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Bachelor Thesis

Role of Initial Soil Moisture in the UK Floods of Winter 13/14

Author(s): Tobler, Kaspar

Publication Date: 2016

Permanent Link: https://doi.org/10.3929/ethz-a-010578604

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ETH Zurich

Department of Environmental Systems Science

Group for Land-Climate Dynamics

Bachelor Thesis

Role of Initial Soil Moisture in the UK Floods of Winter 13/14

A calm Lancaster Canal in May 2015. However, even the most peaceful of waters can become a threat when bursting their banks. Photo taken by the author, in Lancaster, Lancashire, northwestern England.

Kaspar Tobler

[email protected]

Supervisor

Dr. René Orth

Submitted to

Prof. Sonia I. Seneviratne

Zurich, October 2015

Soil Moisture in the UK Floods 13/14 1/21

Abstract

Floods are recurring natural hazards that can cause substantial damage to the environment and soci-

ety. Thus, understanding the key factors that determine flood magnitude is essential to reduce future

risks. This thesis examines the role of initial soil moisture in the floods that struck the United King-

dom in winter 2013/2014. A simple hydrological model was used to compute daily soil moisture and

runoff at a location severely affected by the floods and across the entire UK. To assess the sensitivity

of runoff and flood magnitude to soil moisture, simulations were conducted for reference conditions

and with adjusted initial soil moisture values (on December 1st 2013). The results showed a strong in-

fluence of initial soil moisture on monthly runoff in December, January and on overall winter runoff.

Actual diagnosed soil moisture on December 1st 2013 was found to have been rather dry. Simulations

for the individual location with initial soil moisture close to the long-term mean resulted in a 32% in-

crease in overall winter runoff compared to reference conditions. Simulations for the entire UK with

initial soil moisture at the respective local long-term maximum resulted in overall winter runoff that

was larger than reference values by more than 40% for most of southern England and more than

twice as large for several locations in eastern England. The results suggest that soil moisture played

an important role in mitigating the magnitude of the UK winter-floods, thereby underlining the im-

portant role of initial soil moisture in such flood events.


Contents

Abstract ................................................................................................................................................... 1

1. Declaration of Originality ................................................................................................................ 3

2. Introduction ..................................................................................................................................... 4

3. Methodology ................................................................................................................................... 5

3.1 Theoretical background ........................................................................................................... 5

3.2 Simple Water Balance Model .................................................................................................. 6

3.3 Data ......................................................................................................................................... 7

3.4 Model Simulations ................................................................................................................... 8

3.4.1 For one grid point ............................................................................................................ 8

3.4.2 For the entire grid ............................................................................................................ 9

4. Results ........................................................................................................................................... 11

4.1 Model accuracy ................................................................................................................. 11

4.2 Simulations for one grid point ........................................................................................... 12

4.3 Simulations for the entire grid .......................................................................................... 14

5. Conclusion and Outlook ................................................................................................................ 18

6. Acknowledgement ......................................................................................................................... 20

7. References ..................................................................................................................................... 21


1. Declaration of Originality


2. Introduction

The Floods that struck the United Kingdom (UK) in the winter1 of 2013 and 2014 were extreme in var-

ious aspects: Monthly river-flows in January and February reached record levels in several rivers, es-

pecially in southern England (Huntingford et al., 2014). The total winter rainfall was exceptionally

high for most points of measurement and the highest on record for at least one (Huntingford et al.,

2014). The heavy rainfall caused strong groundwater recharge and consequently groundwater levels

far above the local long-term mean in large parts of the country (Huntingford et al., 2014). Consider-

ing the severe economic and structural damage2 caused by a flood of this magnitude, rigorous under-

standing of the involved processes is vital to take appropriate measures to mitigate the risk of similar

future events. Even more so considering that recent research suggests that it is very likely for such

events to become both more intense and more frequent in the UK in the future (Jones et al., 2013).

The two most comprehensive studies specifically dealing with the winter-floods, the one by Hunting-

ford et al. (2014) and Slingo et al. (2014), describe extensively the role of the large-scale circulation

as well as potential influences of anthropogenic climate change, but pay little attention to the soil’s

potential influence on the development and the magnitude of the floods. My aim was to investigate

that influence, more precisely, the role of the soil’s moisture content at the beginning of December

2013, i.e. the beginning of the period of intense and sustained rainfall3. Since the soil’s capacity to

store water as well as the rate at which water can infiltrate is limited and excess water always forms

runoff at least in part, ISM is expected to be important. I will particularly focus on the extent of this

influence and its dissipation rate within the system and hence its general relevance for overall flood

magnitude.

For my purpose I employed a hydrological model (Orth et al., 2013) at the daily time scale which is

forced with actual precipitation, temperature and net radiation data to derive soil moisture, runoff

and evapotranspiration. By adjusting the initial soil moisture content I obtained simulations with

changes in runoff and evapotranspiration caused entirely by these adjustments to initial soil moisture

values. The derived runoff values were then used as a proxy for the flood magnitude. I conducted

simulations for each day between (and including) 1994 and 2014, for one specific geographical loca-

tion as well as for all of the UK.

