Full Paper
GIS-based Estimation of Flood Damage to Arable
GIS-basierte Abschätzung von Hochwasserschäden landwirt-
schaftlicher Nutzpflanzen
Marco Neubert
, Julia Höhnel
, Reinhard Schinke
Leibniz Institute of Ecological Urban and Regional Development ꞏ m.neubert@ioer.de
Technische Universität Dresden
Floods are a serious risk, particularly in view of their climate change-induced increasing
intensity and frequency. Hence, a further growth of flood consequences is to be expected. The reason
is either, higher flood damage caused by the higher exposure or the rising effort to avoid and to mitigate
the negative consequences. Damage and risk analysis often focus on the built environment because of
its value dimension and its resulting high potential flood losses. In this context, damage to arable crops
are rarely included and almost solely by rough estimates. The reason is the difficulties to assess appro-
priate values especially the seasonal and annual changes. To fill the gap, the paper presents a new
approach for a detailed analysis of damage to arable crops considering the vulnerability of different
crop types and the seasonal crop growth. It results in a wide range of applications including the use for
the evaluation of compensation payments. In cooperation with the responsible authorities, this may be
of particular interest for dike relocations and the flooding of flood polders.
GIS, agriculture, flood vulnerability, risk assessment, damage functions
Hochwasser stellt eine ernstzunehmende Gefahr dar, da durch Klimawandel mit
einer Zunahme von Häufigkeit und Intensität zu rechnen ist. In der Folge werden auch die Konsequen-
zen weiter zunehmen, die entweder durch höhere Schäden oder durch einen höheren Aufwand zur Ver-
meidung bzw. zur Verminderung negativer Folgen entstehen. Im Hinblick auf ökonomische Schäden
steht bei einer Vielzahl von Untersuchungen die wertintensive, gebaute Umwelt im Vordergrund der
Betrachtung. Angesichts dessen, wurden die Schäden an landwirtschaftlichen Nutzpflanzen selten be-
trachtet oder nur mittels überschlägiger Werte in die Risikobetrachtungen einbezogen. Der vorliegende
Beitrag zeigt einen neuen Ansatz auf, mit dem sich landwirtschaftliche Schäden im Überflutungsfall
detailliert ermitteln lassen. Dieser Ansatz berücksichtigt insbesondere die Verletzbarkeit für konkrete
Fruchtarten und die jahreszeitliche Entwicklung der Pflanzen. Insgesamt ist damit ein interessantes
Tool entstanden, welches – in Zusammenarbeit mit den zuständigen Behörden – auch bei der Bewertung
von Ausgleichsleistungen eingesetzt werden kann, was insbesondere bei Deichrückverlegungen und bei
Flutpoldern von Interesse ist.
GIS, Landwirtschaft, Hochwasservulnerabilität, Risikobewertung, Schadensfunktio-
1 Introduction
Floods are one of the major threats to areas inhabited or cultivated by humans worldwide. In
the light of climate change and the expected increase of flood frequency and flood intensity
(Hennegriff et al., 2006), an increase of negative consequences for all receptors exposed to
flood risks is to be expected. For this reasons, it needs a clear understanding of damage pro-
cess and the effects of mitigation measures. This is in line with research activities in recent
AGIT ‒ Journal für Angewandte Geoinformatik, 6-2020, S. 183-194. © Wichmann Verlag, VDE VERLAG GMBH ꞏ
Berlin ꞏ Offenbach. ISBN 978-3-87907-698-7, ISSN 2364-9283, eISSN 2509-713X, doi:10.14627/537698017.
