Zarafshan Valley Groundwater Model
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Ministry of Mining Industry and Geology of the Republic of Uzbekistan
Regional groundwater model of the Zarafshan valley · MODFLOW 6
Modelling and analyticshydrosolutions GmbH

Status review · September 2026

Groundwater in the Zarafshan valley

A regional groundwater model between Ravathoji and Khazara: why it was built, what it is built from, what it shows, where it falls short, and what should happen next.

Overview

Five parts

  • 1 Introduction and problem statement
  • 2 Data and methods of the regional modelling approach
  • 3 Results of the steady-state and the transient model
  • 4 Issues, discussion and required improvements
  • 5 Conclusions

Keys: → next slide, C contents, O overview, N notes with sources, L language, P print to PDF.

Summary

Key messages

The valley's groundwater is under growing pressure: a third more people since 2010, rainfall that swings by half between years, and pumping that the register reports at eight times the population-based estimate.

A working regional model exists. It is built from every dataset available, runs in seconds, reproduces the regional water table, and simulates fifteen years month by month.

It is not yet a calibrated tool. Forty percent of the level targets lie on the piedmont outside the mapped alluvium, and the register pumping cannot be placed without a drawdown the wells do not show.

The way forward is a settled pumping record, a cleaned target set, and a coupled soil–canal–aquifer approach so that recharge is simulated rather than assumed, as already done in the Chu basin.

1 Introduction and problem statement

A valley that lives on one aquifer

Between Ravathoji and Khazara the Zarafshan runs through the most intensively irrigated land of Uzbekistan. Under the fields lies a single shallow alluvial aquifer that the canals, the fields, the river and the towns all draw on.

Samarkand, Kattakurgan and Navoi take their drinking water from it. Farmers pump it where canals do not reach. The river gains from it in winter and loses to it in summer.

Decisions on permits, monitoring and new well fields are taken district by district, but the aquifer does not know district boundaries. A regional model is the only way to see the whole system at once.

1 Introduction and problem statement

The study area: 230 km of valley and everything the model must account for

The study area: 230 km of valley and everything the model must account for

Finding. The model covers the valley floor and terraces, 10 208 km², from the Ravathoji hydroworks to the Khazara gauge. The Zarafshan splits into the Oqdaryo and Qaradaryo branches, feeds the canal systems and the Kattakurgan reservoir, and receives three ungauged tributaries from the north, whose basins lie outside the model.

What it means. Everything the model says is regional: districts and river reaches, not individual wells. The mountain catchments enter only as inflows at the model edge.

Source: OpenStreetMap tiles and canals; SWB outline and grid (NB01a); north_basins (NB01a, hydra-shed); HydroLAKES; inflow points (NB05a); WorldPop city cells.

1 Introduction and problem statement

Four pressures on the same aquifer

Population growth

A third more people in the model area since 2010. Domestic pumping grows with them, and the towns' well fields sit in the most permeable part of the aquifer.

Climate variability and change

Rainfall over the valley varied between 178 and 471 mm a year in 2010–2024. Warmer summers raise crop demand; the snow-fed river peak is expected to shift earlier.

Unmeasured abstraction

The register lists eight times more drinking and industrial pumping than population norms explain, without well coordinates. Agricultural and unregistered pumping are not measured at all.

River diversion

Two thirds of the river is diverted at Ravathoji before it enters the valley. What is left in the channel sets how much the aquifer can exchange with it.

1 Introduction and problem statement

More people, more pumping

The model area is home to almost five million people. Their number grew by a third between 2010 and 2024, and domestic demand in the model grows in step.

Even the population-based estimate, which is the lower bound, rises from 0.09 to 0.12 km³ a year over the simulation period.

More people, more pumping

Source: data/population/yearly_pumping_summary.csv (NB07a; WorldPop 2020 scaled by national growth).

1 Introduction and problem statement

Dry years come in runs

Rain and snow over the valley are the only recharge that does not depend on the canals. Three of the last five years were well below the 2010–2024 mean.

Recharge from rainfall is small in absolute terms, about a tenth of what falls, but the same dry years also cut the river inflow that feeds the canals.