In chapter 3 I will define the basic theoretical concepts and terms needed to understand the process-

es involved in this study, followed by a short introduction to the employed model and datasets as

well as by a description and motivation of the model simulations. The main results are presented in

chapter 4 and the conclusion in chapter 5 of the thesis.

1 „Winter“ refers to the three-month period from beginning of December to end of February

2https://www.abi.org.uk/News/News-releases/2014/03/6-7-million-a-day-in-insurance-claims-from-customers-

hit-by-the-recent-flooding - accessed 09.07.2015 3 In the following referred to as initial soil moisture or ISM

https://www.abi.org.uk/News/News-releases/2014/03/6-7-million-a-day-in-insurance-claims-from-customers-hit-by-the-recent-flooding

https://www.abi.org.uk/News/News-releases/2014/03/6-7-million-a-day-in-insurance-claims-from-customers-hit-by-the-recent-flooding


3. Methodology

3.1 Theoretical background

This section provides short definitions of the most important terms and processes on which the study

is based. They are also important for understanding both the model’s functionality and its limitations.

For more comprehensive discussions of the respective themes, additional sources are provided.

Soil Moisture (soil water content, soil wetness):

The water present in the unsaturated zone of the soil, usually given as a fraction per mass or volume

of soil (Hillel, 2003). Groundwater (saturated zone) does not constitute to soil moisture. There are

various ways to express soil moisture, two of which I shall mention here. Firstly the volumetric soil

moisture 𝜃, which is the ratio of the volume of water (Vw) within a unit volume Vt to the total volume

Vt:

θ =Vw

Vt (1)

Whereas Vt includes the volumes of solid particles, water and air within Vt (Hillel, 2003). Based on

this relative measure we can obtain an absolute estimate by multiplying the volumetric soil moisture

θ obtained from (1) with the soil depth d:

S = θd (2)

S is usually expressed in mm to make it comparable e.g. with precipitation values that are commonly

expressed in mm too. Hillel (2003) refers to S – quite descriptively – as “the equivalent depth that soil

water would have if it were extracted and then ponded over the soil surface” (p. 95) (looking at a

unit area of soil). A soil is saturated when the entire pore volume is filled with water. The field capac-

ity is the remaining soil moisture after free drainage has ceased (what usually takes approximately 2

days) (“Field Capacity”, 2008).

Infiltration:

Infiltration describes quite simply the entry of water into the soil, with the water mainly (but not ex-

clusively) coming from precipitation and – if applicable – melting snow. The rate of infiltration is not

constant and depends – for a specific soil plot and constant application of water to the surface – on

the soil’s current water content. It asymptotically decreases with increasing soil moisture (Rasmussen,

2008)

Of particular importance for my study is the notion of infiltration capacity, which describes the “max-

imum rainfall rate the soil can fully absorb at a given time.” (Morel-Seytoux, 2008). Despite its slightly

misleading name it too denotes a rate (volume flux) and it also depends (for otherwise constant con-

ditions) on the current soil moisture, decreasing with an increase in the latter. Thus, if the water sup-

ply rate exceeds the infiltration capacity, the excess water will run off or accumulate at the surface at

a rate equal to the difference between the supply rate and the infiltration capacity.4 Irrespective of

4 Refer to Hillel (2003; Chapter 14) for an extensive examination of infiltration and infiltration capacity. As a re-

placement for the latter he introduces and defines the – arguably – less confusing term infiltrability.


these relationships, the soil may change its characteristics under very dry conditions and become al-

most impermeable. This is however of negligible relevance for my study as I focus on wet conditions.

(Surface) Runoff:

Surface runoff is water that neither penetrates through nor remains at the soil surface of the ob-

served area (Hillel, 2003). That is it flows down a slope, later contributing to surface waters or infil-

trating at a different place into a different soil profile (“Water Content”, 2008). Whenever not other-

wise stated, if I use the term runoff, I refer to surface runoff as defined here.

Evapotranspiration:

Evapotranspiration (ET) is the sum of evaporation and transpiration. Water mainly evaporates from

bare soil surfaces, plant surfaces (e.g. intercepted water) and from surface waters. Transpiration is

evaporation of water through open stomata in a plant’s leaves (Hillel, 2003). Considering global an-

nual mean values, transpiration accounts for about 50% of total land evapotranspiration (Dirmeyer,

2006).

3.2 Simple Water Balance Model

The employed model used for all computations in this study is introduced and extensively described

in Orth et al (2013). It relies on the general water balance equation:

wn+∆t = wn + (Pn − En − Qn)∆t (3)

Where wn denotes the soil moisture at the beginning of time step n and Pn, En and Qn denote precipi-

tation, evapotranspiration and runoff accumulated between n and n+∆t. As mentioned I used a time

step of one day, i.e. ∆t = 1d. The model computes soil moisture, runoff and evapotranspiration exclu-

sively from meteorological data, such as precipitation, temperature and net radiation. It does not

employ any information on soil or vegetation characteristics.