Dieser Beitrag ist ein Open-Access-Beitrag, der unter den Bedingungen und unter den Auflagen der
Creative Commons Attribution Lizenz verbreitet wird

AGIT – Journal für Angewandte Geoinformatik ꞏ 6-2020
decades. It includes especially the development of methods and tools, which determine po-
tential flood damage and the effects of flood mitigation measures. However, the focus has
mostly been on settlement areas with the highest potential of economic losses (Förster et al.,
2008). Numerous studies are therefore dedicated to the development of depth-dependent
damage functions for the built environment and their application in flood-prone settlement
areas (cf. Merz et al., 2010; Neubert et al., 2016, Schinke et al., 2012). In contrast, damage
assessment for agricultural land is often based on lump sum values or simple estimates
(Förster et al., 2008). The reasons are the lower economic losses on agricultural land in the
event of flooding (cf. Brémond et al., 2013; Förster et al., 2008; Klaus et al., 2016) and the
insufficient data basis regarding the vulnerability of agricultural crops (Dutta et al., 2003).
The diversity of species and varieties of cultivated crops, the varying vulnerability to flood
events and the seasonal variations in damage values are obstacles to this.
Some studies on agricultural flood damage take into account the variation in the vulnerability
of arable land during the course of the year (Förster et al., 2008; Klaus et al., 2016; Win et
al., 2018). Studies addressing the formulation of depth-damage functions for individual crop
types are rarely found (Citeau et al., 2003; Ganji et al., 2012) and mostly concern typical
Asian crops like rice and beans (Dutta et al., 2003; Nguyen, 2017). These damage functions
can be transferred to crops cultivated in Central Europe to a limited extent only.
In a previous study, a method was therefore developed for calculating the economic flood
damage of agricultural land in Central Europe as a function of water depth (Höhnel, 2016).
The basis was a statistics-based distribution of crops on arable and grassland areas in the case
study. In addition, crop-specific water depth damage-functions based on damage compensa-
tion amounts of the flood aid of the federal states Saxony and Saxony-Anhalt for the Elbe
flood 2013 were derived. Monthly damage values have been determined according to average
plant growth heights.
In this study, more detailed statistical crop shares data are used. In addition, updated damage
compensation amounts for Saxony allow recalculating and comparing the results for the same
study area. Further methodological enhancements are intended to serve simplification. For
example, a combined damage function based on crop shares and the use of generalised water
depth data are applied. The resulting potential damage amounts are analysed and discussed.
2 Data and Method
2.1 Study Area
The study area is an agricultural area along the Elbe river floodplain in northern Saxony
(Germany), east of the town Riesa (Figure 1). At this location, the Elbe floodplain widens
after passing the transition from the low mountain range and hilly region (Saxon Loess
Fields) in the south to the lowlands of Northern Saxony (part of Northern Lowland of Ger-
many). Administratively, the study site is part of the district Meißen. Its location is about
40 km northwest of the federal state capital Dresden. The extent of the study area is
9 by 6 km, whereof 15 km² (27.8 %) is located in designated flood areas according to a re-
currence interval of 100 years. 10.8 km² (20 %) of the area is flood-prone agricultural land.

M. Neubert et al.: GIS-based Estimation of Flood Damage to Arable Crops
Fig. 1:
Location of the study area Elbe floodplain close to Riesa – left: Saxony within Ger-
many; right: study area within Saxony including designated flood areas (blue)
Data and Pre-processing
An overview of the data processing and modelling approach is shown in Figure 2. The de-
scription of all steps is to be found in the subsequent sections.
Fig. 2:
Data processing and modelling approach for analysing potential flood damage to
arable crops; GIS processing steps (light orange boxes), spreadsheet/database cal-
culations (light blue boxes)

AGIT – Journal für Angewandte Geoinformatik ꞏ 6-2020
2.2.1 Water Depth
The water depth data used in this study (Figure 3) originate from the previous project VERIS-
Elbe where numerous future flood scenarios for the German Elbe catchment have been mod-
elled. These scenarios incorporate, among others, regional climate change projections as well
as options for action. For the current analyses, we have chosen a reference flood with a 100-
year recurrence interval. The basis for calculating the flood scenarios was a digital terrain
model (DTM) with a raster resolution of 2 m and a height accuracy 0.1 m (Neubert,
A cascading model procedure was applied for calculation including the flood simulation
model LISFLOOD, the hydrodynamic model WAVOS, the flow model Hydro_AS-2D, and
the model SMS for modelling the water depth (Schanze et al., 2015). The water depth values
by centimetre have been simplified into decimetres [dm] by rounding. In addition, the raster
data set was converted into polygons, which thus form partial units of the parcels. Its level of
detail can be lowered to reduce the number of single polygons at the same time by general-
ising the height resolution to e. g. 0.5 m or 1 m.