Dry years come in runs

Source: CHIRPS v3 monthly rasters averaged over the model outline (measured in this review); the 10 % recharge coefficient is the NB10a assumption.

1 Introduction and problem statement

Where the water table is falling

Where the water table is falling

Finding. Of 102 monitoring wells with at least eight years of data, 38 show levels falling by more than 0.1 m a year and only 5 rising. The strongest declines cluster south of Samarkand and in the eastern terraces.

What it means. The median trend is close to zero, so this is not a valley-wide depletion yet. It is a set of local declines that a regional model must be able to attribute to pumping, canal changes or dry years.

Source: data/observations/regional_gw_observations_long.csv, QC-flag OK, least-squares slope per well (measured here).

1 Introduction and problem statement

How much is really pumped? Nobody knows precisely

How much is really pumped? Nobody knows precisely

Finding. For the thirteen Samarkand districts with a purpose breakdown, the MMIG register reports 27.5 m³/s of drinking and industrial abstraction; population and per-capita norms explain 3.6 m³/s. The gap sits almost entirely in the three districts with regional well fields.

What it means. Well fields export water by pipeline to Navoi and Bukhara and return city water through the sewer collector. Without well coordinates, annual series and export volumes, the model cannot place this pumping, and unregistered farm pumping is not in either number.

Source: data/population/modelled_vs_observed_comparison.csv (NB07a; MMIG ОТБОР register).

1 Introduction and problem statement

What the model has to answer

  • How much water reaches the aquifer from canals, fields and rain, and how much leaves through pumping, evaporation and the river?
  • Where and when does the river gain groundwater, and where does it lose it?
  • What happens to levels if pumping grows, if a dry decade comes, or if canal losses are reduced?
  • Which of these answers can be trusted today, and which need more data?

The next three parts follow these questions: the data and methods, the results, and the limits.

2 Data and methods

Everything the model is built from

Everything the model is built from

Finding. Eleven datasets from MMIG, UzHydromet, the Ministry of Water Resources and global satellite products. Groundwater levels cover 2000–2024, river discharge 2010–2024, canal records 2014–2022 with reliable discharge below the intakes only for 2014–2017.

What it means. Every input is traceable to one of these bars. The short bars are the weak points: the canal record and the single-year population snapshot.

Source: data/README.md and the per-folder READMEs; coverage as recorded there.

2 Data and methods

How water moves through the valley aquifer

How water moves through the valley aquifer

Finding. One unconfined aquifer between the land surface and the bedrock. It is fed by canal and field losses, a little rain, the northern tributary fans and underflow from upstream; it is drained by the river, evaporation from shallow groundwater and crops, and pumping.

What it means. This concept is what the model implements. Two of its elements, the bedrock depth and the piedmont margins, are also where the model is weakest, as chapter 4 shows.

Source: Conceptual drawing after the MMIG cross-sections (NB01a) and the model concept in NB12a; no model result is shown.

2 Data and methods

Five geological zones carry the aquifer properties

Five geological zones carry the aquifer properties

Finding. The Quaternary geology map was vectorised and mapped onto the grid: modern river alluvium along the Zarafshan, two generations of Pleistocene terraces on both flanks, small Eopleistocene patches and bedrock, which is inactive.

What it means. Wells screened in the Quaternary and in the deeper Neogene show no systematic head difference, so one hydraulic layer is defensible. The zones follow MMIG's field mapping and can be refined with MMIG geologists.

Source: data/model/aquifer_properties.npz zone_grid (measured cell counts).

2 Data and methods

How easily water moves and how much the aquifer stores

How easily water moves and how much the aquifer stores

Finding. Conductivity comes from pumping tests in the MMIG well registry, one value per zone: the modern alluvium passes water fastest, the old terraces slowest. Specific yield started from literature values.

What it means. These are starting values. The sensitivity analysis in chapter 3 shows which of them the observations can actually pin down.

Source: data/model/aquifer_properties.npz and aquifer_zone_lookup.json (NB03a).

