GenRiver3

• Preface
• 1. GenRiver Model Overview
• 2. Description of Model Sectors
  • Why a River Flow Model
  • GenRiver Model
  • Two alternative explanations for steady-river flow
  • Quantification of ‘buffering’ of river flow by watershed areas
  • Target properties of the model
  • Description of GenRiver Component and its Processes
  • Water Balance
    • Rainfall
    • Interception
    • Infiltration
    • Deep Infiltration
    • Surface Flow
    • Soil Water
    • Actual Evapotranspiration
    • Percolation
    • Subsurface flow or soil discharge
    • Ground Water
    • Irrigation
    • Baseflow
  • Stream Network
    • Total Stream flow
    • Routing time
  • Land Cover
  • Subcatchment Parameter
• 3. Flow Persistence and the FlowPer model
  • Background: temporal autocorrelation of river flow
  • FlowPer Model Overview
  • Starting and Running FlowPer Model
    • Input parameterization
    • Running the model
    • FlowPer model output
• References

Preface

Water flow in rivers is generated by rainfall and modified by landscape topography, vegetation, and soil, but also by human engineering to enhance drainage and/or retention of water. The degree to which river flow is influenced by land cover change (‘deforestation’, ‘reforestation’, ‘agroforestation’, and other code words) is hotly debated, as is the influence of ‘climate change’. A simple tool that relates the logic at the plot level to the river-level consequences was deemed relevant to assist in the analysis of catchment data. Existing models were either too complex and data-hungry or left out important processes, such as the impact of land use change in the soil and its physical condition.

GenRiver is a generic river flow model that responds to spatially explicit rainfall and keeps track of a plot-level water balance that responds to changes in vegetation and soil. The model treats a river as a summation of streams, each originating in a sub-catchment with its own daily rainfall, yearly land cover fractions, and routing time based on the distance to the river outflow (or measurement) point. Interactions between streams in their contribution to the river are considered to be negligible (i.e. there is no ‘backflow’ problem). Spatial patterns in daily rainfall events are translated into average daily rainfall in each sub-catchment in a separate module (Spatrain). The sub-catchment model represents interception, infiltration into the soil, rapid percolation into the subsoil, the surface flow of water, and rapid lateral subsurface flow into streams with parameters that can vary between land cover classes.

1. GenRiver Model Overview

Land cover change can significantly affect watershed functions through a) changes in the fraction of rainfall that reaches the ground, b) the subsequent path-ways of water flow over and through the soil as related to surface and subsurface structure of the soil, surface roughness, and landscape drainage, and c) the rate of water use by plants (Fig. 1.1).

Simple characteristics of the vegetation (monthly pattern of leaf biomass, influencing canopy interception and transpiration, and ability to extract water from deeper soil layers) and soil (especially compaction of the macro pores in the soil that store water between ‘saturation’ and ‘field capacity’) can probably explain a major part of the impacts on river flow.

Empirical assessment of the dynamics of water flows as a function of land cover change and soil properties takes time and resources, and needs to take temporal and spatial variation of rainfall into account. A model based on ‘first principles’ that integrates land cover change and change in soil properties as driving factors of changes in river flow can be used as a tool to explore scenarios of land use change if it passes a ‘validation’ test against observed data.

GenRiver is a generic river model on river flow. As is common in hydrology, it starts the accounting with rainfall or precipitation (P) and traces the subsequent flows and storage in the landscape, which can lead to either evapotranspiration (E), river flow (Q), or change in storage (ΔS) (Figure 1.3):

P = Q + E + ΔS

Models differ in the relations between the different terms of the balance equation and in the way they account for the ‘slow flows’, that derive from water that infiltrates into the soil but can take a range of pathways, with various residence times, to reach the streams and rivers, depending on landform, geology and extractions along the way.

The core of the GenRiver model is a ‘patch’ level representation of a daily water balance, driven by local rainfall and modified by the land cover and land cover change and soil properties of the patch. The patch can contribute to three types of stream flow: surface-quick flow on the day of the rainfall event, soil-quick flow on the next day, and base flow, via the gradual release of groundwater.

A river is treated as a summation of streams, each originating in a sub-catch¬ment with its own daily rainfall, yearly land cover fractions, and constant total area and distance to the river outflow (or measurement) point. Interactions between streams in their contribution to the river are considered to be negligible (i.e. there is no ‘backflow’ problem). Spatial patterns in daily rainfall events are translated into average daily rainfall in each sub-catchment. The sub-catchment model represents interception, infiltration into the soil, rapid percolation into the subsoil, the surface flow of water, and rapid lateral subsurface flow into streams with parameters that can vary between land cover classes.

2. Description of Model Sectors

Why a River Flow Model

At short time span of most observers of river flow it is difficult to distinguish interannual variability of weather from real change in climate and from changes in land cover and soil conditions.

The hydrology of the river basin integrates processes at a range of temporal and spatial scales and the interactions between ‘input’ and ‘water processing’ at patch and river channel scale are not easily unraveled. Purely empirical (data driven) models may need only a few parameter to reconstruct a daily hydrograph from rainfall data, but because their parameter cannot ‘unpacked’ at the land use level, such models are not suited for scenario models where the effect of land cover change (including forest cover) are the main interest (Croke, et. al., 2004).

Spatially explicit models that make use of a basic of understanding of the underlying mechanisms tend to require a large number of spatially explicit parameters more than normally available. If such models are used for ‘model optimization’ there may be to many degrees of freedom for improving the model fit (‘survival of the fitter’), and it is hard to decide which among a range of parameterization options to use for subsequent scenario studies (Thanapakpawin, P., et. al. 2005).

The term ‘watershed functions’ is often used in a rather loose way, suggesting that its various aspects (dimensions) change in a similar way when we make comparisons across climatic zones, land forms and human-induced land cover change. In reality, however, changes in total quantity of water may not be of the same relative magnitude (or even sign) as changes in quality or regularity of flow, and a differentiation among the ‘functions’ is needed. The ‘functionality’ of various aspects of river flow depends on the perspective, however, and thus may differ between various stakeholders. So, we may want to restrict ourselves to the hydrological ‘consequences’ of a watershed, and leave the value judgements of ‘functions’ to a later step in the analysis. The three main outcomes of current interest are:

• Quantity or total water yield
• Evenness of flow, which implies high flows in the ‘dry’ season and an absence of strong peak flows in the wet season
• Quality of water, with respect to its use as drinking water, other domestic uses, industrial use, irrigation or as habitat for fish and other water organisms

The behaviour of streams and rivers in these respects can be seen as the consequence of:

1. Site properties that ‘come with the territory’
   • local rainfall regime (and its temporal autocorrelation or tendency for wet days to follow wet days)
   • slope
   • soil depth and texture, determining the potential water storage, transport and retention
   • underlying landscape and geology that determines potential storage and release of groundwater
   • inherent properties of the riverbed
2. Scale
   • size of the catchment (upstream of the observer/stakeholder) relative to the spatial autocorrelation of rainfall
3. Land use that directly depend on human activities
   • infiltration and supply to groundwater as potentially influenced by soil structure that itself depends on vegetation and land use
   • vegetative aspects of the properties of the riverbed (and temporary storage) that dominate pulse transmission
   • irrigated agriculture and horticulture based on extractions from rivers
4. Engineering structures
   • canalisation of streams and rivers, increasing the rate of drainage
   • regulating structures in the river
   • impediments to rapid drainage in the form of dams and reservoirs

Where much of the public debate attributes most of the changes in ‘watershed functions’ to a change in forest cover (deforestation or reforestation), we need tools to account for the interactions of all four aspects mentioned here, to help us in assessing the causality of changes and the opportunities for interventions.