2.2.2 Field Plot Location
For the spatial location of field plots, we used data of the annually updated InVeKoS
& Nowak, 2014). This dataset is available for download online (SMUL, 2018). Alternatively,
it is possible to use the biotope map BTLNK
, but the current data set for Saxony is from
2005 already. If less detailed data is sufficient, data of ATKIS
can be used.
Fig. 3:
Extent of flooded area and water depth of a flood event with a 100-year recurrence
Integriertes Verwaltungs- und Kontrollsystem
/Integrated Administration and Control System
Biotoptypen- und Landnutzungskartierung
/Biotope Type and Land Use Map
Amtliches Topographisch-Kartographisches Informationssystem
/Authoritative topographic carto-
graphic information system

M. Neubert et al.: GIS-based Estimation of Flood Damage to Arable Crops
InVeKoS (but also BTLNK and ATKIS) allow separating arable land and grassland. Within
the study area 846.6 ha arable land and 230.4 ha grassland based on InVeKoS.
2.2.3 Crop Selection and Spatial Distribution
To estimate potential flood damage on agricultural land as accurately as possible, knowledge
of the crop species cultivated on the individual field plots is necessary. These data are part of
the InVeKoS data set, but they are not available for the public due to data protection regula-
tions. Since the exact spatial location was not available, the statistical share of the crop spe-
cies cultivated was used instead. In previous tests, the identification of crop species shares
was based on a statistical report on land use and harvest of the Statistical State Office of the
Free State of Saxony for the district Meißen (Höhnel, 2016). For this study, we received
detailed annual statistics based on InVeKoS for the designated flood areas (100-year recur-
rence interval) of the district Meißen from 2010 until 2017 (LfULG, 2019b). The data set
was reclassified according to the damage functions available for specific crop types. For the
further calculations, the mean values of the crop shares of the years 2010-2017 were used
(Table 1). The InVeKoS crop types ‘other areas (including permanent crops)’ (7.87 %),
‘other root crops’ (0.01 %), ‘other oleaginous fruits’ (1.32 %), ‘other cereals’ (0.37 %) as
well as ‘horticulture’ (1.16 %) could not be considered due to missing damage functions. The
location of crops was distributed manually on a random basis without consideration of crop-
specific site requirements by attributing the InVeKoS field plots according to the crop type’s
shares as shown in Figure 4.
2.2.4 Flood Damage Compensation Amounts and Damage Functions
After the Elbe flood in June 2013, compensation amounts were paid to farmers in Saxony
and Saxony-Anhalt as flood aid. These crop-specific compensation amounts for the total loss
were the basis for the flood damage calculations in Höhnel (2016). For this study, current
damage amounts (as of January 2019) for Saxony were used (LfULG, 2019a; see Table 1).
Fig. 4:
Location of crop types in the designated flood area (100-year recurrence interval)

AGIT – Journal für Angewandte Geoinformatik ꞏ 6-2020
These were determined based on planning and evaluation database for Saxony's agriculture,
which can be accessed online (LfULG, 2020) and are used, among others, for the calculation
of compensation payments, indemnities and allowances. A 5-year average of agricultural
market performance in Saxony is used as a basis. Saved costs or consequential costs resulting
from the time when the flood occurred in the course of the year were not taken into account
(LfULG, 2019a). For most crop types, there are additional damage amounts for organic farm-
ing available, which are usually significantly higher than for conventional farming. They
were not taken into account, however, as there is no location of organically farmed areas
available (6 % share according to LfULG, 2019b).