2 Data and methods

Where groundwater levels are measured

Where groundwater levels are measured

Finding. 398 wells passed quality control: 148 with monthly time series and 250 with a single measurement. Coverage is dense around Samarkand and along the northern edge, sparse west of Kattakurgan.

What it means. These wells are the currency of calibration. Where they are sparse the model is weakly constrained; where they are stacked in one cell they cannot all be matched.

Source: data/observations/calibration_targets_steady_state.csv (NB11a; MMIG registry, ZRB monitoring wells, UGV time series).

2 Data and methods

Most of the river is diverted before it enters the valley

Most of the river is diverted before it enters the valley

Finding. The Zarafshan brings 134 m³/s to Ravathoji on average. Transfers to the Jizzakh and Kashkadarya basins take 39 m³/s, the valley canals another 49 m³/s. The river below the intakes carries 46 m³/s, a third of the inflow.

What it means. The model's river starts with that lowest line. Canal water enters the aquifer as recharge through the fields, which is why the loss fractions matter so much.

Source: data/mow/mainstem_sfr_inflow.npz (NB09a; UzHydromet daily discharge, Ministry of Water canal records 2014–2017, other years by monthly ratios).

2 Data and methods

Three ungauged tributaries from the north

Oqsoy, Tusunsoy and Qorasuv have no gauges. Their runoff was transferred from 85 gauged analogue basins by elevation and checked against a published estimate for Tusunsoy.

They add 4 m³/s on average, mostly in March and April, and enter the model as groundwater inflow along the northern edge because their water infiltrates on the fans.

Three ungauged tributaries from the north

Source: data/mow/northern_tributary_inflows.csv (NB05a).

2 Data and methods

Where the water is used

Where the water is used

Finding. Landsat maps for 2016–2020 give the irrigated share of every cell: 3 272 km² inside the model area, concentrated on the valley floor and thinning towards the terraces.

What it means. This share splits recharge and evapotranspiration between irrigated and rain-fed land. It is the most decisive single input of the water balance.

Source: data/irrigation/regional_irrigation_fraction.npz (NB04a); area = sum of cell fractions over active cells.

2 Data and methods

What goes in and what is drawn out, every month

What goes in and what is drawn out, every month

Finding. Recharge combines rainfall infiltration with canal and field losses, weighted by the irrigated share. Evapotranspiration combines crop demand, evaporation from shallow groundwater and open reservoirs. Both arrays exist for each of the 180 months.

What it means. The fraction of irrigation losses that actually reaches the water table, set at 45 percent, is the key assumption. Chapter 4 argues it should be simulated, not assumed.

Source: data/model/regional_rcha_evt_arrays.npz, steady-state arrays (NB08a), converted from m/d to mm/yr.

2 Data and methods

Where groundwater is pumped in the model

Where groundwater is pumped in the model

Finding. Domestic and industrial pumping is spread with 100 m population data and Uzbek per-capita norms, 3.4 m³/s in total for the April model. The September candidate instead uses the full register value of 37.8 m³/s, smoothed within districts.

What it means. Neither placement is right: the first is too small, the second puts well-field pumping where the population is rather than where the wells are.

Source: data/population/pumping_mean_annual.npz and population_regional_2024.npz (NB07a, WorldPop 2020 projected).

2 Data and methods

The valley water balance, and what is left for the aquifer

The valley water balance, and what is left for the aquifer

Finding. Inflows of 8.2 km³ a year against 6.8 km³ of measured or estimated outflows leave 1.4 km³ unaccounted. With the assumed loss fractions, 0.96 km³ of that recharges the aquifer and 0.46 km³ is lost in the soil zone.

What it means. The aquifer's recharge is the one term no gauge measures. It is derived as a remainder, which is why every other term must be as good as the data allow.

Source: data/mow/regional_water_balance_constraints.json (NB10a). Recharge = 45 % of canal and field losses plus 10 % of precipitation.

2 Data and methods

How the model talks to its surroundings

How the model talks to its surroundings

Finding. 300 river reaches are routed with MODFLOW's stream package, so the river stage responds to inflow and exchange. 19 fixed-head cells close the valley ends, 75 reservoir cells act as head-dependent boundaries, and pumping is applied cell by cell.