The equations for the dependency of evapotranspiration and runoff on soil moisture are as follows:

Qn

Pn= (

wn

cs)

𝛼

(4)

λρwEn

Rn= β0 (

wn

cs)

𝛾

(5)

Runoff and evapotranspiration are normalized with precipitation and net radiation, respectively, and

as such are functions of soil moisture. It is assumed that there is a maximum fraction of net radiation

that may be transformed to evapotranspiration, denoted by the unitless coefficient β0 in (4). This so

called evaporative fraction (EF) depends, inter alia, on vegetation type and activity. Under strong ra-

diation plants close their stomata to prevent further water loss, which leads to excess net radiation

which is not transferred to evapotranspiration. In (4) and (5) cs is the field capacity of the soil, which

is assumed to be constant at 970mm (Table 1). Since soil moisture in the model cannot exceed field

capacity, the latter is used to scale wn and ensure normalized runoff and normalized ET do not ex-

ceed 1. The density of water ρw and the latent heat of vaporization λ are introduced to convert En to

the same unit as Rn. The two exponents α and λ determine the respective shapes of the functions.

The version of the model used in this study was adapted by Orth et al. (2013) to also account for

snow, and thus both precipitation and net radiation are adjusted accordingly: if temperature is within


a certain range above or below a defined threshold (here: 1°C), a certain percentage of precipitation

falls as snow and does not immediately contribute to runoff and soil moisture. The fraction is linearly

dependent on the current temperature: if the temperature is 1°C below the threshold (here: ≤ 0°C)

100% of precipitation falls as snow, if it’s equal to the threshold 50% falls as snow and if it’s 1°C or

more above it all precipitation falls as liquid water. Similarly, snow melting depends linearly on the

temperature, but with no melting occurring if the temperature is below or equal to the threshold.

Here a melting factor fm is introduced as another model parameter, which determines the strength of

this linear dependency. The melted snow is added to the amount of precipitation on the respective

day. Since the melting requires part of the net radiation as energy input, net radiation is decreased

on days where melting occurs.5

In my study I used the same values for the set of model parameters for all model simulations and across the entire domain. They are summarized in Table 1. Table 1: Overview of the five model parameters with the respective values used in my simulations.

field capacity cs runoff exponent α ET exponent λ max EF β0 melting factor fm

970mm 9 1.1 0.7 3

Thanks to the simplicity of the Simple Water Balance Model (SWBM), the simulations are fast and

straightforward to analyze. However there is an important limitation concerning the dependency of

runoff on soil moisture. As mentioned in section 3.1 the notion of infiltration capacity describes the

maximum rainfall rate the soil can take up at a given time. It is thus closely linked to the concept of

the runoff fraction of precipitation as integrated in the SWBM (normalized runoff). While the runoff

fraction in the model is also negatively proportional to soil moisture, it varies only on a daily scale

due to the model’s time resolution. That means the model does not consider how total daily precipi-

tation is distributed over the day. However, taking into account the infiltration capacity would mean

that the runoff fraction also depends on the intensity of the rainfall: a certain amount of rainfall

spread over an entire day may lead to less runoff than when the same amount occurs within a short

time frame. Even though the runoff on particular days with heavy rainfall may be affected by this lim-

itation, I assume that the analysis of the entire winter as done in this study still yields rather reliable

results.

3.3 Data

Data for precipitation and temperature was obtained from the E-OBS gridded dataset (Haylock et al.,

2008) version 11.0 with a spatial resolution of 0.5x0.5 degrees.

Data for radiation is the same as employed in Orth and Seneviratne (2015), where the dataset from

the NASA/GEWEX SRB project6 is combined with the CERES 2 data product7 for extended time cover-

age (1984-2013). Data for the year 2014 as used in my thesis was obtained from the same sources

and combined applying the same methodology.

I used data from 21 years (1994-2014) to obtain long term means and standard deviations. The mod-

el simulations are performed on a 0.5°x0.5° grid and cover the entire UK except for Northern Ireland.

5 The reduction is proportional to the amount of melting. The constant of proportionality is obtained through

division of the heat of fusion by the heat of vaporization. 6 http://gewex-srb.larc.nasa.gov/

7 http://ceres.larc.nasa.gov/order_data.php


3.4 Model Simulations

3.4.1 For one grid point

In a first step I examined a particular site in the south of England (0°15’E, 51°15’N; Fig. 1) to analyze

the local hydrological dynamics and processes, before moving to the country scale. The location was

not chosen at random but instead in such a way that it represents a region that experienced excep-

tionally strong rainfall during winter 13/14.

I conducted one reference simulation with no manipulations of the model runs whatsoever and 12

simulations where I modified initial soil moisture8 values. The modification consisted of prescribing a

different soil moisture value once December 1st 2013 is reached. The model then proceeds with the

calculations as usual. No other changes to the model were made. I could thus safely infer that any

changes in resulting modelled values are exclusively a consequence of the change in initial soil mois-

ture. Table 2 gives an overview of the adjustments I made and the resulting values for ISM as well as

the corresponding anomalies expressed in number of standard deviations from the 21 years Decem-

ber 1st mean.