Subsequently, water depth damage functions for a one-to-three-day flooding were derived
for the individual crop types based on the following principles:
Compensation amounts for 16 crop types according to LfULG (2019a),
Growth rates of crop types during the year (Gutjahr, 2009; AHDB, 2018a; AHDB, 2018b;
DPI, 2011; Pioneer, 2016),
Vulnerability of crop types to flooding as a function of water depth depending on the
development stage of the plant (LfL, 2005),
Variation of the amount of damage in the course of the year depending on the work al-
ready carried out and the related costs (LfL, 2005; Erftverband, 2009; Dutta et al., 2003).
Table 1:
Share of crop types in the designated flood area (100-year recurrence interval) in
the district Meißen and shares of the assigned crop types in the study area (
is considered separately and is not included in the share of agricultural land)
Crop Type
Average share
of agricultural
land (target)
Share in this
study/field plots
Damage compen-
sation amount
(LfULG, 2019a)
Broad beans
Peas 0.38 0.37 -0.01 683
Field Grass
Oats 0.77 0.74 -0.03 770
Potatoes 0.34 0.32 -0.02 6.160
Grain maize
Legumes 2.31 2.39 0.08 1.445
Rye 6.45 6.44 -0.01 765
Silage maize
Spring Barley
Triticale 6.69 6.65 -0.04 840
Wheat 26.67 26.67 0.00 1.222
Winter Barley
Winter oilseed rape
Sugar Beet
Grassland 30.84 30.84 0.00 1.175

M. Neubert et al.: GIS-based Estimation of Flood Damage to Arable Crops
The following additional assumptions were made (cf. Höhnel 2016):
Compensation amounts represent the maximum total loss occurring during the year.
The maximum losses are decreased, provided that the crop is not yet about to be har-
vested at the time of flooding, thus saving labour and costs for e. g. harvesting. Accord-
ing to LfL (2005), this results in monthly percentage damage amounts. Costs for clearing
and preparing the arable land or a possible new sowing are not taken into account.
If the water depth is lower than the growth height of the crop at the time of flooding, a
linear reduction of the damage is assumed for simplicity. The full monthly percentage
damage amount therefore only applies if the plant is completely flooded.
Grassland, field grass and legumes are treated differently. For these crops with multiple
harvests per year, a total loss is assumed even at a water depth of 1 dm. However, the
amounts of damage vary over the year, as the respective harvests vary in yield.
Damage resulting from impacts like flow velocity, contamination or accumulation/
erosion are excluded in this study.
Figure 5 shows the depth-damage functions for the 16 crops considered exemplified for the
month of June. To further simplify the calculation and since no exact spatial distribution of
the crops is available, a combined damage function was determined, which is composed ac-
cording to the area shares of all crop types including grassland (see Table 1).
Fig. 5:
Depth-damage functions for the 16 crop types considered and combined average
weighted by crop area exemplified for the month of June

AGIT – Journal für Angewandte Geoinformatik ꞏ 6-2020
Flood Damage Calculation
The damage calculation itself is a spatial combination of the aforementioned data sets and is
based on the previous flood damage model HOWAD (Neubert et al., 2016). It can be imple-
mented by GIS procedures, by database queries or by spreadsheet calculations. For this study,
an Excel spreadsheet was created that allows calculating the flood damage to agricultural
land. The only required input is an attribute table, e. g. exported from ArcGIS, of a shape file
containing the arable and grassland areas including following information: Area [ha], crop
type and water depth [dm]. This can be obtained from a combination of the crop type data
with the water depth data (polygons). The damage functions of the individual crop types are
provided as spreadsheet. From the corresponding spreadsheets, the monthly damage values
in percent and the corresponding damage amounts in EUR/ha are calculated according to the
water depth using formulas. The result can be easily combined with the original data set after
calculation. The size of the investigation area as well as the level of detail of the water depth
data (number of single polygons) influences the calculation time, but is only limited by the
software-specific row limit when using Excel.
Results and Discussion
The calculated total damage for the study area considering a 100-year flood event and the
results are summarised in Figure 6. The damage sums are available separately for the respec-
tive months. For better comparability, however, they are shown as a line chart that connects
the damage sums of the individual months representing a damage curve in the course of the
year. At the same time, it is easily possible to re-import the result into GIS by re-joining the
calculation spreadsheet to the original attribute table. This way, the result can be displayed
as a map as exemplified in Figure 7.