What it means. The northern and southern piedmont edges are no-flow boundaries with almost no recharge. That choice is revisited in chapter 4.

Source: data/model/regional_model_setup_v2.json (SFR, CHD), regional_reservoir_mask.npy, regional_wel_data.npz.

2 Data and methods

The model in three dimensions

The model in three dimensions

Finding. Seen as a block with the vertical stretched about 30 times, the model is a thin slab that follows the land surface down the valley: 150 m thick everywhere, 1 300 m above sea level at Ravathoji and 310 m at Khazara, over a length of 230 km.

What it means. The geometry is simple by design: one layer, one thickness. The bedrock depth that would give the slab its true shape is the largest structural unknown.

Source: data/model/regional_model_setup_v2.npz (top, bottom, idomain); drawn in model-grid coordinates.

2 Data and methods

From raw data to a working model

From raw data to a working model

Finding. Twenty notebooks in fixed order turn the raw data into model inputs, run the steady-state model with sensitivity, calibration and concept scenarios, then the transient model, and hand everything over through an open repository, a viewer and a workshop.

What it means. Every figure in this review was regenerated from that pipeline on 14 September 2026. MMIG staff can do the same.

Source: notebooks/README.md pipeline overview.

2 Data and methods

The model in numbers

72 × 223 cells of 1 kmOne unconfined layer, 10 208 active cells
180 monthly periodsTransient simulation 2010–2024
300 river reachesRouted stream, 40–500 m wide
381 wellsSteady-state level targets
17 849 monthly targetsTransient level observations, 144 wells
< 1 s per steady runEnables thousands of test runs

MODFLOW 6 with FloPy, Newton–Raphson solver, open-source throughout.

3 Results

The simulated groundwater surface follows the valley

The simulated groundwater surface follows the valley

Finding. With 2010–2024 mean forcing the model reproduces the regional gradient from about 900 m near Ravathoji to about 330 m at Khazara, with the flat central plain around Kattakurgan and steeper slopes on the flanks.

What it means. The regional picture is right. The question is how well individual wells are matched, and where not.

Source: data/model/regional_ss_workspace/zrb_regional_ss.hds (NB12a run of 2026-09-14).

3 Results

Where the model is too high or too low

Where the model is too high or too low

Finding. Against 381 wells the uncalibrated model has a mean error of −7 m, a mean absolute error of 19 m and a root-mean-square error of 30 m. Red dots south of Samarkand are too high; the dark blue cluster on the northern edge is 30 to 60 m too low.

What it means. The pattern is structural: the northern cluster lies outside the mapped alluvium, on the piedmont the model treats as almost dry.

Source: NB12a workspace heads sampled at target cells; targets from calibration_targets_steady_state.csv without spatial outliers (measured here).

3 Results

Which assumptions matter for the water levels

Which assumptions matter for the water levels

Finding. 5 000 parameter combinations were run, 3 091 converged. Five parameters control the fit: the conductivity of the upper and middle terraces, the evaporation rate from shallow groundwater, precipitation recharge and aquifer thickness. River conductance, boundary heads, bedrock and roughness barely matter.

What it means. Head observations cannot constrain the river parameters at all; that needs discharge data. And no combination brings the mean absolute error below about 15 m.

Source: scratch/lhs_results.csv (NB13a Latin-hypercube sample); Spearman rank correlation with the young-alluvial mean absolute error (measured here).

3 Results

No version beats the April model yet

No version beats the April model yet

Finding. The best of 3 091 sampled parameter sets improves the mean absolute error to 16 m but with a positive bias. The PEST++ calibrated set, re-run in the full model, is worse than the baseline at 27 m. The September candidate with full register pumping sits at 25 m.

What it means. Parameter tuning has reached its limit. The remaining error comes from the model's structure and from the targets themselves, not from the parameter values.

Source: NB12a cell 59 and NB14a cell 30 (executed 2026-09-14); scratch/lhs_results.csv; scratch/ss_rapid_v1_kdown_sfr_connectivity/scenario_metrics.csv.

