git:20261006.6aed70e to git:20261007.44a17f3

- > or hand-coded approximation in place of the real model.

- >

- > **WASP-specific (2026-10-06):** this KI has TWO run paths. The REAL engine is US EPA

- > WASP 8.5.0 (`waspccli.exe` under WINE), run with `tools/run_wasp_engine.py`. The older

- > `tools/run_wasp.py` is an analytic Python SURROGATE ("WASP-inspired"), NOT EPA WASP;

- > its numbers are not WASP results. When a task asks for WASP, use the real engine. If the

- > real engine is missing, report that — never run the surrogate in its place. Use the

- > surrogate only when the task explicitly asks for it, and label its results "surrogate".

- > **DEBUGGING PROTOCOL** — When something goes wrong, follow this order:

- > 2. **Read official docs** — The model's own documentation for expected formats/units

- > 3. **Find working examples** — Check `outputs/` or the model's shipped test data

- > 4. **Fix the tool** — With knowledge of what "correct" looks like

- > Do NOT write custom debug scripts. The answers are in the docs and examples.

- | to run the pipeline stages | `tools/` (5 tools) | the executable pipeline. Read each tool's argparse (`--help`) before composing a command; SKILL.md's stage table says which tool serves which stage. |

- | on ANY error, before debugging | `diagnostics/triplets.yaml` (26 entries) | symptom → diagnosis → remedy for this model's known failure modes. Check here FIRST; the answer usually exists. Never renumber or rewrite entries. |

- *Projected 2026-08-17 from the KI's actual contents — 9 components present. Refresh: `python3 ki_tools_common/generate_skill_map.py --ki_dir <this KI>`.*

- *5 public tools; `_`-prefixed helpers and packaging files excluded.*

- # WASP Knowledge Infrastructure

-

- **Package**: hydrocraft-wasp v1.0.0

- **Model**: WASP (Water Analysis Simulation Program) -- real EPA WASP 8.5.0 engine (`tools/run_wasp_engine.py`) + analytic SURROGATE (`tools/run_wasp.py`, not EPA WASP)

- **Domain**: Lake and reservoir water quality modeling

- **Language**: real engine = EPA Windows binaries under WINE; surrogate = Python (analytic reimplementation of WASP core physics)

- **License**: Public domain (EPA)

- **Created**: 2026-03-30

- **Validation**: SURROGATE only — Lake Erie Central Basin, WQP observations (Temp R=0.885, DO Cal R=0.816); the real EPA engine has only its official repeat-run test case (`test_cases/steady_state/`)

- | Metric | Value |

- |--------|-------|

- | Tools | 5 |

- | Pipeline stages | 6 |

- | Diagnostic triplets | 26 |

- | Validation lake | Lake Erie Central Basin |

- ## 0. Two run paths: REAL EPA WASP engine vs SURROGATE

-

- | | REAL engine | SURROGATE |

- |---|---|---|

- | tool | `tools/run_wasp_engine.py` | `tools/run_wasp.py` (+ `convert_*_to_wasp.py`, `parse_output_wasp.py`) |

- | what runs | US EPA WASP 8.5.0 `C:\WASP8\wasp\bin\waspccli.exe` under WINE 9 (official installer `wasp-version-8.5.0-install-64-bit-10-25-2025.exe`, sha256 `2bb410b59ead76d5ba3681f05f61c4b1193a1be8b5393ab7a33f234957b293d4`; banner "Wasp suite version 8.5.0 … Model master e86fb43") | analytic Python formulas "inspired by" WASP (sections 1, 7, 8 below) |

- | input | a WASP input file `.wif` (built in the WASP GUI or taken from an EPA example) | JSON parameters / WQP CSV |

- | output | `<model>.OUT` (setup echo + run statistics), `<model>.BMD2` (results, binary), CSV via EPA `BMD2_Extract.exe` | JSON / CSV of the analytic model |

- | results are | WASP results | NOT WASP results — always label them "surrogate" |

- Everything below — "Data Preparation" and sections 1–10 (pipeline stages, converters,

- parser, parameters, Streeter-Phelps formulas, Lake Erie validation numbers) — describes the

- SURROGATE, except the subsection "Real engine" in section 2. The converters do not build

- `.wif` files and `parse_output_wasp.py` does not read `.BMD2`.