Based on the damage values of LfULG (2019a), a potential maximum total damage of about
EUR 1.3 million in case of a 100-year flood was calculated for the study area. This calcula-
tion does not consider growth periods of the plants and assumes a maximum flooding. Com-
pared to the use of damage functions, this leads to an overestimation of the damage (cf.
Förster et al., 2008).
The comparison of combined and crop-specific damage functions also revealed different total
loss amounts. The combined damage function leads to a significantly higher total damage
than the use of the crop-specific damage functions. For the month of June, the total damage
sum of EUR 880,000 for the combined damage function is 39 % higher than the total damage
sum of EUR 633,000 for the use of the crop-specific damage function. For the month of
September, the difference between the calculation results for the total damage is even more
pronounced at a plus of 156 %.The considerably higher damage values when using the com-
bined damage functions are caused by the inclusion of the damage functions for grassland,
field grass and legumes. These three crops with a large share are damaged to a maximum
extent already at a water depth of 1 dm and thus lead to an overestimation of the damage. In
this form, the combination has not proven to be meaningful and it indicates a need of further
refinement. A division of the combined damage function into at least two sub-functions
seems to be a reasonable solution. In principle, however, the use of combined damage func-
tions can be considered useful, as the calculation effort can be reduced (especially when an-

M. Neubert et al.: GIS-based Estimation of Flood Damage to Arable Crops
alysing large areas) and the exact spatial distribution of the cultivated areas (which changes
annually anyway) becomes irrelevant.
Fig. 6:
Overview of total losses for all calculated options during the year
Fig. 7:
Example of a flood damage map for the month of June using crop-specific damage
functions (overall damage 634 kEUR); underlying field plots have been generalised
using a reduced height resolution of 0.5 m (14,500 polygons)
In the course of the year, the calculated total losses show clear fluctuations. Using crop-
specific loss functions, the calculated loss is lowest in August at EUR 386,000 and September

AGIT – Journal für Angewandte Geoinformatik ꞏ 6-2020
at EUR 388,000. The highest losses occur in April with EUR 824,000 and in October with
EUR 794,000. Calculations based on the combined damage function also show a similar
monthly fluctuation of the loss amounts. The reasons for this can be found in the damage
functions: After the main harvest period (August, September) the damage is minor; after the
sowing periods (April, October) the young plants are particularly vulnerable.
Conclusions and Outlook
The results show that a detailed calculation of flood damage to agricultural land is possible.
However, there is still potential to improve the results:
Use of specific up-to-date data indicating the distribution of crop types (possibly rec-
orded by mapping, as not available due to data protection regulations from InVeKoS),
possibly also the organically farmed areas;
Use of up-to-date, regionally determined damage values (alternatively simplified: mar-
ket prices, cf. Förster et al., 2008, Klaus et al., 2016, Nguyen et al., 2017) since regional
climate or soil conditions influence the yield;
Optimisation of the combined damage function used (see section 3);
Reduction of the computational effort by generalising the water depths (first tests with
reduced height resolution yielded only slightly different results; also the mere use of an
average water depth for each arable parcel may be considered, as it can be assumed that
farmers make decisions regarding the harvest of a crop for the whole parcel).
The developed method allows the transfer of the procedure to another study area with little
effort if data availability is similar. In principle, a rough estimate of the potential total damage
for each agricultural used flood area in Saxony can be made without changing the damage
functions. For this purpose, only spatial data on the delimitation of the agricultural field plots
as well as on the water depth are necessary. For other regions, an adjustment of the damage
values and functions is necessary, but is easily possible due to the spreadsheet format chosen.
For transferability, it is recommended to use regional data considering specific yields and
prices. Several German federal states are offering similar information. For other countries
this information need to be gathered from respective sources.
The application enables the estimation of flood damage in the case of a specific event, e. g.
in order to be able to predict compensation payments. This procedure is also possible for the
use of flood polders. In addition, potential damage can be determined for dike relocations.