3 Results

Step by step towards a more realistic concept

Step by step towards a more realistic concept

Finding. Five conceptual changes were stacked on the April model in May 2026: intakes taken out of the river, gross-loss recharge with shallow drains, a thicker aquifer, and the full register pumping with local return flows. Each step after the first worsened the head fit, and the river outflow at Khazara stayed at 50 to 59 m³/s against an observed 23.

What it means. Two lessons: the river excess comes from the gross-loss recharge that the drains return to the river, and the register pumping cannot be sustained where the model places it.

Source: scratch/ss_rapid_scenarios and scratch/ss_rapid_v1_kdown_sfr_connectivity scenario_metrics.csv (NB12d, May 2026); observed outflow from the water-balance constraints (NB10a).

3 Results

Fifteen years month by month

Fifteen years month by month

Finding. The transient model runs all 180 months without dry cells and with a mass-balance error below 0.02 percent. Against 15 491 observations after a two-year spin-up the mean absolute error is 9.5 m. Some wells are matched closely, others are offset by several metres, and one shows the seasonal swing the others lack.

What it means. The model can already say how the regional water table responds to wet and dry years. It cannot yet reproduce the seasonal rhythm of most wells.

Source: data/model/transient_residuals.csv (NB15a run of 2026-09-14).

3 Results

The seasonal swing is too small

At 139 wells the simulated yearly rise and fall of the water table is on average a fifth of the observed one. Only a handful of wells swing too much.

Five hundred transient runs traced this to the storage parameters: lower specific yield in the alluvium and terraces brings the amplitude closer to the observations.

The seasonal swing is too small

Source: Yearly max–min per well averaged over 2012–2024 (measured here from transient_residuals.csv); NB16a: 480 of 500 runs converged, best young-alluvial amplitude error 0.23 m against 0.39 m baseline.

3 Results

What controls the seasonal rise and fall

What controls the seasonal rise and fall

Finding. Five hundred transient runs varied the storage and flow parameters together; 480 converged. The specific yield of the modern alluvium and of both terrace generations controls the seasonal amplitude, with a smaller counter-effect from the conductivity of the middle terraces.

What it means. The literature storage values are too high for this valley. Transient data constrain what the steady-state model cannot see, so the next calibration must use both.

Source: scratch/lhs_transient_results.csv (NB16a); Spearman rank correlation with the young-alluvial amplitude error (measured here).

3 Results

The river gains groundwater most of the year and loses it at the irrigation peak

The river gains groundwater most of the year and loses it at the irrigation peak

Finding. Over the whole river and all fifteen years the aquifer discharges about 10 m³/s to the Zarafshan in the gaining months. In June and July, when canal water raises the water table, the direction reverses and the river loses up to 30 m³/s to the aquifer.

What it means. This seasonal reversal is the headline result for management. Its magnitude is uncertain because it is constrained by levels only; the observed discharge at Khazara is the missing check.

Source: data/model/regional_tr_workspace_p2 cell budget, SFR term summed over reaches (NB15a run of 2026-09-14; measured here).

4 Issues, discussion and improvements

Where the error sits

Where the error sits

Finding. 153 of the 381 targets, forty percent, are single-measurement wells on the northern piedmont outside the mapped alluvium. Their median error is −20 m and their spread the widest of any group. Wells in the alluvium and the terraces are centred near zero.

What it means. The model was built for the valley aquifer. It is being judged, to a large degree, on wells in a different unit that it represents with borrowed properties and no recharge.

Source: candidate residuals.csv joined to calibration_targets_steady_state.csv (map_geology); GAPS.md §5 Iteration 3 for the diagnosis.