- Real-engine call (python_env; engine found by `--wineprefix` → `$WASP_WINEPREFIX` →

- `KISSPATH_HOME/engine_builds_20261006/wasp/wineprefix`, wine by `--wine` → `$WASP_WINE` → PATH):

- ```bash

- KISSPATH_PYTHON_ENV/bin/python {KI}/tools/run_wasp_engine.py \

- --wif /path/to/model.wif --run-dir /scratch/wasp_run1 --extract-all

- # list the variables of a result file:

- KISSPATH_PYTHON_ENV/bin/python {KI}/tools/run_wasp_engine.py --list-variables /scratch/wasp_run1/model.BMD2

- ```

- The tool copies the `.wif` into a fresh run dir (`--copy-siblings` also copies the other files

- of its folder; sub-folders and absolute paths inside the `.wif` are not staged), runs the

- engine within one time budget (`--timeout`, default 900 s, max 1080 s, engine + extraction),

- and counts the run as good ONLY if the engine

- prints "run successfully closed out", writes `.OUT` + a valid `.BMD2`, and prints no ERROR

- lines other than "Failed to locate time function" (older example files read by 8.5 print

- these; they are counted in the summary, their effect on results is not checked). The wine

- exit code is not used (waspccli returns 2 even on good runs). Exit codes: 0 ok, 1 run or

- extraction failed, 2 bad command line, 3 WINE/engine missing (nothing run). Results:

- `<run-dir>/<model>_extract.csv` (Date_Time, Segment, one column per variable) and

- `<run-dir>/wasp_engine_summary.json`. Official test case: `test_cases/steady_state/`

- (EPA "Steady State" example river, 10 segments, DO/CBOD).

- ## Data Preparation

-

- ### Forcing data

-

- **Data Sources**: Use `from ki_tools_common.load_forcing import load_daily_forcing` for CMFD/MSWX/NASA POWER.

-

- **Data Validation Reference**: See `data_ki/CMFD/SKILL.md` for meteorological forcing documentation.

- See `data_ki/WQP/SKILL.md` for water quality observations.

- See `data_ki/HydroLAKES/SKILL.md` for lake morphometry.

-

-

- ## 1. Overview

- WASP (Water Analysis Simulation Program) is a generalized water quality model

- developed by US EPA for simulating contaminant fate and transport in surface

- waters. This analytic reimplementation captures WASP's core water quality

- processes for lakes and reservoirs.

- Core physics:

- - **Streeter-Phelps BOD-DO dynamics**: Classic oxygen sag model coupling

- biochemical oxygen demand (BOD) decay with atmospheric reaeration.

- BOD decays first-order: `BOD(t) = BOD0 * exp(-kd * t)`. The oxygen

- deficit evolves as:

- ```

- D(t) = [kd*BOD0/(ka-kd)] * [exp(-kd*t) - exp(-ka*t)] + D0*exp(-ka*t)

- ```

- where `kd` is the BOD deoxygenation rate (1/d), `ka` is the reaeration

- rate (1/d), and `D0` is the initial oxygen deficit (mg/L).

- - **Benson-Krause DO saturation**: Temperature-dependent dissolved oxygen

- saturation concentration (mg/L) following Benson & Krause (1984):

- ```

- ln(DO_sat) = -139.34411 + 1.575701e5/TK - 6.642308e7/TK^2

- + 1.2438e10/TK^3 - 8.621949e11/TK^4

- ```

- where TK is absolute temperature in Kelvin. This is the standard WASP

- formulation. At 20 C, DO_sat ~ 9.09 mg/L; at 0 C, ~14.6 mg/L.