With the resulting data9 I then computed for each simulation the monthly cumulative runoff for De-

cember, January and February as well as for all three months combined (winter runoff) and derived

the respective anomalies in the same way as for ISM. The results gave me an overview of the changes

in monthly and winter runoff caused by adjustments to the initial soil moisture. On its own each sce-

nario offers only limited information: while they show to what extent a certain monthly surface run-

off value is extreme or not, they do not show how it translates into river flows and – if at all – floods.

8 initial soil moisture as defined in chapter 2: soil moisture on December 1

st 2013

9 Thirteen sets of values, each containing 7665 computed values (one per day) for soil moisture, runoff and

evapotranspiration.

Fig. 1: Chosen location in region with heavy rainfall (red square): 0°15’E, 51°15’N


Only through comparing the manipulated simulations with the reference, I obtained an impression –

albeit a non-quantitative one – of the effects on flood magnitude.

Table 2: The 12 adjustments applied to initial soil moisture (in mm) as well as the reference simulation with no adjust-ments, the resulting initial soil moisture values (in mm) and the respective normalized anomalies (as number of standard deviations from the 21 years mean). Set to FC means that the value was directly set to field capacity (FC), representing the maximum soil moisture value the model allows. ISM adjustment ISM Anomaly

Set to FC 970 2.00 +175 951.35 1.76 +150 926.48 1.39 +125 901.59 0.99 +100 876.68 0.55 +75 851.75 0.11 +50 826.81 -0.35 +25 801.851 -0.79 none* 776.89 -1.21 -25 751.91 -1.59 -50 726.93 -1.94 -75 701.95 -2.25 -100 677 -2.51

* representing the reference simulation

Slightly diverging from my main research question, I also had a look at the precipitation-soil moisture

relationship to gain a rough understanding of the soil moisture’s response to occurring rainfall and

the general development of soil moisture prior to the flood period. Precipitation anomalies during

the winter months are also of interest in order to compare them with the runoff anomalies of the

same months, in particular with the respective runoff values resulting from adjusted ISM.

Using the reference simulation I computed monthly precipitation sums in the seven months between

September 2013 and March 2014 and for each month’s last day the respective soil moisture (end-of-

month soil moisture). Since each current day soil moisture value is computed based on the one from

the day before, end-of-month soil moisture is an appropriate measure to depict the overall influence

of sum monthly precipitation on soil moisture of that month. Also, taking a monthly time scale for

precipitation versus daily for soil moisture makes sense, since average daily precipitation values are

several times smaller than average daily soil moisture and thus show weak influence on the latter

when analyzed at a daily time scale.

3.4.2 For the entire grid

In a second step I analyzed daily soil moisture, runoff and evapotranspiration for each on-land grid

point within the UK. Using land points only was important, since soil moisture values obviously only

exist on land. To assure this I used an existing soil moisture data set (introduced in Orth and Sen-

eviratne (2015), downloadable via http://www.iac.ethz.ch/groups/seneviratne/research/SWBM-

Dataset) as a geographical mask based on which the model computed the respective values. To get

an idea of the representativeness of my results I first estimated the accuracy of the modelled values

by comparing them to existing data. For this purpose I made a reference model run with no adjust-

ments to ISM and plotted the results for monthly runoff as a map against the river flow map from

Huntingford et al. (2014).


After the evaluation of the model’s performance I continued by plotting diagnosed initial soil mois-

ture anomalies for every grid point. This yielded a similar map as above, but I refined the color scale

and used the same measure of anomaly as described in section 3.4.1, i.e. number of standard devia-

tions from the long-term mean. In the same manner I replotted monthly runoff for December, Janu-

ary and February as well as for total winter runoff. As in section 3.4.1 I then made a model run with

adjusted ISM, only that now I only made one such simulation (instead of 12). The goal was to simu-

late an extreme but still realistic case which allowed me to test a worst-case scenario: rainfalls equal-

ly large as in winter 13/14 coinciding with very wet initial soil moisture conditions. Or, more informal

yet more descriptive, the other way round: I computed the ‘runoff-reduction-service’ provided by the

soil in the observed floods. Because of the geographic diversity across the whole country there was

no one value both extreme and realistic for every grid point. I thus adapted the model such that it

adjusted ISM for each grid point separately to the long-term (here 21-years) maximum of the respec-

tive point, representing both an extreme and realistic10 value for that location.

10

For 58 of 120 grid points the resulting ISM was larger than the mean by less than 1.5 standard deviations, thus – assuming normal distribution – with a probability to occur larger than 13.4%. 43 points deviated from the mean by 1.5 to 2 standard deviations, resulting in probabilities to occur of between 13.4% and 4.5%. For 19 grid points ISM values were larger than the mean by slightly more than 2 standard deviations, thus oc-curring with slightly less than 4.5% probability (again, assuming normal distribution).