The current experimental calculations will be further developed and improved. A possible
future development would be the implementation of the method in a spatial decision support
system (SDSS).
This study was widely conducted as bachelor thesis and student research project of Julia
Höhnel from TU Dresden supported by the IOER authors. The authors thank the colleagues
of the Saxon State Office for Environment, Agriculture and Geology (LfULG) for supporting
the analysis, providing statistical data on crop shares as well as updated crop-specific damage

M. Neubert et al.: GIS-based Estimation of Flood Damage to Arable Crops
compensation amounts for flooding. Parts of the analyses have been supported by funding
from the European Union (European Regional Development Fund, Interreg VA, Ahoj soused.
Hallo Nachbar. 2014-2020) in the course of the project Saxon-Czech Flood Risk Manage-
ment II (STRIMA-II).
AHDB – Agriculture and Horticulture Development Board (2018a).
Barley growth guide
Retrieved Feb 1, 2020, from https://ahdb.org.uk/barleygg.
AHDB – Agriculture and Horticulture Development Board (2018b).
Wheat growth guide
Retrieved Feb 1, 2020, from https://ahdb.org.uk/wheatgg.
Brémond, P., Grelot, F., & Agenais, A.-L. (2013). Economic evaluation of flood damage to
agriculture. Review and analysis of existing methods.
Nat. Hazards and Earth System
, 2493–2512. Retrieved Feb 1, 2020, from doi:10.5194/nhess-13-2493-2013.
Citeau, J.-M. (2003). A New Control Concept in the Oise Catchment Area: Definition and
Assessment of Flood Compatible Agricultural Activities.
Proceedings FIG Working Week
April 13-17, 2003, Paris, France. Retrieved Feb 1, 2020, from
DPI – Department of Primary Industries of New South Wales (2011).
Canola growth &
. Retrieved Feb 1, 2020, from
Dutta, D., Srikantha, H., & Katumi, M. (2003). A mathematical model for flood loss estima-
Journal of Hydrology, 277
, 24–49.
Erftverband (Ed.) (2009).
Leitfaden zur Erarbeitung eines integrativen Bewirtschaftungskon-
zepts für Trockenbecken und Polder unter besonderer Berücksichtigung der stofflichen
Förster, S., Kuhlmann, B., Lindenschmidt, K.-E., & Bronstert, A. (2008). Assessing flood
risk for a rural detention area.
Natural Hazards and Earth System Sciences, 8
, 311–322.
Retrieved Feb 1, 2020, from doi: 10.5194/nhess-8-311-2008.
Ganji, Z., Shokoohi, A., & Samani, J. (2012). Developing an agricultural flood loss estima-
tion function (case study: Rice).
Natural Hazards, 64
, 405–419.
Retrieved Feb 1, 2020,
from doi:10.1007/s11069-012-0250-1.
Gutjahr, A. (2009).
Feldfrüchte. Ein Führer durch die heimischen Ackerpflanzen.
Hennegriff, W., Kolokotronis, V., Weber, H., & Bartels, H. (2006). Klimawandel und Hoch-
wasser. Erkenntnisse und Anpassungsstrategien beim Hochwasserschutz.
KA – Abwas-
ser, Abfall, 53
(8), 770–779.
Höhnel, J. (2016).
GIS-gestützte Bestimmung von Hochwasserbetroffenheit und -schäden
landwirtschaftlicher Nutzflächen
(unpublished bachelor’s thesis). TU Dresden.
Klaus, S., Kreibich, H., Merz, B., Kuhlmann, B., & Schröter, K. (2016). Large-scale, seasonal
flood risk analysis for agricultural crops in Germany.
Environmental Earth Sciences 75
1289. Retrieved Feb 1, 2020, from doi: 10.1007/s12665-016-6096-1.
LfL – Sächsischen Landesanstalt für Landwirtschaft (Ed.) (2005). Veränderte Landnut-
zungssysteme in hochwassergefährdeten Gebieten.
