**3. The U.S. Great Plains dust bowl of the 1930s**

Before examining the Dust Bowl, it is worth noting that on a global scale dust storms of major proportions have been documented at least for the last 200 years [14]. Furthermore, paleoclimate research has shed light on droughts and dust storms in the last several millennia [24]. **Table 2** gives eight of the more recent major dust storms [15]. Such storms in semi-arid and arid regions of the world have occurred throughout human history and long before, as the sandstone formations discussed in the first section of this paper attest. Of these eight dust storms, one took place in the Middle East, one in China, two in Australia, and four in the U.S. Immediately below the table is a brief damage assessment for each storm.

*A. Black Sunday (Dust Bowl):* 300 million tons of topsoil were lost.

*B. Great Bakersfield dust storm:* Swamp coolers were blown off the roofs of buildings. Windows were shattered. Trees, fences, and swamp coolers had blown down throughout the region. Below-grade freeways, canals, and creeks were buried in sand and dust. The storm resulted in five deaths and \$40 million in damage. Over 25 million cubic feet of topsoil from grazing land alone was moved.

*C. Melbourne dust storm:* The winds brought down power lines and clogged electrical junction boxes with dust, causing them to short-circuit. Railroads could not function.

*D. Interstate-5 dust storm:* This date, the Friday after Thanksgiving, had heavier traffic than usual on Interstate-5. The dust storm caused a series of chain reaction accidents, which mainly occurred in five groups spread across 1.5 miles (2.4 km) of highway; while one 20-car pile-up occurred in the northbound lanes, the remainder of the crashes were in the southbound lanes. In total, 104 vehicles were involved in the accident, including 93 cars and 11 semi-trailer trucks. 17 people died in the accidents, and an additional 150 people were injured.

*E. Australian dust storm:* Vehicular and air transportation were disrupted. Ambulance services received around 140 calls from people having breathing

#### **Table 2.**

*Recent major dust storms: "dam." is for damages from the storms; see the letter keys below. Individual references are not shown here; merely typing the storm's name and date into a search engine brings up the information.*

#### *Bowing Sand, Dust, and Dunes, Then and Now–A North American Perspective DOI: http://dx.doi.org/10.5772/intechopen.98337*

difficulties: more than 50 calls were made from Sydney, 50 from the state's west, 23 from the north and 12 from southern regions.

*F. China dust storms:* In the spring of 2010 many provinces of China were suffering from a severe drought that saw some 51 million citizens enduring water shortages. The series of dust storms that then ensued was in part a consequence of the desertification of extensive regions of the country. The annual direct economic losses attributable to desertification are estimated at US\$ 7.7 billion. It is believed that the indirect economic losses arising from desertification amount to 43.5 billion US\$ per year.

*G. Arizona dust storm of 5 July 2011:* Severe disruption of vehicular and air transportation, although no deaths or injuries were reported.

*H. Tehran dust storm*: 5 men were killed, more than 30 people were injured, and a few cars were destroyed. Falling trees and objects in balconies were destroyed. 65 electric lines of 20 KW were damaged and disconnected.

With these major dust storms enumerated, this review paper now proceeds to examine the Dust Bowl of the U.S. Great Plains, which started in 1930 and lasted for a decade. Severe drought hit the Midwest and southern Great Plains in 1930. Massive dust storms began in 1931. A series of drought years followed, further exacerbating the environmental disaster. By 1934, an estimated 35 million acres of formerly cultivated land had been rendered useless for farming, while another 125 million acres—an area roughly three-quarters the size of Texas—was rapidly losing its topsoil. Regular rainfall returned to the region by the end of 1939, bringing the Dust Bowl years to a close [15].

One of the affected states was Kansas, where in the 1940s an historian at the University of Kansas produced definitive studies of dust storms in the latter half of the nineteenth century [16–18]. Because the following discussion is limited to the Dust Bowl, these works have not been used in this paper, but they are included in the references for the interested reader. Instead, more contemporary research is relied on, and there is no shortage of such scholarship.

For instance, a team of Canadian researchers has assembled a comprehensive historical/scientific review of the Dust Bowl [19]. They present the geographical setting and the severity of the dustiest areas in their **Figure 1**, shown below as **Figure 4**. Although published seven years ago, their paper has perhaps the best and most comprehensive descriptions of the entire Dust Bowl saga, including its natural causes, its anthropogenic causes, and its disastrous consequences of soil erosion, of economic losses, and of forced mass migrations.

The underlying natural causes of the Dust Bowl have been succinctly described [19]:

Through data analysis and modeling, the authors state, that the causal mechanism for Dust Bowl era droughts on the Great Plains has been linked to ocean temperature anomalies. It appears that Pacific sea surface temperatures, especially as expressed by cold tropical temperatures during the La Niña phase of the El Niño Southern Oscillation, have the most direct influence.

The above-summarized work, a magnum opus, with copious geographical, historical, climatic, and economic analyses, is most suitable for the lay person. In sum, the authors cover much ground in a diversity of disciplines in a thorough, straightforward, and comprehensive manner.

The next summary, [24], though more localized, is of comparable analytic detail. Focusing on the region of northeastern Kansas and northwestern Missouri, a research team assembled rainfall records from 20 different cities and towns for the years 1850–2008 (**Figure 5**). They adjusted the records for 1850–1924 to account for negative biases in daily precipitation totals of less than 0.5 inches, which resulted in an overall increase of two percent above the historical records. In any case, the authors put the Dust Bowl into a broader historical perspective and conclude that

#### **Figure 4.**

*The Great Plains and the dust bowl proper: Note that the most severely affected area was limited to NE New Mexico, N Texas, W. Oklahoma, SW Kansas, and SE Colorado; Figure 1 of [19].*

the drought of 1855–1864 may have been the most severe and sustained *spring* moisture deficit over the Kansas-Missouri study area; and that the drought of the Dust Bowl era was by far the most severe and sustained *summer* precipitation deficit over the area. Nonetheless, when the precipitation data are summarized by growing season, the Dust Bowl drought was not remarkably more severe than the droughts of the 1860s, 1910s, and 1950s.

Perhaps the most seminal contribution of the above-summarized work is how the authors put the 1930s Dust Bowl into a much longer historical context. This context is lengthened considerably by the article summarized next.

Another research team [25] gives a much longer view of moisture/drought in the Dust Bowl area with the Palmer Drought Severity Index (PDSI). This index approximates soil moisture relative to 'normal' conditions, using meteorological data and assumptions about soil properties. 'Drought' is here defined as starting *Bowing Sand, Dust, and Dunes, Then and Now–A North American Perspective DOI: http://dx.doi.org/10.5772/intechopen.98337*

#### **Figure 5.**

*Monthly precipitation from 20 Kansas and Missouri meteorological sites: Figure 2 of [24], augmented by marker lines for 1860 and 1935; AMJ, April, May, and June; JA, July, August; AMJJA, April – August. Note the difference in vertical scales: The top two go from zero to 600 mm, the bottom, from zero to 1200 mm.*

the first month when the PDSI is less than −1 for three or more months and ending the month before the PDSI is greater than −1 for two or more months." **Figure 6** presents this drought index for 1,000 years in southeastern Colorado.

This prehistorical to historical reconstruction shows that this drought index sank below −3 about 12 times, with the worst (−4) and longest duration in 1470, compared with the 1935 Dust Bowl value of −2.8. This suggests two points: (1) that severe droughts occur roughly every 80 years, and (2) that the drought conditions of the Dust Bowl were severe but that other droughts have been worse. The strongest point in this research, which relies on tree ring data, is its temporal expansion from years and a century and a half to a complete millennium. In the works summarized so far, then, we go from the 1930s, to 1850–2008, and to 1000–2000 – the short, medium, and long-term views.

Yet another research approach to better understand the Dust Bowl examines changes in the land surface [26]. The authors state that "the drastic land-cover

**Figure 6.**

*Five-year moving average of palmer drought severity index values for … southeastern Colorado … for 1000–2000 AD. The values [come] from tree ring records. (Figure 17 of [25]).*

change from pre-settlement to the 1930s in the Great Plains resulted in a strong increase in the surface albedo. ("Surface albedo" quantifies the fraction of the sunlight reflected by the Earth's surface.) On average, the albedo changes from ~0.16 in the native grassland to ~0.20 in dryland cropland, and such a change can considerably alter the surface energy budget. In [their] simulations, changes in surface albedo from pre-settlement to the 1930s land-cover resulted in a 5 Wm−2 reduction in solar energy absorbed at the surface (averaged over the Great Plains from May to July)." (Incoming solar radiation is often expressed as energy (Watts) per square meter (m−2.) They extend this argument by explaining how these energy budget changes contributed to the 1930s drought. **Figure 7** depicts how surface albedo has changed from the 1930s to the present day.

Although the work just summarized may seem somewhat unrelated to dust emissions, it does analyze dust potential through changes in the surface land cover. Arguably, surface land cover dictates the potential for dust suspension under any given set of intense meteorological conditions. This paper would be accessible to most general readers.