4. Results

4.1 Model accuracy

In the following I present the study’s main results and the corresponding plots. I start with the model

comparison since the conclusion drawn from it – whether approximate model accuracy can be as-

sumed or not – is relevant for all other findings. Fig. 2 shows the map with the computed values and

the one taken from Huntingford et al. (2014). To ensure comparability with the latter I used the same

measure of anomaly – unlike for all other plots not the number of standard deviations but instead

percentage of the long-term mean – and a similar color-scale11. Since the maps employed by

Huntingford et al. (2014) show monthly river flows and not surface runoff, the comparison allows for

a rough and qualitative estimation of model accuracy only. I focused on whether runoff and river

flow show similar regional patterns in their degree of anomaly as this is to be expected under the as-

sumption of accurate runoff values.

Looking at Fig. 2 we see that for February computed runoff indeed matches the river flows pretty

well. This is especially true for the main regions with exceptionally large river flows in southern Eng-

land and Scotland as well as the region with average flows in north-western Scotland. Comparisons

of the other two winter months yielded similar results.

11

The circles’ colors used in Huntingford et al. (2014) are not solely based on the indicated percentage values but also incorporate a not specifically discussed normalization. The two color-scales thus don’t match exactly. A comparison is still possible however.

Fig. 2: Comparing monthly surface runoff for February 2014 as computed by the SWBM (left) with monthly river flows for the same month as illustrated in Huntingford et al. (2014). All expressed as percentages of the long-term mean. The color-scale of the SWBM simulation was adapted to ensure comparability.


4.2 Simulations for one grid point

I proceed with the plots displaying actual normalized precipitation and reference ISM for the seven

months before, during and after winter 13/14 for the chosen geographic location (Fig. 3). The shown

precipitation values will help us to better interpret the 12 simulations with adjusted ISM which are

summarized in Fig. 4. We can see that precipitation in autumn (September through November) was

within normal range, reaching a slightly higher level in December but still within the range of one

standard deviation. Only in January and February rainfalls were exceptionally large, in January 1.97

and in February 1.45 standard deviations larger than the long-term mean.

The actual absolute precipitation values for the seven-month-period are shown in Table 3. December

and February precipitation was similarly strong in absolute terms (though not so in relative terms, as

shown in Fig. 3) and January rainfall was by far the strongest during the observed period, both in ab-

solute and relative terms. End-of-month soil moisture in the autumn months was well below the

mean for that period. Of particular interest is the soil moisture anomaly at the end of November

since it is equivalent to the anomaly of December 1st, hence ISM as defined and used in this study.

We see that the value is exceptionally low, 1.21 standard deviations below the long-term mean. This

marks an important result of this study: the soil was very dry before the heavy rainfall period and

could therefore store a part of the water which would have otherwise constituted to runoff and, ul-

timately, flooding. The steep rise of the end-of-month soil moisture in October despite average Oc-

tober rainfall can be explained by high average October rainfall in absolute terms, the long-term

mean for October being 67% larger than the long-term September mean ([53.48±35.58 vs.

89.35±51.61]mm).

Table 3: Actual precipitation in mm for the seven months before, during and after winter 2013/2014.

Month September October November December January February March

Actual precipita-tion [mm]

42.4 96.9 67.7 119.0 170.9 113.3 28.8

Fig. 3: Monthly sum precipitation anomalies and soil moisture anomalies at the end of each respective month. All as number of standard deviations from the long-term mean. For one geographic grid point in southern Eng-land as described in 3.4.1 (coordinates: 0°15’E, 51°15’N). Results are for a reference model run without any value adjustments. Precipitation represents actual rainfall as delivered by the source data set (see 3.3). The shown soil moisture value at the end of November is equivalent to initial soil moisture as used in this study.

An

om

alie

s re

lati

ve t

o 2

1 y

ears

mea

n


Next I have a look at the results of the 13 simulations for the chosen location in southern England,

which are summarized in Fig. 4 and Table 4. A first very general but no less important finding is that

indeed runoff does react to changes in initial soil moisture. Furthermore it does so not only for large

adjustments and for the time period closest to December 1st, but also for small changes and for the

months further apart from December, i.e. January and February. Still, not surprisingly, the slope is

steepest for December and lowest for February, were the influence of the change in ISM is – as men-

tioned – weak but still significant, with an approximated slope of 0.22 over all data points (obtained

through linear regression). Looking at the monthly runoff values for the reference simulation we see

that only for February the value is well above one standard deviation from the mean (1.47) whereas

for January it is close to one (1.08) and for December even below the long term mean (-0.45 standard

deviations). Overall winter runoff is 0.85 standard deviations above the long term mean for reference

conditions at the analyzed location.

For January and February the slope decreases with growing ISM whereas for December it is increas-

ing until December 1st soil moisture reaches its maximum value, equaling field capacity. Notably, in

the simulations where ISM is set to field capacity, December runoff exceeds February runoff; not just

in terms of the respective anomaly, as shown in Fig. 4, but also in absolute terms, reaching

118.97mm for December vs. 112.14mm for February (precipitation and all other factors being un-

changed compared to reference conditions). Despite much higher precipitation in January than in

February in terms of anomaly (Fig. 3) and in absolute terms (Table 3), runoff for the reference simula-

tion is larger in February than January in relative and almost equal in absolute terms (92mm vs.