Various approaches exist for modelling watershed functions, ranging from directly data-driven (empirical) approaches to models based on concepts of a water balance, soil physics and hydrology. Models differ by temporal and spatial scale: detailed description of rainfall and infiltration may require a minute (or even seconds) time step, especially on slopes where water will become surface runoff if it cannot infiltrate within seconds of reaching the soil surface. At the other end of the spectrum we may find empirical equations relating annual water yield of a catchment to annual rainfall (or precipitation in climate zones where snowfall and ice rains are significant). For some Indonesian catchments, for example, an empirical equation (Rizaldi Boer, pers. comm.) was derived as:

Q = 0.94 P – 1000 mm year –1

with Q as river flow and P as precipitation both in mm year –1. A tentative interpretation of these coefficients is that 6% of rainfall is lost through interception and direct evaporation from wet leaf surfaces and/or a rainfall-dependent increase in plant transpiration, and that the basic value for annual evapotranspiration is 1000 mm year –1. Both these parameters, the interception loss, and the evapotranspiration will vary with the temporal distribution of rainfall and the land cover type, but the intercept is unlikely to change by more than 50% of the values given (so the intercept in unlikely to be more than 1500 or less than 500 mm year-1), while the slope is probably confined to the range 0.8 – 1. The simple model may thus be fairly robust, but it is not sensitive to changes in land use or land cover (these could shift the parameters from the indicated values), and cannot be directly ‘downscaled’ to shorter periods of time (as it does not consider changes in storage terms). More sophisticated models will need to be explicit in the basic value for evapotranspiration of different types of land cover, and the degree to which these land covers induce direct evaporative losses.

Four classes of land cover can be distinguished from the perspective of evapotranspiration :

• open water bodies

  where water loss is determined by the relative humidity of the air and the presence of a stagnant boundary layer of air that reduces the transport of water vapour

• open soil

  which may have a rate of evaporation similar to open water bodies when the surface is wet, but where evaporation may rapidly become limited by the rate of transport to the soil surface; soil cover with a litter layer provides a stagnant air zone, further reducing transport opportunities and mixing with the atmosphere

• seasonally green vegetation

  most plants are able to provide their leaves (evaporating surfaces) with the amount of water that is needed for evaporation similar to an open water surface, during most of the rainy season; during periodic dry spells, plant transpiration is likely to drop below the value of open water, but stay above that of open soil

• evergreen vegetation

  such as evergreen trees (e.g. pines, eucalypts, trees such as grevillea), irrigated rice paddies or vegetable crops will have a rate of transpiration equal to that of open water, or higher if lateral flows of dry air drive the evapotranspiration per unit area to higher levels

If we take for granted that effects of local land use on total annual rainfall are small, the main effect on total water yield of a catchment area is a change in the rate of evapotranspiration, or the return flow of water molecules to the atmosphere. In a simple equation: Q = P – E - S or the total water yield (surface rivers Qr + subsurface lateral flows Qs + groundwater flows Qg) equals precipitation (rainfall plus snow and ice, which in most parts of the tropic can be ignored) minus evapotranspiration minus the changes in storage terms of water in the catrchment. If the time frame for evaluation is sufficiently long relative to the variability of rainfall (e.g. one year for predictable humid climates but multiple years for more erratic drier areas), the S term can be ignored.

Efforts of land users that will reduce evapotranspiration and thus increase total water yield may thus be found in not planting evergeen trees (especially fast growing ones), or not irrigating rice paddies or vegetable crops in the dry season.

By expressing the rainfall and river flow in mm year-1 we essentially use volume of water per unit area as the basis for calculations; if we consider larger areas, where both rainfall and evapotranspiration vary with space, we will need to make an effort to adjust the average value to maintain validity of the equation. For annual water yield, however, an area-based approach to scaling is valid, and values per unit area can be used to estimate values for any scale through multiplication with area. For properties such as ‘evenness of flow’ or probability of flooding, the relation with the scale of consideration is more complex, and a greater sensitivity to both the mean value of land cover fractions as well as the spatial organization of the landscape is probably needed.

If a greater model sensitivity to land use change is important for the question we try to answer or if we are interested in phenomena operating at shorter time scales than a year, we need to take into account the intermediate processes that determine the access to and use of water stored in the soil and the upper groundwater, as well as the rates of transport and temporary storage of water in the river network. The basic framework for a patch or plot level water balance (Figure 4.3) is well accepted, so the various models differ in the details of the time course of describing canopy interception and throughfall, and the way lateral flows over the surface and through the soil are described. As most plot level studies exclude surface inflows, there is a tendency to focus on surface runoff rather than run-on or net transport.

Table 2.1 Models concepts of river flow

A number of existing models address only a single scale, be it a plot or a catchment as a whole (Table 4.1). Other models use a grid-cell approach with interactions between ‘cells’ leading to emergent behaviour at the catchment scale. A third category of models addresses the cross-scale questions in a more direct way by being specific on how properties change with the temporal and spatial scale of consideration.

GenRiver Model

The model was initially designed as a ‘simple’ (few parameters) model that still has a link to process-based models, and that can be gradually spatially differentiated, as the need arises.

The core of the model is a ‘patch level representation of a daily water balance, driven by local rainfall and modified by the land cover and soil properties of the patch. The patch can contribute to three types of stream flow: surface-quick flow on the day of the rainfall event, soil-quick flow on the next day and base flow, via the gradual release of groundwater (Figure 4.4).

Table 2.2. The overall water balance of the model, summed over space and time

For the long-term behaviour the changes in soil and groundwater storage, as well as changes in the volume of streams and rivers will be negligible, while the error term should be negligible at all times if the model is correctly implemented.

Many models for river flow, especially for drier areas, focus on the overland flow directly after rainfall (Quickflow) but do not account for the ‘slow flows’, that derive from water that infiltrates into the soil but can take a range of pathways, with various residence times, to reach the streams and rivers, depending on land form, geology and extractions along the way. To keep things simple, GenRiver distinguishes only two steps in this: a soil quick flow (or ‘inter flow’) that is considered to reach the streams a day after the rainfall event, and a ‘slow flow’ that forms a fraction of the available store of groundwater (leading to an exponential decline of the groundwater store with time and a linear relation ship between the logarithm of the discharge and time in the absence of rainfall).

The GenRiver model was made for data-scarce situations and is therefore based on ‘first principles’, as these may be considered the safest bet for a wide range of applications (acknowledging that directly empirical models may have greater precision within the tested range). The model includes an attempt to relate across spatial scales (Figure 4.5).

Two alternative explanations for steady-river flow

Everybody is probably familiar with the ‘mental model’ of the forests as a sponge, that receive rainfall and gradually feed it to the stream. The concept, clearly formulated in the 1920’s in Indonesia, but is essence much older than that, has been seriously questioned in the 1930’s and internationally in the last two decades (Calder, 2002). The validity of the concept is especially questionable for the humid tropics, where the sponge will be continuously wet and not able to absorb much of the incoming rainfall. Yet, the ‘sponge’ concept still leads to specific expectations that only ‘forest’ can play this role. If we accept that some forms of ‘non-forest’ can maintain infiltration rates, the ‘local buffering’ perspective still leads to strong concerns against any land use intervention that reduces the residence time of water in the system, on its way from rainfall to the river.

There is, however, an alternative explanation for even river flow patterns, that gets much less attention: spatial heterogeneity of rainfall. Simply said: if today it rains here and tomorrow there, the river that receives water from both areas may have a fairly steady flow, despite a poor buffering in either areas (Figure 4.6). If this second model dominates, changes in river flow may be due to a change in the spatial correlation of rainfall, not to land use change in any of the subcatchments per see.

A distinction between these two types of explanation for patterns in river flow is thus essential to evaluate the likely impact of current land use change in forested areas and the types of interventions that may be effective or not. The relative importance of the two explanations clearly depends on the scale of consideration. In small subcatchments there is hardly any space for the second explanation, and the first must dominate.

In areas of several hundreds of square kilometers or at subcontinental scale, the second reason is likely to dominate. So, somewhere at intermediate scale the two may break even – can we assess where this occurs? Unfortunately, most past research was done in small plots and in ‘scaling up’ the possible impact of the second explanation was not recognized. In summarizing data in land use impacts on river flow (Kiersch and Tognetti, 2002) no cases were reported with measurable impacts of land use change on river flow of areas larger than 100 km2.