- - **Logistic thermocline model**: 1-D vertical temperature profile using

- a logistic (sigmoid) function (CE-QUAL-W2 style):

- ```

- T(z) = T_bottom + (T_surface - T_bottom) / (1 + exp((z - z_thermo) / w))

- ```

- where `z` is depth (m), `z_thermo` is thermocline depth (m), and `w` is

- thermocline width (m). This captures the three-layer lake structure:

- epilimnion (warm mixed surface), metalimnion (thermocline), and

- hypolimnion (cold bottom).

- - **Carlson Trophic State Index (TSI)**: Lake trophic classification

- (Carlson 1977):

- ```

- TSI(Chl-a) = 9.81 * ln(Chl-a) + 30.6 [ug/L]

- TSI(Secchi) = 60 - 14.41 * ln(SD) [m]

- TSI(TP) = 14.42 * ln(TP) + 4.15 [ug/L]

- ```

- TSI < 30: oligotrophic, 30-50: mesotrophic, 50-70: eutrophic, >70: hypereutrophic.

- - **Sinusoidal seasonal temperature model**: Surface water temperature

- varies seasonally following:

- ```

- T(doy) = T_mean + amplitude * sin(2*pi*(doy - phase) / 365)

- ```

- where `T_mean` is annual average temperature (deg C), `amplitude` is

- the seasonal swing (deg C), and `phase` is the day of peak temperature

- offset (typically ~130 for Northern Hemisphere lakes).

- - **Seasonal DO model**: Dissolved oxygen coupled to temperature via

- steady-state Streeter-Phelps:

- ```

- DO(doy) = clip(DO_sat(T(doy)) - kd*BOD0/(ka - kd) + DO_offset, 0, 20)

- ```

- This captures the inverse relationship between temperature and DO

- saturation, modulated by BOD loading.

- - **1-D DO profile**: Depth-dependent dissolved oxygen with epilimnion near

- saturation and hypolimnion depleted by sediment oxygen demand:

- ```

- DO(z) = DO_sat(T(z)) * depletion_factor(z, thermocline_depth, SOD_rate)

- ```

- Key characteristics:

- - Designed for lake and reservoir water quality analysis

- - Processes: temperature, dissolved oxygen, BOD, nutrients (Chl-a, TP)

- - Calibration against WQP (Water Quality Portal) lake observations

- - Supports multiple lakes (Erie, DeGray, Jordan, Mead) via WQP data

- - Vertical profiles via discrete profile data and NLA surveys

- - 3 + 4 calibration parameters (temperature model + DO model)

- - Output: seasonal T/DO time series, vertical T/DO profiles, TSI values

- ## 2. Installation

-

- ### Real engine (EPA WASP 8.5.0 under WINE)

-

- Installed on this server in the WINE prefix `KISSPATH_HOME/engine_builds_20261006/wasp/wineprefix`

- (`C:\WASP8`, headless install of the official EPA installer; no display needed). Needs `wine`

- (WINE 9.0, `/usr/bin/wine`). Run it only through `tools/run_wasp_engine.py` (section 0).

-

- ### Analytic reimplementation (Python) — SURROGATE only

- # No compilation needed -- pure Python

- pip install numpy pandas scipy matplotlib

-

- ### Dependencies

-

- | Component | Required | Purpose |

- |--------------|----------|-------------------------------------------------|

- | numpy | Yes | Numerical computation |

- | pandas | Yes | Time series handling, WQP CSV parsing |

- | scipy | Yes | Calibration (Nelder-Mead optimization) |

- | matplotlib | Optional | Validation plots |

- ## 3. Pipeline

- | # | Stage | Tool | Description |

- |---|------------------------|-------------------------------|----------------------------------------------------------|

- | 1 | Forcing preparation | `convert_forcing_to_wasp.py` | WQP/NLA CSV to cleaned T/DO/Chl-a time series |

- | 2 | Parameter setup | `convert_parameters_to_wasp.py`| Lake morphometry + BOD-DO kinetics + thermal params |

- | 3 | Model execution | `run_wasp.py` | Run seasonal T/DO model + profiles + TSI |

- | 4 | Output parsing | `parse_output_wasp.py` | Extract T/DO profiles, compute validation metrics |

- | 5 | Calibration | `run_wasp.py --mode calibrate`| Nelder-Mead against observed T and DO |

- | 6 | TSI analysis | `parse_output_wasp.py` | Carlson TSI computation and NLA percentile ranking |

- **Parallelism**: Stages 1--2 can run in parallel. Stage 3 depends on 1--2.