95mm) because the soil can take up more water in January than in February. With increasing ISM the

difference becomes smaller because of the stronger influence ISM has on January soil moisture. Jan-

uary exceeds February runoff before ISM reaches its long term mean.

Fig. 4: Monthly sum runoff anomalies vs. ISM anomalies (in number of standard deviations from the long term mean). Each point on a graph represents a runoff anomaly associated with the corresponding ISM anomaly for the month(s) indicated by the graph’s color.


Other runoff anomalies and values worth having a closer look at are those resulting from setting the

ISM to the long term mean, as they are indicative of a winter situation where exceptionally high pre-

cipitation (as in winter 13/14) coincides with mean – instead of exceptionally low – December 1st soil

moisture. Table 4 shows induced changes in percent to absolute winter runoff obtained through ma-

nipulations of ISM. A good proxy for conditions with mean ISM is the simulation where ISM is adjust-

ed to 75mm above the reference, resulting in a value merely 0.11 standard deviations above the

long-term mean. The obtained overall winter runoff from such a model run is 0.7 standard deviations

above the actual winter 13/14 runoff, representing a substantial increase of 32% in absolute terms

(dark grey in Table 4). This is another important result of this study, suggesting that indeed runoff

and presumably flooding would have been substantially higher even under average ISM conditions.

4.3 Simulations for the entire grid

The next few paragraphs describe the results I obtained by applying the model to the whole geo-

graphical grid as described in section 3.4.2, i.e. the entire UK with the exception of Northern Ireland. I

underlayed the computed values with a UK map to provide the geographical context. Excluding the

February simulations, the maps are grouped together in pairs in Figures 5 to 8. In every figure the

map to the left depicts the results of the reference simulation with actual ISM values and the map on

the right the corresponding results of the simulations were ISM was set to the 21-year-maximum of

each point (see section 3.4.2).

Fig. 5 shows the geographical distribution of ISM for the entire UK. The difference between the actu-

al and the adjusted ISM is clearly visible in every grid point, indicating that the reference value was

nowhere close to the local maximum of the last 21 years. On the contrary, we see that it indeed was

low throughout the UK, not just for the previously discussed location, in many areas exceptionally

low and only for one area around the English-Scottish border slightly above the long-term mean. The

values mapped on the right on the other hand are high for most areas and exceptionally high for

many, as I have mentioned in detail in section 3.4.2.

Fig. 6 shows the runoff anomalies for December 2013, again for reference conditions and for adjust-

ed ISM. We have seen in the single grid point discussion that reference December runoff was below

the mean, which seems to be true for most parts of southern and central England. The area around

the English-Scottish border however has experienced exceptionally large runoff already in December,

which can be explained with the comparably high soil moisture in that area as found in Fig. 5. In wet

ISM conditions the whole UK witnesses very large December runoff. This is consistent with the steep

slope of the December runoff vs. ISM function we have seen in Fig. 4. An exception constitutes the

area where runoff is already large in the reference simulation. Comparing with Fig. 5 we see that it is

Table 4: Showing some of the applied adjustments to actual winter 13/14 ISM (reference), both in mm added or subtracted from the reference as well as in percent of the reference, the anomalies of the resulting ISM values in number of standard deviations from the long term mean, the resulting absolute winter runoff for each ISM adjustment and the change in run-off induced by the adjustments, depicted as the change in the degree of anomaly as well as in percent of the reference value. The cells shadowed in dark grey represent the values where ISM is close to its long term mean.

Adjustments in mm +100 +75 +50 +25

Reference 776.89 -25 -50 -75 -100

in percent of reference 13% 10% 6% 3% 0 -3% -6% -10% -13%

Standard deviations from mean ISM 0.55 0.11 -0.35 -0.79 -1.21 -1.59 -1.94 -2.25 -2.51 winter runoff [mm] 307 283 260 237 214 193 172 152 133

Change in runoff in percent of reference 43% 32% 21% 10% 0 -10% -20% -29% -38%

in standard deviations 0.9 0.7 0.5 0.2 0 -0.2 -0.5 -0.7 -0.9


also the area with highest ISM for reference conditions. This high starting point of ISM together with

probably high precipitation throughout the month leads to quickly saturated soil and thus large run-

off even without increasing ISM, so that the latter has little to no further impact on overall runoff.

Analogously to Fig. 6, Fig. 7 shows overall January runoff. We now see a similar pattern of difference

between the two simulations for the whole grid as in the area around the English-Scottish border in

Fig. 6. The influence of the higher ISM is strongest in the South-East of England. Interestingly, the re-

sults show equally low runoff for both simulations in the very northwest of Scotland.

Fig. 5: December 1st

2013 soil moisture anomalies for every grid point, in

number of standard deviations from the long term mean. To the left the refer-

ence simulation with clearly visible low soil moisture throughout the country.

To the right the simulation with ISM set to the respective local long term max-

imum, leading to large anomalies in most – but not all – regions.

Fig. 6: December 2014 runoff anomalies. Reference values on the left and val-

ues resulting from the adjusted ISM on the right. The difference being especial-

ly apparent in southern England and negligible in the area around the English-

Scottish border.