4 Issues, discussion and improvements

What is missing in the data

Pumping

  • Coordinates and rates of production wells for Samarkand and Navoi; the register has them only for Bukhara
  • Annual abstraction series 2010–2025 by district; only the 2026 status exists
  • Pipeline exports to Navoi and Bukhara and sewer returns
  • Agricultural and unregistered pumping: no data at all

Aquifer geometry

  • Bedrock depth: no map; the model uses one uniform thickness
  • Filter depths give only lower bounds
  • No transmissivity data for the piedmont and the Eopleistocene zone

Surface water

  • Reliable discharge below the Ravathoji intakes only for 2014–2017
  • Canal deliveries per system, not per field
  • Khazara discharge used as an annual mean; monthly series needed for validation

Observations

  • 250 of 398 wells have a single measurement
  • Several wells share one 1 km cell with levels differing by up to 74 m
  • 47 wells report levels above land surface: elevations need checking
  • No soil-moisture, lysimeter or tracer data for the unsaturated zone

4 Issues, discussion and improvements

Four conceptual problems

Pumping placement. The register magnitude is plausible, but placed by population the model lowers heads around Samarkand by 31 m on average, a cone the observations do not show. Either the fan receives inflow the model lacks, or the effective abstraction over 2010–2024 was lower than the 2026 register, or both.

The piedmont boundary. The northern and southern margins are no-flow edges with almost no recharge, yet forty percent of the targets sit there. Mountain-front recharge, a Neogene zone, or a separate treatment of those wells is needed.

Recharge as a prescribed number. Canal and field losses reach the aquifer through an unsaturated zone the model does not represent. The 45 percent deep-percolation fraction, the 90/50 gross-loss split and the 2 m drains are bookkeeping choices, and the scenario ladder shows they decide both the head fit and the river outflow.

River reaches longer than one cell. Their leakage is forced through a single cell, distorting exchange locally. The fix is known and must precede any flux calibration.

4 Issues, discussion and improvements

The missing link: the soil between canal and aquifer

In this valley the aquifer is fed mainly by irrigation. How much of the diverted water percolates, how much evaporates and how long it takes to arrive depends on soils, crops, canal lining and the state of the water table. The present model replaces all of that with fixed fractions.

The sensitivity analysis and the scenario ladder both point at these fractions as the controls of the head fit and the river outflow. No amount of aquifer-parameter calibration can compensate for a wrong recharge input.

Simulating the land surface and the canal system explicitly, and letting the aquifer model receive recharge from them, is the natural next stage. This is what was proposed and implemented for the Chu River basin plan.

4 Issues, discussion and improvements

How it was done in the Chu basin: one water cycle, three engines

How it was done in the Chu basin: one water cycle, three engines

Finding. In the Chu basin plan the natural hydrology (SWAT+), the managed canal network (TaqSim) and the valley aquifer (MODFLOW 6) are three engines that hand water to each other every day. Soil percolation and canal losses become groundwater recharge; river gains and drain returns flow back to the river.

What it means. The Zarafshan valley has the same anatomy: mountain inflow, a diverted river, canals, irrigated plain and a shallow aquifer. The pattern transfers directly.

Source: Chu River Basin Plan 2027–2031 modelling showcase, hydrosolutions 2026; figure reused unchanged.

4 Issues, discussion and improvements

The Chu water system as one picture: which engine owns what

The Chu water system as one picture: which engine owns what

Finding. Snow and glacier melt, the reservoir, canals and irrigation, pumping, recharge, groundwater flow and the springs that return water to the river, drawn as one block of landscape. The brackets show the reach of each engine and the coupling zone where they hand water to each other.

What it means. Replace the Kyrgyz Ala-Too with the Zarafshan headwaters and the Chu with the Zarafshan, and the picture is the one this valley needs.

Source: Chu River Basin Plan 2027–2031 modelling showcase, hydrosolutions 2026; illustration reused unchanged.

4 Issues, discussion and improvements

How the Chu engines are wired together

How the Chu engines are wired together

Finding. Every transfer between the engines is booked once, with source, receiver and date, and the groundwater compartment closes to a tiny residual. The aquifer model receives simulated recharge instead of a prescribed array, and the river receives simulated gains and losses.

What it means. The coupling code, the ledger checks and the calibration workflow exist and are documented. Reusing them for the Zarafshan is an engineering task, not new research.

Source: Chu River Basin Plan 2027–2031 modelling showcase, hydrosolutions 2026; figure reused unchanged.