In contrast to the above regional analysis, another researcher investigated the relation between meteorology and dust emissions [27] on a micro-scale for a north Texas dust storm that occurred in 1937. **Figure 8** displays the micro-geographic extent of dust emissions for a 4 km2 sand dune area in Texas.

The authors conclude that:

1. Lower-level atmospheric and surface air temperatures are the strongest drivers of Dust Bowl dust events, followed by low relative humidity. Anomalies in this thermal gradient and moisture carried by the Great Plains Low Level Jet occurred on dust event days that were not present on days without dust events within the same season.

*Bowing Sand, Dust, and Dunes, Then and Now–A North American Perspective DOI: http://dx.doi.org/10.5772/intechopen.98337*

**Figure 7.**

*1930s surface albedo minus present-day surface albedo. The tiny dots are water bodies that have distinctively different albedo from the land; Figure 3a of [26]. The differences between the two albedos are lowest in the Rocky Mountains and highest in W. Texas near El Paso.*

This paper is of moderate technical difficulty but would be understandable to most general readers. Admittedly, the authors partake of a somewhat oblique

#### **Figure 8.**

*Potential dust emissions from the Dalhart sand dune area in Dallam County, TX for an event on April 7, 1937. (a) Aerial photograph of the case study area captured on October 5, 1936. (b) Contemporaneous soil texture map produced by the soil conservation service. (c) Available bare surface area in photograph to emit dust by soil texture, the percent silt in the mapped soil unit, and the derived PM10 flux rate. (Figure 15 from [27]).*

#### *Bowing Sand, Dust, and Dunes, Then and Now–A North American Perspective DOI: http://dx.doi.org/10.5772/intechopen.98337*

approach to dust emissions in their concentration on landscape characteristics; but, after all, landscape cover must be considered an essential, if not paramount, ingredient in the severity and frequency of dust storms.

From these more physical-science oriented summaries, this review paper now delves into the human misery of the Dust Bowl -- a tragedy of sad and immense proportions. This section relies on remarks by an Oklahoman physician, as chronicled in [10], p. 173. "In a report delivered to the Southern Medical Association [in April 1935], Dr. John H. Blue of Guymon, Oklahoma, said he treated fifty-six patients for dust pneumonia and all of them showed signs of silicosis; others were suffering early signs of tuberculosis. The doctor had looked inside an otherwise healthy farm hand in his early twenties and told him, "You are filled with dirt". The young man died the next day. The doctor then discusses silicosis, stating that prairie dust has a high silica content, and comparing the respiratory distress of Dust Bowl citizens to that of underground miners. He points out one important difference: silicosis in miners takes many years to build up, whereas doctors in the Dust Bowl were seeing a condition like silicosis after just three years of storms. For those residents who stayed the course, the human toll must have been devastating. For many of those who migrated, as depicted in John Steinbeck's *The Grapes of Wrath*, the outcome was also less than rosy. This reference [10], a clear historical, journalistic account of the Dust Bowl, would be accessible to all readers.

After the sections of this paper on present-day dune fields and the 1930s Dust Bowl, the concluding section first explains the basics of dust storms as described in two lengthy reports. Included in this discussion is a summary of how and why these storms are formed and how the public is alerted to them. Second, from two technical articles, it explains how scientific communities grapple with the difficulties of numerically simulating dust storms, work which might ultimately lead to a better predictive capacity.


Besides already mentioned issues of multi-biometric recognition (see Chapter 2.3) and template protection technologies (see Chapter 3.3), which may be solved through multi-biometric template protection, several further issues might occur which have to be dealt with. From designing a generic framework for multi-biometric template protection at a coarse level different evolving issues will be discussed in detail.

## **5.1. Generic framework for multi-biometric template protection**

The major goal of research in the area of multi-biometric template protection is to generate a *generic framework* of constructing multi-biometric template protection schemes, i.e. a code of practice according to various aspects for incorporating different biometric templates in one or more template protection system(s), yielding multi-biometric template protection. From existing research it appears that biometric fusion on feature level is most suitable for template protection schemes [6, 40–42]. While preliminary scores are not available within the vast majority of biometric cryptosystems, cancelable biometric systems based on score level fusion can be constructed analogue to conventional biometric systems. For both technologies biometric fusion based on decision level can easily be implemented combining final decisions. Figure 5 shows a schematic impression of how such a framework (based on feature level fusion) could look like.

In order to provide generic multi-biometric template protection the system should be capable of incorporating *n* different biometric templates, which need not exhibit a common feature representation, i.e. *k* different representation may be involved. In a fusion module a common representation of feature vectors is established and feature vectors are combined in a sensible manner. Subsequently, an adequate template protection scheme is applied to protect the multi-biometric template. Focusing on a generic fusion of multiple biometric templates in a template protection system several issues evolve.

certain assumptions [53]). The fact that false rejection rates are lower bounded by error correction capacities [54] emerges a great challenge since unbounded use of error correction (if applicable) makes the system even more vulnerable [55]. Other characteristics such as voice or keystroke dynamics (especially behavioral characteristics) were found to reveal only a small amount of stable information [26], but can still be applied to improve the security of an existing secret. While for some characteristics extracting of a sufficient amount of reliable features seems to be feasible it still remains questionable if these features exhibit enough entropy. In case extracted features do not meet requirements of discriminativity, systems become vulnerable to several attacks (e.g. false acceptance attacks). In addition, stability of biometric features is required to limit information leakage of stored helper data as well as a sufficient secret length. Focusing on multi-biometric template protection schemes which perform biometric fusion at feature level a single sensor fusion scenario could be applied in order to overcome the issue of alignment. Any combination of biometric feature vectors extracted from a single biometric signal alleviates the construction of a multi-biometric template protection scheme, in case these feature extractors apply the same mode of operation when analyzing biometric data. For instance, if two different iris biometric feature extractors extract binary iris-codes from pre-processed textures and operate on same block sizes extracting the same number of bits per block, a single optimal alignment for both

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183

Due to the sensitivity of template protection schemes multiple enrollment samples are required and thus, compared to conventional biometric systems, more user-cooperation (compared to conventional biometric systems) is demanded in order to decrease intra-class variation, while sensoring and preprocessing require improvement as well. Furthermore, from the usability side of view it has to be analyzed which combinations of biometric modalities are applicable (e.g. iris and face or fingerprint and hand geometry) [2]. In order to keep the multi-biometric system usable only those modalities should be combined that allow acquisition of multiple samples with "one" single capture device (e.g. capturing two iris images and one face image with multiple cameras that are integrated in one capture device). Only then the capture time and consequently the transaction time will remain constant.

While different template protection systems incorporating multiple biometric traits have been proposed, these turn out to be custom-built according to applied biometric feature representations and applied template protection schemes. Multi-biometric template protection schemes in literature have been proposed for numerous types of template protection requiring different feature representations (a detailed overview can be found in [3]). While some techniques have been applied to distinct template protection systems (e.g. fuzzy commitment scheme or fuzzy vault scheme) a detailed analysis of pros and cons of these schemes regarding the application of multi-biometrics has remained elusive. Such investigation involves factors such as scalability, i.e. the vast majority of template protection schemes require fixed-length feature vectors and are only scalable in discrete iterations (e.g. by adding a distinct number of chunks of error correction codewords). Biometric template protection schemes are designed for a distinct representation of feature vectors, e.g. fuzzy commitment schemes require binary biometric templates as input while fuzzy vault schemes require real-valued biometric templates. A fusion of binary iris-codes and

extracted feature vectors exists.

**5.4. Feature representation**

**Figure 6.** Template alignment within a multi-biometric template protection scheme.

## **5.2. Template alignment**

Focusing on distinct biometric characteristics, e.g. iris, alignment within a template protection scheme can still be feasible. For instance, within an iris biometric fuzzy commitment scheme template alignment can be achieved by applying decommitments at various shifting positions. Within conventional biometric systems align-invariant approaches have been proposed for several biometric characteristics. So far, hardly any suggestions have been made to construct align-invariant biometric cryptosystems or cancelable biometrics. Still, focusing on technologies of biometric template protection, feature alignment significantly effects recognition performance. Biometric templates are obscured within template protection systems, i.e. alignment of protected templates is highly non-trivial [52]. Feature level fusion of biometric templates potentially aggravates a proper alignment of protected templates (optimal alignments vary for incorporated templates), while auxiliary data for the use of alignment may leak information on stored templates. More precisely, a combined feature vector may consist of *t* chunks of feature elements generated by *t* diverse feature extractors. In order to establish a proper alignment of the entire feature vector, chunks of feature elements need to be aligned individually. In general a common optimal alignment which is valid for all chunks of feature elements is rather unlikely. Hence, additional helper data is required which at least has to mark start and end points of such chunks. Figure 6 provides an schematic illustration of this issue.