Fig. 2 already showed that this region did neither experience exceptional runoff nor large river flows

(Huntingford et al., 2014). This does not change when ISM is increased, confirming that actual precip-

itation and therefore its fraction turned into runoff must have been rather low for that region.12

I omit the depiction of the equivalent plot for February as it offers little additional insight and infor-

mation. As we have seen in Fig. 4, the dependency of February runoff on ISM is much lower than that

of December and January runoff for the observed location. The resulting values for the whole grid re-

flect that observation as well, yielding two very similar maps.

To conclude this chapter I present the overall winter runoff for both simulations as summarized in

the maps of Fig. 8. The influence of December 1st soil moisture is clearly evident here too and we see

that the results obtained at the chosen location (section 4.2) are representative across the entire UK:

the induced difference in runoff in terms of anomalies is not as large as for December and compara-

ble to that of January. The same exceptions mentioned in the previous two figures are valid here too:

the very northwest of Scotland is insensible to ISM and the area around the English-Scottish border

reacts only weakly to the adjusted ISM. The impact of the adjustment is most apparent in central

England (the belt around 53°N; denoted by the red rectangle in Fig. 8), where reference runoff is be-

low the mean under reference conditions and well above it under high ISM conditions, in the East by

more than two standard deviations. This is plausible considering the very low reference ISM within

this belt and the low rainfall in December which leads to small contributions to runoff in December

under reference conditions (as seen in Fig. 6). Across southern England (50°-52°N), values obtained

from the high-ISM simulation are on average 1.1 (±0.4) standard deviations, or – in non-normalized

terms – more than 50% above the reference values (more details in Table 5).

12

Plotted UK rainfall for the three winter months in the study by Huntingford et al. (2015) confirms this, show-ing that rainfall in northwestern Scotland was indeed low in January, ranging from average to well below aver-age values.

Fig. 7: January 2014 runoff anomalies. Reference values on the left and values resulting

from the adjusted ISM on the right. In regions, where reference runoff is already high,

the difference is not clearly visible. In northwestern Scotland overall January runoff is

insensible to the change in ISM, most likely due to low precipitation.


Along the eastern coast, simulations with adjusted ISM lead to values that are more than twice as

large as under reference conditions. Table 5 shows the quantitative changes in winter runoff due to

setting ISM to the local long-term maximum for a selection of locations in southern England. It offers

a more precise insight into the effects of the ISM adjustments than the above depicted maps. The

changes correspond to the grid points enclosed by the yellow squares in Fig. 8 and are given both in

percent of the reference values as well as in terms of the difference in standard deviations from the

long-term mean.

Table 5: The effects of setting ISM to the long-term maximum in several locations in southern England. The table shows the in-flicted change upon winter runoff, both as the difference in the number of standard deviations from the long-term mean and as percentage of the reference values. The values are arranged such as to correspond with the maps above, i.e. left corresponds to West and up corresponds to North. The values represent the grid points between 51°N and 52°N as indicated by the yellow rec-tangles in Fig. 8.

change in runoff in southern Eng-land by setting ISM to local long-term mean

in percent of reference 23% 39% 41% 43% 62% 81% 105% 141% 145% 51.75N

in standard deviations 0.8 1 0.9 0.9 1.3 1.6 1.9 2 2

in percent of reference 29% 40% 33% 29% 33% 46% 66% 87% 86% 51.25N

in standard deviations 0.9 1 0.7 0.6 0.7 1 1.4 1.7 1.6

3.25W 0.25E

Fig. 8: Winter 13/14 runoff anomalies. Reference values on the left and values result-

ing from the adjusted ISM on the right. The influence of ISM on overall winter runoff is

clearly visible in most parts of the country, especially in the southeast. The red rectan-

gle encloses the area where the influence of the higher ISM is most visible. The yellow

rectangles enclose all values that are shown in more detail in Table 5 below.


5. Conclusion and Outlook

In this study I have analyzed the influence of initial soil moisture on December 1st 2013 on surface

runoff throughout December 2013, January 2014, February 2014 and winter 13/14. The analysis fo-

cused on the UK and the floods that hit the country during the observed period. The main goal was to

assess the role initial soil moisture played in the flood and whether it would have turned out differ-

ently with different soil moisture conditions in early December. One question addressed was wheth-

er the flood may in part have been caused by exceptionally wet soil prior to the rainfall period, such

that throughout winter a large fraction of the precipitation constituted to runoff and thus to the ex-

traordinarily large river flows. My model simulations for reference conditions show clearly that the

opposite is the case: Fig. 5 illustrates that soil moisture on December 1st was exceptionally low for

almost every analyzed grid point and close to the long-term mean for the rest. I then consequently

focused on the effects caused by initial soil moisture if it had been much higher than actually ob-

served, for example close to its respective local long-term mean or even long-term maximum. This is

a highly relevant scenario since any future heavy rainfall event is likely to coincide with ISM higher

than the one of winter 13/14 than with one equally low or lower.