4 Issues, discussion and improvements

Proposed next stage for the Zarafshan

Proposed next stage for the Zarafshan

Finding. A land-surface and soil model for the irrigated plain and the mountain catchments, an allocation model of the Ravathoji diversions, canals and reservoirs, and the existing MODFLOW 6 model as the aquifer engine. Recharge, evapotranspiration and river exchange become simulated exchanges.

What it means. The regional model built so far is not lost: it becomes the groundwater engine of the coupled system. The steady-state work identified exactly the inputs the coupling would replace.

Source: Pattern: the SWAT+ – TaqSim – MODFLOW 6 coupling of the Chu basin plan (hydrosolutions, 2026); engines for the Zarafshan are a proposal, not an implementation.

4 Issues, discussion and improvements

What was agreed with MMIG in Tashkent, May 2026

What was agreed with MMIG in Tashkent, May 2026

Finding. The working session fixed five management scenarios and their combinations, the output table each run has to deliver, and two data points on pumping: the population-based estimate is only the drinking and industrial part, and agricultural pumping needs MMIG's own data. It also flagged the systematic bias between simulated and observed levels as the first problem to solve.

What it means. The scenarios are the questions the model must answer; the bias diagnosis in this review is the work that makes the answers trustworthy.

Source: Whiteboard of the Tashkent working session with MMIG, May 2026 (photograph).

4 Issues, discussion and improvements

Five scenarios and their combinations

Each run reports river inflow, river outflow, the piezometric water level from the model and its change against 2024. In steady state inflow equals outflow; in the transient runs the difference is the change in aquifer storage.

ScenarioDriverChange tested
S1Hydro-climateRiver inflow at Ravathoji reduced from 140 to 100 m³/s
S2PopulationPopulation of about 3 million grows by 20 %; domestic and industrial pumping scale with it
S3AgricultureGroundwater pumping for irrigation reduced by 40 m³/s
S4ClimatePrecipitation halved
S5ClimatePotential evapotranspiration increased by 25 %
S1 + S2CombinedLess river inflow and more people
S1 + S2 + S3CombinedLess river inflow, more people, less irrigation pumping

Not yet run: under the project workflow, scenario runs start once the steady-state model passes its readiness gate on a cleaned target set (chapter 4, improvements 1–3).

4 Issues, discussion and improvements

Required improvements, in order

1 Settle the pumping record

  • Obtain the annual abstraction series 2010–2025 and well-field coordinates from MMIG
  • Bracket the model period between the 2010 and 2026 registers
  • Route well-field exports and city sewage explicitly instead of local return

2 Clean the target set

  • One target per cell, weighted by well count and spread
  • Exclude edge and above-ground wells; separate static from time-series wells
  • Report all metrics on the cleaned and the full set

3 Fix the piedmont and the river

  • Mountain-front recharge and a Neogene zone along the northern edge, or a separate aquifer for those wells
  • Split river reaches at cell boundaries
  • Validate river outflow against monthly Khazara discharge

4 Couple the unsaturated zone

  • Set up SWAT+ for the plain and the mountain catchments and an allocation model for the canals
  • Hand recharge and river exchange to the existing MODFLOW 6 model daily, as in the Chu basin
  • Only then: joint calibration of levels, seasonal amplitude and river discharge

5 Conclusions

Conclusions

The Zarafshan valley aquifer is under measurable pressure: more people, unmeasured abstraction and a run of dry years, with falling levels at a third of the monitoring wells.

A regional groundwater model of the valley exists, built from every available dataset, fully reproducible, and already handed over to MMIG with training. It reproduces the regional water table and the seasonal reversal of river–aquifer exchange.

Its accuracy is limited by structure, not by parameters: the pumping record, the piedmont wells, and above all a recharge that is assumed rather than simulated.

The next stage is clear. Settle the pumping record with MMIG, clean the calibration targets, and couple the aquifer model to a soil and canal model as in the Chu basin. The five scenarios agreed in Tashkent are then run and reported in the agreed table, and the model becomes a tool for permit and monitoring decisions in the valley.

5 Conclusions

A working platform, ready for its next stage.

hydrosolutions GmbH · Zurich · September 2026

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