- Stage 4 depends on 3.

- ### Stage skill documents

- - [Stage 1: Forcing preparation](docs/s1_forcing_preparation.md)

- - [Stage 2: Parameter setup](docs/s2_parameter_setup.md)

- - [Stage 3: Model execution](docs/s3_model_execution.md)

- - [Stage 4: Output parsing](docs/s4_output_parsing.md)

- - [Stage 5: Calibration](docs/s5_calibration.md)

- - [Stage 6: TSI analysis](docs/s6_tsi_analysis.md)

- ## 4. Output Description

-

- Source: `dag.yaml`. The dag is the authority for output identity; if this

- section ever disagrees with `dag.yaml`, the dag wins and this section should be

- fixed.

- **Headline output** (`validation_rank: 1`, the variable this model is judged by):

- > `do_timeseries` -- Seasonal dissolved-oxygen time series (steady-state Streeter-Phelps). (mg/L)

- Dag outputs restated from the KI:

- | Output variable (dag `var`) | Rank | Unit | Description |

- |-----------------------------|------|------|-------------|

- | `do_timeseries` | 1 | mg/L | Seasonal dissolved-oxygen time series (steady-state Streeter-Phelps). |

- | `temperature_timeseries` | See `dag.yaml` | See `dag.yaml` | See `dag.yaml` |

- | `temperature_profile` | See `dag.yaml` | See `dag.yaml` | See `dag.yaml` |

- | `do_profile` | See `dag.yaml` | See `dag.yaml` | See `dag.yaml` |

- | `do_saturation` | See `dag.yaml` | See `dag.yaml` | See `dag.yaml` |

- | `TSI` | See `dag.yaml` | See `dag.yaml` | See `dag.yaml` |

- The rank-1 output is the seasonal dissolved-oxygen time series,

- `do_timeseries`, in mg/L.

- ## 4a. Unit Table

- This unit table summarizes the model-facing units used by the pipeline. Exact

- I/O shapes live in `docs/format_spec.yaml`; `dag.yaml` is authoritative for

- output variables.

- | Variable | Model unit | Source or context | Notes |

- |----------|------------|-------------------|-------|

- | `do_timeseries` | mg/L | `dag.yaml` rank-1 output | Seasonal dissolved-oxygen time series. |

- | Temperature | deg C | WQP / forcing / seasonal model | Convert Fahrenheit or Kelvin before use. |

- | Dissolved oxygen | mg/L | WQP observations and DO outputs | Values > 20 mg/L are suspect. |

- | Depth | m | WQP profile depth and lake morphometry | Convert ft or cm before use. |

- | Chlorophyll-a | ug/L | TSI input | Used for Carlson TSI. |

- | Total phosphorus | ug/L | TSI input | Used for Carlson TSI. |

- | Secchi depth | m | TSI input | Used for Carlson TSI. |

- | BOD decay rate `kd` | 1/d | DO seasonal model parameter | Convert hourly rates to daily rates. |

- | Reaeration rate `ka` | 1/d | DO seasonal model parameter | Convert hourly rates to daily rates. |

- | Lake area | km2 | Lake morphometry | Convert ha or m2 before use. |

- | Lake volume | m3 | Lake morphometry | Convert acre-ft before use. |

- ## 4b. Unit Trap Table

- These unit conversions cause **silent failures** if wrong. The model performs

- no internal unit conversion; all inputs must be in the expected units.