As previously mentioned an adaption of biometric feature vectors to template protection schemes is considered inevitable in order to achieve practical recognition rates. However, generally a rearrangement of features within biometric templates makes conventional template alignment infeasible. Again, in order to align protected templates properly, additional helper data (e.g. reverse permutations) need to be stored (cf. [22, 23]), in a global or subject-specific manner. The additional storage of helper data is essential but will cause information leakage, i.e. potential impostors may utilize the additional helper data in order to compromise or cross-match protected templates, in case of subject-specific helper data.

## **5.3. Combination of modalities**

In fact it has been shown that distinct biometric modalities (e.g. fingerprint or iris) exhibit enough reliable information to bind or extract sufficiently long keys providing acceptable trade-offs between accuracy and security, where the best performing schemes are based on fuzzy commitment and fuzzy vault. However, practical error correction codes are designed for communication and data storage purposes such that a perfect error correction code for a desired code length has remained evasive (optimal codes exist only theoretically under certain assumptions [53]). The fact that false rejection rates are lower bounded by error correction capacities [54] emerges a great challenge since unbounded use of error correction (if applicable) makes the system even more vulnerable [55]. Other characteristics such as voice or keystroke dynamics (especially behavioral characteristics) were found to reveal only a small amount of stable information [26], but can still be applied to improve the security of an existing secret. While for some characteristics extracting of a sufficient amount of reliable features seems to be feasible it still remains questionable if these features exhibit enough entropy. In case extracted features do not meet requirements of discriminativity, systems become vulnerable to several attacks (e.g. false acceptance attacks). In addition, stability of biometric features is required to limit information leakage of stored helper data as well as a sufficient secret length. Focusing on multi-biometric template protection schemes which perform biometric fusion at feature level a single sensor fusion scenario could be applied in order to overcome the issue of alignment. Any combination of biometric feature vectors extracted from a single biometric signal alleviates the construction of a multi-biometric template protection scheme, in case these feature extractors apply the same mode of operation when analyzing biometric data. For instance, if two different iris biometric feature extractors extract binary iris-codes from pre-processed textures and operate on same block sizes extracting the same number of bits per block, a single optimal alignment for both extracted feature vectors exists.

Due to the sensitivity of template protection schemes multiple enrollment samples are required and thus, compared to conventional biometric systems, more user-cooperation (compared to conventional biometric systems) is demanded in order to decrease intra-class variation, while sensoring and preprocessing require improvement as well. Furthermore, from the usability side of view it has to be analyzed which combinations of biometric modalities are applicable (e.g. iris and face or fingerprint and hand geometry) [2]. In order to keep the multi-biometric system usable only those modalities should be combined that allow acquisition of multiple samples with "one" single capture device (e.g. capturing two iris images and one face image with multiple cameras that are integrated in one capture device). Only then the capture time and consequently the transaction time will remain constant.

## **5.4. Feature representation**

10 New Trends and Developments in Biometrics

**5.2. Template alignment**

... Probe Feature Vector Chunk 1 Chunk 2 Chunk t

... Reference Template Chunk 1 Chunk 2 Chunk t

**Figure 6.** Template alignment within a multi-biometric template protection scheme.

provides an schematic illustration of this issue.

**5.3. Combination of modalities**

Alignment 1 Alignment 2 ... Alignment t

Focusing on distinct biometric characteristics, e.g. iris, alignment within a template protection scheme can still be feasible. For instance, within an iris biometric fuzzy commitment scheme template alignment can be achieved by applying decommitments at various shifting positions. Within conventional biometric systems align-invariant approaches have been proposed for several biometric characteristics. So far, hardly any suggestions have been made to construct align-invariant biometric cryptosystems or cancelable biometrics. Still, focusing on technologies of biometric template protection, feature alignment significantly effects recognition performance. Biometric templates are obscured within template protection systems, i.e. alignment of protected templates is highly non-trivial [52]. Feature level fusion of biometric templates potentially aggravates a proper alignment of protected templates (optimal alignments vary for incorporated templates), while auxiliary data for the use of alignment may leak information on stored templates. More precisely, a combined feature vector may consist of *t* chunks of feature elements generated by *t* diverse feature extractors. In order to establish a proper alignment of the entire feature vector, chunks of feature elements need to be aligned individually. In general a common optimal alignment which is valid for all chunks of feature elements is rather unlikely. Hence, additional helper data is required which at least has to mark start and end points of such chunks. Figure 6

As previously mentioned an adaption of biometric feature vectors to template protection schemes is considered inevitable in order to achieve practical recognition rates. However, generally a rearrangement of features within biometric templates makes conventional template alignment infeasible. Again, in order to align protected templates properly, additional helper data (e.g. reverse permutations) need to be stored (cf. [22, 23]), in a global or subject-specific manner. The additional storage of helper data is essential but will cause information leakage, i.e. potential impostors may utilize the additional helper data in order to compromise or cross-match protected templates, in case of subject-specific helper data.

In fact it has been shown that distinct biometric modalities (e.g. fingerprint or iris) exhibit enough reliable information to bind or extract sufficiently long keys providing acceptable trade-offs between accuracy and security, where the best performing schemes are based on fuzzy commitment and fuzzy vault. However, practical error correction codes are designed for communication and data storage purposes such that a perfect error correction code for a desired code length has remained evasive (optimal codes exist only theoretically under

Database

While different template protection systems incorporating multiple biometric traits have been proposed, these turn out to be custom-built according to applied biometric feature representations and applied template protection schemes. Multi-biometric template protection schemes in literature have been proposed for numerous types of template protection requiring different feature representations (a detailed overview can be found in [3]). While some techniques have been applied to distinct template protection systems (e.g. fuzzy commitment scheme or fuzzy vault scheme) a detailed analysis of pros and cons of these schemes regarding the application of multi-biometrics has remained elusive. Such investigation involves factors such as scalability, i.e. the vast majority of template protection schemes require fixed-length feature vectors and are only scalable in discrete iterations (e.g. by adding a distinct number of chunks of error correction codewords). Biometric template protection schemes are designed for a distinct representation of feature vectors, e.g. fuzzy commitment schemes require binary biometric templates as input while fuzzy vault schemes require real-valued biometric templates. A fusion of binary iris-codes and minutiae triples can still be performed without a successive application of different types of template protection schemes (e.g. in [43]). However, as this is not the case in general, embedding functions are required in order to perform mappings between different feature representations; such mappings must not cause a drastic loss of information.

In the vast majority of approaches to biometric template protection schemes provided security is put on a level with obtained recognition performance, i.e. obtained FAR at a targeted FRR. While analysis with respect to irreversibility and unlinkability is rarely done, some quantities to measure the security of template protection systems have been suggested, e.g. key entropy [56], maximum key size [57], or information leakage of stored helper data [58, 59]. These analysis need to be adapted and extended in order to establish a generic methodology of measuring the security of multi-biometric template protection systems.

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Focusing on security/ privacy of template protection schemes several magnitudes have been proposed for uni-biometric template protection schemes (e.g. [56, 58]). With respect to multi-biometric template protection schemes security measures need to be reformulated and extended since additional factors, such as a separate storage of protected templates, take influence on the security provided by the system [6]. We plan to establish a generic modus operandi of estimating the security of any multi-biometric template protection scheme in an information theoretic way. Emphasis will also be put on irreversibility and unlinkability

The presented bookchapter provides an overview of multi-biometric template protection. While both technologies, multi-biometric recognition [2] and biometric template protection [3], suffer from serious drawbacks a sensible combination of these could eliminate individual disadvantages. Different template protection systems incorporating multiple biometric traits, which have been proposed in literature, are summarized. While, at first glance, multi-biometric template protection seems to solve several drawbacks, diverse issues arise. Based on a theoretical framework for multi-biometric template protection several issues, e.g. template alignment at feature level, are elaborated and discussed in detail. While generic approaches to the construction of multi-biometric template protection schemes have remained elusive we provide several suggestions for designing multi-biometric template

This work has been supported by the Center for Advanced Security Research Darmstadt

da/sec – Biometrics and Internet Security Research Group, Hochschule Darmstadt,

analysis, which is rarely done in existing literature (e.g. in [39]).

**6. Summary**

protection systems.

**Author details**

Darmstadt, Germany

Christian Rathgeb⋆ and Christoph Busch

⋆ Address all correspondence to<EMAIL_ADDRESS>
(CASED).

**Acknowledgement**

Since different template protection schemes require distinct feature representations a generic framework for multi-biometric template protection should be able to handle diverse inputs of feature vectors. This issue can be solve in two ways:

It is expected that the first opportunity degrades discriminativity of feature vectors while the second is expected to cause security vulnerabilities of protected templates. In order to prevent impostors from attacking separately stored protected templates biometric fusion can be performed at "secret level", i.e. each applied template protection scheme contributed a chunk of bits while the final secret is constructed from calculating a hash of the concatenation of all bit chunks. Still, representation of feature vectors represents one of the most critical issues.