My findings illustrate that ISM impacts monthly runoff substantially, not only for December but also

for January and February and therefore – most importantly – for the entire winter. The results for the

single grid point discussed in section 4.2 offer a detailed insight into different magnitudes of impact

ISM has depending on how substantial its adjustment is and depending on the considered month.

The results show that ISM has a strong impact on overall winter runoff not only for extreme adjust-

ments, i.e. adjustments resulting in an extreme ISM value, but also for moderate changes: adding

75mm to reference soil moisture on December 1st results in a value very close to the long term mean

(0.11 standard deviations above). Yet this increase in ISM provokes a 32% increase (0.7 standard de-

viations) of overall winter runoff with otherwise identical conditions. In a winter with mean ISM

these 32% of additional surface runoff would have contributed to river flows and most likely intensi-

fied the floods.

The analysis of the entire UK showed that the results obtained from the single location are repre-

sentative for most parts of the country. For this analysis ISM was adjusted to the long-term maxi-

mum of the respective location. Looking at winter runoff in southern England for example, this re-

sulted in values that were on average 1.1 (±0.4) standard deviations above reference values. For sev-

eral locations, especially along the eastern coast, high-ISM conditions lead to doubling of winter run-

off in non-normalized terms. These substantial differences across wide areas show quite clearly that

similar precipitation conditions coinciding with high ISM would have indeed intensified the floods se-

verely, not just locally but on a large geographical scale.

An in-depth analysis of the statistical significance the computed changes in runoff have for river flows

was not investigated in this study. Note also that a simple conceptual model was employed. However,

the obtained changes and relationships are rather strong such that the main conclusions drawn from

the analysis should be robust with respect to the choice of different types of models. Further anal-

yses could also be conducted with regard to the comparison of the impact of soil moisture versus


impact of monthly precipitation, e.g. by making similar simulations as in this study but with adjusting

precipitation instead of ISM while leaving everything else unchanged.13

Nonetheless the study’s results illustrate clearly the importance of initial soil moisture in extreme

rainfall events with regard to the amount of runoff, which will eventually feed fluvial systems. In the

herein analyzed example of the UK winter-floods 13/14 it played a mitigating role due to being far

below the long-term mean. This may not be the case in future instances and thus it is vital to take

initial soil moisture into account when heavy rainfall periods have been forecast and potential risk of

flooding is evaluated.

13

Looking at the other independent variable of the model – radiation – is less interesting in this context since it influences runoff only indirectly via its function evapotranspiration respectively soil moisture.


6. Acknowledgement

I would first and foremost like to thank my supervisor Dr. René Orth for his valuable, constructive

and incredibly friendly support throughout the entire process of writing this thesis. It was a great

pleasure to work under his guidance.

I also thank Prof. Dr. Sonia I. Seneviratne for providing me the opportunity to conduct my research in

the group for land-climate dynamics which she presides.

Last but not least I would like to thank Julie Pasquier and my family for their emotional support.


7. References

Dirmeyer, P. A., et al. (2006). GSWP-2 - Multimodel anlysis and implications for our perception of the land surface. Bulletin of the American Meteorological Society, 87(10): 1381-1397. doi: 10.1175/BAMS-87-10-1381 Field Capacity (2008). In Chesworth, W. (Ed.), Encyclopedia of Soil Science (p. 270). Dordrecht: Springer Netherlands. Haylock, M.R., et al. (2008). A European daily high-resolution gridded dataset of surface tempera-ture and precipitation. J. Geophys. Res (Atmospheres), 113, D20119, doi:10.1029/2008JD10201 Hillel, D. (2003). Introduction to Environmental Soil Physics. Burlington: Academic Press Huntingford, C., et al. (2014). Potential influences on the United Kingdom's floods of winter 2013/14. Nature Climate Change, 4(9), 769-777. doi: 10.1038/NCLIMATE2314 Jones, M. R., et al. (2013). An assessment of changes in seasonal and annual extreme rainfall in the UK between 1961 and 2009. International Journal of Climatology, 33(5), 1178-1194. doi: 10.1002/joc.3503 Morel-Seytoux, H. J. (2008). Infiltration. In Chesworth, W. (Ed.), Encyclopedia of Soil Science (pp. 350-362). Dordrecht: Springer Netherlands. Orth, R., et al. (2013). Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model. Journal of Hydrometeorology, 14(6), 1773-1790. doi: 10.1175/JHM-D-12-099.1 Orth, R. and Seneviratne, S.I. (2015). Introduction of a simple-model-based land surface dataset for Europe. Env. Res. Lett., 10(4). doi: 10.1088/1748-9326/10/4/044012 Rasmussen, W. (2008). Field Water Cycle. In Chesworth, W. (Ed.), Encyclopedia of Soil Science (pp. 272-275). Dordrecht: Springer Netherlands. Slingo, J. et al. (2014). The recent storms and floods in the UK. Met Office, and Centre for Ecology and Hydrology. http://www.metoffice.gov.uk/media/pdf/n/i/Recent_Storms_Briefing_Final_07023.pdf Water Content (2008). In Chesworth, W. (Ed.), Encyclopedia of Soil Science (p. 813). Dordrecht: Springer Netherlands.

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