- | Variable | Model expects | Common source unit | Conversion | Trap ID |

- |-----------------------|----------------|---------------------|--------------------------------|---------|

- | Temperature | **deg C** | deg F (some WQP) | (F - 32) x 5/9 | dt_001 |

- | Temperature | **deg C** | K | - 273.15 | dt_002 |

- | Dissolved oxygen | **mg/L** | ug/L (some labs) | / 1000 | dt_003 |

- | Dissolved oxygen | **mg/L** | % saturation | x DO_sat(T) / 100 | dt_004 |

- | Depth | **m** | ft (some WQP) | x 0.3048 | dt_005 |

- | Depth | **m** | cm | / 100 | dt_006 |

- | Chlorophyll-a | **ug/L** | mg/L | x 1000 | dt_007 |

- | Total phosphorus | **ug/L** | mg/L | x 1000 | dt_008 |

- | Secchi depth | **m** | ft | x 0.3048 | dt_009 |

- | BOD decay rate kd | **1/d** | 1/h | x 24 | dt_010 |

- | Reaeration rate ka | **1/d** | 1/h | x 24 | dt_011 |

- | Lake area | **km2** | ha | / 100 | dt_012 |

- | Lake area | **km2** | m2 | / 1e6 | dt_013 |

- | Lake volume | **m3** | acre-ft | x 1233.48 | dt_014 |

- **Rule**: If DO values are > 20 mg/L, units are almost certainly ug/L not mg/L.

- If temperature values are > 50, units are likely Fahrenheit or Kelvin.

- If depth values are > 100 in a lake context, units may be feet or cm.

- ## 5. Tools Reference

- | Tool | Stage | Script | Purpose |

- |----------------------------|------------|----------------------------------|-------------------------------------------------------|

- | `convert_forcing_to_wasp` | s1_forcing | `tools/convert_forcing_to_wasp.py` | WQP/NLA CSV to cleaned observation time series |

- | `convert_parameters_to_wasp`| s2_params | `tools/convert_parameters_to_wasp.py`| Lake morphometry + kinetic + thermal parameters |

- | `run_wasp` | s3_execute | `tools/run_wasp.py` | SURROGATE: execute analytic seasonal T/DO model + profiles + TSI (not EPA WASP) |

- | `run_wasp_engine` | real engine | `tools/run_wasp_engine.py` | REAL EPA WASP 8.5.0: run a `.wif` under WINE, check close-out, extract `.BMD2` to CSV |

- | `parse_output_wasp` | s4_output | `tools/parse_output_wasp.py` | Parse T/DO profiles, compute R/RMSE/bias, TSI |

- All tools follow the **validate -> process -> validate** pattern:

- 1. Parse CLI arguments with `argparse`

- 2. Validate all inputs (file existence, unit plausibility, range checks)

- 3. Process (convert, run, parse)

- 4. Validate outputs (physical plausibility, diagnostic warnings)

- 5. Return JSON: `{"status": "success/error", "output": {...}, "log": [...]}`

- ## 6. Critical Domain Knowledge

-

- These non-obvious facts cause silent failures. Each links to a diagnostic triplet.

-

- 1. **dt_001 / dt_002**: WQP temperature data may be in Fahrenheit or Kelvin

- depending on the monitoring organization. Always check

- `ResultMeasure/MeasureUnitCode`. Values > 50 in a temperate lake are

- almost certainly Fahrenheit. Values > 200 are Kelvin.

-

- 2. **dt_003 / dt_004**: WQP dissolved oxygen is typically mg/L but some

- entries report percent saturation. At 20 C, 100% saturation = 9.09 mg/L.

- Using percent values directly gives DO of 80-110 "mg/L" which is absurd.

-

- 3. **dt_005 / dt_006**: WQP depth fields

- (`ActivityDepthHeightMeasure/MeasureValue`) vary in units. The most

- common unit is meters, but some stations report in feet. Check

- `ActivityDepthHeightMeasure/MeasureUnitCode`.

-

- 4. **Benson-Krause temperature sensitivity**: DO saturation drops from

- 14.6 mg/L at 0 C to 7.6 mg/L at 30 C. A 1 C error in temperature

- produces ~0.2 mg/L error in DO saturation. This is the dominant source

- of DO model error in thermally stratified lakes.