## **5.5. System security**

Focusing on the possible levels of fusion existing approaches to feature-level fusion in template protection systems merely involve a trivial concatenation of biometric templates (e.g. [40, 41]). It has been demonstrated, to some extent, that a more-sophisticated feature-level fusion leads to improved accuracy as well as template security [42]. However, more detailed analysis of adapting multiple biometric templates (based on different feature representations) to according template protection schemes on feature level is demanded. While approaches to cancelable biometrics provide a comparison score for each authentication attempt (offering trivial score-level fusion), within biometric cryptosystems subjects are authenticated indirectly via key validities, i.e. comparison scores are not explicitly available. For instance, in [41] comparison scores are equalized with required error correction capacities, however, more sophisticated approaches to multi-biometric cryptosystems based on score level fusion are non-existent. Biometric fusion at decision level implies the incorporation of a significant amount of biometric templates (e.g. to enable majority voting) in a template protection system. For both technologies, biometric cryptosystems and cancelable biometrics, biometric fusion on decision level can be implemented straight-forward. With respect to biometric cryptosystems a way of performing biometric fusion on secret level could be implemented by a (bit-wise) majority vote of released keys. Even tough approaches to cancelable biometric may easily be fused at decision level, recognition performance does not necessarily correlate with results reported for traditional multi-biometric systems. However, by definition, this level of fusion is restricting the system to a separate protection of multiple templates, which need to be secured individually, and can cause further security risks [6].

In the vast majority of approaches to biometric template protection schemes provided security is put on a level with obtained recognition performance, i.e. obtained FAR at a targeted FRR. While analysis with respect to irreversibility and unlinkability is rarely done, some quantities to measure the security of template protection systems have been suggested, e.g. key entropy [56], maximum key size [57], or information leakage of stored helper data [58, 59]. These analysis need to be adapted and extended in order to establish a generic methodology of measuring the security of multi-biometric template protection systems.

Focusing on security/ privacy of template protection schemes several magnitudes have been proposed for uni-biometric template protection schemes (e.g. [56, 58]). With respect to multi-biometric template protection schemes security measures need to be reformulated and extended since additional factors, such as a separate storage of protected templates, take influence on the security provided by the system [6]. We plan to establish a generic modus operandi of estimating the security of any multi-biometric template protection scheme in an information theoretic way. Emphasis will also be put on irreversibility and unlinkability analysis, which is rarely done in existing literature (e.g. in [39]).

## **6. Summary**

12 New Trends and Developments in Biometrics

minutiae triples can still be performed without a successive application of different types of template protection schemes (e.g. in [43]). However, as this is not the case in general, embedding functions are required in order to perform mappings between different feature

Since different template protection schemes require distinct feature representations a generic framework for multi-biometric template protection should be able to handle diverse inputs

1. *Unified representation*: by establishing a common representation of biometric features (e.g.

2. *Different template protection schemes*: by combining different types of template protection schemes according to the provided feature vectors (e.g. fuzzy commitment scheme and

It is expected that the first opportunity degrades discriminativity of feature vectors while the second is expected to cause security vulnerabilities of protected templates. In order to prevent impostors from attacking separately stored protected templates biometric fusion can be performed at "secret level", i.e. each applied template protection scheme contributed a chunk of bits while the final secret is constructed from calculating a hash of the concatenation of all bit chunks. Still, representation of feature vectors represents one of the most critical

Focusing on the possible levels of fusion existing approaches to feature-level fusion in template protection systems merely involve a trivial concatenation of biometric templates (e.g. [40, 41]). It has been demonstrated, to some extent, that a more-sophisticated feature-level fusion leads to improved accuracy as well as template security [42]. However, more detailed analysis of adapting multiple biometric templates (based on different feature representations) to according template protection schemes on feature level is demanded. While approaches to cancelable biometrics provide a comparison score for each authentication attempt (offering trivial score-level fusion), within biometric cryptosystems subjects are authenticated indirectly via key validities, i.e. comparison scores are not explicitly available. For instance, in [41] comparison scores are equalized with required error correction capacities, however, more sophisticated approaches to multi-biometric cryptosystems based on score level fusion are non-existent. Biometric fusion at decision level implies the incorporation of a significant amount of biometric templates (e.g. to enable majority voting) in a template protection system. For both technologies, biometric cryptosystems and cancelable biometrics, biometric fusion on decision level can be implemented straight-forward. With respect to biometric cryptosystems a way of performing biometric fusion on secret level could be implemented by a (bit-wise) majority vote of released keys. Even tough approaches to cancelable biometric may easily be fused at decision level, recognition performance does not necessarily correlate with results reported for traditional multi-biometric systems. However, by definition, this level of fusion is restricting the system to a separate protection of multiple templates, which need to be

representations; such mappings must not cause a drastic loss of information.

of feature vectors. This issue can be solve in two ways:

secured individually, and can cause further security risks [6].

by quantizing feature vectors [24]) or

fuzzy vault scheme [43]).

issues.

**5.5. System security**

The presented bookchapter provides an overview of multi-biometric template protection. While both technologies, multi-biometric recognition [2] and biometric template protection [3], suffer from serious drawbacks a sensible combination of these could eliminate individual disadvantages. Different template protection systems incorporating multiple biometric traits, which have been proposed in literature, are summarized. While, at first glance, multi-biometric template protection seems to solve several drawbacks, diverse issues arise. Based on a theoretical framework for multi-biometric template protection several issues, e.g. template alignment at feature level, are elaborated and discussed in detail. While generic approaches to the construction of multi-biometric template protection schemes have remained elusive we provide several suggestions for designing multi-biometric template protection systems.

## **Acknowledgement**

This work has been supported by the Center for Advanced Security Research Darmstadt (CASED).

## **Author details**

Christian Rathgeb⋆ and Christoph Busch

⋆ Address all correspondence to<EMAIL_ADDRESS>
da/sec – Biometrics and Internet Security Research Group, Hochschule Darmstadt, Darmstadt, Germany

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**Chapter 9**

**Generation of Cryptographic Keys from Personal**

**Biometrics: An Illustration Based on Fingerprints**

Biometric approach for authentication is appealing because of its convenience and possibili‐ ty to offer security with non-repudiation. However, additional hardware such as biometric scanners and complex software for feature extraction and biometric template matching are required if biometric approach is to provide security for protecting sensitive data such as

Cryptographic approach, on the other hand, ties data protection mathematically to the *Key* that is utilized to protect it. This allows a data owner to have complete control over one's personal information without relying on, or relinquishing control to, a third party authority. The protection of personal sensitive information is also not tied to complex software and

Biometric authentication and authorization for data protection could be thought of as ena‐ bling security based on "what one is." The lynchpin of biometric security is the use of suffi‐ ciently unique, but often imprecise, physiological or behavioral traits to characterize an individual for authentication and identification purposes. The characterization is expressed in form of some biometric signature, which often can be reduced to some feature vector or matrix representation. For example, a biometric face could be expressed in terms of a linear‐ ized vector of color distribution [1], EigenMap [2], or Eigen Face components [3]. In our re‐ search a fingerprint is expressed in terms of a 320x1 vector of integers containing minutia point information, and a voice signature is expressed in terms of a 20x1 mean vector and a 20x20 covariance matrix of Mel cepstrum characterizing the multi-variant Gaussian distribu‐ tion of an individual's voiceprint [4]. The security parameter for assessing the strength of a biometrically based approach is typically related to the size of the underlying feature vector (or matrix) and the number of bits for representing a value, as well as the biometric data dis‐

> © 2012 Sy and Kumara Krishnan; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Sy and Kumara Krishnan; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Bon K. Sy and Arun P. Kumara Krishnan

http://dx.doi.org/10.5772/51372

personal health information.

**1. Introduction**

Additional information is available at the end of the chapter

hardware systems that may need constant patches.


*4.1. Relationship between K and Coal Quality Index*

Coal quality indexes mainly include gas emission initial velocity Δ*p*, firmness coefficient *f*, volatile matter *Vdaf*, ash *Aad*, etc. They macroscopically reflect some essential characteristics related to coal and gas desorption. Among them, Δ*p* and *f* are commonly used in regional outburst risk prediction. Because Δ*p*, *f*, *Vdaf*, *Aad*, etc. are all coal quality indicators unrelated to gas pressure, the relationship between them and the values under certain gas pressure conditions can only be considered. In Table 2, the *K*1 value is calculated when the gas pressure *p* = 1 MPa. Through nonlinear fitting of the data in Tables 1 and 2, the relationship between (*K*1)*p*=1 and Δ*p*, *f*, *Vdaf*, *Aad* is obtained, as shown in Figure 1.

**Figure 1.** Relationship between (*K*1)*p*=1 and coal quality index. (**a**) Relationship between (*K*1)*p*=1 and Δ*p*. (**b**) Relationship between (*K*1)*p*=1 and *f* . (**c**) Relationship between (*K*1)*p*=1 and *Aad.* (**d**) Relationship between (*K*1)*p*=1 and *Vdaf*.

