":"03c278b0133a7b01","slug":"environmental-flows","title":"Environmental Flows","thumbnail":"2ac0a60daa61a5684b46dee53c4a31c740af3bf1d7f7feaa847fed8530fd1d0e","access_level":"public","create_time":"$D2020-02-19T08:07:30.215Z","update_time":"$D2025-08-01T08:28:24.694Z","view_time":null,"first_public_time":"$D2020-02-21T16:09:54.522Z","has_unpublished_edits":null,"fork":false,"stars":3,"starred":false,"owner":{"type":"team","id":"3aaf4970407a12be","login":"westernpower","name":"Western Power","avatar_url":"https://avatars-next.observableusercontent.com/0c11a3086911e8caa71a7767a24de09ff93d85f36798166fa6c2d0b61107b761","tier":"starter_2024"},"version":1018,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"96924893937b5930","login":"andyflem","name":"Andy Fleming","avatar_url":"https://avatars-next.observableusercontent.com/fd3844143a4c29872f0df28a8ebce77bb56cebf2fa74bb669872e4def82ad809","tier":null}],"members":[{"type":"team","id":"3aaf4970407a12be","login":"westernpower","name":"Western Power","avatar_url":"https://avatars-next.observableusercontent.com/0c11a3086911e8caa71a7767a24de09ff93d85f36798166fa6c2d0b61107b761","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":3,"collection":{"id":"042ea7275c7e9c32","slug":"ngonye-falls","title":"Ngonye Falls","owner":{"id":"3aaf4970407a12be","login":"westernpower","name":"Western Power"}},"type":"notebook","body":{"titling":"auto","title":"Environmental Flows","cells":[{"id":0,"value":"html`\n

Environmental Flows

Ngonye Falls Hydroelectric Project

\n
\n${book_index('environmental-flows')}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":94,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":130,"value":"md`\n

The proposed Ngonye Falls power generation infrastructure on the Zambezi river (flowing left to right). The weir stretches across the river on the upstream side and diverts flow into the power canal across the center of the image. The 8km river stretch with reduced flows and requiring constant Environmental Flows is highlighted in yellow.

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"html`\nEnvironmental Flows take priority over power generation and on rare occasions where there is not sufficient flow in the river to operate the turbines as well as meeting the Environmental Flow requirements then the power station will be shut down. \n

\nThe economics of the proejct have been calculated on the basis of periodic shutdowns due to low flow and this will not be an unexpected event.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":113,"value":"html`\n

Determining the Environmental Flow Requirements

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":354,"value":"html`\n

Environmental Flow Specification

\nThe result of the Environmental Flow requirements study is a set of minimum flows which must be released through the weir into each of the channels to maintain the environment in the effected reach of the river. These flows vary according to the overall flow in the river so that when overall river flow is higher, for example during flood years, then the environmental flow must also be higher.\n

\nThe environmental flows are also specified separately for each calendar month as many species have seasonal flow requirements to allow activities including migration and breeding which need to be supported.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":230,"value":"html`\n

Process

\n\nThe process used by EcoTone to complete the Environmental Flow assessment has six key steps:\n

Identify Hydrological Resource Units and Cross-sections
Determine Present Ecological State of the Hydrological Resource Units
Determine Ecological Importance and Sensitivity of the Hydrological Resource Units
Determine the Environmental Flow Requirements
Set the recommended Resulting EcoStatus
Multi-disciplinary expert review.

1) Identify Hydrological Resource Units and Cross-sections

\nThe affected river reach is broken into hydrological resource units which are contiguous areas of the river which have common hydrological, hydraulic and ecological characteristic. The final Environmental Flow requirements are specified separately for each of the hydrological resource units.\n

\nSix hydrological resource units were identified and assessed during the field surveys. These units were represented by “critical” cross-sections named Channel A, Channel C, Channel D, Chanel E, Channel F/G and Gorge.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"md`\n

Six Hydrological Resource Units were identified for the Environmental Flow determination and these are represented by \"critical\" cross-sections named Channels A, C, D, E, F/G and Gorge. The final Environmental Flow requirements are specified separately for each cross-section.