The GenRiver models were first designed to answer this rather specific question: how does spatial variability of rainfall influence the ‘evenness’ of river flow that is often attributed to forests as dominant land cover?, or ‘explanation 2’. We first of all need a representation of rainfall with spatial patterns that are intermediate between uncorrelated random and fully coupled. We then need to link this to a model that includes the ‘sponge’ in its essential form, so that we can compare the relative importance of both processes. The two tools described here, GenRiver were developed for such a purpose. We will briefly outline the conceptual basis of both, describe the model implementation and parameter sensitivity, and then proceed with the analysis of the relative impacts of land use change on river flow in catchments with spatially heterogeneous rainfall.

Table 2.3. Well-documented impacts of land use change by basin size (Kiersch and Tognetti, 2002); x = Measured impact; - = No well-documented impact

Quantification of ‘buffering’ of river flow by watershed areas

A basic concept in ‘watershed functions’ is ‘evenness of river flow’, indicating low peak flows’ and high ‘base flows’. The variation in river debit between different rivers, however, is largely due to variation in rainfall, and it is no trivial task to separate this climatic effect (that we assume to be independent of local land use change, for the time being at least) from the impacts of land use change. The following definition of ‘buffering’ can allow us to make this separation.

An efficient way of presenting the input and output of a watershed area in a single graph, is to look at the exceedance probabilities for daily rainfall, daily evapotranspiration and daily river flow. If a sufficiently long time period is considered (at least 1 year), changes in storage in soil, groundwater and surface water may be negligible and the areas to the left of the curves for rainfall and evapotranspiration + river flow should be approximately equal. The point of intersection has to have an X-value that equals the mean daily rainfall. The intersection would be at an exceedance probability of 0.5 if rainfall distribution were symmetrical and there would be no dry days – in reality skewness of rainfall distribution plus the fraction of days without rain cause the point of intersection to have a value on the Y-axis that is above 0.5.

In an ‘asphalted’ watershed, the river flow curve may be expected to coincide with the rainfall curve and there is no buffering. In an ideally buffered situation the river flow may be constant and equal to the mean at every day of the year. In between these two extremes we’ll find real watersheds with a partial ‘buffering’.

Target properties of the model

The model was developed with the following target properties. The model should be:

• based on solid principles of the plot-level water balance and the way this is influenced by land use change, through vegetation and changes in soil structure over time preferably compatible in approach to the WaNuLCAS model that operates at higher spatial resolution of soil zones and layers for mixed copping and Agroforestry situations
• handling processes at less-than-hourly time scale where infiltration is concerned and at daily time scales for stream and river flow
• applicable to multiple subcatchments that together form a catchment and that receive rainfall events partially correlated (so in between the assumptions of ‘homogeneity’ and ‘statistical independence’)
• applicable to any land form and digital elevation model (DEM) at ‘parameter’ level, rather than by modifying model structure
• able to predict river flow (hydrograph) at multiple points of interest
• transparent in structure (assumptions) and easy to operate

Description of GenRiver Component and its Processes

A river is treated as a summation of streams, each originating in a subcatchment with its own daily rainfall, yearly land cover fractions and constant total area and distance to the river outflow (or measurement) point. Interactions between streams in their contribution to the river are considered to be negligible (i.e. there is no ‘backflow’ problem). Spatial patterns in daily rainfall events are translated into average daily rainfall in each subcatchment in a separate module. The subcatchment model represents interception, infiltration into soil, rapid percolation into subsoil, surface flow of water and rapid lateral subsurface flow into streams with parameters that can vary between land cover classes.

GenRiver model consists of several sectors, which are related to one another. Those sectors are:

1. Water Balance is a main sector that calculating the input, output, and storage changes of water in the systems. Some components which are in this sector, rainfall, interception, infiltration, percolation, soil water, surface flow, soil discharge, deep infiltration, ground water area and base flow
2. Stream Network is a sector that estimating the flow of water from the river to the final outlet. Some components which are in this sector, total stream in flow, routing time, direct surface flow, delay surface flow, river flow to final outlet.
3. Land Cover is a sector to generate land cover data per sub catchment for each year.
4. Subcatchment Parameter is a sector stored constant parameters that control to the changes of water balance, land cover dan stream network.

Water Balance

Rainfall

Rainfall at subcatchment level is implemented as daily amounts (I_RainPerDay) from long time records for each subcatchment, stored in an excel spreadsheet (Table 4.4.). The daily rainfall at the sub-catchment can be either derived from actual data or from a “random generator” that takes temporal patterns into account (SpatRain Model). The actual data is stored and distributed in ‘I_DailyRainYear…’ parameters, while rainfall derived from SpatRain Model is stored in “I_SpatRain…” parameters. Each parameter consists four years data as this is the maximum data can be load into STELLA. This option can be decided by switch on I_UseSpatVarRain? into 0 (actual data) or 1 (SpatRain data).

Table 2.4. Rainfall input table in excel spreadsheet

I_RainPerDay = if I_UseSpatVarRain? = 1 then I_SpatRainTime[i] else I_DailyRain[i]

Rainfall at subcatchment level for each landcover type (I_ DailyRainAmount) directly calculated proportionally to the area (I_RelArea) and type of each landcover (I_FracVegClassNow).

I_Daily Rain Amount = I_RainPerDay[i] x I_FracVegClassNow[j,i] x I_RelArea[i]

Another parameter that has relation with rainfall is rainfall duration (I_RainDuration). Rain duration is estimated from the daily amount (I_RainPerDay), and rainfall intensity for the given day (mm hour-1) that is derived from a mean value (I_RainIntensMean), a coefficient of variation (I_Rain_IntensCoefVar) and a random number (I_Rain_GeenSeed).

I_RainDuration = (I_RainPerDay[s]/I_Rain_IntensMean) x MIN(MAX (0,1-3 x I_Rain_IntensCoefVar, NORMAL(1,I_Rain_IntensCoefVar,I_Rain_GenSeed+11250)), 1+3 x  I_Rain_IntensCoefVar)

Rainfall duration determines the ‘fraction of time available for infiltration’ (I_RainTimeAvForInf), this can also be modified by canopy interception of rainfall followed by the duration of the ‘dripping’ phase (D_RainIntercDelay).

I_RainTimeAvForInf = min(24,I_RainDuration[i]+D_RainIntercDelay[i])

Rainfall will be distributed to each component of water balance, interception-evaporation (D_InterceptEvap), infiltration (D_Infiltration), deep infiltration (D_DeepInfiltration) and run off (D_SurfaceFlow).

Interception

Evaporation of intercepted water (D_InterceptEvap) has priority over plant transpiration demand. The proportionality factor for reducing plant transpiration demand on the basis of evaporation of intercepted water can be less than 1 (reflecting the typical time of day of rainfall). The number of interception evaporation value is directly proportional with the storage capacity of land cover class (I_CanIntercAreaClass) and the daily rain amount (I_DailyRainAmount).

D_InterceptEvap = I_CanIntercAreaClass[j,i] x (1-exp(-I_DailyRainAmount[i,j]/I_CanIntercAreaClass[j,i]))

The thickness of the water layer that can be stored on leaves and branches (I_InterceptClass) is treated as a constant value for each land cover type and thus the interception storage capacity is linearly related to leaf area index and it is reflected by its land cover type (I_FracVegClassnow).

I_CanIntercAreaClass = I_InterceptClass[j] x I_FracVegClassNow[j,i] x I_RelArea[i]

Infiltration

Infiltration is calculated as the minimum of:

• the daily potential infiltration capacity (I_MaxInfArea) times the fraction of a day that is available for infiltration (I_RainTimeAvForInf) (the latter reflects rainfall intensity as well as the local storage capacity of the soil surface)
• the amount that can be held by the soil at saturation (I_SoilSatClass) minus the amount already present (D_SoilWater)
• the amount of water that can reach the groundwater level within a day (I_DailyRainAmount-D_InterceptEvap)
• When the surface soil layers are saturated, the rate of outflow will determine the possible rate of inflow on the next day.