It can be seen from Figure 1a that the relationship between (*K*1)*p*=1 and Δ*p* conforms to the power function increasing relationship. With the increase in Δ*p*, the value of (*K*1)*p*=1 also increases correspondingly. The correlation coefficient *R2* is 0.812, indicating that there is a significant correlation between *K*1 and Δ*p*, and Δ*p* significantly affects the positive (*K*1)*p*=1. It can be seen from Figure 1b that (*K*1)*p*=1 is in accordance with the logarithmic function attenuation relationship. As *f* increases, the value of (*K*1)*p*=1 decreases accordingly. The correlation coefficient *R2* is 0.657, indicating that there is also a certain correlation between (*K*1)*p*=1 and *f*, and *f* affects negative (*K*1)*p*=1, but the significance is lower than Δ*p*. It can be seen from Figure 1c that the distribution of 24 coal samples represented by 24 points in the figure is relatively disordered. The relationship between (*K*1)*p*=1 and *Aad* is an approximately logarithmic function, and the correlation coefficient *R2* is only 0.129, indicating that the correlation between (*K*1)*p*=1 and *Aad* is not close. It can be seen from Figure 1d that 24 points are concentrated in the range of 13.10~18.61 of *Vdaf*, and (*K*1)*p*=1 has no correlation with *Vdaf*.

From the above analysis, it can be seen that under the same gas pressure condition, the value of *K*1 mainly depends on Δ*p*. In theory, *K*1 is the same as Δ*p*, which is an index to reflect the risk of outburst by the amount of initial gas desorption. Their fundamental difference is only the difference of adsorption gas pressure; that is, *K*1 index reflects the change of adsorption gas pressure more than Δ*p*. The firmness coefficient *f* mainly reflects the ability of coal to resist damage. In many cases, the lower the strength of a coal seam, the greater the initial gas desorption. The regression analysis shows that under the condition of certain gas pressure, *K*1 decreases with the increase in *f* in a negative exponential law. The volatile matter *Vdaf* of coal reflects the metamorphic degree of coal, and the ash *Aad* reflects the yield of effective carbon. Both of them are not closely related to the initial gas desorption amount. In short, *K1* is most affected by Δ*p* and less affected by *Vdaf* and *Aad*. Therefore, Δ*p* is the main influencing factor of *K*1, and *f*, *Vdaf* and *Aad* are secondary influencing factors.


**4. Coupling of IGB aerosols to the Himalayan region and their possible impacts**

Due to combined effects of IGB topography and the Himalayan orography, aerosols over the IGB region are lifted up quite often and found to be extended up to the Himalayan foothills and also to the other high-altitude regions [23,61,110-113]. Absorbing aerosols in the elevated regions heat the mid-troposphere by absorbing solar radiation, and produce an atmospheric dynamical feedback called as elevated heat pump (EHP) effect. Consequently, this can lead to an increase in the summer monsoon rainfall over India [63] and enhancement in the rate of snow melting in the Himalayan regions [64], which is one of the potential themes for global scientific community and need to be addressed to improve scientific understanding of the regional climate on inter-annual as well as intra-seasonal scales. In particular, the main emphasis of the IGB region coupled with the Himalayan foothills is due to the highest AOD values in this region among the South Asia regions, which are persistent throughout the winter and spring seasons [114].

In a recent study in [115], they have shown a possible influence of desert dust aerosols originated and transported from the Thar Desert region to the high-altitude station at Manora Peak, Nainital in the central Himalayas (Figure 12). The high values of aerosol index (AI) derived from the Ozone Monitoring Instrument (OMI) attest to the presence of absorbing aerosol particles over the region; however, air mass back-trajectory analysis over the station shows different pathways for the transport of air masses from the source region to the experimental site over different time periods (Figure 12). In this study, [115] observed a thick aerosol layer at ~1500 m altitude (Figure 13), above the station level, which was substantiated by the air mass back-trajectory analysis (Figure 14).

Aerosol Characteristics over the Indo-Gangetic Basin: Implications to Regional Climate 67

magnitude) as compared to the urban location in the IGB, but was found to have significant contribution to the total aerosol optical depth (~17%) and the resultant atmospheric forcing (~70%) at Manora Peak [62]. Based on BC measured at two different wavelengths at ultraviolet (370nm) and near-infrared (880nm), [117] have distinguished the potential sources of BC at Delhi (one of the densely populated and industrialized urban megacities in Asia and typically represents the plains of Ganga basin) and Manora Peak (one of the high-altitude and sparsely inhabited clean site in the Indian Himalayan foothills situated in the central Himalayas). Based on the analysis, [117] have found the major contribution of BC at Manora Peak is from biomass burning while fossil fuel is found to be the dominating contributor at

**Figure 14.** Temporal evolution of air masses at 1500 m altitude for three different time intervals on 12

**Figure 15.** Three day back‐air trajectories arriving at Manora Peak during the fire‐impacted periods in

2007, 2008 and 2009 (triangle represents the observation site) (*Adopted from [116]*).

Delhi.

and 13 June 2006 (*Adopted from [115]*).

**Figure 12.** OMI AI images showing the source and the progressive movement of the dust aerosols occurred in June 2006. Five-day air mass back-trajectories at Manora Peak for different time periods are superimposed on respective days AI images (*Adopted from [115]*).

**Figure 13.** Altitude versus time variations in BSR on a dust day (12 June 2006, with color contour) along with the variations in lidar-derived AOD at 532 nm (with solid line, average AOD = 0.83 ± 0.12) (*Adopted from [115]*).

Apart from dust transport from the Desert regions, recent study in [116], they have also demonstrated significant impact of north Indian biomass burning on aerosols and trace gases and the resultant radiation budget over the central Himalayas during the spring period through air mass back-trajectory analysis coupled with fire counts (Figure 15). The same has also been reported in [117] to be one of the major sources of BC over the same station in the central Himalayas, which was observed to be much lower (in terms of magnitude) as compared to the urban location in the IGB, but was found to have significant contribution to the total aerosol optical depth (~17%) and the resultant atmospheric forcing (~70%) at Manora Peak [62]. Based on BC measured at two different wavelengths at ultraviolet (370nm) and near-infrared (880nm), [117] have distinguished the potential sources of BC at Delhi (one of the densely populated and industrialized urban megacities in Asia and typically represents the plains of Ganga basin) and Manora Peak (one of the high-altitude and sparsely inhabited clean site in the Indian Himalayan foothills situated in the central Himalayas). Based on the analysis, [117] have found the major contribution of BC at Manora Peak is from biomass burning while fossil fuel is found to be the dominating contributor at Delhi.

66 Atmospheric Aerosols – Regional Characteristics – Chemistry and Physics

superimposed on respective days AI images (*Adopted from [115]*).

*from [115]*).

**Figure 12.** OMI AI images showing the source and the progressive movement of the dust aerosols occurred in June 2006. Five-day air mass back-trajectories at Manora Peak for different time periods are

**Figure 13.** Altitude versus time variations in BSR on a dust day (12 June 2006, with color contour) along with the variations in lidar-derived AOD at 532 nm (with solid line, average AOD = 0.83 ± 0.12) (*Adopted*

Apart from dust transport from the Desert regions, recent study in [116], they have also demonstrated significant impact of north Indian biomass burning on aerosols and trace gases and the resultant radiation budget over the central Himalayas during the spring period through air mass back-trajectory analysis coupled with fire counts (Figure 15). The same has also been reported in [117] to be one of the major sources of BC over the same station in the central Himalayas, which was observed to be much lower (in terms of

**Figure 14.** Temporal evolution of air masses at 1500 m altitude for three different time intervals on 12 and 13 June 2006 (*Adopted from [115]*).

**Figure 15.** Three day back‐air trajectories arriving at Manora Peak during the fire‐impacted periods in 2007, 2008 and 2009 (triangle represents the observation site) (*Adopted from [116]*).

## **5. Summary and future directions**

The study over IGB region revealed different aerosol characteristics over the region from western to central and to the eastern parts, which show significant gradient in magnitude of most of the aerosol characteristics. Such gradient can be explained due to the gradual changes in weather parameters and/or emission sources apart from geographical heterogeneity. Such gradient is, ultimately, found to have impact on the Earth-atmosphere system by negative radiative forcing, thus causing cooling, at the surface, and positive aerosol forcing, thus causing heating in the atmosphere. Such gradient in heating rate raises several climatic issues, and is needed to be answered on the basis of longer period investigations at several stations to improve the scientific understanding of the regional climate in inter-annual as well as intra-seasonal scale.

Aerosol Characteristics over the Indo-Gangetic Basin: Implications to Regional Climate 69

absorbing aerosols such as black carbon and dust, are significant in the atmosphere, the aerosol optical depth and chemical composition are not the only determinants of aerosol radiative effects, but the altitude of the aerosol layer and its altitude relative to clouds (if present) are also essential. Thus, it is also essential to gather information on vertical

Further, fog over the IGB region is observed to be a common feature, occurs mostly during the winter period. The number of foggy days has been increasing in recent years as compared to earlier decades [124], with strong increasing trends of anthropogenic pollution in the IG plains [125]. Fog formation usually begins in the latter half of December and continues till the end of January, thus blanketing some regions for more than a month [126]. The low topography of the IGB, adjacent to the Himalayan range, favors formation of fog and provides high concentration of air pollutants in the plains which serve as additional CCN for nucleation. Fog affects day to day lives of millions of people living in this region, resulting in poor visibility down to less than 100 meters causing frequent flight and train delays and even a significant number of deaths from vehicular accidents in many severe events [127]. Though few studies were done focusing on fog-induced aerosol characteristics over the IGB region and their impacts to the aerosol radiative forcing [49,128], detailed studies of aerosol composition and inter-annual variation of aerosols are required to better

understand the interaction of winter haze with the formation of fog over the IGB.

optical and radiative characteristics of aerosols with their size distribution.