Schriftenreihe der Sächsischen Lan-

AGIT – Journal für Angewandte Geoinformatik ꞏ 6-2020
desanstalt für Landwirtschaft Heft 12
, Dresden. Retrieved Feb 1, 2020, from
LfULG – Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie (2019a).
denpauschalen für Fruchtarten/Kulturen in Sachsen bei Totalverlust des Aufwuchses
Planungs- und Bewertungsdaten Sachsen, Stand Januar 2019 (unpublished).
LfULG – Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie (2019b).
STRIMA II – Feststellung Fruchtartenanteile in sächsischen Überschwemmungsgebieten
Agrarförderung 2010–2017. Stand Januar 2019 (unpublished).
LfULG – Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie (2020).
bank Planungs- und Bewertungsdaten.
Retrieved Feb 1, 2020, from
Merz, B., Kreibich, H., Schwarze, R., & Thieken, A. (2010). Assessment of economic flood
Natural Hazards and Earth System Sciences, 10
, 1697–1724. Retrieved Feb 1,
2020, from doi: 10.5194/nhess-10-1697-2010.
Neubert, M. (2015). Raum-zeitlich hochauflösende Simulation von Hochwasserschäden mit
HOWAD. In: Schanze, J., Schwarze, R., Horlacher, H.-B., & Deilmann, C. (Eds.),
änderung und Management der Risiken extremer Hochwasserereignisse in großen Fluss-
gebieten – am Beispiel der Elbe
(pp. 245–253). Stuttgart: Schweizerbart.
Neubert, M., Naumann, T., Hennersdorf, J., & Nikolowski, J. (2016): The Geographic Infor-
mation System-based flood damage simulation model HOWAD.
Journal of Flood Risk
Management 1
(9), 36–49. Retrieved Feb 1, 2020, from doi: 10.1111/jfr3.12109.
Nguyen, N., Ichikawa, Y., & Ishidaira, H. (2017). Establishing flood damage functions for
agricultural crops using estimated inundation depth and flood disaster statistics in data-
scarce regions.
Hydrological Research Letters, 11
(1), 12–18. Retrieved Feb 1, 2020, from
doi: 10.3178/hrl.11.12.
PIONEER – Pioneer Hi-Bred Australia (2016).
Growth Potential. Corn Growers' Workshop.
Retrieved Apr 28, 2019, from
tralia_Intl/Publications/Corn_ Workshop_Book.pdf.
Schanze, J., Schwarze, R., Horlacher, H.-B., & Deilmann, C. (2015). Methodischer Gesamt-
ansatz – Konzept, Methoden und Modelle. In: Schanze, J. et al. (Eds.),
Veränderung und
Management der Risiken extremer Hochwasserereignisse in großen Flussgebieten – am
Beispiel der Elbe
(pp. 29–36). Stuttgart: Schweizerbart.
Schinke, R., Neubert, M., Hennersdorf, J., Stodolny, U., Sommer, T., & Naumann, T. (2012).
Damage estimation of subterranean building constructions due to groundwater inundation
– the GIS-based model approach GRUWAD.
Nat. Hazards and Earth System Sciences,
, 2865-2877. Retrieved Feb 1, 2020, from doi: 10.5194/nhess-12-2865–2012.
Siemer, B., & Nowak, E. (2014).
Monitoring der Landwirtschaftsfläche – Methodik und
Ergebnisse von InVeKoS-Auswertungen für Sachsen
. 6. Dresdner Flächennutzungssym-
posium, Dresden, 11./12. Juni 2014. Retrieved Feb 1, 2020, from
SMUL – Sächsisches Staatsministerium für Umwelt und Landwirtschaft (2018).
Online GIS.
Retrieved Apr 28, 2019, from
Win, S., Zin, W. W., Kawasaki, A., & Tin San, Z.M.L. (2018). Establishment of flood dam-
age function models: A case study in the Bago River Basin, Myanmar.
International Jour-
nal of Disaster Risk Reduction, 28
, 688–700. Retrieved Feb 1, 2020, from