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":218,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":223,"value":"html`\n

3) Determine Ecological Importance and Sensitivity of the Hydrological Resource Units

\nThe Ecological Importance and Sensitivity was determined in accordance with the Resource Directed Measures method. The Ecological Importance and Sensitivity categories range from Very High (important on an international scale) to Low (important on a local scale).\n

\nThe Ecological Importance and Sensitivity assessment was also informed by sensitive 'indicator' species identified during the field surveys. These species are particularly responsive to changes in river flow due to changes in flow causing water depth and velocity fluctuations, and a subsequent change in habitat. \nData on the aquatic community composition was used to pin-point sensitive indicator species used as proxies for ecological functioning within the overall Environmental Flow determination.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":226,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":331,"value":"md`\n

The RDRM habitat categories by channel flow for Channel A. As the flow in the channel increases the proportion of the habitat classed as slow-shallow (SVS & SS) decreases while the proportion classed fast-deep (FD) and slow-deep (SD) increases.

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":338,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":369,"value":"html`\n

5) Set the recommended Resulting EcoStatus

\nTo determine the minimum EcoStatus that will be maintained by the Environmental Flows for each resource unit, EcoTone used a combination of the current, unmodified EcoStatus and the Environmental Importance and Sensitivity of each unit. Degradation must be minimised for units with higher Environmental Importance and Sensitivity.\n

\nThe table below shows the final recommended EcoStatus for each of the resource units. The determination of the Environmental Flows is made by reference to these resulting EcoStatuses and the flows calculated as being required to maintain them by the RDRM.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"html`\n\nRecommended EWR EcoStatus Summary\n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n

Section	Resulting EcoStatus
Channel A	B
Channel C	B
Channel D	B
Channel E	B
Channel F/G	B
Gorge	C

\n\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":296,"value":"html`\n

6) Multi-disciplinary expert review

\n\nFollowing the completion of the Environmental Flow requirements study by EcoTone, a multi-disciplinary expert review workshop was conducted. This workshop considered the recommendations of the Environmental Flow requirements study to ensure that the recommended Environmental Flows balanced the requirements for environmental protection, economic and human development and other stakeholders including tourism, agriculture and fisheries. \n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":300,"value":"html`\n

Environmental Flow Requirements Summary

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":507,"value":"html`\nSee also: Ngonye Falls - Catchment Hydrology\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":830,"value":"html`\nThe charts below show the natural proportion - before construction of the power station - of total river flow which pass through each of the critical cross-sections (except the gorge which has all of the flow) under increasing overall flow conditions.\n

\nThe proportion of flow through the smaller inter-island channels (C, D and E) remains mostly constant but as flow increases, an increasing proportion of flow passes through Channels F & G and a decreasing proportion through Channel A.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":443,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":527,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":844,"value":"html`\nThe charts below show the minimum environmental flows specified for Channels A, D and F/G for reducing EcoStatus categories. The selected EcoStatus for each channel is shown in green. The bands show the minimum flows under moderately wet conditions (top of band), normal conditions (solid line) and moderately dry conditions (bottom of band).\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":407,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":615,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":690,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":697,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":683,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":687,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":772,"value":"$42","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":782,"value":"{ \n var data = [\n \n {\n y: ewr_overall.map((e,i)=>Math.round(e.Normal/normal[i]*100)),\n x: [1,2,3,4,5,6,7,8,9,10,11,12],\n mode: 'lines',\n showlegend:true,\n name:'Normal',\n line: {shape: 'spline'},\n marker: {color: colr(1)},\n type: 'scatter'\n } \n ];\n \n \n var layout = {\n width:width,\n height: 270,\n font: font,\n showlegend: false,\n legend: {x: 0,xanchor: 'left',y: 1}, \n margin: { l: 90, r: 90, b: 90, t: 0}, \n xaxis: {\n showgrid: false,\n zeroline: false,\n dtick: 10,\n ticks:'outside',\n tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ],\n ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'],\n },\n yaxis: {\n title: '% of Normal',\n zeroline: false,\n showline: false,\n separatethousands:true,\n }\n };\n \n const div=DOM.element('div')\n plt.newPlot(div, data, layout,{displayModeBar: false});\n \n return div;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":904,"value":"$43","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":302,"value":"html` \n