*Indian Institute of Tropical Meteorology (Branch), Prof. Ramnath Vij Marg, New Delhi, India*

**Author details**

Corresponding Author

A.K. Srivastava\*

 \*

Apart from the measurements for various aerosol characteristics through different groundbased and space-born instrumentations, a 1-D aerosol optical model named as optical properties of aerosols and clouds (OPAC) has been developed by [129], estimating crucial optical properties of aerosols such as AOD and SSA, under the assumption of spherical aerosol particles and external mixing. In [130], they have shown that the optical depth and SSA of aerosol particles have strongest sensitivity on the direct radiative forcing, and these optical properties have found to be large deviation with shape and composition [18]. Further, with the model studies reported in [47,118], the particle composition (i.e. mixing state) and shape (i.e. morphology) attributes to more cooling at both top of the atmosphere and surface, and the combined effect is ~6% more warming than the spherical particles. The significance of consideration of particle shape is more in the regions where black carbon mixes with pure mineral dust, which are the most probable case over the IGB in northern India, because enhancement in the atmospheric warming will be under-estimated if particle morphology is not considered [47]. Thus, there is an urgent need for modeling studies over the IGB region to examine quantitatively the influence of particle morphology along with their mixing states on

distribution of aerosols over this region.

Due to large uncertainty in satellite derived aerosol products over the IGB during premonsoon dust periods, long-term ground-based measurements during different seasons can indeed provide useful information of the characteristics of aerosol types over the region on seasonal and inter-annual basis, which are meager and crucial for the regional climate models. Further, the mixing of natural dust with anthropogenically produced aerosol particles, has been hypothesized in [17] over the IGB region, mostly during the pre-monsoon period and corroborated with the AERONET data [36], suggested the complication of the satellite retrieval of aerosol characteristics and quantifying the climatic effects [118]. Hence, it is also one of the important research areas in understanding aerosol characteristics over the IGB region to make realistic assessments of aerosol-hydro-climate interplay.

The issue of black carbon or soot particles and its relationship with climate change has gained enormous scientific and popular interest over the last few years. The knowledge and understanding on aspects such as vertical distribution and mixing of black carbon with other aerosols, effects of cloud cover and monsoon still remains uncertain and incomplete. Few studies have shown that when sulphate or organics is coated over black carbon aerosols, its absorption effects are enhanced by 50% [119]. In case of black carbon mixed with large dust particles, absorption of the composite dust-black carbon aerosol system is enhanced by a factor of two to three compared to sum of black carbon and dust absorption [120]. However, we have no information on the state of mixing of black carbon. The proper assessment of mixing and/or coating of various aerosol species and their impacts on various aerosol characteristics have not been well quantified [121], which makes the investigation a real challenge [122]. IGB, being in proximity to the Thar Desert region, is found to be affected predominantly by the enhanced dust aerosols, mostly during the pre-monsoon period. As a result, the probability of this interaction (i.e. mixing) was suggested to be more over the region during this period [17,36] and is one of the future perspectives. To better understand these crucial issues, National Carbonaceous Aerosol Program (NCAP) was recently launched in India, focusing on the measurement of black carbon; their role in atmospheric stability and the consequent effect on cloud formation, monsoon and retreating of Himalayan glacier [123].

Based on recent observations using aircraft [61] and satellite measurements ([34,46], it has been reported that during pre-monsoon season, IGB region is characterized by the elevated aerosol layers extended up to the altitude from about 3 to 5 km. When the amount of absorbing aerosols such as black carbon and dust, are significant in the atmosphere, the aerosol optical depth and chemical composition are not the only determinants of aerosol radiative effects, but the altitude of the aerosol layer and its altitude relative to clouds (if present) are also essential. Thus, it is also essential to gather information on vertical distribution of aerosols over this region.

Further, fog over the IGB region is observed to be a common feature, occurs mostly during the winter period. The number of foggy days has been increasing in recent years as compared to earlier decades [124], with strong increasing trends of anthropogenic pollution in the IG plains [125]. Fog formation usually begins in the latter half of December and continues till the end of January, thus blanketing some regions for more than a month [126]. The low topography of the IGB, adjacent to the Himalayan range, favors formation of fog and provides high concentration of air pollutants in the plains which serve as additional CCN for nucleation. Fog affects day to day lives of millions of people living in this region, resulting in poor visibility down to less than 100 meters causing frequent flight and train delays and even a significant number of deaths from vehicular accidents in many severe events [127]. Though few studies were done focusing on fog-induced aerosol characteristics over the IGB region and their impacts to the aerosol radiative forcing [49,128], detailed studies of aerosol composition and inter-annual variation of aerosols are required to better understand the interaction of winter haze with the formation of fog over the IGB.

Apart from the measurements for various aerosol characteristics through different groundbased and space-born instrumentations, a 1-D aerosol optical model named as optical properties of aerosols and clouds (OPAC) has been developed by [129], estimating crucial optical properties of aerosols such as AOD and SSA, under the assumption of spherical aerosol particles and external mixing. In [130], they have shown that the optical depth and SSA of aerosol particles have strongest sensitivity on the direct radiative forcing, and these optical properties have found to be large deviation with shape and composition [18]. Further, with the model studies reported in [47,118], the particle composition (i.e. mixing state) and shape (i.e. morphology) attributes to more cooling at both top of the atmosphere and surface, and the combined effect is ~6% more warming than the spherical particles. The significance of consideration of particle shape is more in the regions where black carbon mixes with pure mineral dust, which are the most probable case over the IGB in northern India, because enhancement in the atmospheric warming will be under-estimated if particle morphology is not considered [47]. Thus, there is an urgent need for modeling studies over the IGB region to examine quantitatively the influence of particle morphology along with their mixing states on optical and radiative characteristics of aerosols with their size distribution.


**Abstract**

The Diffusion Hydrodynamic Model or DHM is a coupled one- and two-dimensional (2-D) surface flow model based upon a diffusion formulation of the well-known Navier–Stokes equations, developed by research hydrologists of the USGS (United States Geological Survey) for use in modeling floodplains and dambreak situations. The Fortran 77 source code and various applications were published in 1987 by the USGS as a Technical Report authored by Hromadka and Yen. The DHM program led to the development of several subsequent computational programs such as the FLO-2D computational model and other similar programs. The original DHM program had a limit of applications to problems with no more than 250 nodes and modeling grids. That limitation was recently removed by a program version named EDHM (Extended DHM), which provides for 9999 nodes and grids. However, the computational code is preserved in order that the baseline code algorithmic procedures are untouched. In this paper, the DHM and EDHM are rigorously compared and examined to identify any variations between the two Fortran codes. It is concluded from this investigation that the two sets of algorithm codes are identical, and outcomes from either program are similar for appropriately sized applications.

**Keywords:** legacy Fortran codes, computational economy, large scale application, overland flow, flow through a constriction

### **1. Introduction**

Legacy Fortran 77 codes that have been developed in the 1980s continue to have a wide spread audience (for both research and commercial applications) across all the Computational Fluid Dynamics disciplines. Their popularity can be largely attributed to the extensive validation that these models have been subjected to with analytical and experimental data for single and multi-dimensional flows. The trust that the audience have in the end results from these legacy codes, and their ability to meet the user goals are other driving factors for making them popular among the modeling community. Few of the legacy Fortran 77 codes, developed by various groups include MFIX [1] (Open source software for simulating Multiphase Flow with Interphase eXchanges), VOF 2D [2] (Two- dimensional, transient, freesurface incompressible fluid dynamics program), FUN3D [3] (fully unstructuredgrid fluid dynamic simulations spanning incompressible flow to transonic flow), LAURA [4] (Langley Aerothermodynamic Upwind Relaxation Algorithm for

structured, multi-block, computational aerothermodynamic simulations) and INS3D [5] (incompressible Navier–Stokes equations in three dimensions for steady and transient flows).

Although theoretically, these legacy codes are on a firm footing, computationally, they are uneconomical. When applied over a large scale application, characterized by thousands of nodes, these codes are constrained by varying degrees. The limitations arise partly from (a) the lack of object-oriented tools and the absence of abstract modeling capabilities in Fortran 77 and (b) the required CPU time when these models are applied across large domains. Balancing the accuracy of simulation with acceptable CPU is a crucial element that the current modelers are looking for. Capturing the physics of some flow phenomena necessitates that the equations be applied across the small spatial grid and temporal scales, which can be an issue in applying legacy codes. Since the codes were written (at that time) for the then available computational resources, applying them, as they are, for large scale domains might not be feasible either due to the large CPU time that they need to complete the simulation or because of the array limitations or modifications that need to be made so that the codes can be compiled using the currently available Fortran 77 compilers. Addressing these limitations will result in a 'modernized' version of the legacy codes. Modernization does not mean a better numerical formulation or a more accurate code. It only refers to a computationally efficient code with perhaps a better user interface for input and for visualizing the results through colorful multi-dimensional graphs and tables. In fact, using any modernized code without extensive benchmark testing can lead to erroneous solutions.