-

- 5. **Thermocline depth varies seasonally**: The logistic thermocline model

- uses a fixed thermocline depth, valid only during summer stratification

- (June-August in Northern Hemisphere). Applying summer profiles to spring

- or fall data produces large errors because the thermocline is developing

- or breaking down.

-

- 6. **Hypolimnetic oxygen depletion**: In productive lakes, the hypolimnion

- becomes hypoxic during summer stratification due to sediment oxygen

- demand (SOD) and decomposition. The DO profile model captures this via

- a depletion rate parameter, but the rate varies by lake productivity

- (oligotrophic: 0.01-0.03, eutrophic: 0.05-0.15 per meter below

- thermocline).

-

- 7. **Carlson TSI independence assumption**: The three TSI indices (Chl-a,

- Secchi, TP) are designed to agree for algae-dominated systems. Deviations

- indicate non-algal turbidity (TSI_Secchi >> TSI_Chl-a) or nutrient

- limitation (TSI_TP >> TSI_Chl-a).

- 8. **WQP data quality flags**: WQP data includes a

- `ResultStatusIdentifier` field. Records marked "Rejected" or

- "Preliminary" should be excluded. Also filter

- `ResultValueTypeName != "Estimated"` for calibration data.

- ## 7. WASP Parameters

-

- ### Temperature Seasonal Model (3 parameters)

- | Parameter | Symbol | Unit | Typical Range | Description |

- |-------------|-----------|---------|-----------------|--------------------------------------|

- | Mean temp | T_mean | deg C | [2, 30] | Annual average surface temperature |

- | Amplitude | amplitude | deg C | [3, 18] | Seasonal temperature swing |

- | Phase | phase | day | [90, 170] | Day-of-year of temperature peak |

- ### DO Seasonal Model (4 parameters, coupled to temperature)

- | Parameter | Symbol | Unit | Typical Range | Description |

- |-------------|-----------|---------|-----------------|--------------------------------------|

- | BOD decay | kd | 1/d | [0.01, 0.5] | Deoxygenation rate coefficient |

- | Reaeration | ka | 1/d | [0.1, 2.0] | Atmospheric reaeration rate |

- | Initial BOD | BOD0 | mg/L | [0.5, 20] | Background BOD loading |

- | DO offset | DO_offset | mg/L | [-3, 3] | Systematic DO correction |

- ### Thermal Profile (4 parameters)

- | Parameter | Symbol | Unit | Typical Range | Description |

- |----------------|-------------|-------|---------------|------------------------------------|

- | Surface temp | T_surface | deg C | [15, 30] | Epilimnion temperature (summer) |

- | Bottom temp | T_bottom | deg C | [4, 15] | Hypolimnion temperature |

- | Thermocline | thermo_depth| m | [3, 30] | Depth of maximum dT/dz |

- | Thermo width | thermo_width| m | [0.5, 10] | Thermocline transition thickness |

- ### Lake Morphometry

- | Parameter | Symbol | Unit | Description |

- |-------------|------------|--------|-----------------------------------------|

- | Max depth | z_max | m | Maximum lake depth |

- | Mean depth | z_mean | m | Volume / surface area |

- | Surface area| area | km2 | Lake surface area |

- | Volume | volume | m3 | Total lake volume |

- ---

- ## 8. Streeter-Phelps BOD-DO Physics

- The Streeter-Phelps model describes the DO sag curve downstream of a

- pollution source (or temporally in a lake with BOD loading):

- ```

- dBOD/dt = -kd * BOD (first-order BOD decay)

- dDO/dt = ka * (DO_sat - DO) - kd * BOD (reaeration - deoxygenation)

- ```

- Analytical solution for oxygen deficit D = DO_sat - DO:

- ```

- D(t) = [kd * BOD0 / (ka - kd)] * [exp(-kd*t) - exp(-ka*t)] + D0 * exp(-ka*t)

- ```

- Critical behavior:

- - If ka > kd: deficit peaks at t_c = ln(ka/kd) / (ka - kd), then recovers

- - If ka = kd: degenerate case, use L'Hopital limit

- - If ka < kd: reaeration dominates, rapid recovery from initial deficit

- - DO is clipped to [0, DO_sat] to prevent supersaturation or negative values

- Typical parameter values for lakes:

- - kd: 0.05-0.3 /d (lower for lakes than rivers due to settling)

- - ka: 0.1-1.0 /d (lower for deep lakes, higher for shallow/windy)

- - BOD0: 1-10 mg/L (varies with pollution loading)

- ## 9. Validated Results (Lake Erie Central Basin) — SURROGATE model, not EPA WASP

-

- | Metric | Calibration | Validation |

- |-------------------------|-------------|------------|

- | Temperature R | 0.878 | 0.885 |

- | Temperature RMSE (C) | 4.31 | 4.10 |

- | Temperature n | 28,988 | 19,326 |

- | DO R | 0.816 | 0.413 |

- | DO RMSE (mg/L) | 2.14 | 3.77 |

- | DO n | 19,795 | 13,198 |

- | Thermal profile R | 0.996 | -- |

- | Thermal profile RMSE (C)| 0.62 | -- |

- | TSI (Chl-a) | 40.7 | -- |

- | NLA percentile | 22.3% | -- |

-

- Temperature model shows strong generalization (R improves from cal to val).

- DO model shows reduced generalization, typical for simple seasonal models

- that cannot capture interannual variability in loading and stratification.

- Thermal profile model achieves near-perfect fit (R=0.996) for summer conditions.

- ### Performance Metrics -- Convention Bars

- Source: `docs/validation_convention.yaml`. These bars are the KI's cited

- field convention for judging model skill. A null band in the convention is

- reported here as "no cited threshold".

- | Dag variable | Metric | Direction | Satisfactory band | Good band | Very good band |

- |--------------|--------|-----------|-------------------|-----------|----------------|

- | `temperature_timeseries` | nse | maximize | 0.5 (`arnold2012`, `efdc2021`, `piccolroaz2013`) | no cited threshold (`arnold2012`, `efdc2021`, `piccolroaz2013`) | 0.75 (`arnold2012`, `efdc2021`, `piccolroaz2013`) |

- | `do_timeseries` | nse | maximize | 0.5 (`arnold2012`, `efdc2021`, `deepavarsa2023`) | no cited threshold (`arnold2012`, `efdc2021`, `deepavarsa2023`) | 0.75 (`arnold2012`, `efdc2021`, `deepavarsa2023`) |

- | `do_timeseries` | r2 | maximize | 0.5 (`deepavarsa2023`, `lushui2021`) | 0.6 (`deepavarsa2023`, `lushui2021`) | 0.75 (`deepavarsa2023`, `lushui2021`) |

- | `temperature_profile` | pbias | zero_centered | 15 (`lushui2021`) | no cited threshold (`lushui2021`) | no cited threshold (`lushui2021`) |

- For the headline dag variable `do_timeseries`, the convention bars are:

- - `nse`, maximize: satisfactory 0.5 (`arnold2012`, `efdc2021`, `deepavarsa2023`), good no cited threshold (`arnold2012`, `efdc2021`, `deepavarsa2023`), very good 0.75 (`arnold2012`, `efdc2021`, `deepavarsa2023`).

- - `r2`, maximize: satisfactory 0.5 (`deepavarsa2023`, `lushui2021`), good 0.6 (`deepavarsa2023`, `lushui2021`), very good 0.75 (`deepavarsa2023`, `lushui2021`).

- ## 10. Supported Lakes

-

- | Lake | WQP Directory | Key Variables | Profile Data |

- |-------------------|--------------------|----------------------|--------------|

- | Lake Erie Central | Lake_Erie_Central | Temp, DO, Chl-a | Yes |

- | DeGray Lake | DeGray_Lake | Temp, DO | Yes |

- | Jordan Lake | Jordan_Lake | Temp, DO, Chl-a | Yes |

- | Lake Mead | Lake_Mead | Temp, DO | Yes |

- NLA 2017 survey data provides additional profile validation for deep lakes

- across the continental US.