D_Infiltration = if L_Lake?[Subcatchement]=1 then 0 else min(min(I_SoilSatClass[j,i]-D_SoilWater[j,i],I_MaxInfArea[j,i] x I_RainTimeAvForInf[i]/24), I_DailyRainAmount[i,j]- D_InterceptEvap[j,i])

If the first constraint is active, the model generates ‘infiltration limited runoff’, in the second case ‘saturation overland flow’.

Infiltration capacity (I_MaxInfArea) driven by maximum infiltration area (I_MaxInf) in patch water balance of GenRiver. The change of the parameter due to land cover change over the time was estimated by power equation and soil bulk density relative to the reference bulk density.

I_MaxInfArea = I_MaxInf x I_RelArea[i] x I_FracVegClassNow[j,i] x (0.7/I_BD_BDRefVegNow[i])I_PowerInfiltRed.

where I_PowerInfiltRed set with range value 3 to 3.5.

The reference bulk density is derived from a pedotransfer function with soil texture (clay and silt) and soil organic matter (SOM) as the main inputs . The BD/BDref ratio depends on land cover, with defaults values: 0.7 for the forest soil, 1 for well manage agricultural soil and 1.3 for compacted and degraded soil (Van Noordwijk et al, 2002). Generic estimation of bulk density reference per soil type is:

If ((Clay+silt)>50 then:  
BDref1 = 1 / (1.984 + 0.01841 x (SOM) + 0.032 x 1 + 0.00003576 x (Clay + Silt)2 + 67.5 / 290 + 0.424 x LN(290))

if ((Clay+Silt)<50 then:   
BDref2=1 / (0.603 + 0.003975 x Clay + 0.00207 x SOM x SOM + 0.01781 x LN(SOM)))  

Deep Infiltration

The amount of deep infiltration is calculated as the minimum of:

• the soil saturation (I_SoilSatClass), soil water (D_SoilWater), infiltration of subsurface (D_Infiltration), rainfall amount (I_DailyRainAmount).
• the amount of infiltration of subsurface (I_MaxInfSubSAreaClass)
• unfilled area of ground water (I_MaxDynGWArea)

D_DeepInfiltration = min(min(min(ARRAYSUM(I_MaxInfArea[*,i]) x I_RainTimeAvForInf[i]/24-ARRAYSUM(I_SoilSatClass[*,i])+ARRAYSUM(D_SoilWater[*,i]),  ARRAYSUM(I_MaxInfSubSAreaClass[*,i])),ARRAYSUM(I_DailyRainAmount[i,*])-ARRAYSUM(D_InterceptEvap[*,i])-ARRAYSUM(D_Infiltration[*,i])),I_MaxDynGWArea[i]-D_GWArea[i])

Surface Flow

When the net rainfall (rainfall minus interception-evaporation (D_InterceptEvap)) exceeds the infiltration capacity of the soil (D_Infiltration and D_DeepInfiltration) possibly become surface flow (D_SurfaceFlow).

D_SurfaceFlow = if L_Lake?[i]=1 then ARRAYSUM(I_DailyRainAmount[i,*]) else 
ARRAYSUM(I_DailyRainAmount[i,*])-ARRAYSUM(D_InterceptEvap[*,i])-ARRAYSUM(D_Infiltration[*,i])-D_DeepInfiltration[i]

Soil Water

During a rain event the soil may get saturated, but within one day it is supposed to drain till ‘field capacity’ (with an operational definition of the soil water content 24 hours after a heavy rainfall event). The difference between saturation and field capacity can be either:

• Used for transpiration (but canopy intercepted rainfall takes priority to meet the demand) (D_ActEvapTransp)

• Drain to the groundwater reserve, calculated as the minimum of the amount that can be transported downwards and the fraction of soil water that will drain on any given day (D_Percolation)

• Drain to the rivers as ‘soil quick flow’: any water left above field capacity by the two preceding processes (D_SoilDischarge)

  

After a rain event, the soil starts to drain and will reach field capacity after one day (or depending on further parameter 1-3 days). The water held between saturation and field capacity is distributed in the order transpiration, drainage to the groundwater reserve or drain to the rivers as “soil quick flow” (van Noordwijk et al, 2003). The Soil water retention curve (saturation, field capacity, wilting point) is estimated based on pedotransfer functions using the soil type to indicate the soil texture. The groundwater was driven by the ‘differential storage’ in ‘active groundwater’ and the groundwater release’ fraction which representing the recession phase of actual river flow during periods without rainfall.

Actual Evapotranspiration

The amount of actual evapotranspiration is proportionally changed by potential evapotranpiration (I_PotEvapTransp), available soil water (D_RelWaterAv) and transpiration of intercepted water (I_InterceptEffectonTransp x D_InterceptEvap).

D_ActEvapTransp =  (I_PotEvapTransp[j,i]-I_InterceptEffectonTransp x D_InterceptEvap[j,i]) x D_RelWaterAv[j,i]

Percolation

The amount of percolation is calculated as the minimum of:
Maximum infiltration of subsurface area (I_MaxInfSubSAreaClass)
Soil water which can percolate into ground water (D_SoilWater x I_PercFracMultiplier x I_GWRelFrac)
Unfilled area of ground water (I_MaxDynGWArea-D_GWArea)

D_Percolation = min(I_MaxInfSubSAreaClass[j,i], min(D_SoilWater[j,i] x I_PercFracMultiplier x I_GWRelFrac[i],I_MaxDynGWArea[i]-D_GWArea[i]))- D_IrrigEfficiency[i] x D_Irrigation[i,j] else - D_IrrigEfficiency[i] x D_Irrigation[i,j]

Subsurface flow or soil discharge

Unused soil water by plants has potential to become sub surface flow or soil discharge (D_SoilWater-I_AvailWaterClass). The actual of soil discharge depend on how fraction of soil discharge is initialized (D_SoilQFlowRelFrac).

D_SoilDischarge = D_SoilQflowRelFrac[i] x (D_SoilWater[j,i]-I_AvailWaterClass[j,i])

Ground Water

Percolation and deep infiltration are the source of ground water. The ground water then will be used for irrigation and base flow. Figure 4.10. shows the flows to and from the ground water level.

Irrigation

The amount of ground water that used for irrigation is controlled by number of input parameters, utilization fraction of ground water (D_GW_Utilization), relative water available (D_RelWaterAv), irrigation efficiency (D_IrrgEfficiency) and potential evapotranspiration (I_PotEvapTransp).

D_WaterEvapIrrigation = D_Irrigation[i,j] x (1-D_IrrigEfficiency[i])

D_Irrigation = min(D_GWArea[i] x D_GWUseFacility?[i,j] x D_GW_Utilization_fraction[i] x (1-D_RelWaterAv[j,i])/D_IrrigEfficiency[i],I_PotEvapTransp[j,i]) 

Baseflow

The portion of stream flow that comes from groundwater (D_GWaDisc) depend on how fraction of released ground water (I_GWRelFrac) is initialized.

D_GWaDisc = D_GWArea[i] x I_GWRelFrac[i]

Stream Network

Total Stream flow

A river in the model is treated as the sum of streams, each originating in a subcatchment with its own daily rainfall, land cover fraction, total area and distance to the outlet of the river. These streams are all streams that are listed in the previous section (surface flow, sub-surface flow and base flow), it will be gathered in the river and become a total stream flow (D_TotalStreamInFlow).

D_TotalStreamInFlow = (D_SurfaceFlow[i]+D_GWaDisch[i] x (1-D_FracGWtoLake[i])+ARRAYSUM(D_SoilDischarge[*,i]))+D_SubCResOutflow[i] x (1-I_DaminThisStream?[i])

Routing time

After entered into river, those streams will flow from subcatchment center to observation point. Routing time controls the flow of water from subcatchment centre to final outlet. Some input parameters controls this routing time are distance from the centre of subcatchment to final outlet, velocity and tortuocity.

D_RoutingTime = I_RoutingDistance[i,ObsPoint]/(I_RivFlowTimeNow[i] x I_RoutVeloc_m_per_s x 3.6 x 24 x I_Tortuosity) 

There are two types of routing time:

1. If the value of routing time is between 0-1 then the water enter to the final outlet on the same day. The amount of direct river flow (D_RivLakeSameDay) is depending on parameters fraction release value (I_ReleaseFrac).