The above first limitation was addressed by researchers either by rewriting the entire code from scratch using an object-oriented programming language or by using an incremental approach in which the computationally intensive modules in legacy codes are identified and replaced with their computationally efficient counterparts. For instance, in an implicit finite difference or finite element formulation, where the system of equations are assembled in the form Ax = B, much of the computational time is spent on solving for 'x' [6]. While for the application audience, a solution module merely represents a means to an end of solving the flow equations, for a solver developer, the application is a source of sparse equations to be solved. Using an appropriate solver and a preconditioner can significantly cut down the simulation time, thus partly addressing the limitation.

The advent of high-performance computing tools and their availability to all the audience (based on commodity processors) saw the evolution of legacy Fortran codes to their full or semi-parallel versions, thus addressing the second limitation in legacy codes. There are many advantages to using parallel codes. For an application modeler using a well-written parallel code, the primary advantage is the reduction in the computational time. This reduction is on the order of the number of processors used in the communicator. Parallelization allows a program to be executed by as many processors available within the sub-complex simultaneously, thus facilitating a steep reduction in the computational time required for the solution to converge to the desired simulation time. This reduction enables the code to be applied over domains with millions of nodes or cells, to better capture the physics of the flow. Using standard parallel libraries that use MPI protocols like PETSc [7], ScaLAPACK [8], and BLAS [9], existing serial codes can be converted to parallel codes with reduced effort. Alternatively, highly optimized parallel versions of legacy codes can be written from scratch, which involves higher costs for developing and testing the code.

Diffusion Hydrodynamic Model (DHM) is a legacy model developed in the 1980s for USGS by the first author and his colleague [10]. The report and the Fortran 77 source code are also available at the DHM companion web page http://

**41**

*Examination of Hydrologic Computer Programs DHM and EDHM*

are discussed in Section 5. Conclusions are presented in Section 6.

field and hydrostatic pressure distribution) can be written as [10].

2

2

h h

h h

The two-dimensional flow continuity and momentum equations along the X and Y axis (assuming a constant fluid density without sources or sinks in the flow

> x y 0 ∂ ∂ ∂ ++= ∂∂ ∂ *H q q txy*

x x q q qx y S 0

y y q qqx y S 0

∂ ∂∂ ∂ + + + += ∂∂ ∂ ∂ *fy*

in which , *qX qy* are the unit flow rates along the spatial directions; , *Sfx Sfy* represents friction slopes; and h, H, h, g denote flow depth, water surface elevation,

The local and convective acceleration terms can be grouped and Eqs. 2 and 3 are

m S 0,z x,y Z ∂ + + == ∂ *fz H*

where mZ represents the sum of the first three terms in Eqs. 1,2 divided by gh. Using Manning's formula to calculate the frictional slope, the flow equation can be

*q H gh tx y X* (2)

*q H gh ty y X* (3)

*Z* (4)

∂ ∂∂ ∂ + + + += ∂∂ ∂ ∂ *fx*

(1)

diffusionhydrodynamicmodel.com/*.* The model was extensively tested in the 1980s for various free surface flow scenarios, and its back engine has laid foundation blocks for other popular models like FLO-2D [11]. The DHM was originally developed as the first (or one of the first) 3-D CFD computational programs but was subsequently revised into the form published due to computer limitations of the day. In the last 30 years, DHM has proven to be a practical and reliable tool for predicting two-dimensional surface flow characteristics associated with gradually varied flows and is popular among the hydraulic community. DHM solves a simplified twodimensional diffusion wave equation, the solution of which is sufficient for many free surface flows that are commonly encountered. For gradually varied flows, the model predicts the values of flow depth and velocity. The model does include any turbulence terms, and it cannot be used for rapidly varying flows. In this work, we address the computational limitations in DHM so that it can be applied over larger domains. The modified DHM, herein, is referred to as Enhanced DHM (EDHM). The layout of this document is as follows. In Section 2, the flow equations and other salient characteristics in DHM are briefly described. Section 3 lists the computational limitations in DHM. In Section 4, the modifications done in DHM to arrive at EDHM are detailed. Performance tests to validate the reliability of EDHM

*DOI: http://dx.doi.org/10.5772/intechopen.94283*

**2. Overview of DHM**

and gravity, respectively.

rewritten as

simplified to

#### *Examination of Hydrologic Computer Programs DHM and EDHM DOI: http://dx.doi.org/10.5772/intechopen.94283*

diffusionhydrodynamicmodel.com/*.* The model was extensively tested in the 1980s for various free surface flow scenarios, and its back engine has laid foundation blocks for other popular models like FLO-2D [11]. The DHM was originally developed as the first (or one of the first) 3-D CFD computational programs but was subsequently revised into the form published due to computer limitations of the day. In the last 30 years, DHM has proven to be a practical and reliable tool for predicting two-dimensional surface flow characteristics associated with gradually varied flows and is popular among the hydraulic community. DHM solves a simplified twodimensional diffusion wave equation, the solution of which is sufficient for many free surface flows that are commonly encountered. For gradually varied flows, the model predicts the values of flow depth and velocity. The model does include any turbulence terms, and it cannot be used for rapidly varying flows. In this work, we address the computational limitations in DHM so that it can be applied over larger domains. The modified DHM, herein, is referred to as Enhanced DHM (EDHM).

The layout of this document is as follows. In Section 2, the flow equations and other salient characteristics in DHM are briefly described. Section 3 lists the computational limitations in DHM. In Section 4, the modifications done in DHM to arrive at EDHM are detailed. Performance tests to validate the reliability of EDHM are discussed in Section 5. Conclusions are presented in Section 6.

### **2. Overview of DHM**

*Hydrology*

and transient flows).

structured, multi-block, computational aerothermodynamic simulations) and INS3D [5] (incompressible Navier–Stokes equations in three dimensions for steady

extensive benchmark testing can lead to erroneous solutions.

down the simulation time, thus partly addressing the limitation.

Although theoretically, these legacy codes are on a firm footing, computationally, they are uneconomical. When applied over a large scale application, characterized by thousands of nodes, these codes are constrained by varying degrees. The limitations arise partly from (a) the lack of object-oriented tools and the absence of abstract modeling capabilities in Fortran 77 and (b) the required CPU time when these models are applied across large domains. Balancing the accuracy of simulation with acceptable CPU is a crucial element that the current modelers are looking for. Capturing the physics of some flow phenomena necessitates that the equations be applied across the small spatial grid and temporal scales, which can be an issue in applying legacy codes. Since the codes were written (at that time) for the then available computational resources, applying them, as they are, for large scale domains might not be feasible either due to the large CPU time that they need to complete the simulation or because of the array limitations or modifications that need to be made so that the codes can be compiled using the currently available Fortran 77 compilers. Addressing these limitations will result in a 'modernized' version of the legacy codes. Modernization does not mean a better numerical formulation or a more accurate code. It only refers to a computationally efficient code with perhaps a better user interface for input and for visualizing the results through colorful multi-dimensional graphs and tables. In fact, using any modernized code without

The above first limitation was addressed by researchers either by rewriting the entire code from scratch using an object-oriented programming language or by using an incremental approach in which the computationally intensive modules in legacy codes are identified and replaced with their computationally efficient counterparts. For instance, in an implicit finite difference or finite element formulation, where the system of equations are assembled in the form Ax = B, much of the computational time is spent on solving for 'x' [6]. While for the application audience, a solution module merely represents a means to an end of solving the flow equations, for a solver developer, the application is a source of sparse equations to be solved. Using an appropriate solver and a preconditioner can significantly cut

The advent of high-performance computing tools and their availability to all the audience (based on commodity processors) saw the evolution of legacy Fortran codes to their full or semi-parallel versions, thus addressing the second limitation in legacy codes. There are many advantages to using parallel codes. For an application modeler using a well-written parallel code, the primary advantage is the reduction in the computational time. This reduction is on the order of the number of processors used in the communicator. Parallelization allows a program to be executed by as many processors available within the sub-complex simultaneously, thus facilitating a steep reduction in the computational time required for the solution to converge to the desired simulation time. This reduction enables the code to be applied over domains with millions of nodes or cells, to better capture the physics of the flow. Using standard parallel libraries that use MPI protocols like PETSc [7], ScaLAPACK [8], and BLAS [9], existing serial codes can be converted to parallel codes with reduced effort. Alternatively, highly optimized parallel versions of legacy codes can be written from scratch, which involves higher costs for devel-

Diffusion Hydrodynamic Model (DHM) is a legacy model developed in the 1980s for USGS by the first author and his colleague [10]. The report and the Fortran 77 source code are also available at the DHM companion web page http://

**40**

oping and testing the code.