\n

Appendix

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":913,"value":"calmonths=(await d3.csv(peFolder + peModelName + '_pe_calmonthly.csv?' + Math.floor(Math.random() * 1000), d3.autoType))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":413,"value":"ewr_flows = (await d3.csv(lookupsFolder + \"\teflows_channel_flows.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":418,"value":"ewr_sets = (await d3.csv(lookupsFolder + \"eflows_assurance_sets.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":584,"value":"monthly_fdc=(await d3.csv(hydrologyFolder + \"calMonthlyFlowExceedance.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":439,"value":"natural_split=(await d3.csv(otherdataFolder + \"flowsplit_natural.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":587,"value":"normal={\n var ex=ewr_bands.filter(e=>e.FlowBand=='Normal')[0].RepExceedance\n return Object.values(monthly_fdc.filter(f=>f.exceedance>=ex)[0])\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":801,"value":"mod_wet={\n var ex=ewr_bands.filter(e=>e.FlowBand=='Mod wet')[0].RepExceedance\n return Object.values(monthly_fdc.filter(f=>f.exceedance>=ex)[0])\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":807,"value":"mod_dry={\n var ex=ewr_bands.filter(e=>e.FlowBand=='Mod dry')[0].RepExceedance/100\n return Object.values(monthly_fdc.filter(f=>f.exceedance>=ex)[0])\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":602,"value":"normal_A={\n return normal.map(n=>n*natural_split.filter(s=>s.Flow>=n)[0].A) \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":679,"value":"normal_C={\n return normal.map(n=>n*natural_split.filter(s=>s.Flow>=n)[0].C) \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":681,"value":"normal_FG={\n return normal.map(n=>n*natural_split.filter(s=>s.Flow>=n)[0].FG) \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":693,"value":"normal_D={\n return normal.map(n=>n*natural_split.filter(s=>s.Flow>=n)[0].D) \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":707,"value":"ewr_overall={\n var vals=Array(12).fill()\n vals.forEach((v,i)=>vals[i]={Month:i+1,Normal:0,Mod_Wet:0,Mod_Dry:0})\n var months=ewr_flows.filter(e=>\n (e.Section=='A' && e.Category=='B') ||\n (e.Section=='C' && e.Category=='B') ||\n (e.Section=='D' && e.Category=='B') ||\n (e.Section=='E' && e.Category=='B') ||\n (e.Section=='FG' && e.Category=='C')\n ).map(e=>{return {Section: e.Section, Month:e.Month,Normal:e.Normal,Mod_Wet:e.Mod_Wet,Mod_Dry:e.Mod_Dry}})\n \n months.forEach(m=>{\n vals[m.Month-1].Normal+=m.Normal\n vals[m.Month-1].Mod_Wet+=m.Mod_Wet\n vals[m.Month-1].Mod_Dry+=m.Mod_Dry \n })\n return vals\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":435,"value":"ewr_bands=(await d3.csv(lookupsFolder + \"ews_flow_bands.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":974,"value":"

Sources

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":996,"value":"otherdataFolder=server + '/other/'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":971,"value":"lookupsFolder=server + '/ngonyePlantModels/lookups/'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":965,"value":"hydrologyFolder = `${server}/syntheticFlowSeries/${hydrologySet}/processed/`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1012,"value":"peFolder = `${server}/ngonyePlantModels/models/${peModelName}/`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":963,"value":"hydrologySet='2024'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1010,"value":"peModelName='sh_2024_7.8mhead'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":961,"value":"server='https://data.westernpower.org'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":982,"value":"

Style

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Libraries

","pinned":true,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":915,"value":"function monthDays(i) {\n if(i === 4 || i === 6 || i === 9 || i === 11) {\n\t\treturn 30\n\t} else if(i === 2) {\n\t\treturn 28\n\t} else {\n\t\treturn 31\n\t}\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":397,"value":"import {plotTitle} from \"@andyflem/commonlibrary\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":395,"value":"import {margin} from \"@andyflem/commonlibrary\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":393,"value":"import {font} from \"@andyflem/commonlibrary\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":387,"value":"d3 = require(\"d3@5\",\"d3-hsv@0.1\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":389,"value":"plt = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":919,"value":"d3hsv = require(\"d3-hsv@0.1\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W44","stdlib":"1"}} 31:"$30:body"