  D_RivLakeSameDay = if D_RoutingTime[i,ObsPoint]>=0 and D_RoutingTime[i,ObsPoint]<1  then D_TotalStreamInflow[i,ObsPoint] x (I_ReleaseFrac[i,ObsPoint]) else 0

2. If the value of routing time is more than 1 then the flow process have delay before enter to the final outlet

Land Cover

Land cover sector is a sector to generate proportion of land cover for each year in each subcathment. Linear interpolation is a method used to generate the land cover. Four of three times series of land cover data with certain year gaps is needed.

There are eleven types of land cover and four time series data (start simulation, first transition, second transition and end transition) as a default that provided by GenRiver model. Fraction of land cover change for each year is a result of interpolation. Figure 4.12. and below equation shows calculation for interpolation of fraction of land cover for each year in each subctachment.

I_FracVegClassNow = if I_RelArea[i]>0  then   
(if I_Flag1 = 1 then (I_FracVegClass1[j,i]+((I_FracVegClass2[j,i]-I_FracVegClass1[j,i]) x (int((I_Simulation_Time)/365)-I_InputDataYears	[Start])/(I_InputDataYears[Trans1]-I_InputDataYears[Start]))/ARRAYSUM(I_FracVegClass1[*,i])) ELSE  
if I_Flag2 = 1 then (I_FracVegClass2[j,i]+((I_FracVegClass3[j,i]-I_FracVegClass2[j,i]) x (int((I_Simulation_Time)/365)-I_InputDataYears[Trans1])/(I_InputDataYears[Trans2]-I_InputDataYears[Trans1]))/ARRAYSUM(I_FracVegClass2[*,i])) else  
(I_FracVegClass3[j,i]+((I_FracVegClass4[j,i]-I_FracVegClass3[j,i]) x (int((I_Simulation_Time)/365)-I_InputDataYears[Trans2])/(I_InputDataYears[End]-I_InputDataYears[Trans2]))/ARRAYSUM(I_FracVegClass3[*,i]))) else 0

Other parameters that also use interpolation method are water availability for plant (I_AvailWatClassNow), permanent willing point (I_PWPSub), bulk density (I_BD_BDRefVegNow), a difference value between saturation water storage capacity and field capacity of the soil (I_SoilSatminFCSubNow), ratio of time arrival of river flow (I_RivFlowTimeNow), ground water release fraction (I_GWRelFracNow) and dynamic groundwater storage capacity (I_MaxDynGWSubNow).

Subcatchment Parameter

This sector provides number of constant parameters to fill in unavailable data. The parameters are ground water release fraction (I_GWRelFrac), Actual Maximum Ground Water Dynamic (I_MaxDynGWat), Soil saturation Class (I_SoilSatClass), Maximum Infiltration Area (I_MaxInfArea), Maximum Infiltration sub surface area (I_MaxInfSubSAreaClass), Potential evapotranspiration (I_PotEvapTransp).

3. Flow Persistence and the FlowPer model

Background: temporal autocorrelation of river flow

Models of river flow, even relatively simple ones such as GenRiver, are over-parameterized relative to the information that we can use to check the statistical validity of the model. There are multiple ways of achieving a similar level of ‘model fit’ between measured and predicted river flow patterns, and the fit obtained may thus be ‘right for the wrong reasons’. Using the ‘validated model’ outside of the calibration range may then be as risky as using a simple regression line. In testing the ‘lack of fit’ of a model we can benefit from having a ‘null-model’, a model that takes basic properties of the data into account, without specific hypotheses about the way rainfall translates into river flow.

In the analysis of watershed functions, we deal with a complex of factors that influence processes and patterns in the landscape that ultimately translate a temporal pattern of rainfall into a temporal pattern of stream flow, which aggregates up to a river. Downstream stakeholders start from what they want to see (‘perfectly regular flow of clean water’) and observe a pattern of stream and river flow that doesn’t match their expectations. They search for interventions on the ‘anthropogenic’ groups of causes (‘deforestation’, ‘degradation’), but need to understand the potential reach of such interventions, given the geological and climatic background. In the absence of knowledge of what happens upstream, an observer of river flow can deduce a fair amount of information from a time series of river flow data.

The FlowPer model is focused on that. It can serve two functions: 1) summarize the key parameters that downstream stakeholders can observe on the flow pattern, e.g. as basis for conditional ES rewards, 2) serve as a parsimonious (parameter-sparse) ‘null model’ that allows quantification of the increments in model prediction that is achieved with spatially explicit models (with a priori parameterization rather than parameter tuning to the data).

FlowPer Model Overview

The FlowPer.xls model provides a parsimonious null-model, that is based on temporal autocorrelation or an empirical ‘flow persistence’ in the river flow data. The basic form is a recursive relationship between river flow Q at subsequent days:

Qt+1 = fp Qt + Qadd

where Qt and Qt+1 represent the river flow on subsequent days, fp is the flow persistence factor ([0< fp <1]) and Qadd is a random variate that reflects inputs from recent rainfall.

Qadd and fp are related, as Σ Qadd i = (1 – fp) ΣQ. Thus, if fp = 1, Qadd = 0 and river flow is constant, regardless of rainfall (the ideally buffered system…). If fp = 0 there is no relation between river flow on subsequent days and the river is extremely ‘flashy’, alternating between high and low flows without temporal predictability within the frequency distribution of Qadd.

The term Qadd,i can be described as a statistical distribution with a probability of a non-zero value, a mean and a measure of variance, plus two parameters that describe a seasonal pattern (peak and shape of the distribution, e.g. Weibull). This makes for 5 parameters for Qadd,i (and six for the whole model) that are derived from the data. It leaves many degrees of freedom for more specific models that, for example, make use of measured rainfall.
Table 3.1. Multiple influences on process and pattern of river flow and the downstream perceptions of ‘ecosystem services’ (modified from van Noordwijk et al. 2006)

Figure 3.1. Example of the type of ‘fit’ that can be achieved for the 6-parameters FlowPer model

If we partition the total flow Qtot into water flow by three pathways (surface runoff, interflow and groundwaterflow), we can obtain Qtot = Qrunoff + Qinterflow + Qgwflow. Each type of flow pathway will typically have a different flow persistence, fp,runoff , fp,interflow and fp,gwflow, respectively.

Qtot,t+1 = (fp,runoff(Qrunoff,t/Qtot,t)+ fp,interflow(Qinterflow,t/Qtot,t)+ fp,gwflow (Qgwflow,t/Qtot,t))Qtot,t+ Qadd,t

As we can expect values for fp,runoff , fp,interflow and fp,gwflow of about 0, 0.5 and close to 1, respectively, we can interpret the relative contributions of the 3 flow pathways from the overall fp value.

In a more detailed model, the daily value of fp will shift according to the predicted contributions of the three types of flow, rather than being a constant. Together with the way Qadd,i relates to rainfall, this gives space for improved model fits.

Part of the ‘flow persistence’ may in fact derive from ‘rainfall persistence’, or the increased probability of daily rainfall after a rainy day, and/or from the increased probability of dry days to follow dry days, even after a monthly pattern ion rainfall is accounted for.

Starting and Running FlowPer Model

Input parameterization

FlowPer Model in excel file is organized into seven sheets, labeled: “READ ME”, “DebitData”, “FlowPerModel”, “Calculation”, “Graphics”, “QAddon”, “FP”, and “Shape and TimeMax” (Figure 3.2).

The only input required is a (partial) time series of daily river flow data, to be entered in the “DebitData” sheet, as in GenRiver.xls. Once river flow data have passed minimum quality checks, we can use them to parameterize the FlowPer model, esp. the Fp parameter. The “FlowPerModel” sheet (Figure 5.3) then provide options to run the model for each year that data are available and derive four auxiliary parameters to run FlowPer model (Table 5.2). The four auxiliary parameters are flow persistence (“FP” sheet), the mean random variate that reflects inputs from recent rainfall (“QAddon” sheet), and two parameters that describe a seasonal pattern, peak and shape of the distribution (“Shape and TimeMax” sheet). The only parameters that based on trial and fit is coefficient variation of measurement noise.