The two-dimensional flow continuity and momentum equations along the X and Y axis (assuming a constant fluid density without sources or sinks in the flow field and hydrostatic pressure distribution) can be written as [10].

$$\frac{\partial H}{\partial t} + \frac{\partial q\_x}{\partial x} + \frac{\partial q\_y}{\partial y} = 0 \tag{1}$$

$$\frac{\partial \mathbf{q}\_{\mathbf{x}}}{\partial t} + \frac{\partial}{\partial \mathbf{x}} \left[ \frac{\mathbf{q}\_{\mathbf{x}}}{\mathbf{h}}^2 \right] + \frac{\partial}{\partial \mathbf{y}} \left[ \frac{\mathbf{q}\_{\mathbf{x}} \mathbf{q}\_{\mathbf{y}}}{\mathbf{h}} \right] + gh \left[ \mathbf{S}\_{\delta \mathbf{x}} + \frac{\partial H}{\partial \mathbf{X}} \right] = \mathbf{0} \tag{2}$$

$$
\frac{\partial \mathbf{q}\_y}{\partial t} + \frac{\partial}{\partial \mathbf{y}} \left[ \frac{\mathbf{q}\_y^{-2}}{\mathbf{h}} \right] + \frac{\partial}{\partial \mathbf{y}} \left[ \frac{\mathbf{q}\_x \mathbf{q}\_y}{\mathbf{h}} \right] + gh \left[ \mathbf{S}\_{fi} + \frac{\partial H}{\partial \mathbf{X}} \right] = \mathbf{0} \tag{3}
$$

in which , *qX qy* are the unit flow rates along the spatial directions; , *Sfx Sfy* represents friction slopes; and h, H, h, g denote flow depth, water surface elevation, and gravity, respectively.

The local and convective acceleration terms can be grouped and Eqs. 2 and 3 are rewritten as

$$\mathbf{m}\_{\mathbf{z}} + \left[ \mathbf{S}\_{f\mathbf{z}} + \frac{\partial H}{\partial \mathbf{Z}} \right] = \mathbf{0}, \mathbf{z} = \mathbf{x}, \mathbf{y} \tag{4}$$

where mZ represents the sum of the first three terms in Eqs. 1,2 divided by gh. Using Manning's formula to calculate the frictional slope, the flow equation can be simplified to

*Hydrology*

$$\mathbf{q}\_{\mathbf{z}} = \frac{\mathbf{1}.486}{\mathbf{n}} \mathbf{h}^{5/3} \mathbf{S}\_{\text{fz}}^{1/2}, \ \mathbf{z} = \mathbf{x}, \mathbf{y} \tag{5}$$

Eq. 5 can be rewritten in the general case as

$$\mathbf{q}\_{\rm Z} = -\mathbf{K}\_{\rm Z} \frac{\partial H}{\partial \mathbf{Z}} - \mathbf{K}\_{\rm Z} \mathbf{m}\_{\rm Z}, \quad \mathbf{z} = \mathbf{x}, \mathbf{y} \tag{6}$$

where

$$\mathbf{K}\_{\mathbf{z}} = \frac{\mathbf{1}.486}{\mathbf{n}} \stackrel{\text{h}^{5/3}}{\underset{\beta\mathbf{S}}{\left|\beta\mathbf{S}\right|}} \stackrel{\text{h}^{5/3}}{\underset{\beta\mathbf{S}}{\left|\alpha\mathbf{S}\right|}} + \mathbf{m}\_{\mathbf{s}} \stackrel{\text{h}^{1/2}}{\underset{\beta\mathbf{S}}{\left|\alpha\mathbf{S}\right|}} \tag{7}$$

The symbol S in Eq. 7 indicates the flow direction which makes an angle of θ = ( ) 1 y x tan q / q − with the positive x-direction. By assuming the value of m to be negligible, the diffusion model can be expressed as,

$$\mathbf{q}\_{\rm Z} = -\mathbf{K}\_{\rm Z} \frac{\partial H}{\partial \mathbf{Z}}, \quad \mathbf{z} = \mathbf{x}, \mathbf{y} \tag{8}$$

Two-dimensional DHM is formulated by substituting Eq. 8 into Eq. 1

$$\frac{\partial \mathbf{H}}{\partial t} = \frac{\partial}{\partial \mathbf{X}} \mathbf{K}\_{\mathbf{x}} \frac{\partial \mathbf{H}}{\partial \mathbf{X}} + \frac{\partial}{\partial \mathbf{y}} \mathbf{K}\_{\mathbf{y}} \frac{\partial \mathbf{H}}{\partial \mathbf{y}}.\tag{9}$$

If the momentum term groupings were retained, Eq. 9 can be written as

$$\frac{\partial \mathbf{H}}{\partial t} = \frac{\partial}{\partial \mathbf{x}} \mathbf{K}\_{\mathbf{x}} \frac{\partial H}{\partial \mathbf{x}} + \frac{\partial}{\partial y} \mathbf{K}\_{\mathbf{y}} \frac{\partial H}{\partial y} + \mathbf{S} \tag{10}$$

**43**

*Examination of Hydrologic Computer Programs DHM and EDHM*

b.Inflow and outflow boundary nodes are limited to 10

domain, warranting modifications to DHM, as discussed next.

cells and the inflow hydrograph at each of them need to be inputted. The number and the outflow boundary cell numbers should be identified. Since the formulation is explicit, the choice of time step (Δt) is limited by the Courant-Friedrich-Lewy stability condition. Starting from time = 0, the explicit solution is marched in the direction of time, until the required transient time level is reached. DHM gives the option of printing the output variables at any time level. The output at the center of each cell includes the flow depth, elevation, and flow velocities along the four directions.

For modeling flows over large sizes domain, the two primary shortcomings in

a.The maximum number of cells (nodes) that can be accommodated in DHM is

Both these limitations were largely due to the computational resources that were available to the developers in the 1980's. Application of DHM over large flow domains would require using a higher number of nodes in the computational

The changes made in DHM can be grouped into three categories, as detailed below. No changes were made to the format or structure of variables input and

a.Increased the array size of variables (FP,FC) that stores the location of cells and the initial depth, average elevation, roughness coefficient values, velocity (VEL), maximum water depth, and the corresponding time at the cell (DMAX,

b.Increased the array size of variables that store the stage curve data for the

d.Increased the array size of variables relating to the inflow and outflow hydrograph nodes (KIN, KOUT), depth of the specified stage-discharge curve (HOUT), inflow boundary condition nodal points (KINP) and the inflow

e.Increased of the array which stores the nodal points where outflow hydro-

The changes in the above array sizes were done in the main code and the

c.Increased effective rainfall intensity data pairs from 10 to 99.

graphs are being printed (NODFX, NODCC) from 50 to 99

associated subroutines FLOODC, QFP, QFC, and CHANPL.

*DOI: http://dx.doi.org/10.5772/intechopen.94283*

**3. Computational limitations in DHM**

**4. Features in extended DHM (EDHM)**

DHM are

output files.

**4.1 Major enhancements**

TIMEX) from 250 to 9999.

channel (NOSTA, STA) from 10 to 99.

hydrograph details (HP) from 10 to 99

limited to 250

where

$$\mathbf{S} = \frac{\widehat{\boldsymbol{\mathcal{O}}}}{\widehat{\boldsymbol{\mathcal{O}}} \mathbf{x}} (\mathbf{K}\_{\mathbf{x}} \mathbf{m}\_{\mathbf{x}}) + \frac{\widehat{\boldsymbol{\mathcal{O}}}}{\widehat{\boldsymbol{\mathcal{O}}} \mathbf{y}} (\mathbf{K}\_{\mathbf{y}} \mathbf{m}\_{\mathbf{y}}),$$

and Kx , Ky are also functions of mx , my respectively.

To maintain continuity in the discussion, while salient aspects of the DHM numerical algorithm are presented here, readers are referred to [10] for a detailed description of the numerical formulation and the input file format. The domain is divided into uniform square grids or cells. For each interior grid, its connectivity with adjacent grids along the North, East, South, and West directions is specified. For grids that are on the boundaries, '0' is specified along the directions that do not have adjacent nodes. The flow equation is solved using the nodal domain integration method. Apart from the grid connectors, at the center of each cell, the required input variables are the roughness value, ground elevation, initial flow depth. The number of inflow

*Examination of Hydrologic Computer Programs DHM and EDHM DOI: http://dx.doi.org/10.5772/intechopen.94283*

cells and the inflow hydrograph at each of them need to be inputted. The number and the outflow boundary cell numbers should be identified. Since the formulation is explicit, the choice of time step (Δt) is limited by the Courant-Friedrich-Lewy stability condition. Starting from time = 0, the explicit solution is marched in the direction of time, until the required transient time level is reached. DHM gives the option of printing the output variables at any time level. The output at the center of each cell includes the flow depth, elevation, and flow velocities along the four directions.