Running the model

Once you open FlowPer model and enter the river flow data on the “DebitData” sheet, then simply click “FlowPerModel” button and you will see something like Figure 5.3. Then and run the model, table 5.2 present the location of each input parameter in excel file:

1. Change the starting year to begin the simulation as entered in “DebitData” sheet.
2. Type year of simulation in the year colum to to look at the type of fit obtained then simply click “RUN” button.
3. Once the running finish, then copy predicted value of flow persistence (FP) and random variate of rainfall (QAdd) into table of input FP & Qadd.
4. Click “RUN” again until you got fit result by comparing the summary of predicted value and observation value in colum D25 and D26.

Table 3.2. Input parameters of FlowPerModel

FlowPer model output

There are two types of FlowPer model output, flow persistence value and predicted daily river flow. The predicted daily river flow is presented in graph and table. There are two types of graph in presenting the predicted daily river flow (Figure 5.1).

The flow persistence value can be considered as input and output. As input, the FP is used to generate the predicted daily river flow, while as an output, the FP is a indicator value of watershed condition that range from 0 - 1. If fp = 0, then there is no relation between river flow on subsequent days and the river is extremely ‘flashy’, alternating between high and low flows without temporal predictability within the frequency distribution of Qadd.

References

Brouwer C; Prins K; Kay M and Heibloem M. 1990. Irrigation Water Management Training Manual No. 5: Irrigation Methods. FAO. http:// www.fao.org/docrep/S8684E/s8684e0a.htm. 4 Nopember 2003.

Bandaragoda C, David G and Ross Woods T. 2004. Application of TOPNET in the distributed model intercomparison project. Journal of hydrology 298: 178-201.

Croke BFW, Merritt, WS and Jakeman AJ. 2004. A dynamic model for predicting hydrologic response to land cover changes in gauged and ungauged catchments. Journal of Hydrology 291 (1-2): 115-131.

Croke BFW, Andrews F, Jakeman A.J, Cuddy S and Luddy A.. 2005. Redesign of the IHACRES rainfall-runoff model, to appear in the proceedings of the 29th Hydrology and Water Resources Symposium, Engineers Australia, February 2005.

Dairaku K, Emori S and Oki T. 2004. Rainfall Amount, Intensity, Duration, and Frequency Relationships in the Mae Chaem Watershed in Southeast Asia, J. Hydrometeor (5): 458-470.

Dye PJ. and Croke BFW. 2003. Evaluation of stream flow predictions by the IHACRES rainfall-runoff model in two South African catchments. Environmental Modelling and Software, vol 18, pp 705-712.

Jakeman AJ and Hornberger GM. 1993. How much complexity is warranted in a rainfall-runoff model? Water Resources Research 29(8): pp2637-2649.

Jakeman AJ, Littlewood IG and whitehead PG. 1990. Computation of the instantaneous unit hydrograph and identifiable components flows with application to the small upland catchments. Journal of hydrology 117: 275-300.

Moriasi DNJG, Arnold MW, Van Liew RL, Bingner RD, Harmel TL and Veith. 2007. Model Evaluation Guidelines For Systematic Quantification Of Accuracy In Watershed Simulations.

Transactions of the ASABE Vol. 50(3): 885−900. American Society of Agricultural and Biological Engineers ISSN 0001−2351.

Merritt, WS, Croke BFW, Jakeman AJ, Letcher RA, Perez P. 2005. A Biophysical toolbox for assessment and management of land and water resources in rural catchments in northern Thailand. Ecological Modelling 171: 279–300.

Anonymous. 1993. Storm Rainfall Depth. National Engineering Handbook. USDA

Olivera PE. 2001. Extracting hydrologic information from spatial data For hms modeling. Journal of Hydrologic Engineering 6.

Ortiz C. 2005. Calibration of GenRiver with GLUE for Northern Vietnamese conditions. M.Sc. thesis 27 pp.

Post DA, Jakeman AJ, Littlewood IG,Whitehead PG and Jayasuriya MDA.. 1996. Modelling land cover induced variations in hydrologic response: Picaninny Creek. Victoria. Ecological Modelling 86:177-182.

Saipothong P, Onpraphai T, Ratnamhin A., Sangsrichan T, Natee S, Sepan S, Pitakam A. and Chanpo C. 2007. Spatial Information on Land Use/Land Cover and Its Change in Mae Chaem Watershed.

Subagyono K, Marwanto S, Tafakresno C and Dariah A. 2005. Delineation of Erosion Prone Areas In Sumberjaya Lampung, Indonesia. In Fahmuddin Agus and Meine Van Noordwijk (editors). Alternatives to slash and burn in Indonesia: Fascilitating the development of agroforestry systems:Phase 3 Synthesis and summary report. World Agroforestry centre, Southeast Asia, Bogor, Indonesia.

Thanapakpawin PJ, Richey D, Thomas S, Rodda B, Campbell and Logsdon M. 2007. Effects of Land use Change on the hydrologic regime of the Mae Chaem river Basin, N.W Thailand’.Journal of Hydrology 334 (1-2):215-230.

Van Noordwijk M, Farida A, Suyamto D and Lusiana B. 2002. GenRiver and Spatrain Model Description. World Agroforestry Centre, ICRAF-SE Asia.

Van Noordwijk M, Richey J and Thomas D. 2003. Functional value of biodiversity – phase 2. Technical Report For Activity 2. Landscape and (Sub) Catchment Scale Modelling of Effects of forest Conversion on Watershed Functions and Biodiversity in Southeast Asia. Alternatives to Slash and Burn program, “Forest and Agro ecosystem Tradeoffs in the Humid Tropics”. ICRAF/World Agroforestry Centre, SEA.

Van Noordwij M. 2004. GenRiver 1.0. Presentation of GenRiver in PowerPoint.

Van Noordwijk M, Farida A, Suyamto DA, Lusiana B and Khasanah N. 2003. Spatial Variability of Rainfall Governs river flow and reduces effects of land use change at landscape scale: GenRiver And Spatrain Simulations. Modsim. Australia.

Van Noordwijk M, Farida A, Saipothong P, Agus F, Hairiah K, Suprayogo D and Verbist B. 2006. Watershed functions in productive agricultural landscapes with trees.World Agroforestry Centre.

Verbist B, Ekadinata A and Budidarsono B. 2005. Factors driving land use change: Effects on watershed functions in a coffee agroforestry system in Sumberjaya (Lampung, Sumatra). Agricultural Systems 85: 254-270.

Verbist B, Widodo RH, Susanto S, van Noordwijk M, Poesen J, Deckers S. (2008). Assessment of Flows and Sediment Loads in a Largely Deforested Catchment in Sumberjaya, Lampung, Sumatra, Indonesia.

• How to run the software
  • Installation Instructions
  • Launching the App
• Home Screen
• Input
  • Land Cover
    • Land Cover Map
    • Hydrological Properties
    • Evapotranspiration
  • Watershed
    • Watershed Map
    • 3D View
    • Lake and DAM
    • Ground Water and River Flow
  • Soil
    • Physical and Chemical Properties
    • Hydraulic Properties
    • Soil and Plant Water
    • Soil Erosion and Sedimentation
  • Rainfall and Rivers
    • Rainfall
    • River
    • Consistency Check
  • Options
• Simulation
  • Water Balance
  • Watershed Indicator
  • Buffering Indicator
• FlowPer

How to run the software

GenRiver3 software is web application and the online version is available at: https://genriver.agroforestri.id/

The software can also be launched as a standalone app using R and RStudio. The following is a step-by-step guide for running the software from the source code as a standalone app.

Installation Instructions

1. Install R from CRAN.
2. Install RStudio from RStudio.
3. Install the Shiny package in R:

   install.packages("shiny")
   

Launching the App

1. Open RStudio.
2. Load the Shiny library:

   library(shiny)
   

3. Run the app directly from GitHub source code:

   shiny::runGitHub("genriver", "degi")
   

   Another option is to download all the source codes from https://github.com/degi/genriver. Extract all files to a local folder and execute the script below

   runApp("path/to/your/app")
   

If you have the source code on the local folder, you will need an internet connection to run it for the first time. An internet connection is required to update and initialize the R libraries. Once the updates are completed, you will be able to run the app without an internet connection.

Note:
You might still need an internet connection to download the DEM. However, you can go without it once it is in your saved parameters.

Home Screen

GenRiver model is a generic model of river flow in a catchment that is subdivided into sub-catchments, with separate dynamics of land cover change and rainfall, and different properties for soil parameters and routing distance if desired. The model was developed as a tool to analyze river flow in two catchments in SE Asia: the Way Besai (Sumberjaya) watershed in Lampung Indonesia and Mae Chaem in Northern Thailand; default input parameters are based on the Sumberjaya case.

The model treats a river as a summation of streams, each originating in a sub-catchment with its own daily rainfall, yearly landcover fractions, and constant total area and distance to the river outflow or measurement point. Interactions between streams in their contribution to the river are considered to be negligible (i.e. there is no ‘backflow’ problem). Spatial patterns in daily rainfall events are translated into average daily rainfall in each sub-catchment in a separate module. The sub-catchment model represents interception, infiltration into the soil, rapid percolation into the subsoil, surface flow of water, and rapid lateral subsurface flow into streams with parameters that can vary between land cover classes.

The main interface for working with the model is divided into sections titled Input, Simulation, and Flopper.

• Input: The input parameters required for running the model
• Simulation: Run the model simulation after the required parameters are completed
• Flopper: Analyzing the river flow persistence

Input

Land Cover

Land cover is one of the main factors in the watershed dynamics. A time series of land cover maps follows the dynamic changes in soil properties. You may provide land cover maps for the observed periods. The boundary box of the land cover map will be used as the area for the DEM query. The DEM is later delineated to generate the watershed boundary.

Land Cover Map

Once the map is uploaded, it will be displayed on the page. The map IDs will be displayed on the right side as landcover IDs. A default landcover label is generated for all the IDs. You may edit the landcover label in place, or upload it from a predefined CSV file.

Hydrological Properties

The GenRiver model was set up to compare the impacts of land cover change on hydrology. These impacts are based on four steps in the water cycle: interception by the canopy, impact on the topsoil structure (BD/BDref) that influences infiltration (or runoff generation as its complement), the seasonal pattern of water use (here expressed as the fraction of the potential ET per month), and a drought threshold that indicates the relative soil water content where evapotranspiration is affected. Distinctions between land cover types for any simulation should be based on the primary research question, the availability of data on land cover fractions, and the importance of the land cover type in the catchment area. Most spatial data include a “no data” category (clouds and cloud shadow). normally the fractions of land cover elsewhere are assumed to apply to these pixels as well.

The first column of the first table is the storage capacity for intercepted water (I_InterceptClass) of each land cover type, mm day-1. It is treated as a linear function of leaf + branch area index of the land cover, with the option of modifiers for surface properties that determine the thickness of the water film, forest = 4, young secondary forest/young agroforestry = 3.

The second column is drought limitation to transpiration per land cover class relative to field capacity (I_RelDroughtFact). The values depend on drought resistance, the highest resistance = 1 (teak), and the lowest resistance = 0.1 (Durian).

The third column is BD/BDref, which is the bulk density of a soil layer relative to the ‘reference bulk density’ that can be expected for soil of similar texture under natural forest conditions

The second table is the monthly pattern of potential evapotranspiration for each land cover type is calculated by multiplying these monthly values by daily potential evapotranspiration (I_MultiplierEvapoTrans[LandCoverType]). These multiplier values follow the seasonal pattern of crop, tree, and paddy. The highest value = 1 (rice field, pine), and the lowest = 0.1 (houses).

Evapotranspiration

Evapotranspiration is a term used to describe the sum of evaporation and plant transpiration from the earth’s land surface to the atmosphere. Evaporation accounts for the movement of water to the air from sources such as the soil, canopy interception, and waterbodies. Transpiration accounts for the movement of water within a plant and the subsequent loss of water as vapor through stomata in its leaves.

The potential evapotranspiration, mm day-1 data can be either daily data or monthly data. These values can be derived from open pan evaporation measurements or from equations such as Penman’s that calibrate on such data.

Watershed

The DEM data is acquired from opentopography.org. OpenTopography provides open and free access to the DEM dataset. Please visit the website for more information about its data collection. Find the instructions at https://opentopography.org/citations for the citation

If the DEM download fails. You may do the following:

• Make sure the internet connection is stable
• Try another DEM sources
• Register to opentopography.org and get your own API key. Make sure the key was correctly copied into ‘API key’ input

Watershed Map

3D View

Lake and DAM

Ground Water and River Flow

Soil

The soil data is acquired from Harmonized World Soil Database version 2.0 (HWSD v2.0). HWSD is a comprehensive global soil inventory that offers detailed insights into soil properties, including their morphology, chemistry, and physical characteristics, with a focus on a 1 km resolution. Please visit FAO SOILS PORTAL for more information on the database and their suggested citation

Physical and Chemical Properties

Average texture (or soil type in a way that allows texture to be estimated) as input to ‘pedotransfer’ functions to estimate soil water retention curve (saturation, field capacity, wilting point)

Hydraulic Properties

Field capacity is the volumetric soil water content measured 1 day after a saturating rainfall event, when rapid drainage and interflow have removed excess water to streams or groundwater

Soil hydraulic at a potential of 0 kPa is in a state of saturation. At saturation, all soil pores are filled with water, and water typically drains from large pores by gravity. At a potential of −33 kPa, or −⅓ bar, (−10 kPa for sand), soil is at field capacity. Typically, at field capacity, air is in the macropores, and water in the micropores. Field capacity is viewed as the optimal condition for plant growth and microbial activity. At a potential of −1500 kPa, the soil is at its permanent wilting point, at which plant roots cannot extract the water through osmotic diffusion. https://en.wikipedia.org/wiki/Water_potential

Soil and Plant Water

Soil Erosion and Sedimentation

Rainfall and Rivers

Rainfall

Rainfall or precipitation is all forms of water particles, whether liquid or solid, that fall from the atmosphere to the ground. Distinguished from cloud, fog, dew, and frost, precipitation includes rain, drizzle, snow, and hail.

A number of formats are possible, as long as they allow a reconstruction of monthly exceedance curves of daily rainfall intensity:

• 30 (or at least 20) years of daily rainfall records for a station that can represent the area (or multiple stations if these are supposed to be similar) or
• Any ‘rainfall simulator’ equation with the appropriate parameters that can be used to generate a 30-year dataset for the site (e.g. MarkSim).

River

If available, river debit data for any period of time (expressed in m3 s-1 in the river or mm day-1 over the whole contributing subcatchments) will be valuable in ‘constraining’ the simulations. If not available, we will simply have to ‘believe’ the model predictions as such

Consistency Check

Options

Simulation

Water Balance

• O_CumRain is the cumulative amount of daily rainfall for the whole sub-catchment and vegetation class
• O_CumPercolation is the cumulative amount of percolation water for the whole sub-catchment
• O_CumDeepInfilt is a cumulative amount of water deeply infiltrated to the soil for the whole sub-catchment
• O_CumBaseFlow is the cumulative amount of base flow the whole sub-catchment and vegetation class
• O_CumSoilQFlow is the cumulative amount of soil quick flow for the whole sub-catchment and vegetation class

Watershed Indicator

Evaluation of Model Performance

Evaluation of model performance can be done by comparing simulation results to measurement data. Statistical indicators proposed by Nash and Sutcliffe (1970) are used for checking the performance of the model. The performance of the model can also be checked using coefficient correlation or double mass cumulative rainfall-river flow curve. “PerformanceTestGenRiver” is a file consists an explanation of the process of this evaluation.

Nash-Sutcliffe Efficiency

The Nash-Sutcliffe efficiency (NSE) is a normalized statistic that determines the relative magnitude of the residual variance (“noise”) compared to the measured data variance (Nash and Sutcliffe, 1970). NSE indicates how well the plot of observed versus simulated data fits the 1:1 line

where Yiobs is the observation for the constituent being evaluated, Yisim is the simulated value for the constituent being evaluated, Ymean is the mean of observed data for the constituent being evaluated, and n is the total number of observations.

NSE ranges between −∞ and 1.0 (1 inclusive), with NSE = 1 being the optimal value. Values between 0.0 and 1.0 are generally viewed as acceptable levels of performance, whereas values < 0.0 indicate that the mean observed value is a better predictor than the simulated value, which indicates unacceptable performance. The performance of the model and the result will be evaluated annually and will be accepted when NSE criteria are more than 0.50 (Table 2.4).

Table 2.4. Reference Stream flow model Performance (Moriasi, D.N. et. al., 2007)

Coefficient of correlation

The coefficient of correlation representing the change direction of simulation data compare with the observation data.

where xi is observation data, yi is simulation result, xmean is mean observation data and ymean is mean simulation.

When applying the GenRiver model to landscapes where at least some river flow data are available, there is an opportunity to assess the ‘lack of fit’ between the model and measurements. Lack of fit can be due to 1) inaccuracy or error in the data (e.g. with incomplete representation of spatial variability on rainfall, and/or errors in the data records), 2) suboptimal model parameterization, 3) error and/or oversimplification in the model process description. Component 3 can only be assessed if components 1 and 2 can be quantified. Tests of data consistency can be used to assess component 1, e.g. at the seasonal aggregate level. Steps can include:

1. SP - SQ gives an estimate of top total evapotranspiration. Values below 500 or above 1500 mm/year are suspect. These may indicate errors in P or Q registration, errors in the area, or deviation from the ‘closed catchment’ assumption (e.g. subsurface flows out of or into the catchment are non-negligible).
2. ‘Double Mass’ curves of cumulative SQ versus SP during the year: large jumps will require explanation (see next section).
3. Flow persistence Qi+I versus Qi plots may indicate gaps in the data or ‘outliers’ that indicate errors (see further in Chapter 5).

Analysis of Indicators of Watershed Functions: water quantity and quality

The assessment of the hydrological situation of the watershed is determined by the criteria and indicators of water transmission (total water yield per unit rainfall), buffering capacity (relationship of peak river flow and peak rainfall, linked to flooding risk), and gradual release of (ground) water in the dry season, based on recharge in the rainy season (Table 2.5). These indicators all relate the flows of water to the preceding rainfall and by doing so; allow the analysis of the relatively small land use effects, superimposed on substantial year-to-year variation in rainfall. We provide a file “IndicatorWatershed” to ease users doing this analysis.

Table 2.5. Criteria and indicators of watershed hydrological functions that are relevant to downstream stakeholders (Van Noordwijk, et al., 2006)

If there is a shortage of reliable data on river flow, you can first calibrate and validate a water balance model for the area, and then used this for further exploration of scenarios. If no continuous data on sedimentation or erosion exists, you can assess the risk of erosion through the level of runoff. This is with an underlying assumption that high run-off would lead to a high risk of erosion or you can use the run-off output as the input of other erosion models on the Catchment level.

Buffering Indicator

FlowPer

Flow persistence is the minimum volume of river flow that can be expected as a fraction of flow on the previous day

To be updated…

van Noordwijk, M., Widodo, R.H., Farida, A., Suyamto, D., Lusiana, B., Tanika, L. and Khasanah, N., 2011. GenRiver and FlowPer: Generic River and Flow Persistence Models: User Manual Version 2.0. World Agroforestry Centre.

van Noordwijk, M., van Oel, P., Muthuri, C., Satnarain, U., Sari, R.R., Rosero, P., Githinji, M., Tanika, L., Best, L., Comlan Assogba, G.G., Kimbowa, G., Andreotti, F., Lagneaux, E., Wamucii, C.N., Hakim, A.L., Miccolis, A., Abdurrahim, A.Y., Farida, A., Speelman, E., Hofstede, G.J., 2022. Mimicking nature to reduce agricultural impact on water cycles: A set of mimetrics. Outlook Agric 003072702110738. https://doi.org/10.1177/00307270211073813

To be updated..

Required R Libraries

• Glossary
• Variables

Glossary

• Base flow is the portion of stream flow that derives from groundwater and is not related to current or recent rainfall.
• BD/BDref is the bulk density of a soil layer relative to the ‘reference bulk density’ that can be expected for soil of similar texture under natural forest conditions
• Buffering capacity is the ability of a system to reduce the impact of external variation on internal properties, e.g. reducing the variation in stream flow relative to variation in rainfall.
• Buffering indicator is derived from the ratio of above-average stream flow and above-average rainfall.
• Buffering for peak events is the ‘buffer’ function demonstrated at peak rainfall events.
• C/Cref is the organic soil carbon content of a soil relative to the ‘reference soil Corg concentration’ that can be expected for soil of similar texture, pH, and mineralogy under natural forest conditions at the given elevation (temperature regime) The discharge or Outflow of a river is the volume of water it transports in a certain amount of time. The unit used is usually m³/s (cubic meters per second).
• Evapotranspiration is a term used to describe the sum of evaporation and plant transpiration from the earth’s land surface to the atmosphere. Evaporation accounts for the movement of water to the air from sources such as the soil, canopy interception, and waterbodies. Transpiration accounts for the movement of water within a plant and the subsequent loss of water as vapor through stomata in its leaves.
• Field capacity is the volumetric soil water content measured 1 day after a saturating rainfall event, when rapid drainage and interflow have removed excess water to streams or groundwater
• Flash floods are floods caused by heavy or excessive rainfall in a short period, generally under 6 hours. They cause stream flow and water levels to rise and fall rapidly.
• Flow persistence is the minimum volume of river flow that can be expected as a fraction of flow on the previous day
• Gradual water release is a gradual release of (ground) water during periods without rainfall (‘dry season)
• Groundwater discharge is the release of groundwater to streams or subsurface flows.
• Interflow see Quickflow
• Low flow is flow through a watercourse after a prolonged absence of rainfall.
• Overland flow see surface runoff
• Overflow or Bank overflow is a flow of water outside of the regular river bed during conditions where recent inflow minus outflow has exceeded the storage capacity.
• Peak flows is the maximum flow through a watercourse.
• Precipitation is all forms of water particles, whether liquid or solid, that fall from the atmosphere to the ground. Distinguished from cloud, fog, dew, and frost, precipitation includes rain, drizzle, snow, and hail.
• Quickflow or Interflow is part of a storm rainfall that moves laterally through hillslope soils to a stream channel; it infiltrates the soil, but cannot be retained by the soil at its ‘field capacity’; shallow groundwater or interflow may emerge at the surface at the bottom of slopes and flow across the ground surface to the stream.
• Relative buffering indicator is the ‘buffer’ function adjusted for relative annual water yield
• River flow is the flow of water in the river channel
• Storage capacity is the total amount of water that can be stored in a reservoir before overflow occurs.
• Stream flow is the flow of water in streams, rivers, and other channels.
• Surface runoff or Overland flow is the flow across the land surface of the water when the rainfall rate exceeds the infiltration capacity of the soil. The rate of infiltration, and therefore the possibility of surface runoff, is determined by such factors as soil type, vegetation, and the presence of shallow, relatively impermeable, soil horizons. Saturated overland flow can occur when a temporary rise of the water table inhibits infiltration and causes flow over the surface.
• Total discharge fraction is the total water yield (discharge) per unit of rainfall, usually on an annual basis.
• Water balance is the comparison over a certain period (e.g. month or year) of inflow of water (precipitation) and outflows by evapotranspiration, stream flow, and subsurface flows.
• Water quality is the chemical, physical, and biological characteristics of water concerning its suitability for a particular use.
• Water storage is the volume of water that can be (temporarily) withheld from evapotranspiration, stream flow, or subsurface flows, either above ground in lakes, rivers, and other waterways or below ground as groundwater.
• Water transmission is the fraction of incoming precipitation that is converted into stream flow.

Variables